New submitter nichogenius writes: The latest observation run of LIGO and VIRGO only started April 1st, but has already observed another black hole merger. The LIGO detectors have been offline since the 25th of August, 2017 for a series of upgrades. The latest observational run is the first run where gravitational wave events are being publicly announced as they happen rather than being announced weeks or months later. Few details of the merger are available at this time, but there is some information available on LIGO's twitter and raw details can be obtained from LIGO's event database page.

Gravitational detection events are being publicly broadcast using NASA's VOEvent system. If you know a bit of python, you can setup your own VOEvent client using the pygcn module with example code available in this tutorial.

C++ has knocked machine-learning favorite Python out of the top 3 in the TIOBE Index of popular programming languages. From a report: It marks a reversal of fortune for C++, which, after years of occupying third place in the index, was pushed down to fourth place by Python in September last year. First and second place in the list remain unchanged, with Java in pole position and C at number two. The TIOBE Index attempts to estimate the popularity of languages worldwide based on results from major search engines. The index is sometimes criticized for being a rather blunt measure, likely to be influenced by a range of factors beyond a language's popularity, but its rankings are broadly in line with others, with a similar mix of languages albeit arranged in a different order.

In an analysis alongside the latest figures, TIOBE attributes the comeback of C++ to a surge in its popularity, rather than a fall in the use of Python. "This is certainly not because Python is in decline: Python is scoring all time highs almost every month. It is just that C++ is also getting more and more popular," it writes. The report credits this growing interest in C++ to C++11, the version of the language released in 2011 that TIOBE said made C++ "much simpler, safer and more expressive."

Lego Education, the education-focused arm of the veteran Denmark company, is making its biggest product debut in three years, unveiling Spike Prime, a new kit that aims to mix the company's familiar bricks with motors, sensors and introductory coding lessons. The company is targeting kids aged between 11 to 14. From a report: Lego Mindstorms have been around for years. The Mindstorms EV3 robotics kit remains a staple of many learning centers and robotics classrooms. Lego's newest kit looks more like Lego Boost, a programmable kit that aimed to win over families in 2017 and was compatible with regular Lego bricks. It's compatible with Lego Boost, Lego Technic sets and classic Lego pieces, but not with Lego's previous Mindstorms accessories. Lego Mindstorms EV3 is remaining alongside Lego Spike Prime in Lego Education's lineup, and looks like it's aiming more at the high school crowd, while Lego Spike Prime could bridge to that higher-end projects.

The Spike Prime set is created specifically for grades six to eight. It uses an app that uses visual Scratch programming and aims to adopt the Python programming language by the end of the year, according to Lego Education executives. The robots made by Spike Prime look cute, and Lego Boost-like, but not necessarily as complicated as some Mindstorm kits. The central processing hub that drives the Lego Spike Prime robotics creations has six input and output ports, and connects with sensors including an RGB color and light sensor, a force-sensitive touch sensor, and an ultrasonic distance sensor for measurement and navigation.

An anonymous reader shares a report: In 2004, Geoffrey Hinton doubled down on his pursuit of a technological idea called a neural network. It was a way for machines to see the world around them, recognize sounds and even understand natural language. But scientists had spent more than 50 years working on the concept of neural networks, and machines couldn't really do any of that. Backed by the Canadian government, Dr. Hinton, a computer science professor at the University of Toronto, organized a new research community with several academics who also tackled the concept. They included Yann LeCun, a professor at New York University, and Yoshua Bengio at the University of Montreal.

On Wednesday, the Association for Computing Machinery, the world's largest society of computing professionals, announced that Drs. Hinton, LeCun and Bengio had won this year's Turing Award for their work on neural networks. The Turing Award, which was introduced in 1966, is often called the Nobel Prize of computing, and it includes a $1 million prize, which the three scientists will share. More: The Godfathers of the AI Boom Win Computing's Highest Honor; Hinton Says We Need To Start Over; Bengio is Worried About Its Future; and Deep Learning May Need a New Programming Language That's More Flexible Than Python, LeCun Says.

