# Decoding Decoding is the process of recovering original data from an encoded signal or representation, often despite noise or interference. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Encoding Schemes in FHE DevFeed: [Encoding Schemes in FHE]() Original publisher: [Read original article]() Published: 2023-09-18T11:08:39Z Content type: tutorial Language: en Sources: [Jeremy Kun]() Topics: [Encoding](), [FHE](), [homomorphic encryption](), [Cryptography](), [Decoding]() Tags: [bits](), [complex-numbers](), [cryptography](), [decoding](), [encoding](), [fhe](), [homomorphic-encryption](), [learning-with-errors](), [lwe](), [mathematics](), [number-theory](), [programming](), [residue-number-system](), [rlwe](), [scaling](), [scheme]() ### AI overview This article explains how cleartexts are transformed into plaintexts through encoding, and reversed through decoding, in homomorphic encryption. It catalogs encoding approaches for different HE schemes, including bit-field encoding for LWE and considerations for noise, scaling, bit positions, and programmable bootstrapping in CGGI/TFHE. ### Source excerpt In cryptography, we need a distinction between a cleartext and a plaintext. A cleartext is a message in its natural form. A plaintext is a cleartext that is represented in a specific way to prepare it for encryption in a specific scheme. The process of taking a cleartext and turning it into a plaintext is called encoding, and the reverse is called decoding. In homomorphic encryption, the distinction matters. Cleartexts are generally all integers, though the bit width of allowed integers can be restricted (e. ## Using createImageBitmap to reduce image-decoding jank DevFeed: [Using createImageBitmap to reduce image-decoding jank]() Original publisher: [Read original article]() Author: Paul Lewis Published: 2016-01-25T00:00:00Z Content type: article Language: en Sources: [Paul Lewis]() Topics: [Decoding](), [browser](), [Canvas](), [Chrome Canary](), [Firefox](), [JavaScript]() Tags: [browser](), [canvas](), [chrome-canary](), [decoding](), [firefox](), [images](), [jank](), [main-thread](), [perf](), [pixel]() ### AI overview The article explains how image decoding can block other paint tasks and cause checkerboarding or jank. It introduces createImageBitmap as a browser-managed way to decode images off the main paint flow, noting availability in Chrome Canary behind an experimental canvas flag and in Firefox 42 onward. ### Source excerpt Image decoding can be a source of checkboarding and jank. What if there was a way to work around it without resorting to a cluster of horror hacks? Step right up and meet my new friend createImageBitmap! ## The Codes of Solomon, Reed, and Muller DevFeed: [The Codes of Solomon, Reed, and Muller]() Original publisher: [Read original article]() Published: 2015-03-23T09:00:00Z Content type: tutorial Language: en Sources: [Jeremy Kun]() Topics: [Code](), [Decoding]() Tags: [code](), [coding-theory](), [decoding](), [hamming-code](), [information-theory](), [linear-codes](), [projection](), [reed-muller-code](), [reed-solomon-code]() ### AI overview This article introduces Reed-Solomon codes, explains the Singleton bound for codes with parameters (n,k,d)q, proves the bound using a projection map, and relates codes that meet the bound to maximum distance separable codes. It also places Reed-Muller codes in the broader discussion. ### Source excerpt Last time we defined the Hamming code. We also saw that it meets the Hamming bound, which is a measure of how densely a code can be packed inside an ambient space and still maintain a given distance. This time we'll define the Reed-Solomon code which optimizes a different bound called the Singleton bound, and then generalize them to a larger class of codes called Reed-Muller codes. In future posts we'll consider algorithmic issues behind decoding the codes, for now we just care about their existence and optimality properties. ## PostgreSQL 9.4 - Looking up (with JSONB and logical decoding) DevFeed: [PostgreSQL 9.4 - Looking up (with JSONB and logical decoding)]() Original publisher: [Read original article]() Author: Map Published: 2014-03-24T20:55:56Z Content type: article Language: en Sources: [Craig Kerstiens]() Topics: [PostgreSQL](), [JSON](), [Decoding](), [Database](), [Replication]() Tags: [decoding](), [indexing](), [jsonb](), [logical](), [postgres](), [postgresql]() ### AI overview This article examines PostgreSQL 9.4 features, focusing on JSONB and logical decoding. It explains how JSONB provides binary JSON document storage and how GIN indexes can automatically