Reporter ohne Grenzen: Studie kritisiert Darknet-Paragraf als unnötig Die Betreiber von Plattformen im Darknet können laut einer Studie bereits nach geltendem Recht geahndet werden, wenn dort mit Illegalem gehandelt wird. Ein eigens dafür geschaffener Straftatbestand könne jedoch den Betrieb von Plattformen im Internet und Anonymisierungsdiensten gefährden ▻https://www.golem.de/news/reporter-ohne-grenzen-studie-kritisiert-darknet-paragraf-als-unnoetig-1907-142 #Internet #Darknet #Tor #Computer #Handy #Nutzer
Instant Meshes algorithm — an interview with Dr Wenzel Jakob
« An interview with assistant professor Dr Wenzel, leading the Realistic Graphics Lab at EPFL in Lausanne, Switzerland; his research revolves around rendering, appearance modeling, and geometry processing. […] »
2019 Significant New Researcher Award: Wenzel Jakob / citation by ACM SIGGRAPH
« ACM SIGGRAPH is pleased to present the Significant New Researcher Award to Wenzel Jakob for his work in rendering and geometry.
Wenzel’s broad contributions to computer graphics are centered in rendering, where his work spans surfaces and volumes, path tracing and Markov Chain Monte Carlo, rendering software, and surface reflectance and appearance models. His light transport work has expanded both the problems being solved—for instance by generalizing volume scattering to anisotropic materials like textiles—and the methods for solving them—for instance by applying the differential geometry of manifolds to high dimensional structures in path space. He often applies intricate numerical methods to impressive effect, such as in reviving classical methods from atmospheric physics to solve new problems in layered surface appearance. Wenzel’s research also connects to the physical world through substantial experimental work in surface reflectance measurement and fabrication of refractive surfaces.
During his postdoc, Wenzel expanded his repertoire into the area of surface geometry, creating a highly scalable and practical method for re-meshing complex surfaces; it produces smooth, well-structured triangle and quad meshes that align to geometric features, while remaining very fast and scalable to large models.
Besides his considerable theoretical and algorithmic contributions, Wenzel also makes invaluable contributions through practical, functional software. His Mitsuba open-source renderer provides solid implementations of many difficult methods, making it a favorite testbed for rendering researchers. He has also contributed considerably to the PBRT renderer, which plays an important role in advanced instruction and as a reference implementation for production systems. His open-source field-aligned meshing software has been adopted in multiple practical applications. He is leading by example in helping to move our field towards viewing solid, practical implementations as an integral part of graphics research. […] »
Renesas Electronics Develops Low-Power Technology for Embedded Flash Memory Based on SOTB™ Process to Enable Energy Harvesting and Eliminate Need for Batteries.... Achieves Read Energy of 0.22 pJ/bit at 64 MHz – Among the World’s Lowest Levels for Embedded Flash Memory on an MCU. #technology #electronics #science #physics #Energy #Harvesting #Flash #Memory #hardware #computer ▻https://www.renesas.com/eu/en/about/press-center/news/2019/news20190612.html
CERN Ditches Microsoft to ‘Take Back Control’ with Open Source Software.... The European Organisation for Nuclear Research, better known as CERN, and also known as home of the Large Hadron Collider, has announced plans to migrate away from Microsoft products and on to open-source solutions where possible. Why? Increases in Microsoft license fees. Microsoft recently revoked the organisations status as an academic institution, instead pricing access to its services on users. This bumps the cost of various software licenses 10x, which is just too much for CERN’s budget. #CERN #Microsoft #Open_Source #Software #computer #technology #Switzerland #Geneva (...)
Using managed machine learning services (MLaaS) as your baseline
Build versus Buy: does MLaaS fit your data science project’s needs and how do you evaluate across vendors?Making a build or buy decision at the start of any data science project can seem daunting — let’s review aAlmost every major cloud provider now offers a custom machine learning service— from Google Cloud’s AutoML Vision Beta, to Microsoft Azure’s Custom Vision Preview, and IBM Watson’s Visual Recognition service, the field of computer vision is no exception.Perhaps your team has been in this Build or Buy predicament?From the marketing perspective, these managed ML services are positioned for companies that are just building up their data science teams or whose teams are primarily composed of data analysts, BI specialists, or software engineers (who might be transitioning to data (...)
