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  QANDA

  QANDA (stands for 'Q and A') is an AI-based learning platform developed by Mathpresso Inc., a South Korea-based education technology company. Its best known feature is a solution search, which uses optical character recognition technology to scan problems and provide step-by-step solutions and learning content. As of March 2024, QANDA solved over 6.3 billion questions. QANDA has 90 million total registered users and has reached 8 million monthly active users (MAU) in 50 countries. 90% of the cumulative users are from overseas such as Vietnam and Indonesia. In January 2024, its MathGPT, a math-specific small large language model set a new world record, surpassed Microsoft's 'ToRA 13B', the previous record holder in benchmarks assessing mathematical performance such as 'MATH' (high school math) and 'GSM8K' (grade school math). 'MathGPT' was co-developed with Upstage and KT. In March 2024, Mathpresso launched 'Cramify' (formerly known as Prep.Pie), an AI-powered study material generator designed to create personalized exam prep materials for U.S. college students. It uses generative AI to create customized study materials uploaded by students. Its features include a range of tools including study summarizer and question solver. == History == Co-founder Jongheun ‘Ray’ Lee first came up with the idea of QANDA during his freshman year in college. While he was tutoring to earn money, Lee realized that the quality of education a student receives is greatly based on their location. Lee saw his K-12 students were regularly asking similar questions and realized that these questions were from a pre-selected number of textbooks currently being used in schools. He decided to team up with his high school friend, Yongjae ‘Jake’ Lee to build a platform whereby, one uses a mobile app to scan and submit questions, and students can ask and receive detailed responses. Lee's school friends, Wonguk Jung and Hojae Jeong, joined the team. In June 2015, Mathpresso, Inc. was founded in Seoul, South Korea. In January 2016, Mathpresso's first product QANDA was launched. It supported a Q&A feature between students and tutors. In October 2017, QANDA introduced an AI-based search capability that permitted users to search for answers in seconds. In April 2020, Jake Yongjae Lee(CEO & co-founder) and Ray Jongheun Lee (co-founder) were selected as Forbes 30 under 30 Asia. In June 2021, QANDA raised $50 million in series C funding. Jake Yongjae Lee was recognized as an Innovator Under 35 by MIT Technology Review. In November 2021, QANDA secured a strategic investment from Google. Since its inception, it has received backing in Series C funding from investors namely Google, Yellowdog, GGV Capital, Goodwater Capital, KDB, and SKS Private Equity with participation from SoftBank Ventures Asia, Legend Capital, Mirae Asset Venture Investment, and Smilegate Investment. In September 2023, Mathpresso has raised $8 million (10 billion KRW) from Korea's telecom giant, KT. The total cumulative investment is about 130 million US dollars. The partnership aims to accelerate the development of an education-specific Large Language Model. The company intends to incorporate the LLM model to fortify its AI tutor, which later will be integrated into the existing services: QANDA App, B2B & B2G Saas, and 1:1 online tutoring (QANDA Tutor). == Features == QANDA features OCR-based solution search, one-on-one Q&A tutoring, a study timer. In 2021, QANDA launched additional features, including the premium subscription model that offers unlimited “byte-sized” micro-video lectures and the community feature that enhances collaborative learning. In 2021, QANDA launched QANDA Tutor, a tablet-based 1:1 tutoring service and QANDA Study, a 1:N online school in Vietnam. In 2022, QANDA launched an exam prep feature that offers past exam materials from school via online. This feature is currently available in South Korea. In August 2023, QANDA launched a beta version of an LLM-powered AI Tutor. == Awards and recognition == Best Hidden Gems of 2017 by Google Playstore 2018 AWS AI Startup Challenge Award National representative for the Google AI for Social Good APAC, 2018 Best Self-Improvement Apps of 2018 by Google Playstore GSV Edtech 150 — the Most Transformational Growth Companies in Digital Learning Speaker at the Google App Summit, 2021 Selected as a prospect unicorn company by Korea Technology Finance Corporation in 2023 Winner of G20-DIA Global Pitching in 2023 2021, 2022, 2023 East Asia EdTech 150 by HolonIQ

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  Polygraphic substitution

  Polygraphic substitution is a substitution cipher in which a uniform substitution is performed on blocks of letters. When the length of the block is specifically known, more precise terms are used: for instance, a cipher in which pairs of letters are substituted is bigraphic. As a concept, polygraphic substitution contrasts with monoalphabetic (or simple) substitutions in which individual letters are uniformly substituted, or polyalphabetic substitutions in which individual letters are substituted in different ways depending on their position in the text. In theory, there is some overlap in these definitions; one could conceivably consider a Vigenère cipher with an eight-letter key to be an octographic substitution. In practice, this is not a useful observation since it is far more fruitful to consider it to be a polyalphabetic substitution cipher. == Specific ciphers == In 1563, Giambattista della Porta devised the first bigraphic substitution. However, it was nothing more than a matrix of symbols. In practice, it would have been all but impossible to memorize, and carrying around the table would lead to risks of falling into enemy hands. In 1854, Charles Wheatstone came up with the Playfair cipher, a keyword-based system that could be performed on paper in the field. This was followed up over the next fifty years with the closely related four-square and two-square ciphers, which are slightly more cumbersome but offer slightly better security. In 1929, Lester S. Hill developed the Hill cipher, which uses matrix algebra to encrypt blocks of any desired length. However, encryption is very difficult to perform by hand for any sufficiently large block size, although it has been implemented by machine or computer. This is therefore on the frontier between classical and modern cryptography. == Cryptanalysis of general polygraphic substitutions == Polygraphic systems do provide a significant improvement in security over monoalphabetic substitutions. Given an individual letter 'E' in a message, it could be encrypted using any of 52 instructions depending on its location and neighbors, which can be used to great advantage to mask the frequency of individual letters. However, the security boost is limited; while it generally requires a larger sample of text to crack, it can still be done by hand. One can identify a polygraphically-encrypted text by performing a frequency chart of polygrams and not merely of individual letters. These can be compared to the frequency of plaintext English. The distribution of digrams is even more stark than individual letters. For example, the six most common letters in English (23%) represent approximately half of English plaintext, but it takes only the most frequent 8% of the 676 digrams to achieve the same potency. In addition, even in a plaintext many thousands of characters long, one would expect that nearly half of the digrams would not occur, or only barely. In addition, looking over the text one would expect to see a fairly regular scattering of repeated text in multiples of the block length and relatively few that are not multiples. Cracking a code identified as polygraphic is similar to cracking a general monoalphabetic substitution except with a larger 'alphabet'. One identifies the most frequent polygrams, experiments with replacing them with common plaintext polygrams, and attempts to build up common words, phrases, and finally meaning. Naturally, if the investigation led the cryptanalyst to suspect that a code was of a specific type, like a Playfair or order-2 Hill cipher, then they could use a more specific attack.

