AI Writing Tools

Explore the best AI Writing Tools — independent reviews, comparisons, pricing and step-by-step how-to guides, curated by Aizhi.

Unit of work

A unit of work is a behavioral pattern in software development. Martin Fowler has defined it as everything one does during a business transaction which can affect the database. When the unit of work is finished, it will provide everything that needs to be done to change the database as a result of the work. A unit of work encapsulates one or more code repositories[de] and a list of actions to be performed which are necessary for the successful implementation of self-contained and consistent data change. A unit of work is also responsible for handling concurrency issues, and can be used for transactions and stability patterns.[de]
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SIGINT Activity Designator

A SIGINT Activity Designator (or SIGAD) identifies a signals intelligence (SIGINT) line of collection activity associated with a signals collection station, such as a base or a ship. For example, the SIGAD for Menwith Hill in the UK is USD1000. SIGADs are used by the signals intelligence agencies of Australia, Canada, New Zealand, the United Kingdom, and the United States (the Five Eyes). There are several thousand SIGADs including the substation SIGADs denoted with a trailing alpha character. Several dozen of these are significant. The leaked Boundless Informant reporting screenshot showed that it summarized 504 active SIGADs during a 30-day period in March 2013. == General format == A SIGAD consists of five to eight case insensitive alphanumeric characters. It takes the general form of an alphanumeric designator normally composed of a two- or three-letter prefix followed by one to three numbers. Often a dash is used to separate the alphabetic and numeric characters in the primary part of the designator, but less frequently a space is used as a separator or the alphabetic and numeric characters are concatenated together. An additional alphabetic character can be added to denote a sub-designator for a subset of the primary unit, such as a detachment. Lastly, a numeric character can be added after the aforementioned alphabetic to provide for a sub-sub-designator. In the examples below an X represents an alphabetic character and an N represents a numeric character that are part of the primary designator. Likewise, an x represents an alphabetic character and an n represents a numeric character that are part of a sub-designator. Here are valid generalized examples of SIGADs: The first two characters show which country operates the particular SIGINT facility, which can be US for the United States, UK for the United Kingdom, CA for Canada, AU for Australia and NZ for New Zealand. A third letter shows what sort of staff runs the station. SIGADs beginning with US without a third letter are used for intercept facilities run by the NSA. == PRISM SIGAD == One prominent SIGAD as of April 2013 is US-984XN, with an unclassified codename of PRISM. It is "the number one source of raw intelligence used for NSA analytic reports" according to National Security Agency sources in a document leaked by Edward Snowden. The President's Daily Brief, an all-source intelligence product, cited SIGAD US-984XN as a source in 1,477 items in 2012. The U.S. government operates the PRISM electronic surveillance collection program through NSA's Special Source Operations, an alliance with trusted telecommunications providers. == SIGADs for spy ships == The declassified SIGAD for the USS Liberty (AGTR-5) was USN-855. The USS Liberty incident occurred on 8 June 1967, during the Six-Day War, when Israeli Air Force jet fighter aircraft and Israeli Navy motor torpedo boats attacked the USS Liberty in international waters. The USS Pueblo (AGER-2) was a technical research ship, which was boarded and captured by North Korean forces on 23 January 1968, in what is known as the Pueblo incident. The declassified SIGAD for the NSA Direct Support Unit (DSU) from the Naval Security Group (NSG) on the USS Pueblo patrol involved in the incident was USN-467Y. The USS Pueblo, which officially remains a commissioned vessel of the United States Navy, is the only ship of the U.S. Navy currently being held captive. == Vietnam War SIGADs == The following are the Vietnam War-era declassified SIGADs from inside South Vietnam during the period of 1969 to 1975: Some locations have multiple SIGADs due to different types of collection activities and/or collection at different times during the period. The SIGADs beginning with USA were operated by the United States Air Force's United States Air Force Security Service (USAFSS). The SIGADs beginning with USM were operated by the United States Army's Army Security Agency (ASA). Lastly, the SIGADs beginning with USN were operated by the United States Navy's Naval Security Group (NAVSECGRU). All three of these units have been merged into other units or inactivated. The above list consists of the higher-echelon SIGADs. It does not include the numerous miscellaneous and temporary detachments, or direction finding stations belonging to major units or sites unless that detachment or site was the only one stationed in South Vietnam. Many of the "dets" were short-lived, often formed to support ongoing MACV operations or forward deployments of combat operational or maneuver units. These detachments usually were designated by a letter suffix attached to the higher-echelon SIGAD such as "USM-633J," which was a detachment of the 372d Radio Research Company, USM-633, supporting the United States Army's 25th Infantry Division. === Supporting Southeast Asia SIGADs === The following declassified SIGADs were highly relevant to the Vietnam Campaign, but were located in areas outside of South Vietnam in Southeast Asia. Again, detachments are not listed separately. In the case of the USS Maddox, naval Direct Support Units (DSUs) used the SIGAD USN-467 as a generic designator for their missions. Each specific patrol received a letter suffix for its duration. The subsequent mission would receive the next letter in an alphabetic sequence. Thus, SIGAD USN-467N specifically designates the USS Maddox patrol involved with the Gulf of Tonkin incident. == Joint Base SIGADs == In November 2005, the US Congress performed a fifth round of Base Realignment and Closure. This 2005 law also created twelve joint bases by merging adjacent installations belonging to different services in an effort to reduce costs and improve efficiencies. Joint bases with a primarily SIGINT mission have SIGADs that begin with USJ. A joint base would have a primary SIGAD in the general form of USJ-NNN, where NNN are numeric characters. An actual example is not given, since these units are currently active.
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SocialIQ

