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Jan "Starbug" Krissler, the Chaos Computer Club researcher who broke the fingerprint reader security on the new Iphone, had given a long interview to Zeit Online explaining his process and his thoughts on biometrics in general. The CCC's Alex Antener was good enough to translate the interview for us; I've included some of the most interesting bits after the jump.

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jcatcw writes "A recent study shows that a single random up-vote, randomly chosen, created a herding behavior in ratings that resulted in a 25% increase in the ratings but the negative manipulation had no effect. An intuitive explanation for this asymmetry is that we tend to go along with the positive opinions of others, but we tend to be skeptical of the negative opinions of others, and so we go in and correct what we think is an injustice. The third major result was that these effects varied by topic. So in business and society, culture, politics, we found substantial susceptibility to positive herding, whereas in general news, economics, IT, we found no such herding effects in the positive or negative direction."

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ComicStix asked:

I'm a freshman Computer Science student and we just started doing some actual projects in Python. I have found I'm very efficient when I use the pen and paper method that my professor suggested in class. But when I can't write my problem down and work my algorithms out on paper I am really slow. During labs, I always seem to have to take the assignment back to my dorm. When I get there and write it out I solve the problem that took me the whole class in like 5 minutes.

Maybe it's because I get stressed seeing people solving labs before me. Or maybe it's the pen and paper method.

I was browsing through forums and someone wrote that if you have to write your programs on paper then you shouldn't be a programmer. I'm really worried because I'm so much better when I can see what the program is doing and track my way through it before typing actual code. Am I doing something wrong?

See the original question here.

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The National Security Agency and its UK counterpart have made repeated and determined attempts to identify people using the Tor anonymity service, but the fundamental security remains intact, as top-secret documents published on Friday revealed.

The classified memos and training manuals—which were leaked by former NSA contractor Edward Snowden and reported by The Guardian, show that the NSA and the UK-based Government Communications Headquarters (GCHQ) are able to bypass Tor protections, but only against select targets and often with considerable effort. Indeed, one presentation slide grudgingly hailed Tor as "the king of high-secure, low-latency Internet anonymity." Another, titled "Tor Stinks," lamented: "We will never be able to de-anonymize all Tor users all the time."

An article published separately by The Washington Post also based on documents provided by Snowden concurred.

"There is no evidence that the NSA is capable of unmasking Tor traffic routinely on a global scale," the report said. "But for almost seven years, it has been trying."

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Hugh Pickens DOT Com writes "Computer Scientist Daniel Lemire has had an interesting discussion going on at his site about the ideas in software that are universally recognized as useful. 'Let me put it this way: if you were to meet a master of software programming, what are you absolutely sure he will recommend to a kid who wants to become a programmer?' Lemire's list currently includes structured programming; Unix and its corresponding philosophy; database transactions; the 'relational database;' the graphical user interface; software testing; the most basic data structures (the heap, the hash table, and trees) and a handful of basic algorithms such as quicksort; public-key encryption and cryptographic hashing; high-level programming and typing; and version control. 'Maybe you feel that functional and object-oriented programming are essential. Maybe you think that I should include complexity analysis, JavaScript, XML, or garbage collection. One can have endless debates but I am trying to narrow it down to an uncontroversial list.' Inspired by Lemire, Philip Reames has come up with his own list of 'Things every practicing software engineer should aim to know.'"

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Charlie Stross really, really hates Microsoft Word. So much so that he's written a 1600-word essay laying out the case for Word as a great destroyer of creativity, an agent of anticompetitive economic destruction, and an enemy of all that's decent and right in the world. It's actually a pretty convincing argument.

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This aricle, F1: A Distributed SQL Database That Scales by Srihari Srinivasan, is republished with permission from a blog you really should follow: Systems We Make - Curating Complex Distributed Systems.

With both the F1 and Spanner papers out its now possible to understand their interplay a bit holistically. So lets start by revisiting the key goals of both systems.

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With both the F1 and Spanner papers out its now possible to understand their interplay a bit holistically. So lets start by revisiting the key goals of both systems.

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Abstract
This paper presents Polybase, a feature of SQL Server PDW V2 that allows users to manage and query data stored in a Hadoop
cluster using the standard SQL query language. Unlike other database systems that provide only a relational view over HDFSresident data through the use of an external table mechanism, Polybase employs a split query processing paradigm in which
SQL operators on HDFS-resident data are translated into MapReduce jobs by the PDW query optimizer and then executed on the Hadoop cluster. The paper describes the design and implementation of Polybase along with a thorough performance evaluation that explores the benefits of employing a split query processing paradigm for executing queries that involve both structured data in a relational DBMS and unstructured data in Hadoop. Our results demonstrate that while the use of a splitbased query execution paradigm can improve the performance of some queries by as much as 10X, one must employ a cost-based query optimizer that considers a broad set of factors when deciding whether or not it is advantageous to push a SQL operator to Hadoop. These factors include the selectivity factor of the predicate, the relative sizes of the two clusters, and whether or not their nodes are co-located. In addition, differences in the semantics of the Java and SQL languages must be carefully considered in order to avoid altering the expected results of a query.

Link to the paper

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