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  Hybrid cryptosystem

  In cryptography, a hybrid cryptosystem is one which combines the convenience of a public-key cryptosystem with the efficiency of a symmetric-key cryptosystem. Public-key cryptosystems are convenient in that they do not require the sender and receiver to share a common secret in order to communicate securely. However, they often rely on complicated mathematical computations and are thus generally much more inefficient than comparable symmetric-key cryptosystems. In many applications, the high cost of encrypting long messages in a public-key cryptosystem can be prohibitive. This is addressed by hybrid systems by using a combination of both. A hybrid cryptosystem can be constructed using any two separate cryptosystems: a key encapsulation mechanism, which is a public-key cryptosystem a data encapsulation scheme, which is a symmetric-key cryptosystem The hybrid cryptosystem is itself a public-key system, whose public and private keys are the same as in the key encapsulation scheme. Note that for very long messages the bulk of the work in encryption/decryption is done by the more efficient symmetric-key scheme, while the inefficient public-key scheme is used only to encrypt/decrypt a short key value. == Implementations and standards == All practical implementations of public key cryptography today employ a hybrid system. Examples include the TLS protocol and the SSH protocol, that use a public-key mechanism for key exchange (such as Diffie-Hellman) and a symmetric-key mechanism for data encapsulation (such as AES). The OpenPGP file format and the PKCS#7 file format are other examples. Hybrid Public Key Encryption (HPKE, published as RFC 9180) is a modern standard for generic hybrid encryption. HPKE is used within multiple IETF protocols, including Messaging Layer Security (MLS), Oblivious DNS over HTTPS, Oblivious HTTP, Privacy Preserving Measurement, and TLS Encrypted Client Hello. Envelope encryption is an example of a usage of hybrid cryptosystems in cloud computing. In a cloud context, hybrid cryptosystems also enable centralized key management. == Example == To encrypt a message addressed to Alice in a hybrid cryptosystem, Bob does the following: Obtains Alice's public key. Generates a fresh symmetric key for the data encapsulation scheme. Encrypts the message under the data encapsulation scheme, using the symmetric key just generated. Encrypts the symmetric key under the key encapsulation scheme, using Alice's public key. Sends both of these ciphertexts to Alice. To decrypt this hybrid ciphertext, Alice does the following: Uses her private key to decrypt the symmetric key contained in the key encapsulation segment. Uses this symmetric key to decrypt the message contained in the data encapsulation segment. == Security == If both the key encapsulation and data encapsulation schemes in a hybrid cryptosystem are secure against adaptive chosen ciphertext attacks, then the hybrid scheme inherits that property as well. However, it is possible to construct a hybrid scheme secure against adaptive chosen ciphertext attacks even if the key encapsulation has a slightly weakened security definition (though the security of the data encapsulation must be slightly stronger). == Envelope encryption == Envelope encryption is term used for encrypting with a hybrid cryptosystem used by all major cloud service providers, often as part of a centralized key management system in cloud computing. Envelope encryption gives names to the keys used in hybrid encryption: Data Encryption Keys (abbreviated DEK, and used to encrypt data) and Key Encryption Keys (abbreviated KEK, and used to encrypt the DEKs). In a cloud environment, encryption with envelope encryption involves generating a DEK locally, encrypting one's data using the DEK, and then issuing a request to wrap (encrypt) the DEK with a KEK stored in a potentially more secure service. Then, this wrapped DEK and encrypted message constitute a ciphertext for the scheme. To decrypt a ciphertext, the wrapped DEK is unwrapped (decrypted) via a call to a service, and then the unwrapped DEK is used to decrypt the encrypted message. In addition to the normal advantages of a hybrid cryptosystem, using asymmetric encryption for the KEK in a cloud context provides easier key management and separation of roles, but can be slower. In cloud systems, such as Google Cloud Platform and Amazon Web Services, a key management system (KMS) can be available as a service. In some cases, the key management system will store keys in hardware security modules, which are hardware systems that protect keys with hardware features like intrusion resistance. This means that KEKs can also be more secure because they are stored on secure specialized hardware. Envelope encryption makes centralized key management easier because a centralized key management system only needs to store KEKs, which occupy less space, and requests to the KMS only involve sending wrapped and unwrapped DEKs, which use less bandwidth than transmitting entire messages. Since one KEK can be used to encrypt many DEKs, this also allows for less storage space to be used in the KMS. This also allows for centralized auditing and access control at one point of access.

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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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  Layers (digital image editing)

  Layers are used in digital image editing to separate different elements of an image. A layer can be compared to a transparency on which imaging effects or images are applied and placed over or under an image. Today they are an integral feature of image editors. In the early days of computing, memory was at a premium and the idea of using multi-layered images was considered infeasible in personal computer applications as the tradeoffs were image size and color depth. As the price of memory fell it became feasible to apply the concept of layering to raster images. The first software known to apply the concept of layers was LALF, which was released in 1989 for the NEC PC-9801. LALF's terminology for layers is "cells", after the concept of drawing animation frames over-top of a stencil. Layers were introduced in Western markets by Fauve Matisse (later Macromedia xRes), and then available in Adobe Photoshop 3.0, in 1994, which lead to widespread adoption. In vector image editors that support animation, layers are used to further enable manipulation along a common timeline for the animation; in SVG images, the equivalent to layers are "groups". == Layer types == There are different kinds of layers, and not all of them exist in all programs. They represent a part of a picture, either as pixels or as modification instructions. They are stacked on top of each other, and depending on the order, determine the appearance of the final picture. In graphics software, layers are the different levels at which one can place an object or image file. In the program, layers can be stacked, merged, or defined when creating a digital image. Layers can be partially obscured allowing portions of images within a layer to be hidden or shown in a translucent manner within another image. Layers can also be used to combine two or more images into a single digital image. For the purpose of editing, working with layers allows for applying changes to just one specific layer. == Layer (basic) == The standard layer available to most programs consists of a rectangular, semitransparent picture which may be superimposed over other layers. Some programs require that layers cover the same area as the final canvas, but others offer layers of multiple sizes. Each layer may bear individual settings, such as opacity, blending modes, dynamic filters, and potentially hundreds of other properties. == Layer mask == A layer mask is linked to a layer and hides part of the layer from the picture. What is painted black on the layer mask will not be visible in the final picture. What is grey will be more or less transparent depending on the shade of grey. As the layer mask can be both edited and moved around independently of both the background layer and the layer it applies to, it gives the user the ability to test a lot of different combinations of overlay. == Adjustment layer == An adjustment layer typically applies a common effect like brightness or saturation to other layers. However, as the effect is stored in a separate layer, it is easy to try it out and switch between different alternatives, without changing the original layer. In addition, an adjustment layer can easily be edited, just like a layer mask, so an effect can be applied to just part of the image.

