Getting the Most out of Your Open Source Investments
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A Framework for Real-Time Communications
Anton is a leading experts on Real Time Anton Venema Communications solutions, and the 1 Chief Technology Officer visionary lead architect behind IceLink, WebSync and LiveSwitch. iRTC Internet-Based Real Time Communications Introduction What’s the first thought that comes into your mind when you think about real-time communications? Is it a phone call you had a few minutes ago? A text message sent to your mobile? Maybe something more modern, like a tweet or video broadcast? All of these fall under the umbrella of what we like to call Internet-based real time communications, or iRTC for short. Real-time communications have been a part of our lives for a long time. From public telephone networks to radios all the way back to the telegraph, humanity has a history of seeking out new and better ways to use technology to improve communication. In the past decade, technology has arguably advanced more than the past century before it. Smartphones have stormed the market, mobile processors are advancing in line with Moore’s law, LTE rollouts are delivering unprecedented Internet speeds across the world, and WiFi hotspots are becoming ubiquitous. iRTC Includes Many Applications The availability of high-speed Internet services just about everywhere is causing a fundamental shift in the way people want to communicate and consume media. Cable networks are finding it more difficult to distinguish between their Internet and TV services, especially when companies like Netflix and HBO are able to publish their content directly to consumers over the Internet. Even live broadcasts, TV’s last stronghold, are being slowly replaced as platforms like YouTube allow content to be broadcast live to millions of users simultaneously over the Internet. -
Alexander Vaos [email protected] - 754.281.9609 - Alexvaos.Com
SENIOR FullStack SOFTWARE ENGINEER Alexander Vaos [email protected] - 754.281.9609 - alexvaos.com PERSONA Portfolio My name is Alexander Vaos and I love changing the world. Resume alexvaos.com/resume I’ve worn nearly ever hat I could put on in the last 18 years. I truly love being a part of something WEBSITE alexvaos.com meaningful. github github.com/kriogenx0 linkedin linkedin.com/pub/alex-vaos I love every part of creating a product: starting from an abstract idea, working through the experience, writing up it’s features, designing it, architecting it, building the front-end and back-end, integrating with stackoverflow stackoverflow.com/users/327934/alex-v other apps, bringing it together, testing, and ending at a physical result that makes a difference in Behance behance.net/kriogenx someone’s life. At an early age, I pursued design and programming, eventually learning some of the other Flickr flickr.com/alexvaos components of a business: marketing, product management, business development, sales. I worked as a part of every company size, from startup to enterprise. I've learned the delicacy of a startup, and how essential ROI can be for the roadmap of any company. I'd like to make an impact and create products people love, use, and can learn things from. I'd like the change the world one step at a time. FULL-TIME POSITIONS 2015-2018 SENIOR FullSTack ENGINEER Medidata Mdsol.com Overseeing 8 codebases, front-end applications and API services. Configured countless new applications from scratch with Rails, Rack, Express (Node), and Roda, using React/Webpack for front-end. -
FOSDEM 2017 Schedule
FOSDEM 2017 - Saturday 2017-02-04 (1/9) Janson K.1.105 (La H.2215 (Ferrer) H.1301 (Cornil) H.1302 (Depage) H.1308 (Rolin) H.1309 (Van Rijn) H.2111 H.2213 H.2214 H.3227 H.3228 Fontaine)… 09:30 Welcome to FOSDEM 2017 09:45 10:00 Kubernetes on the road to GIFEE 10:15 10:30 Welcome to the Legal Python Winding Itself MySQL & Friends Opening Intro to Graph … Around Datacubes Devroom databases Free/open source Portability of containers software and drones Optimizing MySQL across diverse HPC 10:45 without SQL or touching resources with my.cnf Singularity Welcome! 11:00 Software Heritage The Veripeditus AR Let's talk about The State of OpenJDK MSS - Software for The birth of HPC Cuba Game Framework hardware: The POWER Make your Corporate planning research Applying profilers to of open. CLA easy to use, aircraft missions MySQL Using graph databases please! 