Social IQ (formerly Soovox Inc.) was a San Diego-based influencer marketing platform that measured users' online social influence and connected them with brands for word-of-mouth marketing campaigns. The company was founded in 2009 by Akram Benmbarek and was headquartered in San Diego, California. == History == Akram Benmbarek, who had previously worked in technology finance at Advanced Equities Financial Corp and in wealth management at Morgan Stanley, Merrill Lynch, and UBS, founded the company in mid-2009 under the name Soovox. In October 2011, Benmbarek rebranded the company as SocialIQ. At that time, the company was seeking a Series A round of venture capital, having raised under $1 million in angel seed funding. == Similar metrics == Klout PeerIndex
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Kaeli McEwen

Kaeli Mae McEwen (born May 10, 2000), known professionally as Kaeli Mae, is an American content creator and social media influencer from Seattle, Washington, known for her TikTok videos about cleaning and organizing and contributing to the "Clean Girl" Internet aesthetic. She has Type 1 diabetes. Her fame was attributed to an increase in use of the name Kaeli for newborn girls in the United States in 2023.
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Emergent algorithm

An emergent algorithm is an algorithm that exhibits emergent behavior. In essence an emergent algorithm implements a set of simple building block behaviors that when combined exhibit more complex behaviors. One example of this is the implementation of fuzzy motion controllers used to adapt robot movement in response to environmental obstacles. An emergent algorithm has the following characteristics: it achieves predictable global effects it does not require global visibility it does not assume any kind of centralized control it is self-stabilizing Other examples of emergent algorithms and models include cellular automata, artificial neural networks and swarm intelligence systems (ant colony optimization, bees algorithm, etc.).
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Convergent encryption

Convergent encryption, also known as content hash keying, is a cryptosystem that produces identical ciphertext from identical plaintext files. This has applications in cloud computing to remove duplicate files from storage without the provider having access to the encryption keys. The combination of deduplication and convergent encryption was described in a backup system patent filed by Stac Electronics in 1995. This combination has been used by Farsite, Permabit, Freenet, MojoNation, GNUnet, flud, and the Tahoe Least-Authority File Store. The system gained additional visibility in 2011 when cloud storage provider Bitcasa announced they were using convergent encryption to enable de-duplication of data in their cloud storage service. == Overview == The system computes a cryptographic hash of the plaintext in question. The system then encrypts the plaintext by using the hash as a key. Finally, the hash itself is stored, encrypted with a key chosen by the user. == Known Attacks == Convergent encryption is open to a "confirmation of a file attack" in which an attacker can effectively confirm whether a target possesses a certain file by encrypting an unencrypted, or plain-text, version and then simply comparing the output with files possessed by the target. This attack poses a problem for a user storing information that is non-unique, i.e. also either publicly available or already held by the adversary - for example: banned books or files that cause copyright infringement. An argument could be made that a confirmation of a file attack is rendered less effective by adding a unique piece of data such as a few random characters to the plain text before encryption; this causes the uploaded file to be unique and therefore results in a unique encrypted file. However, some implementations of convergent encryption where the plain-text is broken down into blocks based on file content, and each block then independently convergently encrypted may inadvertently defeat attempts at making the file unique by adding bytes at the beginning or end. Even more alarming than the confirmation attack is the "learn the remaining information attack" described by Drew Perttula in 2008. This type of attack applies to the encryption of files that are only slight variations of a public document. For example, if the defender encrypts a bank form including a ten digit bank account number, an attacker that is aware of generic bank form format may extract defender's bank account number by producing bank forms for all possible bank account numbers, encrypt them and then by comparing those encryptions with defender's encrypted file deduce the bank account number. Note that this attack can be extended to attack a large number of targets at once (all spelling variations of a target bank customer in the example above, or even all potential bank customers), and the presence of this problem extends to any type of form document: tax returns, financial documents, healthcare forms, employment forms, etc. Also note that there is no known method for decreasing the severity of this attack -- adding a few random bytes to files as they are stored does not help, since those bytes can likewise be attacked with the "learn the remaining information" approach. The only effective approach to mitigating this attack is to encrypt the contents of files with a non-convergent secret before storing (negating any benefit from convergent encryption), or to simply not use convergent encryption in the first place.
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Torus interconnect