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  Cryptographic Service Provider

  A cryptographic service provider (CSP) is a package that "provides a concrete implementation of certain cryptographic services." A CSP offers operations and protocols to support a variety of use cases. The cryptographic application programming interface (API) provided by the CSP provides common solutions for different platforms, for example hardware and cloud services. == Microsoft Windows == In Microsoft Windows, a Cryptographic Service Provider is a software library that implements the Microsoft CryptoAPI (CAPI). CSPs implement encoding and decoding functions, which computer application programs may use, for example, to implement strong user authentication or for secure email. CSPs are independent modules that can be used by different applications. A user program calls CryptoAPI functions and these are redirected to CSPs functions. Since CSPs are responsible for implementing cryptographic algorithms and standards, applications do not need to be concerned about security details. Furthermore, each application can define which CSP it is going to use on its calls to CryptoAPI. In fact, all cryptographic activity is implemented in CSPs. CryptoAPI only works as a bridge between the application and the CSP. CSPs are implemented basically as a special type of DLL with special restrictions on loading and use. Every CSP must be digitally signed by Microsoft and the signature is verified when Windows loads the CSP. In addition, after being loaded, Windows periodically re-scans the CSP to detect tampering, either by malicious software such as computer viruses or by the user him/herself trying to circumvent restrictions (for example on cryptographic key length) that might be built into the CSP's code. To obtain a signature, non-Microsoft CSP developers must supply paperwork to Microsoft promising to obey various legal restrictions and giving valid contact information. As of circa 2000, Microsoft did not charge any fees to supply these signatures. For development and testing purposes, a CSP developer can configure Windows to recognize the developer's own signatures instead of Microsoft's, but this is a somewhat complex and obscure operation unsuitable for nontechnical end users. The CAPI/CSP architecture had its origins in the era of restrictive US government controls on the export of cryptography. Microsoft's default or "base" CSP then included with Windows was limited to 512-bit RSA public-key cryptography and 40-bit symmetric cryptography, the maximum key lengths permitted in exportable mass market software at the time. CSPs implementing stronger cryptography were available only to U.S. residents, unless the CSPs themselves had received U.S. government export approval. The system of requiring CSPs to be signed only on presentation of completed paperwork was intended to prevent the easy spread of unauthorized CSPs implemented by anonymous or foreign developers. As such, it was presented as a concession made by Microsoft to the government, in order to get export approval for the CAPI itself. After the Bernstein v. United States court decision establishing computer source code as protected free speech and the transfer of cryptographic regulatory authority from the U.S. State Department to the more pro-export Commerce Department, the restrictions on key lengths were dropped, and the CSPs shipped with Windows now include full-strength cryptography. The main use of third-party CSPs is to interface with external cryptography hardware such as hardware security modules (HSM) or smart cards. === Smart Card CSP === These cryptographic functions can be realized by a smart card, thus the Smart Card CSP is the Microsoft way of a PKCS#11. Microsoft Windows is identifying the correct Smart Card CSP, which have to be used, analyzing the answer to reset (ATR) of the smart card, which is registered in the Windows Registry. Installing a new CSP, all ATRs of the supported smart cards are enlisted in the registry. === Use of CSP in MS Office password protection === Cryptographic service providers can be used for encryption of Word, Excel, and PowerPoint documents starting from Microsoft Office XP. A standard encryption algorithm with a 40-bit key is used by default, but enabling a CSP enhances key length and thus makes decryption process more continuous. This only applies to passwords that are required to open document because this password type is the only one that encrypts a password-protected document.

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  Malleability (cryptography)