A new report from the open source security company WhiteSource asks the question, "Is one programming language more secure than the rest?"

An anonymous reader quotes TechRepublic: To answer this question, the report compiled information from WhiteSource's database, which aggregates information on open source vulnerabilities from sources including the National Vulnerability Database, security advisories, GitHub issue trackers, and popular open source projects issue trackers. Researchers focused in on open source security vulnerabilities in the seven most widely-used languages of the past 10 years to learn which are most secure, and which vulnerability types are most common in each...

The most common vulnerabilities across most of these languages are Cross-SiteScripting (XSS); Input Validation; Permissions, Privileges, and Access Control; and Information Leak / Disclosure, according to the report.
Across the seven most widely-used programming languages, here's how the vulnerabilities were distributed:

• C (47%)
• PHP (17%)
• Java (11%)
• JavaScript (10%)
• Python (5%)
• C++ (5%)
• Ruby (4%)

But the results are full of disclaimers -- for example, that C tops the list because it's the oldest language with "the highest volume of written code" and "is also one of the languages behind major infrastructure like Open SSL and the Linux kernel."

The report also notes a "substantial rise" across all languages for known open source security vulnerabilities over the last two years, attributing this to more awareness about vulnerable components -- thanks to more research, automated security tools, and "the growing investment in bug bounty programs" -- as well as the increasing popularity of open source software. And it also reports a drop in the percentage of critical vulnerabilities for most languages -- except JavaScript and PHP.

The report then concludes that "the Winner Of Most Secure Programming Language is...no one and everyone...! It is not about the language itself that makes it any more or less secure, but how you use it. If you are mitigating your vulnerabilities throughout the software development lifecycle with the proper management approach, then you are far more likely to stay secure."

Coincidentally, WhiteSource sells software which monitors open source components throughout the software development lifecycle to provide alerts about security (and licensing) issues.

A recent experiment by Josh Frantz, a senior security consultant at Rapid7, suggests that users are taking few if any steps to protect their private information before releasing their used devices back out into the wild. From a report: For around six months, he collected used desktop, hard disks, cellphones and more from pawn shops near his home in Wisconsin. It turned out they contain a wealth of private data belonging to their former owners, including a ton of personally identifiable information (PII) -- the bread and butter of identity theft. Frantz amassed a respectable stockpile of refurbished, donated, and used hardware: 41 desktops and laptops, 27 pieces of removable media (memory cards and flash drives), 11 hard disks, and six cellphones. The total cost of the experiment was a lot less than you'd imagine. "I visited a total of 31 businesses and bought whatever I could get my hands on for a grand total of around $600," he said.

Frantz used a Python-based optical character recognition (OCR) tool to scan for Social Security numbers, dates of birth, credit card information, and other sensitive data. And the result was, as you might expect, not good. The pile of junk turned out to contain 41 Social Security numbers, 50 dates of birth, 611 email accounts, 19 credit card numbers, two passport numbers, and six driver's license numbers. Additionally, more than 200,000 images were contained on the devices and over 3,400 documents. He also extracted nearly 150,000 emails.

Deep learning may need a new programming language that's more flexible and easier to work with than Python, Facebook AI Research director Yann LeCun said today. From an interview: It's not yet clear if such a language is necessary, but the possibility runs against very entrenched desires from researchers and engineers, he said. LeCun has worked with neural networks since the 1980s. "There are several projects at Google, Facebook, and other places to kind of design such a compiled language that can be efficient for deep learning, but it's not clear at all that the community will follow, because people just want to use Python," LeCun said in a phone call with VentureBeat. "The question now is, is that a valid approach?" Further reading: Facebook joins Amazon and Google in AI chip race.

Over 20,000 programmers from more than 150 different countries provided answers for the second annual Python Developers Survey (conducted by the Python Software Foundation and JeBrains).