A torus interconnect is a switch-less network topology for connecting processing nodes in a parallel computer system. == Introduction == In geometry, a torus is created by revolving a circle about an axis coplanar to the circle. While this is a general definition in geometry, the topological properties of this type of shape describes the network topology in its essence. === Geometry illustration === In the representations below, the first is a one dimension torus, a simple circle. The second is a two dimension torus, in the shape of a 'doughnut'. The animation illustrates how a two dimension torus is generated from a rectangle by connecting its two pairs of opposite edges. At one dimension, a torus topology is equivalent to a ring interconnect network, in the shape of a circle. At two dimensions, it becomes equivalent to a two dimension mesh, but with extra connection at the edge nodes. === Torus network topology === A torus interconnect is a switch-less topology that can be seen as a mesh interconnect with nodes arranged in a rectilinear array of N = 2, 3, or more dimensions, with processors connected to their nearest neighbors, and corresponding processors on opposite edges of the array connected.[1] In this lattice, each node has 2N connections. This topology is named for the lattice formed in this way, which is topologically homogeneous to an N-dimensional torus. == Visualization == The first 3 dimensions of torus network topology are easier to visualize and are described below: 1D Torus: one dimension, n nodes are connected in closed loop with each node connected to its two nearest neighbors. Communication can take place in two directions, +x and −x. A 1D Torus is the same as ring interconnection. 2D Torus: two dimensions with degree of four, the nodes are imagined laid out in a two-dimensional rectangular lattice of n rows and n columns, with each node connected to its four nearest neighbors, and corresponding nodes on opposite edges connected. Communication can take place in four directions, +x, −x, +y, and −y. The total nodes of a 2D Torus is n2. 3D Torus: three dimensions, the nodes are imagined in a three-dimensional lattice in the shape of a rectangular prism, with each node connected with its six neighbors, with corresponding nodes on opposing faces of the array connected. Each edge consists of n nodes. communication can take place in six directions, +x, −x, +y, −y, +z, −z. Each edge of a 3D Torus consist of n nodes. The total nodes of 3D Torus is n3. ND Torus: N dimensions, each node of an N dimension torus has 2N neighbors, Communication can take place in 2N directions. Each edge consists of n nodes. Total nodes of this torus is nN. The main motivation of having higher dimension of torus is to achieve higher bandwidth, lower latency, and higher scalability. Higher-dimensional arrays are difficult to visualize. The above ruleset shows that each higher dimension adds another pair of nearest neighbor connections to each node. == Performance == A number of supercomputers on the TOP500 list use three-dimensional torus networks, e.g. IBM's Blue Gene/L and Blue Gene/P, and the Cray XT3. IBM's Blue Gene/Q uses a five-dimensional torus network. Fujitsu's K computer and the PRIMEHPC FX10 use a proprietary three-dimensional torus 3D mesh interconnect called Tofu. === 3D Torus performance simulation === Sandeep Palur and Dr. Ioan Raicu from Illinois Institute of Technology conducted experiments to simulate 3D torus performance. Their experiments ran on a computer with 250GB RAM, 48 cores and x86_64 architecture. The simulator they used was ROSS (Rensselaer’s Optimistic Simulation System). They mainly focused on three aspects: Varying network size Varying number of servers Varying message size They concluded that throughput decreases with the increase of servers and network size. Otherwise, throughput increases with the increase of message size. === 6D Torus product performance === Fujitsu Limited developed a 6D torus computer model called "Tofu". In their model, a 6D torus can achieve 100 GB/s off-chip bandwidth, 12 times higher scalability than a 3D torus, and high fault tolerance. The model is used in the K computer and Fugaku. === Cost === While long wrap-around links may be the easiest way to visualize the connection topology, in practice, restrictions on cable lengths often make long wrap-around links impractical. Instead, directly connected nodes—including nodes that the above visualization places on opposite edges of a grid, connected by a long wrap-around link—are physically placed nearly adjacent to each other in a folded torus network. Every link in the folded torus network is very short—almost as short as the nearest-neighbor links in a simple grid interconnect—and therefore low-latency.
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HKDF