  Malleability is a property of some cryptographic algorithms. An encryption algorithm is said to be malleable if it is possible to transform a ciphertext into another ciphertext which decrypts to a related plaintext. That is, given an encryption of a plaintext m {\displaystyle m} , it is possible to generate another ciphertext which decrypts to f ( m ) {\displaystyle f(m)} , for a known function f {\displaystyle f} , without necessarily knowing or learning m {\displaystyle m} . Malleability is often an undesirable property in a general-purpose cryptosystem, since it allows an attacker to modify the contents of a message. For example, suppose that a bank uses a stream cipher to hide its financial information, and a user sends an encrypted message containing, say, "TRANSFER $0000100.00 TO ACCOUNT #199." If an attacker can modify the message on the wire, and can guess the format of the unencrypted message, the attacker could change the amount of the transaction, or the recipient of the funds, e.g. "TRANSFER $0100000.00 TO ACCOUNT #227". Malleability does not refer to the attacker's ability to read the encrypted message. Both before and after tampering, the attacker cannot read the encrypted message. On the other hand, some cryptosystems are malleable by design. In other words, in some circumstances it may be viewed as a feature that anyone can transform an encryption of m {\displaystyle m} into a valid encryption of f ( m ) {\displaystyle f(m)} (for some restricted class of functions f {\displaystyle f} ) without necessarily learning m {\displaystyle m} . Such schemes are known as homomorphic encryption schemes. A cryptosystem may be semantically secure against chosen-plaintext attacks or even non-adaptive chosen-ciphertext attacks (CCA1) while still being malleable. However, security against adaptive chosen-ciphertext attacks (CCA2) is equivalent to non-malleability. == Example malleable cryptosystems == In a stream cipher, the ciphertext is produced by taking the exclusive or of the plaintext and a pseudorandom stream based on a secret key k {\displaystyle k} , as E ( m ) = m ⊕ S ( k ) {\displaystyle E(m)=m\oplus S(k)} . An adversary can construct an encryption of m ⊕ t {\displaystyle m\oplus t} for any t {\displaystyle t} , as E ( m ) ⊕ t = m ⊕ t ⊕ S ( k ) = E ( m ⊕ t ) {\displaystyle E(m)\oplus t=m\oplus t\oplus S(k)=E(m\oplus t)} . In the RSA cryptosystem, a plaintext m {\displaystyle m} is encrypted as E ( m ) = m e mod n {\displaystyle E(m)=m^{e}{\bmod {n}}} , where ( e , n ) {\displaystyle (e,n)} is the public key. Given such a ciphertext, an adversary can construct an encryption of m t {\displaystyle mt} for any t {\displaystyle t} , as E ( m ) ⋅ t e mod n = ( m t ) e mod n = E ( m t ) {\textstyle E(m)\cdot t^{e}{\bmod {n}}=(mt)^{e}{\bmod {n}}=E(mt)} . For this reason, RSA is commonly used together with padding methods such as OAEP or PKCS1. In the ElGamal cryptosystem, a plaintext m {\displaystyle m} is encrypted as E ( m ) = ( g b , m A b ) {\displaystyle E(m)=(g^{b},mA^{b})} , where ( g , A ) {\displaystyle (g,A)} is the public key. Given such a ciphertext ( c 1 , c 2 ) {\displaystyle (c_{1},c_{2})} , an adversary can compute ( c 1 , t ⋅ c 2 ) {\displaystyle (c_{1},t\cdot c_{2})} , which is a valid encryption of t m {\displaystyle tm} , for any t {\displaystyle t} . In contrast, the Cramer-Shoup system (which is based on ElGamal) is not malleable. In the Paillier, ElGamal, and RSA cryptosystems, it is also possible to combine several ciphertexts together in a useful way to produce a related ciphertext. In Paillier, given only the public key and an encryption of m 1 {\displaystyle m_{1}} and m 2 {\displaystyle m_{2}} , one can compute a valid encryption of their sum m 1 + m 2 {\displaystyle m_{1}+m_{2}} . In ElGamal and in RSA, one can combine encryptions of m 1 {\displaystyle m_{1}} and m 2 {\displaystyle m_{2}} to obtain a valid encryption of their product m 1 m 2 {\displaystyle m_{1}m_{2}} . Block ciphers in the cipher block chaining mode of operation, for example, are partly malleable: flipping a bit in a ciphertext block will completely mangle the plaintext it decrypts to, but will result in the same bit being flipped in the plaintext of the next block. This allows an attacker to 'sacrifice' one block of plaintext in order to change some data in the next one, possibly managing to maliciously alter the message. This is essentially the core idea of the padding oracle attack on CBC, which allows the attacker to decrypt almost an entire ciphertext without knowing the key. For this and many other reasons, a message authentication code is required to guard against any method of tampering. == Complete non-malleability == Fischlin, in 2005, defined the notion of complete non-malleability as the ability of the system to remain non-malleable while giving the adversary additional power to choose a new public key which could be a function of the original public key. In other words, the adversary shouldn't be able to come up with a ciphertext whose underlying plaintext is related to the original message through a relation that also takes public keys into account.

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  Clustered file system

  A clustered file system (CFS) is a file system which is shared by being simultaneously mounted on multiple servers. There are several approaches to clustering, most of which do not employ a clustered file system (only direct attached storage for each node). Clustered file systems can provide features like location-independent addressing and redundancy which improve reliability or reduce the complexity of the other parts of the cluster. Parallel file systems are a type of clustered file system that spread data across multiple storage nodes, usually for redundancy or performance. == Shared-disk file system == A shared-disk file system uses a storage area network (SAN) to allow multiple computers to gain direct disk access at the block level. Access control and translation from file-level operations that applications use to block-level operations used by the SAN must take place on the client node. The most common type of clustered file system, the shared-disk file system – by adding mechanisms for concurrency control – provides a consistent and serializable view of the file system, avoiding corruption and unintended data loss even when multiple clients try to access the same files at the same time. Shared-disk file-systems commonly employ some sort of fencing mechanism to prevent data corruption in case of node failures, because an unfenced device can cause data corruption if it loses communication with its sister nodes and tries to access the same information other nodes are accessing. The underlying storage area network may use any of a number of block-level protocols, including SCSI, iSCSI, HyperSCSI, ATA over Ethernet (AoE), Fibre Channel, network block device, and InfiniBand. There are different architectural approaches to a shared-disk filesystem. Some distribute file information across all the servers in a cluster (fully distributed). === Examples === == Distributed file systems == Distributed file systems do not share block level access to the same storage but use a network protocol. These are commonly known as network file systems, even though they are not the only file systems that use the network to send data. Distributed file systems can restrict access to the file system depending on access lists or capabilities on both the servers and the clients, depending on how the protocol is designed. The difference between a distributed file system and a distributed data store is that a distributed file system allows files to be accessed using the same interfaces and semantics as local files – for example, mounting/unmounting, listing directories, read/write at byte boundaries, system's native permission model. Distributed data stores, by contrast, require using a different API or library and have different semantics (most often those of a database). === Design goals === Distributed file systems may aim for "transparency" in a number of aspects. That is, they aim to be "invisible" to client programs, which "see" a system which is similar to a local file system. Behind the scenes, the distributed file system handles locating files, transporting data, and potentially providing other features listed below. Access transparency: clients are unaware that files are distributed and can access them in the same way as local files are accessed. Location transparency: a consistent namespace exists encompassing local as well as remote files. The name of a file does not give its location. Concurrency transparency: all clients have the same view of the state of the file system. This means that if one process is modifying a file, any other processes on the same system or remote systems that are accessing the files will see the modifications in a coherent manner. Failure transparency: the client and client programs should operate correctly after a server failure. Heterogeneity: file service should be provided across different hardware and operating system platforms. Scalability: the file system should work well in small environments (1 machine, a dozen machines) and also scale gracefully to bigger ones (hundreds through tens of thousands of systems). Replication transparency: Clients should not have to be aware of the file replication performed across multiple servers to support scalability. Migration transparency: files should be able to move between different servers without the client's knowledge. === History === The Incompatible Timesharing System used virtual devices for transparent inter-machine file system access in the 1960s. More file servers were developed in the 1970s. In 1976, Digital Equipment Corporation created the File Access Listener (FAL), an implementation of the Data Access Protocol as part of DECnet Phase II which became the first widely used network file system. In 1984, Sun Microsystems created the file system called "Network File System" (NFS) which became the first widely used Internet Protocol based network file system. Other notable network file systems are Andrew File System (AFS), Apple Filing Protocol (AFP), NetWare Core Protocol (NCP), and Server Message Block (SMB) which is also known as Common Internet File System (CIFS). In 1986, IBM announced client and server support for Distributed Data Management Architecture (DDM) for the System/36, System/38, and IBM mainframe computers running CICS. This was followed by the support for IBM Personal Computer, AS/400, IBM mainframe computers under the MVS and VSE operating systems, and FlexOS. DDM also became the foundation for Distributed Relational Database Architecture, also known as DRDA. There are many peer-to-peer network protocols for open-source distributed file systems for cloud or closed-source clustered file systems, e. g.: 9P, AFS, Coda, CIFS/SMB, DCE/DFS, WekaFS, Lustre, PanFS, Google File System, Mnet, Chord Project. === Examples === == Network-attached storage == Network-attached storage (NAS) provides both storage and a file system, like a shared disk file system on top of a storage area network (SAN). NAS typically uses file-based protocols (as opposed to block-based protocols a SAN would use) such as NFS (popular on UNIX systems), SMB/CIFS (Server Message Block/Common Internet File System) (used with MS Windows systems), AFP (used with Apple Macintosh computers), or NCP (used with OES and Novell NetWare). == Design considerations == === Avoiding single point of failure === The failure of disk hardware or a given storage node in a cluster can create a single point of failure that can result in data loss or unavailability. Fault tolerance and high availability can be provided through data replication of one sort or another, so that data remains intact and available despite the failure of any single piece of equipment. For examples, see the lists of distributed fault-tolerant file systems and distributed parallel fault-tolerant file systems. === Performance === A common performance measurement of a clustered file system is the amount of time needed to satisfy service requests. In conventional systems, this time consists of a disk-access time and a small amount of CPU-processing time. But in a clustered file system, a remote access has additional overhead due to the distributed structure. This includes the time to deliver the request to a server, the time to deliver the response to the client, and for each direction, a CPU overhead of running the communication protocol software. === Concurrency === Concurrency control becomes an issue when more than one person or client is accessing the same file or block and want to update it. Hence updates to the file from one client should not interfere with access and updates from other clients. This problem is more complex with file systems due to concurrent overlapping writes, where different writers write to overlapping regions of the file concurrently. This problem is usually handled by concurrency control or locking which may either be built into the file system or provided by an add-on protocol. == History == IBM mainframes in the 1970s could share physical disks and file systems if each machine had its own channel connection to the drives' control units. In the 1980s, Digital Equipment Corporation's TOPS-20 and OpenVMS clusters (VAX/ALPHA/IA64) included shared disk file systems.