index document keys and values. The supplied text begins discussing logical decoding but is incomplete. ### Source excerpt Just a few weeks back I wrote a article discussing many of the things that were likely to miss making the 9.4 PostgreSQL release. Since that post a few weeks ago the landscape has already changed, and much more for the positive. The lesson here, is never count Postgres out. As Bruce discussed in a recent interview, Postgres is slow and steady, but much like the turtle can win the race. So onto the actual features: JSONB JSON has existed for a while in Postgres. Though the JSON that exists today simply validates that your text is valid JSON, then goes on to store it in a text field. This is fine, but not overly performant. If you do need some flexibility of your schema and performance without much effort then hstore may already work for you today, you can of course read more on this in an old post comparing hstore to json. But let's assume you do want JSON and a full document store, which is perfectly reasonable. Your option today is still best with the JSON datatype. And if you're retrieving full documents this is fine, however if you're searching/filtering on values within those documents then you need to take advantage of some functional indexing. You can do this some of the built-in operators or with full JS in Postgres. This is a little more work, but also very possible to get good performance. Finally, onto the perfect world, where JSON isn't just text in your database. For some time there's been a discussion around hstore and its future progress and of course the future of JSON in Postgres. These two worlds have finally heavily converged for PostgreSQL 9.4 giving you the best of both worlds. With what was known as hstore2, by The Russians under the covers, and collective efforts on JSONB (Binary representation of JSON) which included all the JSON interfaces you'd expect. We now have full document storage and awesome performance with little effort. Digging in a little further, why does it matter that its a binary representation? Well under the covers building o ## Stripping layer 2 in pcap DevFeed: [Stripping layer 2 in pcap]() Original publisher: [Read original article]() Author: Marek Published: 2013-01-28T23:00:00Z Content type: tutorial Language: en Sources: [Marek Majkowski]() Topics: [Network](), [TCP/IP](), [Code](), [Decoding]() Tags: [code](), [decoding](), [network](), [tcp-ip]() ### AI overview The article explains why extracting IP headers from captured TCP/IP packets requires removing the layer 2 header first. It discusses data-link types, hardcoded offsets, VLAN headers, and differing strategies for decoding packet layouts. ### Source excerpt Stripping layer 2 in pcap Say you want to sniff TCP/IP packets on your network. That's pretty easy, right? Use , receive packets from the network interface and we're done. But before you can extract the IP header from a received packet you need to strip layer 2 header. It's not that easy. tries to help only a bit - it is possible to get the data link type of the network interface. But this is useless without the knowledge of how to extract IP from given data link type. ## Cryptanalysis with N-Grams DevFeed: [Cryptanalysis with N-Grams]() Original publisher: [Read original article]() Published: 2012-02-03T13:52:16Z Content type: tutorial Language: en Sources: [Jeremy Kun]() Topics: [Computing](), [Cryptography](), [Decoding](), [Algorithm](), [Python](), [Encryption]() Tags: [algorithm](), [code](), [computing](), [cryptanalysis](), [decoding](), [linguistics](), [mathematics](), [natural-language-data](), [ngrams](), [programming](), [python](), [substitution-cipher](), [techniques]() ### AI overview This tutorial explains how to decode substitution ciphers using n-gram-based word segmentation and a local-search algorithm over possible decryptions. It introduces the relevant concepts, implements the approach in Python, and tests it on historical substitution ciphers. ### Source excerpt This post is the third post in a series on computing with natural language data sets. For the first two posts, see the relevant section of our main content page. A Childish Bit of Fun In this post, we focus on the problem of decoding substitution ciphers. First, we'll describe a few techniques humans use to crack ciphers. We'll find these unsatisfactory, and move on to a simplistic algorithm which does a local search on the space of all possible decryptions, where we utilize our word segmentation algorithm from last time to determine the likelihood that a decryption is correct.