HKDF is a multi-purpose key derivation function (KDF) based on the HMAC message authentication code. HKDF follows "extract-then-expand" paradigm, where the KDF logically consists of two modules: the first stage takes the input keying material and "extracts" from it a fixed-length pseudorandom key, and then the second stage "expands" this key into several additional, independent pseudorandom keys as the output of the KDF. == Mechanism == HKDF is the composition of two functions, HKDF-Extract and HKDF-Expand: HKDF(salt, IKM, info, length) = HKDF-Expand(HKDF-Extract(salt, IKM), info, length) === HKDF-Extract === HKDF-Extract (XTR) takes "input key material" or "source key material" (IKM or SKM) such as a shared secret generated using Diffie-Hellman; an optional, non-secret, random or pseudorandom salt (r); and generates a cryptographic key called the PRK ("pseudorandom key"). HKDF-Extract acts as a "randomness extractor", specifically a "computational extractor", taking a potentially non-uniform value of sufficient min-entropy and generating a value indistinguishable from a uniform random value (pseudorandom). Computational extractors assume attackers are computationally bounded and source entropy may only exist in a computational sense. Such extractors can be built using cryptographic functions under suitable assumptions, modeled as universal hash function (in the generic case) or a random oracle (in constrained scenarios like sources with weak entropy). Salt (r) acts as a "source-independent extractor", strengthening HKDF's security guarantees. Using a fixed public r is safe for multiple invocations of HKDF (on "independent" but secret IKMs which may or may not be derived from the same source), provided r isn't chosen or manipulated by an attacker. Ideally, r is a random string of hash function's output length. Even low quality r (weak entropy or shorter length) is recommended as they contribute "significantly" to the security of the OKM. Without or with a low-entropy, non-secret r, if an attacker can influence the IKMs source in a way that specifically exploits HKDF-Extract's underlying hash function (finding a collision or a specific bias), XTR provides no protection. A random r, even if fixed by the application (for example, random number generators using r as seed), would strengthen protections for that specific extractor session. In such a setting, sufficiently long IKMs also provide better entropy extraction. However, allowing the attacker to influence enough of the IKM after seeing r may result in a completely insecure KDF. HKDF-Extract is the result of HMAC with r as the key (all zeros up to length of the underlying extractor hash function, if not provided) and the IKM as the message. The underlying hash function used for HKDF-Extract step may be different to the one used by HKDF-Expand. It is recommended that HKDF-Extract uses strongest hash function available to the application, as it "concentrates" the entropy already present in IKM but may not necessarily "add" to it. Truncated output from a stronger underlying hash function for XTR (for example, SHA512/256) offers stronger extraction properties. The attacker is assumed to have partial knowledge about IKM (publicly known values in the case of Diffie-Hellman) or partial control over it (entropy pools). HKDF-Extract may be skipped if the IKM is itself a cryptographically strong key (and hence can assume the role of PRK), though it is recommended that HKDF-Extract be applied for the sake of compatibility with the general case, especially if r is available to the application. === HKDF-Expand === HKDF-Expand (PRF) takes the PRK (or any random key-derivation key if HKDF-Extract step is skipped), optional info (CTXinfo), and a length (L), to generate output key material (OKM) of length L. Multiple OKMs can be generated from a single PRK by using different values for CTXinfo, which must be "independent" of the IKM passed in HKDF-Extract. Even if an attacker, who knows r and some auxillary information about the secret IKM, can force the use of the same IKM (and PRK, by extension), in two or more HKDF-Expand contexts (represented by CTXinfo), the OKMs output are computationally independent (leak no useful information on each other). HKDF-Expand, acting as a variable-output-length pseudorandom function (PRF) keyed on PRK, calls HMAC on CTXinfo as the message (empty string, if unspecified) appended to a 8-bit counter i initialized to 1. Subsequent calls to HMAC are chained in "feedback mode" by prepending the previous HMAC output to CTXinfo and incrementing i. OKM is a function of the output size (k bits) of HMAC's underlying hash function; i.e., SHA-256 outputs OKM in segments of k=256 bits for up to a maximum of length i × k bits (255 × 256 bits = 8160 bytes) truncated to desired length L. HKDF-Expand may be skipped if PRK is at least desired length L, though it is recommended that HKDF-Expand be applied for additional "smoothing" of the OKM. == Standardization == HKDF was proposed as a building block in various protocols and applications, as well as to discourage the proliferation of multiple KDF mechanisms by its authors. It is formally described in RFC 5869 with detailed analysis in a paper published in 2010. NIST SP800-56Cr2 specifies a parameterizable extract-then-expand scheme, noting that RFC 5869 HKDF is a version of it and citing its paper for the rationale for the recommendations' extract-and-expand mechanisms. == Applications == HKDF is used in the Signal Protocol for end-to-end encrypted messaging where it generates the message keys, in conjunction with the triple Elliptic-curve Diffie-Hellman handshake (X3DH) key agreement protocol. Signal's "Secure Value Recovery" and "Sealed Sender" are based on HKDF. HKDF is a main component in the Noise Protocol Framework, Message Layer Security, and is used in widely deployed protocols like IPsec Internet Key Exchange and TLS 1.3. The "multi-purpose" nature of HKDF is meant to serve applications that require key extraction, key expansion, and key hierarchies in key wrapping, key exchange, PRNG, and password-based key derivation schemes. == Implementations == There are implementations of HKDF for C#, Go, Java, JavaScript, Perl, PHP, Python, Ruby, Rust, and other programming languages. RFC6234 lays out a reference C implementation of HKDF based on the Secure Hash Standard. === Example in Python ===
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ImHex