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  Glyph (data visualization)

  In the context of data visualization, a glyph is any marker, such as an arrow or similar marking, used to specify part of a visualization. This is a representation to visualize data where the data set is presented as a collection of visual objects. These visual objects are collectively called a glyph. It helps visualizing data relation in data analysis, statistics, etc. by using any custom notation. In the context of data visualization, a glyph is the visual representation of a piece of data where the attributes of a graphical entity are dictated by one or more attributes of a data record. == Constructing glyphs == Glyph construction can be a complex process when there are many dimensions to be represented in the visualization. Maguire et al proposed a taxonomy based approach to glyph-design that uses a tree to guide the visual encodings used to representation various data items. Duffy et al created perhaps one of the most complex glyph representations with their representation of sperm movement.

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  Pamphlet war

  A pamphlet war is a protracted argument or discussion through printed media, especially between the time the printing press became common, and when state intervention like copyright laws made such public discourse more difficult. The purpose was to defend or attack a certain perspective or idea. Pamphlet wars have occurred multiple times throughout history, as both social and political platforms. Pamphlet wars became viable platforms for this protracted discussion with the advent and spread of the printing press. Cheap printing presses, and increased literacy made the late 17th century a key stepping stone for the development of pamphlet wars, a period of prolific use of this type of debate. Over 2200 pamphlets were published between 1600–1715 alone. Pamphlet wars are generally credited for powering many key social changes of the era, including the Reformation and the Revolution Controversy, the English philosophical debate set off by the French Revolution. == History of the pamphlet in England == Throughout Europe in the 16th century, printed tracts were used to argue religious doctrine and foment support for religious causes. In England, Henry VIII used print literature to justify his break from the Catholic Church. During the subsequent reigns of Edward and Mary, print polemics escalated into propaganda warfare, as print media gained enormous potential to sway common opinion. By the 1560s, print was widely used to convey news. In 1562, the first pamphlets appeared, which discussed the English forces sent to aid the Protestant French Huguenots. In 1569, pamphlets reported the revolt of the Northern Earls and the subsequent Rebellion of the same year. In the 1580s, pamphlets began to replace broadsheet ballads as the means to convey information to the general public. Over the next century, the pamphlet became the principal means of garnering support for a cause or an idea, and was particularly influential during the English Civil Wars (1642-1651) and the Glorious Revolution of 1688. Through the ensuing decades, the pamphlet lost some popularity due to the emergence of newspapers and journals, but continued to be an important medium of public debate, as illustrated by the Revolution Controversy a full century later in the 1790s. == Pamphlet printing == Coming from a Latin word, "pamphlet" literally means "small book." In the early days of printing, the format of the book or pamphlet depended on the size of the paper used and the number of times it was folded. If a page was only folded once, it was called a folio. If it was folded twice, it was known as a quarto. An octave was a paper folded three times. A pamphlet was usually 1-12 sheets of paper folded in quarto, or 8-96 pages. It was sold for one or two pennies apiece. The printing of a pamphlet involved many people: the author, the printer, suppliers, print-makers, compositor, correctors, pressmen, binders, and distributors. Once the pamphleteer had written the pamphlet, it was sent to the printing house to be corrected, set into type, and printed. The papers were then given to the printer's warehouse-keeper, who bundled the copies and sent them to the bookseller, who was probably the one financing the printing. He was responsible to bind the pamphlets, usually by sewing them, and then sold them wholesale to individual bookselling vendors. The booksellers then sold them from a stall in the marketplace. == Pamphlet subjects == Pamphlets began as the means of conveyance for religious debates, and therefore religious topics were one of the main subjects they dealt with. The definition of a pamphlet came to mean a short work dealing with social, political, or religious issues. Typical topics included the Civil war, Church of England doctrines, Acts of Parliament, the Popish Plot (see below), the Stuart Era, and Cromwell propaganda. In addition, pamphlets were also used for romantic fiction, autobiography, scurrilous personal abuse, and social criticism. They contained much of the propaganda of the 17th century in the midst of the religious and political turmoil. They were also used for debates between the Puritans and the Anglican. During the Glorious Revolution, pamphlets were political weapons. == Authors == There were many authors of pamphlets. However some of the more popular authors include Daniel Defoe, Thomas Hobbes, Jonathan Swift, John Milton, and Samuel Pepys. Also included in the midst are Thomas Nashe, Joseph Addison, Richard Steele, and Matthew Prior. In 1591–1592, Robert Greene released a series of pamphlets which later inspired many other authors including Thomas Middleton and Thomas Dekker. == Critics == Pamphlets, along with their vast popularity, received criticism. There were many in the time period who believed that pamphlets were full of foolishness. They thought the pamphlets were not good enough literature and that they would turn people from "good" writing. They believed that pamphlets would be the end of the great volumes of literature and that great writing would be forgotten. == News reporting == Pamphlets made a great difference in the way news was reported to the general public. With the publication of pamphlets, it was no longer difficult for people to hear of events taking place far away. The closer the occurrence was to London, the easier and faster people heard of it. For example, the Battle of Edgehill took place on 23 October 1642. The first pamphlet reporting the incident was printed on 25 October 24 hours after some of the orders reported had been given. While not entirely accurate, and hurriedly made, the pamphlet nonetheless was able to tell the general public what had happened in the battle. A more accurate, specific, and readable account was available in a pamphlet printed on 26 October, and the "authorized" version was available only five days after the battle took place. == Marprelate pamphlets == In 1588, a series of pamphlets marked a turning point for the Puritans, dividing them from other Protestants in the country. The authors wrote under the pseudonym of Martin Marprelate and his two sons of the same name. The true identities of the authors were never discovered. The pamphlets aimed to provoke authorities to take action against censorship. The series was among the first to ask questions directly of its readers. == Early pamphlet wars == === Elizabethan pamphlet wars === As a means of forming or swaying public opinion, pamphlets like these had a part in influencing society, even as the content was itself influenced by society. During the 16th century and continuing for a short while in the early 17th century in England there was rise in the use of pamphlet wars to discuss a myriad of issues spanning from the civil war, to religious freedoms and the roles of women in society. The Queen herself participated in these discussions, making sure that she was widely read and understood by her people in order to gain favour and establish herself as the monarch despite being a woman. Examples of her use of this medium appear in To the Troops at Tilbury written in 1588, On Mary's Execution written in 1586, and many more. Another famous writer of this period to take advantage of the pamphlet was Emilia Lanier, famous for her arguments about the role of women. A common idea promoted by many literary works and the general attitude towards women, Lanier's work "Eve's Apology in Defence of Women" refuted the belief that Eve is responsible for the fall of man. A very uncommon and unpopular stance to take, Lanier accomplishes her defence through structuring it as an apology, one of the earliest subversive feminist texts. Similarly, Francis Bacon wrote his Essays to promote his idea of morality and other complicated social issues. For example, his work, "Of Love" examines the various understandings of the concept of love, particularly as it was perceived during the Elizabethan era. === Eikon Series === From 1649 until 1651, some five pamphlets were published in a debate about the execution of King Charles I of England (1600-1649). Prior to his execution, King Charles wrote the first pamphlet in the discussion, Eikon Basilike’’ (from the Greek “eikon” for image and “basileus” for king). The subtitle of this work - Portraiture of His Sacred Majesty in His Solitudes and Sufferings - indicates that Charles sought to portray himself as a martyr to the cause of regal prerogative. In the following months, several response pamphlets were published (collectively known as the "Eikon" series), including: Eikon Alethine, Eikon e Pistes, Eikonoklastes, and Eikon Aklastos,” alternately attacking or defending the king, his regicide, and his self-portrait in “Eikon Basilike.” == Popish Plot and Elizabeth Cellier == In the 1680s, after being acquitted of the "Meal-Tub Plot" for which she was accused, Elizabeth Cellier wrote Malice Defeated, which, along with The Matchless Picaro, sparked a pamphlet war surrounding debate of the ascension of a Catholic king to the thro

