It was well-known that PGP is vulnerable to short-ID collisions,
and many experiments were done to demonstrate that. [0]

Nevertheless, real attacks started in June, some developers found
their fake keys with same name, email, and even "same" fake signatures
by more fake keys in the wild, on the keyservers. [1]

All these keys have same short-IDs, created by collision attacks, led
with some discussions about the danger of short-IDs. Now, it is worth
to mention this issue again, since fake keys of Linus Torvalds, Greg Kroah-Hartman,
and other kernel devs are found in the wild recently.

> We don't know who is behind this, or what his purpose is. We just know this
> looks very evil.

Source: LKML: <1471274895@i2pmail …: Fake Linus Torvalds’ Key Found in the Wild, No More Short-IDs.

Clicks. Once upon a time they were the most powerful tool in assessing online ad performance. A humble beginning, but much has changed. The data-driven measurement and predictive analytics technologies that launched adtech and expanded to marketing are now being applied to nearly everything — and yet, it’s easy to forget the road that led here.

The genesis of big data

October 27, 1994: The first ad to hit the web is published.

It was difficult to recognize the significance at the time, but that single, floating rectangle at the top of my computer screen would bring into being an entirely unique market.

Adtech was invented — designed for this emerging sector, and built to measure the return of the new media creators produced.

Finally, ads could be understood.

Source: Big data’s humble beginnings | TechCrunch

Last year, I wrote a post entitled “Academics are humans with human emotions and human problems.” That post was motivated in part by the overwhelmingly positive response to my post on crying in science, and also by having read about a prominent philosopher, Peter Railton, who gave a talk at the American Philosophical Association meeting about his personal battles with depression. In my post, I said, “I very much agree with Railton and others that we need to be more open in these discussions. Being able to be a positive voice on these topics is a very important reason why I blog.” I then went on and talked about different things, including anxiety. But it was anxiety, with a little “a”, because, at that time, I wasn’t ready to be more open about having an anxiety disorder. But now I am. My goals with this post are two-fold: first, to state more openly and definitively that I have an anxiety disorder, and, second, to talk some about how I have managed that.

Source: Life as an anxious scientist | Dynamic Ecology

The Foundations of Data Science

By Ani Adhikari and John DeNero

Contributions by David Wagner

This is the textbook for the Foundations of Data Science class at UC Berkeley.

View this textbook online on Gitbooks.

Source: Introduction | Computational and Inferential Thinking

Computational biology enables and often drives biomedical research. The contribution of computational researchers may involve “routine” data analysis for supporting fundamental research, techniques for generating and testing complex biological hypotheses, and approaches to diagnosing diseases or guiding their treatment.

The journal Cell Systems recently asked 15 top researchers: “What Is the Key Best Practice for Collaborating with a Computational Biologist?” Answers to this question are crucial because biomedical research significantly relies on inter-disciplinary cooperation.

Source: Make hay while the sun shines – Stactivist

This post is part of a series covering the exercises from Andrew Ng’s machine learning class on Coursera. The original code, exercise text, and data files for this post are available here.

Part 1 – Simple Linear Regression
Part 2 – Multivariate Linear Regression
Part 3 – Logistic Regression
Part 4 – Multivariate Logistic Regression
Part 5 – Neural Networks
Part 6 – Support Vector Machines
Part 7 – K-Means Clustering & PCA
Part 8 – Anomaly Detection & Recommendation

One of the pivotal moments in my professional development this year came when I discovered Coursera. I’d heard of the “MOOC” phenomenon but had not had the time to dive in and take a class. Earlier this year I finally pulled the trigger and signed up for Andrew Ng’s Machine Learning class. I completed the whole thing from start to finish, including all of the programming exercises. The experience opened my eyes to the power of this type of education platform, and I’ve been hooked ever since.

Source: Machine Learning Exercises In Python, Part 1

Genome-wide association studies, which try to find correlations between particular genetic variations and disease diagnoses, are a staple of modern medical research.But because they depend on databases that contain people’s medical histories, they carry privacy risks. An attacker armed with genetic information about someone — from, say, a skin sample — could query a database for that person’s medical data. Even without the skin sample, an attacker who was permitted to make repeated queries, each informed by the results of the last, could, in principle, extract private data from the database.In the latest issue of the journal Cell Systems, researchers from MIT’s Computer Science and Artificial Intelligence Laboratory and Indiana University at Bloomington describe a new system that permits database queries for genome-wide association studies but reduces the chances of privacy compromises to almost zero.It does that by adding a little bit of misinformation to the query results it returns. That means that researchers using the system could begin looking for drug targets with slightly inaccurate data. But in most cases, the answers returned by the system will be close enough to be useful.And an instantly searchable online database of genetic data, even one that returned slightly inaccurate information, could make biomedical research much more efficient.

Source: Protecting privacy in genomic databases | MIT News

Editors think that scholars are bad writers, and they say so often and rudely. Academics think that journalists are lazy thinkers, and they’re no more polite. Neither is right, I think, but the fields are so twain that nobody really bothers to think about the why or the how or the what next except super-intellectual magazines that nobody reads.
In the hope of addressing some of the issues that rub up at the academic/writerly tectonic ridge, I spoke with an old friend, David Wolf, my first-ever editor, who is now the commissioning editor of The Guardian’s Long Read section. Nobody’s ever pissed me off more on this topic, nor do I care so much about what anybody else has to say.

Source: Can the Academic Write? — Part I — The Awl

Source: DjangoCon US 2016 – Django and React: Perfect Together by Jack McCloy – YouTube

DjangoCon US 2016: Philly

Source: DjangoConUS – YouTube