ImHex is a free cross-platform hex editor available on Windows, macOS, and Linux. ImHex is used by programmers and reverse engineers to view and analyze binary data. == History == The initial release of the project in November 2020, saw significant interest on GitHub. == Features == Features include: Hex editor Custom pattern matching and analysis scripting language Visual, node based data pre-processor Disassembler Running and visualizing of YARA rules Bookmarks Binary data diffing Additional Tools MSVC, Itanium, D and Rust name demangler ASCII table Calculator Base converter File utilities IEEE 754 floating point decoder Division by invariant multiplication calculator TCP/IP client and server Support for: Data importing and exporting ASCII string, Unicode string, numeric, hexadecimal and regular expressions search Byte manipulation File hashing Plug-ins
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AS2

AS2 (Applicability Statement 2) is a specification on how to transport structured business-to-business data securely and reliably over the Internet. Security is achieved by using digital certificates and encryption. == Background == AS2 was created in 2002 by the IETF to replace AS1, which they created in the early 1990s. The adoption of AS2 grew rapidly throughout the early 2000s because major players in the retail and fast-moving consumer goods industries championed AS2. Walmart was the first major retailer to require its suppliers to use the AS2 protocol instead of relying on dial-up modems for ordering goods. Amazon, Target, Lowe's, Bed, Bath, & Beyond and thousands of others followed suit. Many other industries use the AS2 protocol, including healthcare, as AS2 meets legal HIPAA requirements. In some cases, AS2 is a way to bypass expensive value-added networks previously used for data interchange. == Technical overview == AS2 is specified in RFC 4130, and is based on HTTP and S/MIME. It was the second AS protocol developed and uses the same signing, encryption and MDN (as defined by RFC3798) conventions used in the original AS1 protocol introduced in the late 1990s by IETF. In other words: Files are encoded as "attachments" in a standardized S/MIME message (an AS2 message). AS2 does not specify the contents of the files. Usually, the file contents are in a standardized format that is separately agreed upon, such as XML or EDIFACT. AS2 messages are always sent using the HTTP or HTTPS protocol (Secure Sockets Layer — also known as SSL — is implied by HTTPS) and usually use the "POST" method (use of "GET" is rare). Messages can be signed, but do not have to be. Messages can be encrypted, but do not have to be. Messages may request a Message Disposition Notification (MDN) back if all went well, but do not have to request such a message. If the original AS2 message requested an MDN: Upon the receipt of the message and its successful decryption or signature validation (as necessary) a "success" MDN will be sent back to the original sender. This MDN is typically signed but never encrypted (unless temporarily encrypted in transit via HTTPS). Upon the receipt and successful verification of the signature on the MDN, the original sender will "know" that the recipient got their message (this provides the "Non-repudiation" element of AS2). If there are any problems receiving or interpreting the original AS2 message, a "failed" MDN may be sent back. However, part of the AS2 protocol states that the client must treat a lack of an MDN as a failure as well, so some AS2 receivers will not return an MDN in this case. Like any other AS file transfer, AS2 file transfers typically require both sides of the exchange to trade X.509 certificates and specific "trading partner" names before any transfers can take place. AS2 trading partner names can usually be any valid phrase. === MDN options === Unlike AS1 or AS3 file transfers, AS2 file transfers offer several "MDN return" options instead of the traditional options of "yes" or "no". Specifically, the choices are: ==== AS2 w/ "Sync" MDNs ==== Return Synchronous MDN via HTTP(S) ("AS2 Sync") - This popular option allows AS2 MDNs to be returned to AS2 message sender clients over the same HTTP connection they used to send the original message. This "MDN while you wait" capability makes "AS2 Sync" transfers the fastest of any type of AS file transfer, but it also keeps this flavor of MDN requests from being used with large files (which may time out in low-bandwidth situations). ==== AS2 w/ "ASync" MDNs ==== Return Asynchronous MDN via HTTP(S) (a.k.a. "AS2 Async") - This popular option allows AS2 MDNs to be returned to the AS2 message sender's server later over a different HTTP connection. This flavor of MDN request is