11:15 in popular open source CMSs 11:30 Jockeying the Jigsaw The power of duck Instrumenting plugins Optimized and Mixed License FOSS typing and linear for Performance reproducible HPC Projects algrebra Schema Software deployment 11:45 Incremental Graph Queries with 12:00 CloudABI LoRaWAN for exploring Open J9 - The Next Free It's time for datetime Reproducible HPC openCypher the Internet of Things Java VM sysbench 1.0: teaching Software Installation on an old dog new tricks Cray Systems with EasyBuild 12:15 Making License 12:30 Compliance Easy: Step Diagnosing Issues in Webpush notifications Putting Your Jobs Under Twitter Streaming by Open Source Step. Java Apps using for Kinto Introducing gh-ost the Microscope using Graph with Gephi Thermostat and OGRT Byteman. -
Naiad: a Timely Dataflow System
Naiad: A Timely Dataflow System Derek G. Murray Frank McSherry Rebecca Isaacs Michael Isard Paul Barham Mart´ın Abadi Microsoft Research Silicon Valley {derekmur,mcsherry,risaacs,misard,pbar,abadi}@microsoft.com Abstract User queries Low-latency query are received responses are delivered Naiad is a distributed system for executing data parallel, cyclic dataflow programs. It offers the high throughput Queries are of batch processors, the low latency of stream proces- joined with sors, and the ability to perform iterative and incremental processed data computations. Although existing systems offer some of Complex processing these features, applications that require all three have re- incrementally re- lied on multiple platforms, at the expense of efficiency, Updates to executes to reflect maintainability, and simplicity. Naiad resolves the com- data arrive changed data plexities of combining these features in one framework. A new computational model, timely dataflow, under- Figure 1: A Naiad application that supports real- lies Naiad and captures opportunities for parallelism time queries on continually updated data. The across a wide class of algorithms. This model enriches dashed rectangle represents iterative processing that dataflow computation with timestamps that represent incrementally updates as new data arrive. logical points in the computation and provide the basis for an efficient, lightweight coordination mechanism. requirements: the application performs iterative process- We show that many powerful high-level programming ing on a real-time data stream, and supports interac- models can be built on Naiad’s low-level primitives, en- tive queries on a fresh, consistent view of the results. abling such diverse tasks as streaming data analysis, it- However, no existing system satisfies all three require- erative machine learning, and interactive graph mining. -
Getting Started with Openbts BUILD OPEN SOURCE MOBILE NETWORKS
Compliments of Getting Michael Iedema Started with Foreword by Harvind Samra OpenBTS BUILD OPEN SOURCE MOBILE NETWORKS Getting Started with OpenBTS Michael Iedema Getting Started with OpenBTS by Michael Iedema Copyright © 2015 Range Networks. All rights reserved. Printed in the United States of America. Published by O’Reilly Media, Inc., 1005 Gravenstein Highway North, Sebastopol, CA 95472. O’Reilly books may be purchased for educational, business, or sales promotional use. Online editions are also available for most titles (http://safaribooksonline.com). For more information, contact our corporate/ institutional sales department: 800-998-9938 or [email protected]. Editor: Brian MacDonald Indexer: WordCo Indexing Services Production Editor: Melanie Yarbrough Cover Designer: Karen Montgomery Copyeditor: Lindsy Gamble Interior Designer: David Futato Proofreader: Charles Roumeliotis Illustrator: Rebecca Demarest January 2015: First Edition Revision History for the First Edition: 2015-01-12: First release See http://oreilly.com/catalog/errata.csp?isbn=9781491910658 for release details. The O’Reilly logo is a registered trademark of O’Reilly Media, Inc. Getting Started with OpenBTS, the cover image of a Sun Conure, and related trade dress are trademarks of O’Reilly Media, Inc. Many of the designations used by manufacturers and sellers to distinguish their products are claimed as trademarks. Where those designations appear in this book, and O’Reilly Media, Inc. was aware of a trademark claim, the designations have been printed in caps or initial caps. While the publisher and the author have used good faith efforts to ensure that the information and instruc‐ tions contained in this work are accurate, the publisher and the author disclaim all responsibility for errors or omissions, including without limitation responsibility for damages resulting from the use of or reliance on this work. -