An anonymous reader submitted this condensed version of their results: 84% of Python users in our survey use Python as their main language...up 5 percentage points from 79% in 2017. But half of all Python users in the survey also use JavaScript, and 47% more say they use HTML/CSS. Reported use of Bash/Shell has also grown from 36% in 2017 to 45% in 2018. [Later 93% of respondents said that their activities included Software testing/Writing automated tests.] Python users who report that they also use Go and SQL have both increased by 2 percentage points, while many other languages (including C/C++, Java, and C#) have decreased their share...

When asked "What do you use Python for?" data analysis has become more popular than Web development, growing from 50% in 2017 to 58% in 2018. Machine learning also grew by 7 percentage points. These types of development are experiencing faster growth than Web development, which has only increased by 2 percentage points when compared to the previous year...

Almost two-thirds of respondents selected Linux as their development environment OS. Most people are using free or open source databases such as PostgreSQL, MySQL, or SQLite... Twenty-something was the prevalent age range among our respondents, with almost a third being in their thirties. [31% more were between the ages of 30 and 39.]

An AI-enhanced tool that suggests code snippets for Python developers in real time just raised $17 million in VC funding to expand its R&D team "with a focus on accelerating developer productivity."

An anonymous reader quotes VentureBeat: "Our mission is to bring the latest advancements in AI and machine learning (ML) to make writing code fluid, effortless, and more enjoyable," explained [founder Adam] Smith. "Developers using Kite can focus their productive energy toward solving the next big technical challenges, instead of searching the web for code examples illustrating mundane and frequently repeated code patterns...."

Instead of relying on the cloud to run its AI engine, Kite now runs locally on a user's computer, letting developers use it offline and without having to upload any code. (Kite still trains its machine learning models with thousands of publicly available code sources from highly rated developers.) Furthermore, running locally allows Kite to fully operate with lower latencies... In addition to ditching the cloud, the new version of Kite brings a feature the team calls Line-of-Code Completions. Until now, Kite's machine learning models could only suggest the next "token" in a line of code. Line-of-Code Completions can complete entire function calls with a single keystroke... The team boasts that Kite is "the only developer product on the market to offer such advanced completions."
"Today, Kite is used by more than 30,000 Python developers worldwide," reports VentureBeat, adding it locally-based ML plugin is available for top Python IDEs including Visual Studio Code, Atom, Sublime Text, PyCharm, IntelliJ, and Vim.

Kite's investors include the CEO of GitHub, as well as the founders of Dropbox, Paypal, and Twitch.tv, and the company hopes to eventually support more languages, starting with either Java, JavaScript, or Go.

Python "is often described as being slow when it comes to performance... But is that truly the case?" writes Patrick Wohlschlegel, Arm's senior product manager for infrastructure and high-performance computing tools.

Slashdot reader igor.sfiligoi writes: Effectively profiling Python has always been a pain. Arm recently announced that their Arm Forge is now able to profile both Python and compiled code.
It's available for any hardware architecture, Wohlschlegel writes, adding that developers "typically assume that most of the execution time is spent in compiled, optimized C/C++ or Fortran libraries (e.g. NumPy) which are called from Python..."

"How confident are you that your application is not wasting your precious computing resources for the wrong reasons?"

Tech Republic re-visits the story of the earliest attempts to build the Raspberry Pi, and the dramatic launch of a quest "to rekindle the curiosity about computing in a generation immersed in technology but indifferent to how it worked." [T]he dominant computers -- games consoles and later tablets and smartphones -- no longer offered an invitation to create, but rather to consume. Eben Upton recalls a bonfire party in 2007 where an 11-year-old boy told him he wanted to be an electrical engineer, and his disappointment at realizing the boy didn't have access to a computer he could program on. "I said, 'Oh, what computer have you got?'. He said, 'I've got a Nintendo Wii'. And there was just that awful feeling about there being a kid who was excited, a kid who was showing concrete interest in our profession, and who didn't have access to a programmable computer, a computer of any sort. He just had a games console."

