vpnMentor blog runs a post with a lengthy infographic ranking online censorship in different countries. There’s plenty of interesting data regarding torrents, social media, political media, pornography, and other types of online censorship targets.
Federacy has an interesting research in Docker image vulnerabilities. The bottom line is:
24% of latest Docker images have significant vulnerabilities
This can and should be improved, especially given the whole hierarchical structure of Docker images. It’s not like improving security of all those random GitHub repositories.
Here’s an interactive collection of the world’s biggest data breaches. It goes back to 2004, where about 92,000,000 email addresses and screen names were stolen by an AOL employee, and covers most of the major events up until and including 2016. There are a few ways to filter the data and change the representation.
Overall, should give you a pretty good idea of how safe and secure your online data is. Oh, and how private it is too.
For a large project at work, we need to integrate or develop a workflow engine. I worked a little bit with workflow engines in the past, but the subject is way to big and complex for me to claim any expertise in it.
So, I am looking at what’s available these days and what are our options. This post is a collection of initial links and thoughts, and it’s goal is mostly to document my research process and findings, and not to provide any answers or solutions yet.
Social Media Research Toolkit — a list of 50+ social media research tools curated by researchers at the Social Media Lab at Ted Rogers School of Management, Ryerson University. The kit features tools that have been used in peer-reviewed academic studies. Many tools are free to use and require little or no programming. Some are simple data collectors such as tweepy, a Python library for collecting Tweets, and others are a bit more robust, such as Netlytic, a multi-platform (Twitter, Facebook, and Instagram) data collector and analyzer, developed by our lab. All of the tools are confirmed available and operational.
This GitHub repository contains a list of the 10,000 most common English words, sorted by frequency, as seen by the Google Machine Translation Team.
Here at Google Research we have been using word n-gram models for a variety of R&D projects, such as statistical machine translation, speech recognition, spelling correction, entity detection, information extraction, and others. While such models have usually been estimated from training corpora containing at most a few billion words, we have been harnessing the vast power of Google’s datacenters and distributed processing infrastructure to process larger and larger training corpora. We found that there’s no data like more data, and scaled up the size of our data by one order of magnitude, and then another, and then one more – resulting in a training corpus of one trillion words from public Web pages.
We believe that the entire research community can benefit from access to such massive amounts of data. It will advance the state of the art, it will focus research in the promising direction of large-scale, data-driven approaches, and it will allow all research groups, no matter how large or small their computing resources, to play together. That’s why we decided to share this enormous dataset with everyone. We processed 1,024,908,267,229 words of running text and are publishing the counts for all 1,176,470,663 five-word sequences that appear at least 40 times. There are 13,588,391 unique words, after discarding words that appear less than 200 times.
There are a few variations of the list – with and without the swear words and such. I took a quick look at it and was surprised to find that “cyprus” is at position 4,993 (pretty high), immediately after the word “emails“. Weird!
(found via the link from this article)
Amitj Aggarwal, former Staff Engineer at Google (2008-2012), has collected a whole bunch of data in regards to engineers salaries, in USA and worldwide. His points seem to be overly optimistic at times, but I don’t have any links handy to contradict his research.
Here are a few points to get you started:
- Zoho, Salesforce pay 40% more than Oracle, Cisco, GE!!!
- Top 7% or so engineers at Netflix, Amazon, Google, Facebook are paid more than $1.4M per year. Next 10% make $700K on average.
- Facebook has lost relevance to Slack, LinkedIn, Snapchat, Pinterest and Quora. If you are working at Facebook ask for a 50% raise else move to a startup.
- Oracle is loosing to cloud startups. If you are working at Oracle ask for a 60% raise else move to a startup.
- ENGINEERS DO NOT WASTE MONEY ON AN MBA. You will make 2X more on average as an engineer.
- Tableau, Splunk, Slack, Airbnb, Quora, Twitter, Facebook, Google pay more than $320K salary to their top hires. Definitely interview at these fine places. Uber top engineer salaries are $190-340K per year.
- Starting salaries for fresh software engineering graduates is now $130K-160K. Ask shamelessly. For the best ones its ~$180K.
- Apple pays 60% more than Samsung.
An international team of astronomers have released two petabytes of data from the Pan-STARRS project that’s also known as the “world’s largest digital sky survey.” Two petabytes of data, according to the team, is equivalent to any of the following: a billion selfies, one hundred Wikipedias or 40 million four-drawer filing cabinets filled with single-spaced text. The scientists spent four years observing three-fourths of the night sky through their 1.8 meter telescope at Haleakala Observatories on Maui, Hawaii, scanning three billion objects in the Milky Way 12 times in five different filters. Those objects included stars, galaxies, asteroids and other celestial bodies.
Wow … this is mind blowing at the very least …
See the image above? That’s the result of half a million 45-second exposures taken over four years. They’re releasing even more detailed images and data in 2017 — for now, you can check out what the team released to the public on the official Pan-STARRS website.
“Nope. There’s at least 12 pubs missing from the north coast of Scotland. Thurso alone has more than 6, 2 in Bettyhill, Tongue and Melvich plus a few others all missing”, writes shaidy64
The source of the map is here referencing 24,727 UK pubs. And I’ve only been to like, what, 3? This situation urgently needs correction.
Pascal Birchler of the WordPress blogs some interesting research he did in the area of handling preferred language and how different systems – ranging from browsers, wikis, and social networks to all kinds of content management systems – approach and solve the problem.
Drupal 8 has a rather powerful user interface text language detection mechanism. There is a per session, per user and per browser option in the detection settings. However, users can only choose one language, so they cannot say (in core at least) that they want German primarily and Spanish if German is not available. But the language selected by the user is part of the larger fallback system, so it may fall back further down to other options.
The Language fallback module allows defining one fallback for a language, while the Language Hierarchy module provides a GUI to change the language fallback system. It allows setting up language hierarchies where translations of a site’s content, settings and interface can fall back to parent language translations, without ever falling back to English. This module might be the most interesting one for our research.
Apart from the research itself, I think this is an interesting example of how complex some seemingly simple features are.