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  Experimental SAGE Subsector

  The Experimental Semi-Automatic Ground Environment (SAGE) Sector (ESS, Experimental SAGE Subsector until planned Sectors/Subsectors were renamed NORAD Regions, Divisions, and Sectors) was a prototype Cold War Air Defense Sector for developing the Semi Automatic Ground Environment. The Lincoln Laboratory control center in a new building was at Lexington, Massachusetts. == ESS Computer System == The network's Direction Center was completed in a new 1954 building (Building F, 42°27′37″N 071°16′04″W) with prototype peripherals and a single IBM XD-1 computer, a successor to Lincoln Lab's Whirlwind I computer (WWI). In 1955, Air Force personnel began IBM training at the Kingston, New York, prototype facility, and the "4620th Air Defense Wing (experimental SAGE) was established at Lincoln Laboratory"—its "primary mission was computer programming". ESS had a capacity of 48 tracks and used a pre-SAGE ground environment in a "prototype intercept monitor room [at] MIT's Barta building" with "track situation displays, which geographically showed Air Defense Identification Zone lines and antiaircraft circles [and] each console also had a 5-inch CRT for digital information display. Audible alert signals were used, with a different signal for each symbol on a situation display." == Radar stations == Initial service test models of the Burroughs AN/FST-2 Coordinate Data Transmitting Set were placed with radars at South Truro and West Bath, Maine; followed by Texas Tower#2 (TT2) in the Atlantic Ocean, which provided a "triangular pattern with overlap" radar coverage (TT2 later had a connection from the XD-1 via the GE G/A Data Link Output Subsystem through North Truro Air Force Station.) By August 1955, 13 radar stations were networked by the subsector, e.g.: Chatham Clinton, Massachusetts with gap-filler radar Great Boars Head Halibut Point Killingly, Connecticut (41.865734°N 71.820958°W / 41.865734; -71.820958).with gap-filler radar Rockport Air Force Station Scituate, Massachusetts South Truro West Bath, Maine (43°54′7″N 69°50′43″W) with AN/FPS-31 on Jug Handle Hill: ("Lincoln Laboratories experimental radar station") Required by 21 November 1955 were 44 consoles: 38 for the operations floor, 3 on the computer floor for display maintenance, and 3 near the maintenance console (program checkout). WWI was connected to the Experimental SAGE Subsector to verify crosstelling (collateral communication) with the ESS DC, and WWI was also used for a Ground-to-Air (G/A) experiment using a transmitter of the GE G/A Data Link Output Subsystem on Prospect Hill, Waltham, MA sending data to simulated airborne equipment at Lexington. Transmissions from the WWI SAGE Evaluation (WISE) computer system to XD-1 and back were without error by December 1955 when operational software specifications were frozen. Operating procedures for the ESS external sites were complete in March 1956, and == System Operation Testing == From November 15, 1955, to November 7, 1956, three System Operation Tests were conducted which used voice "Ground-to-Air" communication from the Barta control room to aircraft outfitted with SAGE receivers (F-86 interceptors modified to F-86L models in "Project FOLLOW-ON".) Test teams included employees of Bell Telephone Laboratories, Western Electric-ADES, IBM, the RAND Corporation, and Lincoln Labs' Division 6, Division 3, & Division 2 (Division 6 had been created for ESS support.) The North Truro P-10 AN/FST-2 was moved to Almaden Air Force Station (M-96)c. 1957-8 and on August 7, 1958, control of an airborne BOMARC missile that had malfunctioned transferred from the "Experimental SAGE Sector" to a Westinghouse AN/GPA-35 Ground Environment system and the missile crashed into the Atlantic Ocean. By December 31, 1958, ADC Manual 55-28 described the Model 3 SAGE System. == 1959 Experimental Testing == "To prove out the revised SAGE computer program" for Automatic Targeting and Battery Evaluation and ADDC-AADCP crosstelling, a "SAGE/Missile Master" test was conducted beginning in September 1959 with communications between the ESS XD-1 and Martin AN/FSG-1 Antiaircraft Defense System equipment at Fort Banks planned for the CONAD Joint Control Center at Fort Heath—a "SAGE ATABE Simulation Study" (SASS) was also completed 1959–60 by MITRE Corporation.

