nature556

2 yıl öncediscuss

lol, it's wierd

2 yıl öncediscuss

What does the discovery of the Bootkitty UEFI bootkit for Linux systems suggest about the evolving landscape of cybersecurity threats?

nature556

2 yıl öncediscuss

This is a nice parallel to the real world: it can't function without a reasonably sized middle class that supports and sustains it.

nature556

2 yıl öncediscuss

Love this brand. I've been meaning to buy something Polyend for a while, looks amazing.

nature556

2 yıl öncediscuss

The FaCT DSL is used to define functions with predictable execution times to prevent timing leaks. In this notebook, functions have been created to simulate varying computational complexity (e.g., sorting, searching).

nature556

2 yıl öncediscuss

Abstract Real-world cryptographic code is often written in a subset of C intended to execute in constant-time, thereby avoiding timing side channel vulnerabilities. This C subset eschews structured programming as we know it: if-statements, looping constructs, and procedural abstractions can leak timing information when handling sensitive data. The resulting obfuscation has led to subtle bug

nature556

2 yıl öncediscuss

Hello hackers, I made this app of what happens behind SHA-256 algorithm, and some cool graphs.

nature556

2 yıl öncediscuss

This will be crazy

nature556

2 yıl öncediscuss

:/ Wierd

nature556

2 yıl öncediscuss

This is nice usecase for faker and dbm, cool

nature556

2 yıl öncediscuss

I don't get it at all why to use a pre-print servers?

nature556

2 yıl öncediscuss

Oh cool, thank you for the example notebook

nature556

2 yıl öncediscuss

The 20 minutes notebook is great for quick explanation, thx

nature556

2 yıl öncediscuss

In MapReduce, the *reducer* function processes the output generated by the *mapper* function. Here’s a breakdown of how it works and what calculations it typically performs:

1. *Mapper Function*: This function processes input data and produces a set of key-value pairs. For example, if the task is to count words in a text, the mapper function would output pairs like `("word", 1)` for each occurrence of a word.

2. *Shuffle and Sort*: After mapping, the MapReduce framework shuffles and sorts these key-value pairs by key, grouping together all values associated with the same key. This organizes the data so that the reducer can work on each key individually.

3. *Reducer Function*: The reducer function then takes each key and the list of values associated with it. It performs a calculation, typically an aggregation, on this list of values.

   - **Example Calculation (Sum)**: For a word count, if the key is `"word"`, the reducer would receive all the `1`s associated with that word and sum them up, giving the total count for that word. 
   - **Other Calculations**: The reducer might perform other aggregations like finding the maximum or minimum value, averaging, or concatenating values depending on the task.

In summary, the reducer aggregates or processes each key’s values returned by the mapper function, completing the overall transformation.

nature556

2 yıl öncediscuss

I think it's important to have high quality TTS on arbitrary web articles. reply

Submissions

Timing-Sensitive Analysis in Python

Fact [pdf]

Visualize Your SHA-256

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