Microsoft SQL Server Master Data Services (MDS) is a Master Data Management (MDM) product from Microsoft that ships as a part of the Microsoft SQL Server relational database management system. Master data management (MDM) allows an organization to discover and define non-transactional lists of data, and compile maintainable, reliable master lists. Master Data Services first shipped with Microsoft SQL Server 2008 R2. Microsoft SQL Server 2016 introduced enhancements to Master Data Services, such as improved performance and security, and the ability to clear transaction logs, create custom indexes, share entity data between different models, and support for many-to-many relationships. == Overview == In Master Data Services, the model is the highest level container in the structure of your master data. You create a model to manage groups of similar data. A model contains one or more entities, and entities contain members that are the data records. An entity is similar to a table. Like other MDM products, Master Data Services aims to create a centralized data source and keep it synchronized, and thus reduce redundancies, across the applications which process the data. Sharing the architectural core with Stratature +EDM, Master Data Services uses a Microsoft SQL Server database as the physical data store. It is a part of the Master Data Hub, which uses the database to store and manage data entities. It is a database with the software to validate and manage the data, and keep it synchronized with the systems that use the data. The master data hub has to extract the data from the source system, validate, sanitize and shape the data, remove duplicates, and update the hub repositories, as well as synchronize the external sources. The entity schemas, attributes, data hierarchies, validation rules and access control information are specified as metadata to the Master Data Services runtime. Master Data Services does not impose any limitation on the data model. Master Data Services also allows custom Business rules, used for validating and sanitizing the data entering the data hub, to be defined, which is then run against the data matching the specified criteria. All changes made to the data are validated against the rules, and a log of the transaction is stored persistently. Violations are logged separately, and optionally the owner is notified, automatically. All the data entities can be versioned. Master Data Services allows the master data to be categorized by hierarchical relationships, such as employee data are a subtype of organization data. Hierarchies are generated by relating data attributes. Data can be automatically categorized using rules, and the categories are introspected programmatically. Master Data Services can also expose the data as Microsoft SQL Server views, which can be pulled by any SQL-compatible client. It uses a role-based access control system to restrict access to the data. The views are generated dynamically, so they contain the latest data entities in the master hub. It can also push out the data by writing to some external journals. Master Data Services also includes a web-based UI for viewing and managing the data. It uses ASP.NET in the back-end. The Silverlight front-end was replaced with HTML5 in SQL Server 2019. Master Data Services provides a Web service interface to expose the data, as well as an API, which internally uses the exposed web services, exposing the feature set, programmatically, to access and manipulate the data. It also integrates with Active Directory for authentication purposes. Unlike +EDM, Master Data Services supports Unicode characters, as well as support multilingual user interfaces. SQL Server 2016 introduced a significant performance increase in Master Data Services over previous versions. == Terminology == Model is the highest level of an MDS instance. It is the primary container for specific groupings of master data. In many ways it is very similar to the idea of a database. Entities are containers created within a model. Entities provide a home for members, and are in many ways analogous to database tables. (e.g. Customer) Members are analogous to the records in a database table (Entity) e.g. Will Smith. Members are contained within entities. Each member is made up of two or more attributes. Attributes are analogous to the columns within a database table (Entity) e.g. Surname. Attributes exist within entities and help describe members (the records within the table). Name and Code attributes are created by default for each entity and serve to describe and uniquely identify leaf members. Attributes can be related to other attributes from other entities which are called 'domain-based' attributes. This is similar to the concept of a foreign key. Other attributes however, will be of type 'free-form' (most common) or 'file'. Attribute Groups are explicitly defined collections of particular attributes. Say you have an entity "customer" that has 50 attributes — too much information for many of your users. Attribute groups enable the creation of custom sets of hand-picked attributes that are relevant for specific audiences. (e.g. "customer - delivery details" that would include just their name and last known delivery address). This is very similar to a database view. Hierarchies organize members into either Derived or Explicit hierarchical structures. Derived hierarchies, as the name suggests, are derived by the MDS engine based on the relationships that exist between attributes. Explicit hierarchies are created by hand using both leaf and consolidated members. Business Rules can be created and applied against model data to ensure that custom business logic is adhered to. In order to be committed into the system data must pass all business rule validations applied to them. e.g. Within the Customer Entity you may want to create a business rule that ensures all members of the 'Country' Attribute contain either the text "USA" or "Canada". The Business Rule once created and ran will then verify all the data is correct before it accepts it into the approved model. Versions provide system owners / administrators with the ability to Open, Lock or Commit a particular version of a model and the data contained within it at a particular point in time. As the content within a model varies, grows or shrinks over time versions provide a way of managing metadata so that subscribing systems can access to the correct content.
