Academic Ranking of World Universities | ARWU | First World University Ranking | Shanghai Ranking java - Monotonic Pair - Codility Pseudonymization vs. Anonymization and How They Help With GDPR January 5, 2017 Pseudonymization and Anonymization are two distinct terms that are often confused in the data security world. With the advent of GDPR, it is important to understand the difference, since anonymized data and pseudonymized data fall under very different categories in the regulation. Pseudonymization and Anonymization are different in one key aspect. You can think about it in terms of authors. In practice, let’s look at tokenization. Here, with the pseudonymized data, we may not know the identity of the data subject, but we can correlate entries with specific subjects (records 1 and 7 reference the same person, records 2 and 5 reference the same person, records 3 and 4 reference the same person). Pseudonymization is a method to substitute identifiable data with a reversible, consistent value. With Anonymization, we must also be concerned about “indirect re-identification”. “50 people went to this coffee shop every morning.” “100 people got money from this ATM every Friday.”
the museum of science, art and human perception Walking The Beat - Mining Seattle's Police Report Data This week marks the completion of Y Combinator for Bayes Impact! As our Fall 2014 Fellowship ramps up (250+ applicants!), we wanted to do a blog post illustrating how exactly we can use data to understand public services better. Tip 1: before analyzing the data we should understand when the events happened, and if the system that records the data, also known as the data generating mechanism, is biased to a particular period of time. Our natural intuition may want to ask does criminal activity vary according to the day of the week? First we will just check how many Mondays, Tuesdays, etc have data recorded in the dataset: Great! There are lots of different types of crimes here, some that are very similar to each other and some that are very different. Tip 2: We can simplify large categorical variables by binning them into a few major categories We solve this problem by defining a simpler category for crime type which can be "minor", "serious" or "violent".
* Algorithm (Photography) - Definition - Lexicon & Encyclopedia AlgorithmAn algorithm describes a set procedure to complete a task in computing. It comprises the actions needed to complete a specific task, or for solving a specific problem. [>>>] How do algorithms listen to music?From copyright monitoring and cover song detection to classifications of all kinds (genre, style, mood, key, year, epoch), all the way to the music curation war waged by the music streaming titans, ... [>>>] Demosaicing Algorithms: Color FilteringPrev NEXTA more economical and practical way to record the primary colors is to permanently place a filter called a color filter array over each individual photosite. [>>>] The one clear thing about how the Instagram ~[⇑] works is that posts with high engagement rates show up first on users' feeds. [>>>] ~[⇑]s[edit]Simple interpolation[edit]These ~[⇑]s are examples of multivariate interpolation on a uniform grid, using relatively straightforward mathematical operations on nearby instances of the same color component. [>>>]
What’s the “problem” with MOOCs? « EdTechDev In case the quotes didn’t clue you in, this post doesn’t argue against massive open online courses (MOOCs) such as the ones offered by Udacity, Coursera, and edX. I think they are very worthy ventures and will serve to progress our system of higher education. I do however agree with some criticisms of these courses, and that there is room for much more progress. I propose an alternative model for such massive open online learning experiences, or MOOLEs, that focuses on solving “problems,” but first, here’s a sampling of some of the criticisms of MOOCs. Criticisms of MOOCs Khan Academy The organization is unclear and it lacks sufficient learner support.The videos aren’t informed by research and theory on how people learn, and this may diminish the effectiveness of his videos. Are MOOCs a Horseless Carriage? In the book How People Learn (which can be read free online), John Bransford shared the story of Fish is Fish. MOOC or MMORPG? From MOOC to MOOLE Who’s the teacher in a MOOLE?
Lecture 1 - How to Start a Startup This text is annotated! Click on the highlights to read what others are saying. If you'd like to add your own insights, comments, or questions to specific parts of the lecture, visit the lecture page on Genius, highlight the relevant text, and click the button that pops up. Your annotation will appear both here and on Genius. Welcome to CS183B. We've taught a lot of this class at YC and it's all been off the record. I'm only teaching three. All of the advice in this class is geared towards people starting a business where the goal is and eventually building a very large company. Ideas, Products, Teams and Execution Part I So the four areas: You need a great idea, a great product, a great team, and great execution. You may still fail. One of the exciting things about startups is that they are a surprisingly even playing field. Before we jump in on the how, I want to talk about why you should start a startup. The specific passion should come first, and the startup second. Thank you.
Experts on the Pros and Cons of Algorithms Algorithms are instructions for solving a problem or completing a task. Recipes are algorithms, as are math equations. Computer code is algorithmic. The internet runs on algorithms and all online searching is accomplished through them. Email knows where to go thanks to algorithms. Algorithms are often elegant and incredibly useful tools used to accomplish tasks. The British pound dropped 6.1% in value in seconds on Oct. 7, 2016, partly because of currency trades triggered by algorithms.Microsoft engineers created a Twitter bot named “Tay” this past spring in an attempt to chat with Millennials by responding to their prompts, but within hours it was spouting racist, sexist, Holocaust-denying tweets based on algorithms that had it “learning” how to respond to others based on what was tweeted at it.Facebook tried to create a feature to highlight Trending Topics from around the site in people’s feeds. Theme 1: Algorithms will continue to spread everywhere Theme 2: Good things lie ahead