At this time Upton was working as a system-on-a-chip architect at chip designer Broadcom, and realized he had the skills to try to halt this drift away from computers that encouraged users to code.
Upton describes the Raspberry Pi as "a very conscious attempt" to bring back the easily programmable home computers that he remembered as a child in the 1980s -- and he was gratified at its success. "Even early on you started to see those pictures of kids lying on the living room floor, looking up at the TV with Raspberry Pi plugged into it, the same way we used to."

It was named "Pi" because it booted into a version of Python, and Raspberry because "There's a lot of fruit-named computer companies, and the 'blowing a raspberry' thing was also deliberate."

It's gone on to become the world's third best-selling general-purpose computer.

Free online courses -- also called Massive Open Online Courses -- have quite a year. An anonymous reader shares a blog post: It's been seven years since these courses rose to prominence, when a few Stanford professors decided to offer their courses online free. Since then over over 900 universities have launched over 11,000 courses. And in its seven years these Massive Online Open Courses (MOOCs) have achieved new milestone: 100 million plus learners. [...] At the end of every year, I do an extensive analysis of the MOOC space. To help me with analysis, I send the top MOOC providers a set of questions, one of them being the top enrolled courses of 2018.

The list below contains the top enrolled courses from the major MOOC providers: Coursera, edX, Udacity, and FutureLearn. Combined, these providers represent a big chunk of the MOOC learners (70 million plus). Without further ado, here are the most popular free online university courses of 2018:
Learning How to Learn: Powerful mental tools to help you master tough subjects from University of California, San Diego.
Machine Learning from Stanford University.
The Science of Well-Being from Yale University.
Introduction to Computer Science and Programming Using Python from Massachusetts Institute of Technology.
CS50's Introduction to Computer Science from Harvard University.

A Bloomberg columnist asks whether this week's rise in bitcoin's price is a turning point -- or just a "dead cat bounce"? After hitting a year's low of about $3,143, down about 80 percent from January highs, Bitcoin has risen 27 percent this week. Short-sellers are closing their positions, while fans smell fresh opportunity. Even more eye-watering market moves are happening elsewhere in the digital currency's ecosystem. Bitcoin Cash, a spin-off intended to be more usable as a payments mechanism, has almost tripled this week from about $80 to $225. That this is happening at the same time as a U.S. stock-market selloff will no doubt warm the hearts of crypto-evangelists, who believe their currencies offer genuine alternatives for where to put money in times of trouble....

A cursory glance at the price of Bitcoin Cash over the past year shows that it has fallen about 95 percent from its December 2017 record. So, anyone refusing to crystallize their losses this year has seen their 98-percent loss narrow over the past few days to, well, 95 percent. Celebrating now is like the Monty Python knight calling it a draw after losing all his limbs. It's not entirely clear either what kind of investor has the appetite, let alone the resources, to make meaningful bets on digital currencies today after a boom-and-bust cycle driven entirely by speculative hype rather than the adoption of Bitcoin in the real world. The long-awaited wave of money from Wall Street looks as far away as ever. So we're probably getting back to more natural territory for crypto: True believers and small-time gamblers.
Their conclusion? "One still can't rule out that these particular crypto-cats are dead."

The Python Software Foundation has settled on a new governance model for the programming language Python. The decision to come up with a new model was made after Python creator and chief Guido van Rossum stepped down as the "Benevolent Dictator For Life" (BDFL). SDTimes: The new governance model will rely on a five-person steering council to establish standard practices for introducing new features to the Python programming language. Based on tested methods, the proposal was designed to be "boring," comprehensive, flexible and lightweight, the steering council model document explained. "We're not experts in governance, and we don't think Python is a good place to experiment with new and untried governance models," software developers Nathaniel Smith and Donald Stufft explained in the Python documentation.