ROOT Package Management: “Lazy Install” Approach
ROOT package management: “lazy install” approach Oksana Shadura ROOT Monday meeting Outline ● How we can improve artifact management (“lazy-install”) system for ROOT ● How to organise dependency management for ROOT ● Improvements to ROOT CMake build system ● Use cases for installing artifacts in the same ROOT session Goals ● Familiarize ROOT team with our planned work ● Explain key misunderstandings ● Give a technical overview of root-get ● Explain how root-get and cmake can work in synergy Non Goals We are not planning to replace CMake No change to the default build system of ROOT No duplication of functionality We are planning to “fill empty holes” for CMake General overview Manifest - why we need it? ● Easy to write ● Easy to parse, while CMakeLists.txt is impossible to parse ● Collect information from ROOT’s dependencies + from “builtin dependencies” + OS dependencies + external packages to be plugged in ROOT (to be resolved after using DAG) ● It can be easily exported back as a CMakeLists.txt ● It can have extra data elements [not only what is in CMakeLists.txt, but store extra info] ○ Dependencies description (github links, semantic versioning) ■ url: "ssh://[email protected]/Greeter.git", ■ versions: Version(1,0,0)..<Version(2,0,0) Manifest is a “dump” of status of build system (BS), where root-get is just a helper for BS Manifest - Sample Usage scenarios and benefits of manifest files: LLVM/Clang LLVM use CMake as a LLVMBuild utility that organize LLVM in a hierarchy of manifest files of components to be used by build system llvm-build, that is responsible for loading, verifying, and manipulating the project's component data. -
Webrtc About
WebRTC About WebRTC provides Real-Time Communications directly from better web browsers and devices without requiring plug-ins such as Adobe Flash nor Silverlight. WebRTC always operates in secure mode.FreeSWITCH provides a WebRTC portal to its public conference bridge to demonstrate the possibilities for handling telephony via a web page; join us for our weekly conference calls. The process for configuring FreeSWITCH with WSS certificates is the same whether for use with classic WebRTC or the FreeSWITCH Verto endpoint. Installation The configuration for Secure Web Sockets is slightly different than for TLS over SIP. This guide covers WSS certificate setup. Debian 7 (Wheezy) Install Debian 7 (Wheezy) minimal. Building FreeSWITCH Building apt-get install git build-essential automake autoconf libtool wget python zlib1g-dev libjpeg-dev libncurses5- dev libssl-dev libpcre3-dev libcurl4-openssl-dev libldns-dev libedit-dev libspeexdsp-dev libspeexdsp-dev libsqlite3-dev apache2 cd /usr/src/ git clone https://freeswitch.org/stash/scm/fs/freeswitch.git cd freeswitch ./bootstrap.sh -j ./configure -C make make install cd-sounds-install cd-moh-install mkdir -p /usr/local/freeswitch/certs edit /usr/local/freeswitch/conf/sip_profiles/internal.xml # Set these params and save the file: <param name="tls-cert-dir" value="/usr/local/freeswitch/certs"/> <param name="wss-binding" value=":7443"/> If behind N.A.T. make sure to set the ext-sip-ip and ext-rtp-ip in vars.xml to the public IP address of your FreeSWITCH. If talking to clients both inside and outside the N.A.T. you must set the local-network-acl rfc1918.auto, and prefix the ext-sip-ip and ext-rtp-ip to autonat:X.X. -
A Package Manager for Curry