Computer Vision: The #future of the Future in More Ways Than One
As computer vision expands its influence in the human world, there are many things to consider in regard to how it will change the way we view our lives and how we actually live it. We look now at just a few of the advances computer vision has given usSource: dribbble.comSky’s The LimitAll around us — and most of the time without us even realizing it — computer vision (CV) is being used to enhance our lives. With our iPhones and its Face ID #technology to unlock your smartphone as a case in point, not to mention the countless other services and apps that have pooped up on the market of late, we’re headed in the right direction as far as innovation is concerned.Technology is progressing at an unbelievable pace.Things that were only a dream in 2010 are now the de facto reality. The algorithms of (...)
Curious Case of PLATO: the cold war #internet
Curious Case of PLATO: The cold war InternetMillennials, myself included, would never get it.Because millennials are accustomed to hear of revolutions born in a garage (Google, Apple, Amazon) or a dorm room (Facebook, Wordpress), hitting eight figure revenues in the cradle, sometimes despite being loss-making.They miss a point: those innovations are built from solid blocks invented decades ago - inside the lonesome corridors of military labs and Universities.PLATO, a contrived acronym that stands for Programmed Logic for Automatic Teaching Operations, was one such founding stone.On the face of it, it was merely a classroom teaching simulator system, powered by military grade vacuum tube mainframes and arrays of dumb terminals made of displays and keyboards, spread through out university (...)
The 10 Computer Scientists That Made Computers Mainstream
These are scientists that made a significant contribution to the field and will be forever remembered for their work.Here are 10 Computer Scientists who made history.1. Alan TuringAlan Turing is an English computer scientist, widely considered to be the father of computer #science. The prestigious “Turing Award” was named after him — an award given to those in computer science who make a significant contribution to the industry. Turing worked for the British Government, playing a pivotal role in cracking intercepted coded messages and enabling the Allies to defeat the Nazis in many crucial engagements. Despite the sheer brilliance of his work, he was not fully recognised for his contributions as he was a homosexual, which was illegal in the UK at the time.Alan Turing’s biography2. Tim (...)
A Hiring Platform’s Nine-Part Guide for ProgrammersBY AMMON BARTRAM ON MAR 8, 2016. This post started as the preparation material we send to our candidates, but we decided to post it publicly.Being a good programmer has a surprisingly small role in passing programming interviews. To be a productive programmer, you need to be able to solve large, sprawling problems over weeks and months. Each question in an interview, in contrast, lasts less than one hour. To do well in an interview, then, you need to be able to solve small problems quickly, under duress, while explaining your thoughts clearly. This is a different skill . On top of this, interviewers are often poorly trained and inattentive (they would rather be programming), and ask questions far removed from actual work. They bring (...)
Histogram Equalization in #python from Scratch
Histogram Equalization is one of the fundamental tools in the image processing toolkit. It’s a technique for adjusting the pixel values in an image to enhance the contrast by making those intensities more equal across the board. Typically, the histogram of an image will have something close to a normal distribution, but equalization aims for a uniform distribution. In this article, we’re going to program a histogram equalizer in python from scratch. If you want to see the full code, I’ve included a link to a Jupyter notebook at the bottom of this article. Now, if you’re ready, let’s dive in!Before anything, we have to do some setup. Let’s import the libraries we’ll be using throughout the program, load in the image, and display (...)
CS Degrees Are Mostly Just Signaling — An Interview With Economist Bryan Caplan
CS Degrees Are Mostly Just Signaling — An Interview With Economist Bryan CaplanBY CHARLES TREICHLERThe main thing I’d say about computer science is that [programmers] have a self-concept of being totally skills-driven, but if you actually look at employment in computer science, that’s not how it works. Degrees from leading schools really do seem to matter. They really do seem to open doors.In his controversial book, The Case Against #education, Dr. Bryan Caplan, Professor of Economics at George Mason University, uses statistical analysis to argue that our eduction system is a big waste of time and money. But he isn’t suggesting that you should drop out. Data shows that education pays big dividends (the so-called education premium), and Caplan does not dispute that fact.What he does question is (...)
Interview with Kaggle Grandmaster, Senior CV Engineer at Lyft: Dr. Vladimir I. Iglovikov
Interview with Kaggle Grandmaster, Senior Computer Vision Engineer at Lyft: Dr. Vladimir I. IglovikovPart 24 of The series where I interview my heroes.Today, I’m honored to be talking to another great kaggler from the ODS community: (kaggle: iglovikov) Competitions Grandmaster (Ranked #97), Discussions Expert (Ranked #30): Dr. Vladimir I. IglovikovVladimir is currently working as the Senior Computer Vision Engineer at Level5, Self-Driving Division, Lyft Inc.Prior to Lyft, Vladimir was working as a Senior Data Scientist at TrueAccord. He has a background in Physics and holds a Ph.D. in Physics from UC Davis.About the Series:I have very recently started making some progress with my Self-Taught Machine Learning Journey. But to be honest, it wouldn’t be possible at all without the amazing (...)