BioBIKE
BioBike(nee. BioLingua ) is a cloud-based, through-the-web programmable (Paas) symbolic biocomputing and bioinformatics platform that aims to make computational biology, and especially intelligent biocomputing (that is, the application of Artificial Intelligence to computational biology) accessible to research scientists who are not expert programmers. == Unique capabilities == BioBIKE is an integrated symbolic biocomputing and bioinformatics platform, built from the start as an entirely (what is now called) cloud-based architecture where all computing is done in remote servers, and all user access is accomplished through web browsers. BioBIKE has a built-in frame system in which all objects, data, and knowledge are represented. This enables code written either in the native Lisp, in the visual programming language, or systems of rules expressed in the SNARK theorem prover to access the whole of biological knowledge in an integrated manner. For its time (released in 2002) it was unique in permitting users to create fully functional biocomputing programs that run on the back-end servers entirely through the web browser UI. (In modern terms it was one of the first PaaS (Platform as a Service) systems, predating even Salesforce in this capability.) Initially this programming was carried out in raw Lisp, but Jeff Elhai's team at VCU, with NSF funding, created an entirely graphical programming environment on top of BioBIKE based upon the Boxer-style programming environments. Being a multi-headed, multi-threaded, multi-user, multi-tenancy cloud-based system, BioBIKE users were able to directly work together through their web browsers, remotely sharing the same listener and memory space. This permitted a unique sort of collaboration, discussed in Shrager (2007). A specialized offshoot of BioBIKE called "BioDeducta" includes SRI's SNARK theorem prover, offering unique "deductive biocomputing" capabilities. == Implementation == BioBIKE is open-source software implemented using the Lisp programming language. Continuing development takes place by the BioBIKE team centered at Virginia Commonwealth University . == History == BioBIKE was originally called "BioLingua", and was developed by Jeff Shrager at The Carnegie Inst. of Washington Dept. of Plant Biology, and JP Massar with funding from NASA's Astrobiology Division. Shrager and Massar wanted to create a web-based, multi-user Lisp Machine, specialized for bioinformatics. Other early contributors to the project included Mike Travers, and Jeff Elhai of VCU. Elhai obtained continuing funding from the National Science Foundation for the project, which was renamed BioBIKE. Elhai and colleagues added BioBIKE's unique visual programming language. Shrager, meanwhile, collaborated with Richard Waldinger at SRI to build SRI's (SNARK) theorem prover into BioBIKE, creating a deductive biocomputing system, called BioDeducta. == Instances == There used to be a number of BioBIKE verticals in different biological domains, including viral pathogens, cyanobacteria and other bacteria, Arabidopsis thaliana, and several others described in the references.
List of cryptography journals
List of cryptography journals includes notable peer-reviewed academic journals that focus on cryptography, cryptanalysis, information security, and related areas in computer science and mathematics. == Notable journals == Cryptologia Designs, Codes and Cryptography IEEE Transactions on Information Theory International Journal of Information Security Journal of Cryptology Journal of Computer Security ACM Transactions on Privacy and Security Information Processing Letters Information and Computation
List of cryptosystems
A cryptosystem is a set of cryptographic algorithms that map ciphertexts and plaintexts to each other. == Private-key cryptosystems == Private-key cryptosystems use the same key for encryption and decryption. Caesar cipher Substitution cipher Enigma machine Data Encryption Standard Twofish Serpent Camellia Salsa20 ChaCha20 Blowfish CAST5 Kuznyechik RC4 3DES Skipjack Safer IDEA Advanced Encryption Standard, also known as AES and Rijndael. == Public-key cryptosystems == Public-key cryptosystems use a public key for encryption and a private key for decryption. Diffie–Hellman key exchange RSA encryption Rabin cryptosystem Schnorr signature ElGamal encryption Elliptic-curve cryptography Lattice-based cryptography McEliece cryptosystem Multivariate cryptography Isogeny-based cryptography
Data deduplication
In computing, data deduplication is a technique for eliminating duplicate copies of repeating data. Successful implementation of the technique can improve storage utilization, which may in turn lower capital expenditure by reducing the overall amount of storage media required to meet storage capacity needs. It can also be applied to network data transfers to reduce the number of bytes that must be sent. The deduplication process requires comparison of data 'chunks' (also known as 'byte patterns') which are unique, contiguous blocks of data. These chunks are identified and stored during a process of analysis, and compared to other chunks within existing data. Whenever a match occurs, the redundant chunk is replaced with a small reference that points to the stored chunk. Given that the same byte pattern may occur dozens, hundreds, or even thousands of times (the match frequency is dependent on the chunk size), the amount of data that must be stored or transferred can be greatly reduced. A related technique is single-instance (data) storage, which replaces multiple copies of content at the whole-file level with a single shared copy. While possible to combine this with other forms of data compression and deduplication, it is distinct from newer approaches to data deduplication (which can operate at the segment or sub-block level). Deduplication is different from data compression algorithms, such as LZ77 and LZ78. Whereas compression algorithms identify redundant data inside individual files and encodes this redundant data more efficiently, the intent of deduplication is to inspect large volumes of data and identify large sections – such as entire files or large sections of files – that are identical, and replace them with a shared copy. == Functioning principle == For example, a typical email system might contain 100 instances of the same 1 MB (megabyte) file attachment. Each time the email platform is backed up, all 100 instances of the attachment are saved, requiring 100 MB storage space. With data deduplication, only one instance of the attachment is actually stored; the subsequent instances are referenced back to the saved copy for deduplication ratio of roughly 100 to 1. Deduplication is often paired with data compression for additional storage saving: Deduplication is first used to eliminate large chunks of repetitive data, and compression is then used to efficiently encode each of the stored chunks. In computer code, deduplication is done by, for example, storing information in variables so that they don't have to be written out individually but can be changed all at once at a central referenced location. Examples are CSS classes and named references in MediaWiki. == Benefits == Storage-based data deduplication reduces the amount of storage needed for a given set of files. It is most effective in applications where many copies of very similar or even identical data are stored on a single disk. In the case of data backups, which routinely are performed to protect against data loss, most data in a given backup remain unchanged from the previous backup. Common backup systems try to exploit this by omitting (or hard linking) files that haven't changed or storing differences between files. Neither approach captures all redundancies, however. Hard-linking does not help with large files that have only changed in small ways, such as an email database; differences only find redundancies in adjacent versions of a single file (consider a section that was deleted and later added in again, or a logo image included in many documents). In-line network data deduplication is used to reduce the number of bytes that must be transferred between endpoints, which can reduce the amount of bandwidth required. See WAN optimization for more information. Virtual servers and virtual desktops benefit from deduplication because it allows nominally separate system files for each virtual machine to be coalesced into a single storage space. At the same time, if a given virtual machine customizes a file, deduplication will not change the files on the other virtual machines—something that alternatives like hard links or shared disks do not offer. Backing up or making duplicate copies of virtual environments is similarly improved. == Classification == === Post-process versus in-line deduplication === Deduplication may occur "in-line", as data is flowing, or "post-process" after it has been written. With post-process deduplication, new data is first stored on the storage device and then a process at a later time will analyze the data looking for duplication. The benefit is that there is no need to wait for the hash calculations and lookup to be completed before storing the data, thereby ensuring that store performance is not degraded. Implementations offering policy-based operation can give users the ability to defer optimization on "active" files, or to process files based on type and location. One potential drawback is that duplicate data may be unnecessarily stored for a short time, which can be problematic if the system is nearing full capacity. Alternatively, deduplication hash calculations can be done in-line: synchronized as data enters the target device. If the storage system identifies a block which it has already stored, only a reference to the existing block is stored, rather than the whole new block. The advantage of in-line deduplication over post-process deduplication is that it requires less storage and network traffic, since duplicate data is never stored or transferred. On the negative side, hash calculations may be computationally expensive, thereby reducing the storage throughput. However, certain vendors with in-line deduplication have demonstrated equipment which performs in-line deduplication at high rates. Post-process and in-line deduplication methods are often heavily debated. === Data formats === The SNIA Dictionary identifies two methods: Content-agnostic data deduplication – a data deduplication method that does not require awareness of specific application data formats. Content-aware data deduplication – a data deduplication method that leverages knowledge of specific application data formats. === Source versus target deduplication === Another way to classify data deduplication methods is according to where they occur. Deduplication occurring close to where data is created, is referred to as "source deduplication". When it occurs near where the data is stored, it is called "target deduplication". Source deduplication ensures that data on the data source is deduplicated. This generally takes place directly within a file system. The file system will periodically scan new files creating hashes and compare them to hashes of existing files. When files with same hashes are found then the file copy is removed and the new file points to the old file. Unlike hard links however, duplicated files are considered to be separate entities and if one of the duplicated files is later modified, then using a system called copy-on-write a copy of that changed file or block