usually used if large files are involved or if your trading partner's AS2 server has poor Internet service. ==== AS2 w/ "Email" MDNs ==== Return (Asynchronous) MDN via Email - This rarely used option allows AS2 MDNs to be returned to AS2 message senders via email rather than HTTP. Otherwise, it is similar to "AS2 Async (HTTP)". ==== AS2 w/ No MDNs ==== Do not return MDN - This option works like it does in any other AS protocol: the receiver of an AS2 message with this option set simply does not try to return an MDN to the AS2 message sender. ==== Filename preservation ==== AS2 filename preservation feature will be used to communicate the filename to the trading partner. The banking industry relies on filenames being communicated between trading partners. AS2 vendors are currently certifying that implementation of filename communication conforms to the standard and is interoperable. There are two profiles for filename preservation being optionally tested under AS2 testing: Filename preservation without MDN responses Filename preservation with an associated MDN response certification Walmart recommends contacting Drummond Group, LLC for more information on EDIINT AS2, or for a list of interoperable-testing AS2 software providers. == Benefits == For many businesses, the use of AS2 and electronic data interchange (EDI) is not a choice so much as it is a requirement of doing business with a large customer or partner. That said, AS2 is a universal protocol that has benefits, from both business and technology vantage points. === Business case === Cut costs by using the web for EDI file transfers, AS2 reduces the cost of transactions from expensive VANs. Extend EDI to more partners; with lower costs and universal web connectivity, AS2 allows organizations to implement EDI with partners worldwide that have little EDI infrastructure. Save time by eliminating the need to manually process orders. Eliminate errors by turning manual processes into automated processes. Universal solution — AS2 is established and tested, so no one has to re-invent the wheel. === Technological advantages === Leverage the web: if an organization can share data securely via the web, they already have much of the infrastructure for AS2. Unlimited EDI data — there are no practical limitations on transaction sizes via the web, and AS2 includes features for managing large transfers. Payload Agnostic — AS2 can be used to transport any type of document. While EDI X12, EDIFACT and XML are common, any mutually agreed-upon format may be transferred.
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Client-side encryption

Client-side encryption is the cryptographic technique of encrypting data on the sender's side, before it is transmitted to a server such as a cloud storage service. Client-side encryption features an encryption key that is not available to the service provider, making it difficult or impossible for service providers to decrypt hosted data. Client-side encryption allows for the creation of applications whose providers cannot access the data its users have stored, thus offering a high level of privacy. Applications utilizing client-side encryption are sometimes marketed under the misleading or incorrect term "zero-knowledge", but this is a misnomer, as the term zero-knowledge describes something entirely different in the context of cryptography. == Details == Client-side encryption seeks to eliminate the potential for data to be viewed by service providers (or third parties that compel service providers to deliver access to data), client-side encryption ensures that data and files that are stored in the cloud can only be viewed on the client-side of the exchange. This prevents data loss and the unauthorized disclosure of private or personal files, providing increased peace of mind for its users. Current recommendations by industry professionals as well as academic scholars offer great vocal support for developers to include client-side encryption to protect the confidentiality and integrity of information. === Examples of services that use client-side encryption by default === Tresorit MEGA Cryptee Cryptomator === Examples of services that optionally support client-side encryption === Apple iCloud offers optional client-side encryption when "Advanced Data Protection for iCloud" is enabled. Google Drive, Google Docs, Google Meet, Google Calendar, and Gmail — However, as of Jul 2024, optional client-side encryption features are only available to paid users. === Examples of services that do not support client-side encryption === Dropbox === Examples of client-side encrypted services that no longer exist === SpiderOak Backup
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SFINKS