Introduction to The Machine Learning Stack
Introduction to the machine learning stackData science is the underlying force that is driving recent advances in artificial intelligence (AI), and machine learning (ML). This has lead to the enormous growth of ML libraries and made established #programming languages like Python more popular than ever before.It makes sense to put them all together (even though they’re not interchangeable) because there’s significant overlap. In some ways we can say that data science is about producing insights, while AI is about producing actions, and ML is focused on making predictions.To better understand the inner workings of data science in AI and ML, you will have to dive right into the machine learning engineering stack listed below to understand how it’s used.As part of our research for Springboard’s (...)
What if there was a way to remove opinions and personal preferences from the equation and unambiguously determine what code is better given two competing solutions?The only thing that developers have to agree upon is the axiom itself. With unanimity reached on this single point, mountains of subjective discussions suddenly become irrelevant and valuable time is reclaimed.Deference to unwanted authority is unnecessary as we make progress to a decentralized world.A handrail becomes available for developers to help them make countless decisions throughout their days. The haunting feeling of uncertainty is replaced with welcomed confidence.I came up with this idea about 5 years ago and it has withstood intense scrutiny from developers and architects at various companies since then. I (...)
Public Key #cryptography Simply Explained
Photo by Liam Macleod on UnsplashPublic key cryptography seems magical to everyone, even those who understand it. In this post, I’m going to explain public key cryptography. Public Key Cryptography is based on asymmetric cryptography, so first let us talk about symmetric cryptography.▻https://medium.com/media/c28f9fc84629b8f11d5c569ae4d99c81/hrefSymmetric CryptographyYour front door is usually locked by a key. This key unlocks & locks your front door. With symmetric cryptography, you have one key which you use to unlock and lock things.Only people with the key or a copy of the key can unlock the door. Now, imagine you’re on holiday in Bali. You want to invite your friend around to look after your cat ? while you’re on the beautiful beaches ?️.Before the holiday, you give your friend the (...)
A Brief History of Computer Vision (and Convolutional Neural Networks)
Although Computer Vision (CV) has only exploded recently (the breakthrough moment happened in 2012 when AlexNet won ImageNet), it certainly isn’t a new scientific field.Computer scientists around the world have been trying to find ways to make machines extract meaning from visual data for about 60 years now, and the history of Computer Vision, which most people don’t know much about, is deeply fascinating.In this article, I’ll try to shed some light on how modern CV systems, powered primarily by convolutional neural networks, came to be.I’ll start with a work that came out in the late 1950s and has nothing to do with software engineering.One of the most influential papers in Computer Vision was published by two neurophysiologists — David Hubel and Torsten Wiesel — in 1959. Their publication, (...)
What is Amazon Lightsail?
Amazon Lightsail LogoIf you are new to #aws and looking to deploy some servers for your applications then AWS Lightsail may be the best starting point for you.Unlike Amazon EC2, you are given a nice interface where you can select preconfigured plans that may cover most of your use cases.Just with 3–4 clicks you can launch a WordPress website running on Linux server.You also don’t have to worry about determining the cost since the cost is fixed monthly.Along with servers, you can also create databases, load balancers, and storage on Lightsail.Now let’s get practical and quickly launch a Node server with Lightsail!Launching an InstanceFirst, log into your AWS console and in the All Services tab under Compute you will find Lightsail. Click on it, it will open the Lightsail dashboard in a new (...)
An Introduction to Ridge, Lasso, and Elastic Net #regression
A guide to Ridge, Lasso, and Elastic Net Regression and applying it in RRegression analysis is a statistical technique that models and approximates the relationship between a dependent and one or more independent variables. This article will quickly introduce three commonly used regression models using #r and the Boston housing data-set: Ridge, Lasso, and Elastic Net.First we need to understand the basics of regression and what parameters of the equation are changed when using a specific model. Simple linear regression, also known as ordinary least squares (OLS) attempts to minimize the sum of error squared. The error in this case is the difference between the actual data point and its predicted value.Visualization of the squared error (from Setosa.io)The equation for this model is (...)
