Microsoft Issues Windows Out-of-Band Update That Disables Spectre Mitigations

An anonymous reader quotes BleepingComputer: Microsoft has issued on Saturday an emergency out-of-band Windows update that disables patches for the Spectre Variant 2 bug (CVE-2017-5715). The update — KB4078130 — targets Windows 7 (SP1), Windows 8.1, all versions of Windows 10, and all supported Windows Server distributions. Microsoft shipped mitigations for the Meltdown and Spectre bugs on January 3. The company said it decided to disable mitigations for the Spectre Variant 2 bug after Intel publicly admitted that the microcode updates it developed for this bug caused “higher than expected reboots and other unpredictable system behavior” that led to “data loss or corruption.” HP, Dell, and Red Hat took previous steps during the past week.

“We are also offering a new option — available for advanced users on impacted devices — to manually disable and enable the mitigation against Spectre Variant 2 (CVE 2017-5715) independently via registry setting changes…” Microsoft


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Microsoft issues emergency Windows update to disable Intel’s shoddy Spectre variant 2 mitigation

The computer industry is in utter chaos right now. Despite a slight increase in PC sales for Q4 2018, the market is still extremely unhealthy. Not to mention, pretty much all existing hardware is fundamentally flawed thanks to both Spectre and Meltdown vulnerabilities. At least major companies such as Intel, AMD, and Microsoft are working together to mitigate these risks, right? Wrong. These patches have proven to be problematic — for instance, some AMD computers were rendered unbootable. Ugh, what a failure. To make matters even worse, Intel’s Spectre variant 2 mitigation is causing instability (random reboots) on some Windows… [Continue Reading]


Original URL: https://betanews.com/2018/01/28/microsoft-windows-intel-spectre/

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How Generative Music Works

Let’s define a generative grammar for additive rhythm.

An additive rhythm grammar

Additive rhythm patterns consist of rhythmic cells.
A cell can be either two or three beats long.

Consists of cells that are two or three beats long

A two-beat cell is either two eighths or one quarter.

A two beat cell is two eights or one quarter

A three-beat cell is either three eighths, or an eighth and a quarter in either order.

A three beat cell is three eighths, or an eighth and a quarter.

Each quarter and eighth can be either a note or a rest.
Now we have hierarchically defined what a “cell”
means in this language, all the way down to the atomic level.

Quarters and eighths are notes or rests


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Netflix Hadoop Big Data Marketing Use Case

Netflix is a video streaming service that has a wealth of information about their user base likes, dislikes, general consumer habits, retention lengths and much more.
Netflix uses their big data to commission original programming content that it knows will succeed and be accepted in relevant published markets (O’Neill, 2016).
They perform various A/B tests to determine which variant of similar things perform higher, for example, when showing cover images for series or movies, they will at random show alternative images to determine which proves more reactive from their user base.
Volume:As of Q4 2017, Netflix has around 120 million subscribed users and counting (Statista, 2017). With a steady growth rate year on year, it is important that the company uses its immense data aggregation and analytics to drive new business and support investment into the platform.

The number of titles in Netflix’s database varies wildly from country to country. A recent report of


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Uber’s Schemaless design on Mysql

The making of Schemaless, Uber Engineering’s custom designed datastore using MySQL, which has allowed us to scale from 2014 to beyond. This is part one of a three-part series on Schemaless.

In Project Mezzanine we described how we migrated Uber’s core trips data from a single Postgres instance to Schemaless, our fault-tolerant and highly available datastore. This article further describes its architecture and the expanded role Schemaless has had in Uber’s infrastructure, and how it came to be.

Our Race for a New Database
In early 2014, we were running out of database space due to flourishing trip growth. Each new city and trip milestone pushed us to the precipice, to the point where we realized Uber’s infrastructure would fail to function by the end of the year: we simply couldn’t store enough trip data with Postgres. Our mission was to implement the next generation of database technology for Uber, a task that


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