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  Critical data studies

  Critical data studies is the exploration of and engagement with social, cultural, and ethical challenges that arise when working with big data. It is through various unique perspectives and taking a critical approach that this form of study can be practiced. As its name implies, critical data studies draws heavily on the influence of critical theory, which has a strong focus on addressing the organization of power structures. This idea is then applied to the study of data. Interest in this unique field of critical data studies began in 2011 with scholars danah boyd and Kate Crawford posing various questions for the critical study of big data and recognizing its potential threatening impacts on society and culture. It was not until 2014, and more exploration and conversations, that critical data studies was officially coined by scholars Craig Dalton and Jim Thatcher. They put a large emphasis on understanding the context of big data in order to approach it more critically. Researchers such as David Ribes, Robert Soden, Seyram Avle, Sarah E. Fox, and Phoebe Sengers focus on understanding data as a historical artifact and taking an interdisciplinary approach towards critical data studies. Other key scholars in this discipline include Rob Kitchin and Tracey P. Lauriault who focus on reevaluating data through different spheres. Various critical frameworks that can be applied to analyze big data include Feminist, Anti-Racist, Queer, Indigenous, Decolonial, Anti-Ableist, as well as Symbolic and Synthetic data science. These frameworks help to make sense of the data by addressing power, biases, privacy, consent, and underrepresentation or misrepresentation concerns that exist in data as well as how to approach and analyze this data with a more equitable mindset. == Motivation == In their article in which they coin the term 'critical data studies,' Dalton and Thatcher also provide several justifications as to why data studies is a discipline worthy of a critical approach. First, 'big data' is an important aspect of twenty-first century society, and the analysis of 'big data' allows for a deeper understanding of what is happening and for what reasons. Big data is important to critical data studies because it is the type of data used within this field. Big data does not necessarily refer to a large data set, it can have a data set with millions of rows, but also a data set that just has a wide variety and expansive scope of data with a smaller type of dataset. As well as having whole populations in the data set and not just sample sizes. Furthermore, big data as a technological tool and the information that it yields are not neutral, according to Dalton and Thatcher, making it worthy of critical analysis in order to identify and address its biases. Building off this idea, another justification for a critical approach is that the relationship between big data and society is an important one, and therefore worthy of study. Ribes et. al. argue there is a need for an interdisciplinary understanding of data as a historical artifact as a motivating aspect of critical data studies.The overarching consensus in the Computer-Supported Cooperative Work (CSCW) field, is that people should speak for the data, and not let the data speak for itself. The sources of big data and it’s relationship to varied metadata can be a complicated one, which leads to data disorder and a need for an ethical analysis. Additionally, Iliadis and Russo (2016) have called for studying data assemblages. This is to say, data has innate technological, political, social, and economic histories that should be taken into consideration. Kitchin argues data is almost never raw, and it is almost always cooked, meaning that it is always spoken for by the data scientists utilizing it. Thus, Big Data should be open to a variety of perspectives, especially those of cultural and philosophical nature. Further, data contains hidden histories, ideologies, and philosophies. Big data technology can cause significant changes in society's structure and in the everyday lives of people, and, being a product of society, big data technology is worthy of sociological investigation. Moreover, data sets are almost never completely without any influence. Rather, data are shaped by the vision or goals of those gathering the data, and during the data collection process, certain things are quantified, stored, sorted and even discarded by the research team. A critical approach is thus necessary in order to understand and reveal the intent behind the information being presented.One of these critical approaches has been through feminist data studies. This method applies feminist principles to critical studies and data collecting and analysis. The goal of this is to address the power imbalance in data science and society. According to Catherine D’Ignazio and Lauren F. Klein, a power analysis can be performed by examining power, challenging power, evaluating emotion and embodiment, rethinking binaries and hierarchies, embracing pluralism, considering context, and making labor visible. Feminist data studies is part of the movement towards making data to benefit everyone and not to increase existing inequalities. Moreover, data alone cannot speak for themselves; in order to possess any concrete meaning, data must be accompanied by theoretical insight or alternative quantitative or qualitative research measures. Based on different social topics such as anti-racist data studies, critical data studies give a focus on those social issues concerning data. Specifically in anti-racist data studies they use a classification approach to get representation for those within that community. Desmond Upton Patton and others used their own classification system in the communities of Chicago to help target and reduce violence with young teens on twitter. They had students in those communities help them to decipher the terminology and emojis of these teens to target the language used in tweets that followed with violence outside of the computer screens. This is just one real world example of critical data studies and its application. Dalton and Thatcher argue that if one were to only think of data in terms of its exploitative power, there is no possibility of using data for revolutionary, liberatory purposes. Finally, Dalton and Thatcher propose that a critical approach in studying data allows for 'big data' to be combined with older, 'small data,' and thus create more thorough research, opening up more opportunities, questions and topics to be explored. == Issues and concerns for critical data scholars == Data plays a pivotal role in the emerging knowledge economy, driving productivity, competitiveness, efficiency, sustainability, and capital accumulation. The ethical, political, and economic dimensions of data dynamically evolve across space and time, influenced by changing regimes, technologies, and priorities. Technically, the focus lies on handling, storing, and analyzing vast data sets, utilizing machine learning-based data mining and analytics. This technological advancement raises concerns about data quality, encompassing validity, reliability, authenticity, usability, and lineage. The use of data in modern society brings about new ways of understanding and measuring the world, but also brings with it certain concerns or issues. Data scholars attempt to bring some of these issues to light in their quest to be critical of data. Technical and organizational issues could include the scope of the data set, meaning there is too little or too much data to work with, leading to inaccurate results. It becomes crucial for critical data scholars to carefully consider the adequacy of data volume for their analyses. The quality of the data itself is another facet of concern. The data itself could be of poor quality, such as an incomplete or messy data set with missing or inaccurate data values. This would lead researchers to have to make edits and assumptions about the data itself. Addressing these issues often requires scholars to make edits and assumptions about the data to ensure its reliability and relevance. Data scientists could have improper access to the actual data set, limiting their abilities to analyze it. Linnet Taylor explains how gaps in data can arise when people of varying levels of power have certain rights to their data sources. These people in power can control what data is collected, how it is displayed and how it is analyzed. The capabilities of the research team also play a crucial role in the quality of data analytics. The research team may have inadequate skills or organizational capabilities which leads to the actual analytics performed on the dataset to be biased. This can also lead to ecological fallacies, meaning an assumption is made about an individual based on data or results from a larger group of people. These technical and organizational challenges highlight the complexity of working with data and