"So this proposal sticks to mature, well-known, previously tested processes as much as possible. The high-level approach of a mostly-hands-off council is arguably the most common across large successful F/OSS projects, and low-level details are derived directly from Django's governance." The steering council will serve as the "court of final appeal" for changes to the language and will have broad authority over the decision-making process, including the ability to accept or reject PEPs (Python Enhancement Proposals) (such as the one used to introduce this governance model), enforce and update the project's code of conduct, create subcommittees and manage project assets. But the intended goal of the council is to take a more hands-off and occasional approach to flexing its powers, Smith and Stufft explained.

quantic_oscillation7 shares a report: The latest notes from the Debian anti-harassment team on Wednesday caught my attention when reading, "We were requested to advice on the appropriateness of a certain package in the Debian archive. Our decision resulted in the package pending removal from the archive." Curiosity got the best of me... What package was deemed too inappropriate for the Debian archive?

When digging further, the package raised to the Debian Anti-Harassment Team was "Weboob." Weboob is short for "Web Outside of Browsers" as it's an open-source collection of software to script and automate the parsing/scraping/gathering-via-API of web data so that it can be consumed by different modules/applications. Weboob.org describes itself as "Weboob is a collection of applications able to interact with websites, without requiring the user to open them in a browser. It also provides well-defined APIs to talk to websites lacking one."

Weboob is Python-based and offers Qt-based user interfaces for accessing these different modules for reading data from different web-sites outside of any conventional web browser. Those interested can learn more about the software at Weboob.org. But, yes, the name is juvenile and likely inappropriate in most professional/corporate environments.

Steve Dower, a Python developer at Microsoft, describes how the language become popular internally: In 2010, our few Pythonistas were flying under the radar, in case somebody noticed that they could reassign a few developers to their own project. The team was small, leftover from a previous job, but was chipping away at a company culture that suffered from "not invented here" syndrome: Python was a language that belonged to other people, and so Microsoft was not interested. Over the last eight years, the change has been dramatic. Many Microsoft products now include Python support, and some of the newest only support Python. Some of our critical tools are written in Python, and we are actively investing in the language and community....

In 2018, we are out and proud about Python, supporting it in our developer tools such as Visual Studio and Visual Studio Code, hosting it in Azure Notebooks, and using it to build end-user experiences like the Azure CLI. We employ five core CPython developers and many other contributors, are strong supporters of open-source data science through NumFOCUS and PyData, and regularly sponsor, host, and attend Python events around the world.
"We often felt like a small startup within a very large company" Downer writes, in a post for the Medium community "Microsoft Open Source Stories."

Microsoft's Visual Basic .NET now ranks above JavaScript, PHP, SQL on TIOBE's index of programming language popularity, which ZDNet notes is "the highest it's ever been since [TIIOBE] started tracking the Microsoft language in 2001." Tiobe analysts said it was "very surprising" that Visual Basic .Net is now the fifth most popular language, only behind C++, Python, C, and Java. It's even ahead of JavaScript, which currently lies in seventh place, down from sixth a year ago. C# meanwhile fell from fifth spot a year ago to sixth this month. The language index still reckons Visual Basic .Net will "sooner or later go into decline", but concedes it's popular for dedicated office applications in small and medium enterprises, and is probably still used by many developers because it's easy to learn.
TIOBE's methodology "basically...comes down to counting hits for the search query +"<language> programming," TIOBE explains on its web page -- though its results don't always agree with other analysts.

InfoWorld points out that on this month's PyPL Popularity of Programming Language index, which analyzes how often language tutorials are searched for on Google, VB.NET "doesn't even register Visual Basic.Net or Visual Basic among its Top 10 languages" -- and JavaScript comes in third, behind only Python and Java.