Interview with Radiologist, fast.ai fellow and Kaggle expert: Dr. Alexandre Cadrin-Chenevert
Part 20 of The series where I interview my heroes.Index to “Interviews with ML Heroes”Today, I’m super excited to be interviewing one of the domain experts in #medical Practice: A Radiologist, a great member of the fast.ai community and a kaggle expert: Dr. Alexandre Cadrin-Chenevert.Alexandre is an MD, Radiologist and a Computer Engineer. He is also a Deep Learning Practitioner, Kaggle Competition Expert (Ranked #72). He is actively working in the application of Deep Learning in the Medical Domain.About the Series:I have very recently started making some progress with my Self-Taught Machine Learning Journey. But to be honest, it wouldn’t be possible at all without the amazing community online and the great people that have helped me.In this Series of Blog Posts, I talk with People that have (...)
Les décrocheurs des cours en ligne Agence Science-Presse - 4 février 2019 - Le devoir
Ce n’est peut-être plus une surprise que d’apprendre que les étudiants qui s’inscrivent aux cours en ligne, ou MOOC (Massive open online courses), sont très peu nombreux à se rendre jusqu’au bout. Mais à partir de quel seuil pourrait-on parler d’un échec ? Une étude amène pour la première fois des chiffres ▻http://science.sciencemag.org/content/363/6423/130 permettant de conclure que les MOOC sont devenus quelque chose de différent de ce qu’ils annonçaient au départ.
Photo : iStock Parmi 1,1 million d’étudiants inscrits pour la première fois en 2015-2016, seulement 12% étaient à nouveau inscrits l’année suivante.
Leur promesse, c’était celle d’un accès gratuit, pour la planète entière, à une éducation de qualité. Ils sont plutôt devenus une aide technique aux établissements d’enseignement qui veulent offrir des cours en ligne. Deux chercheurs du Laboratoire des systèmes d’enseignement du Massachusetts Institute of Technology, Justin Reich et José A. Ruipérez-Valiente, décrivent dans la revue Science six années — de 2012 à 2018 — couvrant 12,6 millions d’inscriptions.
La principale découverte derrière ces données n’est pas que seul un petit nombre de ces inscrits avaient l’intention d’obtenir un diplôme. C’est plutôt que même le pourcentage de ceux qui ont terminé leur formation a diminué d’année en année. Y compris chez ceux qui avaient payé pour suivre les cours dits « vérifiés », quoique dans leur cas, la diminution ne s’observe que dans la dernière année.
En chiffres : parmi l’ensemble des inscrits, 6 % avaient terminé leur formation en 2013-2014, contre 3,13 % l’an dernier. Parmi les « vérifiés », le pourcentage avait augmenté de 50 à 56 % entre 2014-2015 et 2016-2017, pour retomber à 46 % l’an dernier.
Et le faible taux de retour est également troublant : parmi 1,1 million d’étudiants inscrits pour la première fois en 2015-2016, seulement 12 % étaient à nouveau inscrits l’année suivante. Ce « taux de retour » est en déclin depuis la deuxième année du programme (où il était de 38 %).
Les auteurs concluent par une leçon de prudence pour les milieux universitaires : une démocratisation de l’enseignement supérieur ne pourra pas se contenter de s’appuyer sur de nouvelles technologies, que ce soit la réalité virtuelle ou l’intelligence artificielle. « Une expansion significative des possibilités d’éducation chez les populations mal desservies nécessitera des efforts politiques pour changer l’orientation, le financement et les objectifs de l’enseignement supérieur. »
Working through Sipser, a textbook on Theoretical Computer Science
— with John K. GibbonsRepresentation of a Turing Machine,My friend John and I have been meeting weekly to workout, have dinner, and in some cases, to study together.Recently, John and I decided to work through a textbook on theoretical computer science together. For John, this was new material, and he wanted to apply to graduate school. For me, it was old hat; I had taken one undergraduate and one graduate course on this, about 30 years ago. Both of us are older than most students, but we are both dedicated to perpetual learning.More importantly, John was fascinated by the famous “P=NP” problem, and I am interested in the edges of “Church’s Thesis” (more on both below.)We chose Introduction to the Theory of Computation (Second Edition) by Michael Sipser. According to the preface, “This book is (...)
A realistic roadmap to becoming a #python developer
This is a highly opinionated, pseudo-motivational, unconventional and almost rant-like developer roadmap article.This article is more than a compilation of best books/videos/courses to learn Python and covers the bigger issues that a beginner/early-intermediate faces on their journey. These are the undocumented problems Stack Overflow does not solve.Why Python ?Why should you learn Python anyway? Why not one of the 20 other languages trending right now? As you’re beginning your journey, this questions crops up multiple times (a day).Picking your first #programming language is a lot like picking a starter pokemon.The inherent capabilities of a language are less significant than the skill of the programmer in using said language and their grit to make it into the big league.If you want to (...)