A Package Manager for Curry Jonas Oberschweiber Master-Thesis eingereicht im September 2016 Christian-Albrechts-Universität zu Kiel Programmiersprachen und Übersetzerkonstruktion Betreut durch: Prof. Dr. Michael Hanus und M.Sc. Björn Peemöller Eidesstattliche Erklärung Hiermit erkläre ich an Eides statt, dass ich die vorliegende Arbeit selbstständig ver- fasst und keine anderen als die angegebenen Quellen und Hilfsmittel verwendet habe. Kiel, Contents 1 Introduction 1 2 The Curry Programming Language 3 2.1 Curry’s Logic Features 3 2.2 Abstract Curry 5 2.3 The Compiler Ecosystem 6 3 Package Management Systems 9 3.1 Semantic Versioning 10 3.2 Dependency Management 12 3.3 Ruby’s Gems and Bundler 16 3.4 JavaScript’s npm 19 3.5 Haskell’s Cabal 21 4 A Package Manager for Curry 25 4.1 The Command Line Interface 26 4.2 What’s in a Package? 29 4.3 Finding Packages 35 4.4 Installing Packages 37 4.5 Resolving Dependencies 38 vi A Package Manager for Curry 4.6 Interacting with the Compiler 43 4.7 Enforcing Semantic Versioning 46 5 Implementation 51 5.1 The Main Module 52 5.2 Packages and Dependencies 56 5.3 Dependency Resolution 58 5.4 Comparing APIs 71 5.5 Comparing Program Behavior 73 6 Evaluation 85 6.1 Comparing Package Versions 85 6.2 A Sample Dependency Resolution 88 6.3 Performance of the Resolution Algorithm 90 6.4 Performance of API and Behavior Comparison 96 7 Summary & Future Work 99 A Total Order on Versions 105 B A Few Curry Packages 109 C Raw Performance Figures 117 D User’s Manual 121 1 Introduction Modern software systems typically rely on many external libraries, reusing func- tionality that can be shared between programs instead of reimplementing it for each new project. -
ML Cheatsheet Documentation
ML Cheatsheet Documentation Team Sep 02, 2021 Basics 1 Linear Regression 3 2 Gradient Descent 21 3 Logistic Regression 25 4 Glossary 39 5 Calculus 45 6 Linear Algebra 57 7 Probability 67 8 Statistics 69 9 Notation 71 10 Concepts 75 11 Forwardpropagation 81 12 Backpropagation 91 13 Activation Functions 97 14 Layers 105 15 Loss Functions 117 16 Optimizers 121 17 Regularization 127 18 Architectures 137 19 Classification Algorithms 151 20 Clustering Algorithms 157 i 21 Regression Algorithms 159 22 Reinforcement Learning 161 23 Datasets 165 24 Libraries 181 25 Papers 211 26 Other Content 217 27 Contribute 223 ii ML Cheatsheet Documentation Brief visual explanations of machine learning concepts with diagrams, code examples and links to resources for learning more. Warning: This document is under early stage development. If you find errors, please raise an issue or contribute a better definition! Basics 1 ML Cheatsheet Documentation 2 Basics CHAPTER 1 Linear Regression • Introduction • Simple regression – Making predictions – Cost function – Gradient descent – Training – Model evaluation – Summary • Multivariable regression – Growing complexity – Normalization – Making predictions – Initialize weights – Cost function – Gradient descent – Simplifying with matrices – Bias term – Model evaluation 3 ML Cheatsheet Documentation 1.1 Introduction Linear Regression is a supervised machine learning algorithm where the predicted output is continuous and has a constant slope. It’s used to predict values within a continuous range, (e.g. sales, price) rather than trying to classify them into categories (e.g. cat, dog). There are two main types: Simple regression Simple linear regression uses traditional slope-intercept form, where m and b are the variables our algorithm will try to “learn” to produce the most accurate predictions. -
A Survey of Open Source Products for Building a SIP Communication Platform
Hindawi Publishing Corporation Advances in Multimedia Volume 2011, Article ID 372591, 21 pages doi:10.1155/2011/372591 Research Article A Survey of Open Source Products for Building a SIP Communication Platform Pavel Segec and Tatiana Kovacikova Department of InfoCom Networks, University of Zilina, Univerzitna 8215/1, 010 26 Zilina, Slovakia Correspondence should be addressed to Tatiana Kovacikova, [email protected] Received 29 July 2011; Revised 31 October 2011; Accepted 15 November 2011 Academic Editor: T. Turletti Copyright © 2011 P. Segec and T. Kovacikova. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Session Initiation Protocol (SIP) is a multimedia signalling protocol that has evolved into a widely adopted communication standard. The integration of SIP into existing IP networks has fostered IP networks becoming a convergence platform for both real- time and non-real-time multimedia communications. This converged platform integrates data, voice, video, presence, messaging, and conference services into a single network that offers new communication experiences for users. The open source community has contributed to SIP adoption through the development of open source software for both SIP clients and servers. In this paper, we provide a survey on open SIP systems that can be built using publically available software. We identify SIP features for service deve- lopment and programming, services and applications of a SIP-converged platform, and the most important technologies support- ing SIP functionalities. We propose an advanced converged IP communication platform that uses SIP for service delivery. -