Sfinks (Polish for "Sphynx") was also the initial name of the Janusz A. Zajdel Award In cryptography, SFINKS is a stream cypher algorithm developed by An Braeken, Joseph Lano, Nele Mentens, Bart Preneel, and Ingrid Verbauwhede. It includes a message authentication code. It has been submitted to the eSTREAM Project of the eCRYPT network. In 2005, Nicolas T. Courtois noted that, while the cipher is elegant and secure against some simple algebraic attacks, it is vulnerable to more elaborate known attacks.
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International Conference on Language Resources and Evaluation

The International Conference on Language Resources and Evaluation is an international conference organised by the ELRA Language Resources Association every other year (on even years) with the support of institutions and organisations involved in Natural language processing. The series of LREC conferences was launched in Granada in 1998. == History of conferences == The survey of the LREC conferences over the period 1998-2013 was presented during the 2014 conference in Reykjavik as a closing session. It appears that the number of papers and signatures is increasing over time. The average number of authors per paper is higher as well. The percentage of new authors is between 68% and 78%. The distribution between male (65%) and female (35%) authors is stable over time. The most frequent technical term is "annotation", then comes "part-of-speech". == The LRE Map == The LRE Map was introduced at LREC 2010 and is now a regular feature of the LREC submission process for both the conference papers and the workshop papers. At the submission stage, the authors are asked to provide some basic information about all the resources (in a broad sense, i.e. including tools, standards and evaluation packages), either used or created, described in their papers. All these descriptors are then gathered in a global matrix called the LRE Map. This feature has been extended to several other conferences.
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Data lake

A data lake is a system or repository of data stored in its natural/raw format, usually object blobs or files. A data lake is usually a single store of data including raw copies of source system data, sensor data, social data etc., and transformed data used for tasks such as reporting, visualization, advanced analytics, and machine learning. A data lake can include structured data from relational databases (rows and columns), semi-structured data (CSV, logs, XML, JSON), unstructured data (emails, documents, PDFs), and binary data (images, audio, video). A data lake can be established on premises (within an organization's data centers) or in the cloud (using cloud services). == Background == James Dixon, then chief technology officer at Pentaho, coined the term by 2011 to contrast it with data mart, which is a smaller repository of interesting attributes derived from raw data. In promoting data lakes, he argued that data marts have several inherent problems, such as information siloing. PricewaterhouseCoopers (PwC) said that data lakes could "put an end to data silos". In their study on data lakes, they noted that enterprises were "starting to extract and place data for analytics into a single, Hadoop-based repository." == Examples == Many companies use cloud storage services such as Google Cloud Storage and Amazon S3 or a distributed file system such as Apache Hadoop distributed file system (HDFS). There is a gradual academic interest in the concept of data lakes. For example, Personal DataLake at Cardiff University is a new type of data lake which aims at managing big data of individual users by providing a single point of collecting, organizing, and sharing personal data. Early data lakes, such as Hadoop 1.0, had limited capabilities because it only supported batch-oriented processing (Map Reduce). Interacting with it required expertise in Java, map reduce and higher-level tools like Apache Pig, Apache Spark and Apache Hive (which were also originally batch-oriented). == Criticism == Poorly managed data lakes have been facetiously called data swamps. In June 2015, David Needle characterized "so-called data lakes" as "one of the more controversial ways to manage big data". PwC was also careful to note in their research that not all data lake initiatives are successful. They quote Sean Martin, CTO of Cambridge Semantics: We see customers creating big data graveyards, dumping everything into Hadoop distributed file system (HDFS) and hoping to do something with it down the road. But then they just lose track of what’s there. The main challenge is not creating a data lake, but taking advantage of the opportunities it presents. They describe companies that build successful data lakes as gradually maturing their lake as they figure out which data and metadata are important to the organization. Another criticism is that the term data lake is used with many different meanings. It may be used to refer to, for example: any tools or data management practices that are not data warehouses; a particular technology for implementation; a raw data reservoir; a hub for ETL offload; or a central hub for self-service analytics. While critiques of data lakes are warranted, in many cases they apply to other data projects as well. For example, the definition of data warehouse is also changeable, and not all data warehouse efforts have been successful. In response to various critiques, McKinsey noted that the data lake should be viewed as a service model for delivering business value within the enterprise, not a technology outcome. == Data lakehouses == Data lakehouses are a hybrid approach that can ingest a variety of raw data formats like a data lake, while also providing ACID transactions and enforced data quality like a data warehouse.
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Storyful