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  AI washing

  AI washing is a deceptive marketing tactic that consists of promoting a product or a service by overstating the role of artificial intelligence (AI) and the integration of it. Companies often involve in the practice to mislead customers to boost their offerings, and to secure funding from investors. The practice raises concerns regarding transparency, and legal issues. == Definition == AI washing is a deceptive marketing practice. It involves promoting a product or a service by overstating the role of artificial intelligence (AI) and its integration in the design and manufacture of the same. The practice raises concerns regarding transparency, compliance with security regulations, and consumer trust in the AI industry potentially hampering legitimate advancements in AI. The term was first defined by the AI Now Institute, a research institute based at New York University in 2019. The term is derived from greenwashing, another deceptive marketing technique that misrepresents a product's environmental impact in a similar manner. AI washing might involve a company claiming to have used AI in the development or enhancement of its products or services without its actual involvement, or using buzzwords such as "smart" or "AI-powered" without the product actually offering it or making use of it. A company may overstate the usage of AI or misuse the term, which is also construed as AI washing. In 2026, The Washington Post defined AI washing as "a trend for bosses to blame layoffs on the productive capabilities of AI and its ability to replace workers, even when job cuts may have little to do with the technology". == Usage and effects == AI washing can lead to deception of customers and misleading of investors. It is also an illegal and unethical practice that lacks transparency regarding disclosing the details of a product or a service. Companies get involved in such a practice often in response to competition who might have used AI in their offerings. It might also be used as a ploy to secure funding and investment, assuming that it will attract them towards it. AI washing has been compared to dot-com bubble, when businesses appended "dot-com" to the end of the business name to boost their valuation. In September 2023, Coca-Cola released a new product called Coca-Cola Y3000, and the company stated that the Y3000 flavor had been "co-created with human and artificial intelligence". The company was accused of AI washing due to no proof of AI involvement in the creation of the product, and critics believed that AI was used as a way to grab consumer attention more than it was used in the actual product creation. In 2026, mass tech layoffs were attributed to AI washing from AI innovation instead of balance sheet restructuring. == Mitigation == Companies are expected to be transparent and clearer in communicating the usage of AI in their products or services. Consumers can mitigate the same by requesting for hard evidence from the companies regarding the usage of AI tools. Customers should evaluate the product or service as a whole rather than being swayed by the usage of AI. Informed decision making and purchasing can keep them from falling for such marketing gimmicks. The United States Securities and Exchange Commission (SEC) imposes penalties for companies indulging in such practices. In March 2024, the SEC imposed the first civil penalties on two companies for misleading statements about their use of AI, and in July 2024, it charged a corporate executive from a supposed AI hiring startup with fraud for the usage of buzzwords related to AI.

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  Voice inversion

  Voice inversion scrambling is an analog method of obscuring the content of a transmission. It is sometimes used in public service radio, automobile racing, cordless telephones and the Family Radio Service. Without a descrambler, the transmission makes the speaker "sound like Donald Duck". Despite the term, the technique operates on the passband of the information and so can be applied to any information being transmitted. == Forms and details == There are various forms of voice inversion which offer differing levels of security. Overall, voice inversion scrambling offers little true security as software and even hobbyist kits are available from kit makers for scrambling and descrambling. The cadence of the speech is not changed. It is often easy to guess what is happening in the conversation by listening for other audio cues like questions, short responses and other language cadences. In the simplest form of voice inversion, the frequency p {\displaystyle p} of each component is replaced with s − p {\displaystyle s-p} , where s {\displaystyle s} is the frequency of a carrier wave. This can be done by amplitude modulating the speech signal with the carrier, then applying a low-pass filter to select the lower sideband. This will make the low tones of the voice sound like high ones and vice versa. This process also occurs naturally if a radio receiver is tuned to a single sideband transmission but set to decode the wrong sideband. There are more advanced forms of voice inversion which are more complex and require more effort to descramble. One method is to use a random code to choose the carrier frequency and then change this code in real time. This is called Rolling Code voice inversion and one can often hear the "ticks" in the transmission which signal the changing of the inversion point. Another method is split band voice inversion. This is where the band is split and then each band is inverted separately. A rolling code can also be added to this method for variable split band inversion (VSB). Common carrier frequencies are: 2.632 kHz, 2.718 kHz, 2.868 kHz, 3.023 kHz, 3.107 kHz, 3.196 kHz, 3.333 kHz, 3.339 kHz, 3.496 kHz, 3.729 kHz and 4.096 kHz. Voice inversion offers no security at all and software is available to restore the original voice, which is why it is no longer used to protect conversations today. However, voice inversion is still found in low-end Chinese walkie talkies.

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  Cover-coding

  Cover-coding is a technique for obscuring the data that is transmitted over an insecure link, to reduce the risks of snooping. An example of cover-coding would be for the sender to perform a bitwise XOR (exclusive OR) of the original data with a password or random number which is known to both sender and receiver. The resulting cover-coded data is then transmitted from sender to the receiver, who uncovers the original data by performing a further bitwise XOR (exclusive OR) operation on the received data using the same password or random number. ISO 18000-6C (EPC Class 1 Generation 2) RFID tags protect some operations with a cover code. The reader requests a random number from the tag, and the tag responds with a new random number. The reader then encrypts future communications with this number, using bitwise XOR, to the data it sends. Cover coding is secure if the tag signal can't be intercepted and the random number is not re-used. Compared to the loud transmissions from the reader, tag backscatter is much weaker and difficult -- but not impossible -- to intercept.

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