A recent TechCrunch article claimed to have identified the best indicator of programming language popularity: GitHub's annual "State of the Octoverse" reports. So Austin-based technology reporter Mike Melanson explored the new verdict in GitHub's 2018 report: It felt to me like the overarching theme of the numbers was one of quiet stasis for the year past, at least when it comes to those languages deemed the cream of the crop. One of the first graphics offered in the post shows the top languages according to the number of repositories created and we see that everything seems to be flowing along, just as it has for the last decade. While GitHub points to a "steady uptick" for JavaScript after 2011, it looks like this list of languages hasn't changed much over time. [The graphic shows the four most popular languages -- every year since early 2014 -- have been JavaScript, Java, Python, and PHP.]

When we look at the top languages according to the number of contributors, we see a similar story, with the top four languages mirrored. In this chart, of course, we see that Ruby is on a steady decline, while Typescript is on a steady rise. The only surprise to be seen here is that C, after a brief uptick in popularity, has taken a bit of a nosedive over the past year. Either way, seven of 10 languages have the same exact ranking....

Finally, beyond the language rankings themselves, GitHub offers a wonderful analysis of just what it is that makes a particular language popular in 2018, boiling it down to three key characteristics: thread safety, interoperability, and being open source.
GitHub's report also identifies its fastest growing languages over the last year -- including Kotin, TypeScript, Rust, Python, and Go. "This year, TypeScript shot up to #7 among top languages used on the platform overall, after making its way in the top 10 for the first time last year," the report notes.

"TypeScript is now in the top 10 most used languages across all regions GitHub contributors come from -- and across private, public, and open source repositories."

GitHub saw more than 67 million pull requests this year -- more than a third of GitHub's "lifetime" total of 200 million pull requests since its launch in 2008. It now hosts 96 million repositories, and has over 31 million contributors -- including 8 million who just joined within the last 12 months.

These are among the facts released in GitHub's annual "State of the Octoverse" report -- a surprising number of which involve Microsoft.

• GitHub's top project this year, by contributor count, was Microsoft's Visual Studio Code (with 19,000 contributors), followed by Facebook's React Native (10,000), TensorFlow (9,300) and Angular CLI (8,800) -- as well as Angular (7,600) -- and the open source documentation for Microsoft Azure (7,800).

• Microsoft now has more employees contributing to open source projects than any other company or organization (7,700 employees), followed by Google (5,500), Red Hat (3,300), U.C. Berkeley (2,700), and Intel (2,200).

• The open source documentation for Microsoft Azure is GitHub's fastest-growing open source project, followed by PyTorch (an open source machine learning library for Python).

• Among the "Cool new open source projects" is an Electron app running Windows 95.

But more than 2.1 million organizations are now using GitHub (including public and private repositories) -- which is 40% more than last year -- and the report offers a fun glimpse into the minutiae of life in the coding community.

Read on for more details.

What do Heartbleed, WannaCry, and million dollar iPhone bugs have in common? From a report: One bug affects iPhones, another affects Windows, and the third affects servers running Linux. At first glance these might seem unrelated, but in reality all three were made possible because the software that was being exploited was written in programming languages which allow a category of errors called "memory unsafety." By allowing these types of vulnerabilities, languages such as C and C++ have facilitated a nearly unending stream of critical computer security vulnerabilities for years.

Imagine you had a program with a list of 10 numbers. What should happen if you asked the list for its 11th element? Most of us would say an error of some sort should occur, and in a memory safe programming language (for example, Python or Java) that's what would happen. In a memory unsafe programming language, it'll look at wherever in memory the 11th element would be (if it existed) and try to access it. Sometimes this will result in a crash, but in many cases you get whatever happens to be at that location in memory, even if that portion of memory has nothing to do with our list. This type of vulnerability is called a "buffer-overflow," and it's one of the most common types of memory unsafety vulnerabilities. HeartBleed, which impacted 17 percent of the secure web servers on the internet, was a buffer-overflow exploit, letting you read 60 kilobytes past the end of a list, including passwords and other users' data.