Storyful (stylized as storyful.) is a social media intelligence company headquartered in Dublin, Ireland that is a subsidiary of News Corp, offering services such as social news monitoring, video licensing, and reputation risk management tools for corporate clients. The startup was launched as the first social media newswire, a content aggregator, verifying news sources and online content in Dublin in 2010 by Mark Little, a former journalist with RTÉ News. Storyful was acquired by News Corp in 2013 for USD$25 million. == Background == Mark Little, who had worked as a television journalist for RTÉ One, founded startup Storyful in Dublin, Ireland, in 2010, as a service that "verified news sources and online content". According to Nieman Lab, Storyful had a reputation for content aggregation as a social news agency—finding, verifying, distributing, licensing, and commercializing user-generated content, social media and online content from social networking services, including videos about stories in the news, such as the Syrian Civil War, Arab Spring protests, as well as "smaller viral moments". Storyful aimed to provide authority through its verification and monitoring tools while providing authenticity through user-generated content. On 20 December 2013 News Corp purchased Storyful for US$25 million and opened a New York office in the same building as Fox News' main studios. Little left Storyful in 2015 and Gavin Sheridan, Storyful's director of innovation left in 2014. News Corp CEO Robert Thomson said that through Storyful, News Corp would "define the opportunities that the digital landscape presents, rather than simply adapt to them." After the acquisition, the company expanded its service to include "commercial and creative work". After Murdoch acquired the company, from 2014 through to February 2018, losses "swelled", requiring a series of cash injections from News Corp. During that time the company expanded aggressively globally with a staff of about 200 worldwide up from about 30 in 2014. According to The Guardian, in 2016, journalists were encouraged by Storyful to use the social media monitoring software called Verify developed by Storyful. By installing Verify's web browser extension on their computers, Verify would inform the journalists when social media content had been "verified and cleared". The Guardian revealed that through the Verify plugin, dozens of staff in four offices had access to the journalists browsing activity without them knowing. This data allowed Storyful to actively monitor its own clients' activities on social media and to "turn it into an internal feed" at Storyful that "updates in real time". In November 2018, when a video circulated by Infowars' Paul Joseph Watson appeared to prove that CNN's Jim Acosta's contact with a White House intern was a physical blow, Storyful was able to prove that the 15-second-long clip had been doctored. According to a 21 January 2019 article in CNN Business, Rob McDonagh, the editor of Storyful's U.S. news team, had proven that one of the viral videos that served as catalysts in the January 2019 Lincoln Memorial confrontation at 18 January 2019 Indigenous Peoples March, was posted by a suspicious account, under the handle @2020fight. McDonagh's team validates videos and posts before adding them to their "digest", distinguishing true stories from those that are not. Storyful attempts to validate each post or video before including it in its digest. McDonagh reviewed previous content from @2020fight's account, and found it suspicious because it had a high follower count, a "highly polarized and yet inconsistent political messaging", an "unusually high rate of tweets", and "the use of someone else's image in the profile photo." reporter Donie O'Sullivan said that the @2020fight video that had been posted on 18 January, which had 2.5 million views by 22 January, was the one that "helped frame the news cycle". Currently the website offers a service by which video can be commercially brokered. == Services == Services include a newswire service—one of their "core pillars"—and social news monitoring. By February 2018, Storyful was developing "risk and reputation monitoring" services through which they would source and verify social news, fact-checking it and contextualising it for corporate clients. They were "developing tech tools" to "explore obscure or closed networks" for their intelligence team. can use to explore obscure or closed networks. They "track deviations in social conversations around brands and organisations and catch potential risks before they blow up. Like an alerts system." The company "released a re-booted version of its Newswire platform in 2018. According to FORA, Storyful was developing new tools to combat fake news online. == Clients == When Storyful was acquired by News Corp in 2013, the company already had the Wall Street Journal, the BBC, New York Times, YouTube, ITN and Channel 4 News as clients. By 2018 their clients included CNN, ABC News and Fox News, The New York Times, the Washington Post, in the United States, the Australian Broadcasting Corporation and all of News Corp’s own publications. Most of their "reputation-conscious corporate customers" clients prefer to not be named.
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