1.2 Le Zhang, Microsoft
Objective • “Taking recommendation technology to the masses” • Helping researchers and developers to quickly select, prototype, demonstrate, and productionize a recommender system • Accelerating enterprise-grade development and deployment of a recommender system into production • Key takeaways of the talk • Systematic overview of the recommendation technology from a pragmatic perspective • Best practices (with example codes) in developing recommender systems • State-of-the-art academic research in recommendation algorithms Outline • Recommendation system in modern business (10min) • Recommendation algorithms and implementations (20min) • End to end example of building a scalable recommender (10min) • Q & A (5min) Recommendation system in modern business “35% of what consumers purchase on Amazon and 75% of what they watch on Netflix come from recommendations algorithms” McKinsey & Co Challenges Limited resource Fragmented solutions Fast-growing area New algorithms sprout There is limited reference every day – not many Packages/tools/modules off- and guidance to build a people have such the-shelf are very recommender system on expertise to implement fragmented, not scalable, scale to support and deploy a and not well compatible with enterprise-grade recommender by using each other scenarios the state-of-the-arts algorithms Microsoft/Recommenders • Microsoft/Recommenders • Collaborative development efforts of Microsoft Cloud & AI data scientists, Microsoft Research researchers, academia researchers, etc. • Github url: https://github.com/Microsoft/Recommenders • Contents • Utilities: modular functions for model creation, data manipulation, evaluation, etc. • Algorithms: SVD, SAR, ALS, NCF, Wide&Deep, xDeepFM, DKN, etc. • Notebooks: HOW-TO examples for end to end recommender building. • Highlights • 3700+ stars on GitHub • Featured in YC Hacker News, O’Reily Data Newsletter, GitHub weekly trending list, etc. -
Alibaba Amazon
Online Virtual Sponsor Expo Sunday December 6th Amazon | Science Alibaba Neural Magic Facebook Scale AI IBM Sony Apple Quantum Black Benevolent AI Zalando Kuaishou Cruise Ant Group | Alipay Microsoft Wild Me Deep Genomics Netflix Research Google Research CausaLens Hudson River Trading 1 TALKS & PANELS Scikit-learn and Fairness, Tools and Challenges 5 AM PST (1 PM UTC) Speaker: Adrin Jalali Fairness, accountability, and transparency in machine learning have become a major part of the ML discourse. Since these issues have attracted attention from the public, and certain legislations are being put in place regulating the usage of machine learning in certain domains, the industry has been catching up with the topic and a few groups have been developing toolboxes to allow practitioners incorporate fairness constraints into their pipelines and make their models more transparent and accountable. AIF360 and fairlearn are just two examples available in Python. On the machine learning side, scikit-learn has been one of the most commonly used libraries which has been extended by third party libraries such as XGBoost and imbalanced-learn. However, when it comes to incorporating fairness constraints in a usual scikit- learn pipeline, there are challenges and limitations related to the API, which has made developing a scikit-learn compatible fairness focused package challenging and hampering the adoption of these tools in the industry. In this talk, we start with a common classification pipeline, then we assess fairness/bias of the data/outputs using disparate impact ratio as an example metric, and finally mitigate the unfair outputs and search for hyperparameters which give the best accuracy while satisfying fairness constraints.