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17 Dec '12

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Re: [cryptography] Gmail and SSL
by coderman 17 Dec '12

17 Dec '12

On Sat, Dec 15, 2012 at 12:23 PM, Andy Steingruebl <andy(a)steingruebl.com> wrote: > I think what you really want is the ability within Google's interface to > specify how you'd like the certificate verified. yes; this is what i want. for Google to arbitrarily enforce a decision is dumb and not useful.

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by ERO SEX SEX 17 Dec '12

17 Dec '12

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Re: [serval-project-dev] Implementing a different routing protocol
by Eugen Leitl 17 Dec '12

17 Dec '12

----- Forwarded message from Jeremy Lakeman <jeremy(a)servalproject.org> -----

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[liberationtech] Social Media Combatants
by Eugen Leitl 17 Dec '12

17 Dec '12

----- Forwarded message from Gregory Foster <gfoster(a)entersection.org> -----

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too big to jail
by Eugen Leitl 17 Dec '12

17 Dec '12

Historical times. http://www.guardian.co.uk/business/2012/dec/11/hsbc-fine-prosecution-money-…

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Analyzing the Veracity of Tweets during a Major Crisis
by Eugen Leitl 17 Dec '12

17 Dec '12

http://irevolution.net/2010/09/19/veracity-of-tweets-during-a-major-crisis/ Analyzing the Veracity of Tweets during a Major Crisis Posted on September 19, 2010 | 16 Comments A research team at Yahoo recently completed an empirical study (PDF) on the behavior of Twitter users after the 8.8 magnitude earthquake in Chile. The study was based on 4,727,524 indexed tweets, about 20% of which were replies to other tweets. What is particularly interesting about this study is that the team also analyzed the spread of false rumors and confirmed news that were disseminated on Twitter. The authors b manually selected some relevant cases of valid news items, which were confirmed at some point by reliable sources.b In addition, they b manually selected important cases of baseless rumors which emerged during the crisis (confirmed to be false at some point).b Their goal was to determine whether users interacted differently when faced with valid news vs false rumors. The study shows that about 95% of tweets related to confirmed reports validated that information. In contrast only 0.03% of tweets denied the validity of these true cases. Interestingly, the results also show that b the number of tweets that deny information becomes much larger when the information corresponds to a false rumor.b In fact, about 50% of tweets will deny the validity of false reports. The table below lists the full results. The authors conclude that b the propagation of tweets that correspond to rumors differs from tweets that spread news because rumors tend to be questioned more than news by the Twitter community. Notice that this fact suggests that the Twitter community works like a collaborative filter of information. This result suggests also a very promising research line: it could posible to detect rumors by using aggregate analysis on tweets.b I think these findings are particularly important for projects like *Swift River, which try to validate crowdsourced crisis information in real-time. I would also be interested to see a similar study on tweets around the Haitian earthquake to explore whether this b collaborative filterb dynamic is an emergent phenomena in this complex systems or simply an artifact of something else. Interested in learning more about b information forensicsb? See this link and the articles below: Predicting the Credibility of Disaster Tweets Automatically Automatically Ranking Credibility of Tweets During Major Events Six Degrees of Separation: Implications for Verifying Social Media How to Verify Crowdsourced Information from Social Media Truth in Age of Social Media: Social Computing & Big Data Challenge Truthiness as Probability: Moving Beyond the True or False Dichotomy when Verifying Social Media How to Verify and Counter Rumors in Social Media Crowdsourcing for Human Rights Monitoring: Challenges and Opportunities for Information Collection & Verification Rapidly Verifying the Credibility of Sources on Twitter Accelerating the Verification of Social Media Content

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Author Identification
by Eugen Leitl 17 Dec '12

17 Dec '12

http://www.uni-weimar.de/medien/webis/research/events/pan-13/pan13-web/auth… Author Identification Authorship attribution is an important problem in many areas including information retrieval and computational linguistics, but also in applied areas such as law and journalism where knowing the author of a document (such as a ransom note) may be able to save lives. The most common framework for testing candidate algorithms is a text classification problem: given known sample documents from a small, finite set of candidate authors, which if any wrote a questioned document of unknown authorship? It has been commented, however, that this may be an unreasonably easy task. A more demanding problem is author verification where given a set of documents by a single author and a questioned document, the problem is to determine if the questioned document was written by that particular author or not. This may more accurately reflect real life in the experiences of professional forensic linguists, who are often called upon to answer this kind of question. Given a small set (no more than 10, possibly as few as one) of "known" documents by a single person and a "questioned" document, the task is to determine whether the questioned document was written by the same person who wrote the known document set. One problem comprises a set of known documents by a single person and a questioned document. There will be several such problems covering English, Greek, and Spanish (about 20 cases per language) and a varying number of known documents (1-10). All documents within a single problem will be in the same language and best efforts will be applied to assure that within-problem documents are matched for genre, register, theme, and date of writing. The documents will possibly be fragmentary, with a minimum length of 1,000 words. View details ; Download data (Release by mid-December) Participants are asked to provide a simple "yes/no" binary answer for each problem. Grading will be based on the percentage of correct answers. Beyond the accuracy on the entire corpus, separate rankings will be provided for the subsets of problems for each language. In addition, participants may also provide a score, a real number in the set [0,1] inclusive, where 0 corresponds to NO and 1 to YES. In that case, ROC curves will be produced and the area under the curve will be used to grade participant systems. We refer you to: PAN @ CLEF'12 (overview paper), PAN @ CLEF'11 (overview paper), Patrick Juola. Authorship Attribution. In Foundations and Trends in Retrieval, Volume 1, Issue 3, December 2006. Moshe Koppel, Jonathan Schler, and Shlomo Argamon. Computational Methods Authorship Attribution. Journal of the American Society for Information Science and Technology, Volume 60, Issue 1, pages 9-26, January 2009. Efstathios Stamatatos. A Survey of Modern Authorship Attribution Methods. of the American Society for Information Science and Technology, Volume 60, Issue 3, pages 538-556, March 2009. We ask you to prepare your software so that in can be executed via a command line. However, you can choose freely among the available programming languages and among the operating systems Microsoft Windows 7 and Ubuntu 12.04. We will ask you to deploy your software onto a virtual machine that will be made accessible to you after registration. You will be able to reach the virtual machine via ssh and via remote desktop.

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Re: [liberationtech] Mailvelope: OpenPGP Encryption for Webmail
by Eugen Leitl 17 Dec '12

17 Dec '12

----- Forwarded message from Karel Bmlek <kb(a)karelbilek.com> -----

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[liberationtech] DIY Drones
by Eugen Leitl 17 Dec '12

17 Dec '12

----- Forwarded message from Jeffrey Burdges <burdges(a)gmail.com> -----

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