is created. The deduplication process is transparent to the users and backup applications. Backing up a deduplicated file system will often cause duplication to occur resulting in the backups being bigger than the source data. Source deduplication can be declared explicitly for copying operations, as no calculation is needed to know that the copied data is in need of deduplication. This leads to a new form of link on file systems, called a reference-counted link, or reflink, in some systems (e.g. Linux), or a cloned file on macOS, where one or more inodes (file information entries) are made to share some or all of their data. It is named analogously to hard links, which work at the inode level, and symbolic links, which work at the filename level.The individual entries have a copy-on-write behavior that is non-aliasing, i.e. changing one copy afterwards will not affect other copies. Microsoft's ReFS also supports this operation. Target deduplication is the process of removing duplicates when the data was not generated at that location. Example of this would be a server connected to a SAN/NAS, The SAN/NAS would be a target for the server (target deduplication). The server is not aware of any deduplication, the server is also the point of data generation. A second example would be backup. Generally this will be a backup store such as a data repository or a virtual tape library. === Deduplication methods === One of the most common forms of data deduplication implementations works by comparing chunks of data to detect duplicates. For that to happen, each chunk of data is assigned an identification, calculated by the software, typically using cryptographic hash functions. In many implementations, the assumption is made that if the identification is identical, the data is identical, even though this cannot be true in all cases due to the pigeonhole principle; other implementations do not as
Structural similarity index measure
The structural similarity index measure (SSIM) is a method for predicting the perceived quality of digital television and cinematic pictures, as well as other kinds of digital images and videos. It is also used for measuring the similarity between two images. The SSIM index is a full reference metric; in other words, the measurement or prediction of image quality is based on an initial uncompressed or distortion-free image as reference. SSIM is a perception-based model that considers image degradation as perceived change in structural information, while also incorporating important perceptual phenomena, including both luminance masking and contrast masking terms. This distinguishes from other techniques such as mean squared error (MSE) or peak signal-to-noise ratio (PSNR) that instead estimate absolute errors. Structural information is the idea that the pixels have strong inter-dependencies especially when they are spatially close. These dependencies carry important information about the structure of the objects in the visual scene. Luminance masking is a phenomenon whereby image distortions (in this context) tend to be less visible in bright regions, while contrast masking is a phenomenon whereby distortions become less visible where there is significant activity or "texture" in the image. == History == The predecessor of SSIM was called Universal Quality Index (UQI), or Wang–Bovik index, which was developed by Zhou Wang and Alan Bovik in 2001. This evolved, through their collaboration with Hamid Sheikh and Eero Simoncelli, into the current version of SSIM, which was published in April 2004 in the IEEE Transactions on Image Processing. In addition to defining the SSIM quality index, the paper provides a general context for developing and evaluating perceptual quality measures, including connections to human visual neurobiology and perception, and direct validation of the index against human subject ratings. The basic model was developed in the Laboratory for Image and Video Engineering (LIVE) at The University of Texas at Austin and further developed jointly with the Laboratory for Computational Vision (LCV) at New York University. Further variants of the model have been developed in the Image and Visual Computing Laboratory at University of Waterloo and have been commercially marketed. SSIM subsequently found strong adoption in the image processing community and in the television and social media industries. The 2004 SSIM paper has been cited over 50,000 times according to Google Scholar, making it one of the highest cited papers in the image processing and video engineering fields. It was recognized with the IEEE Signal Processing Society Best Paper Award for 2009. It also received the IEEE Signal Processing Society Sustained Impact Award for 2016, indicative of a paper having an unusually high impact for at least 10 years following its publication. Because of its high adoption by the television industry, the authors of the original SSIM paper were each accorded a Primetime Engineering Emmy Award in 2015 by the Television Academy. == Algorithm == The SSIM index is calculated between two windows of pixel values x {\displaystyle x} and y {\displaystyle y} of common size, from corresponding locations in two images to be compared. These SSIM values can be aggregated across the full images by averaging or other variations. === Special-case formula === In one simple special case, further explained in the next section, the SSIM measure between x {\displaystyle x} and y {\displaystyle y} is: SSIM ( x , y ) = ( 2 μ x μ y + c 1 ) ( 2 σ x y + c 2 ) ( μ x 2 + μ y 2 + c 1 ) ( σ x 2 + σ y 2 + c 2 ) {\displaystyle {\hbox{SSIM}}(x,y)={\frac {(2\mu _{x}\mu _{y}+c_{1})(2\sigma _{xy}+c_{2})}{(\mu _{x}^{2}+\mu _{y}^{2}+c_{1})(\sigma _{x}^{2}+\sigma _{y}^{2}+c_{2})}}} with: μ x {\displaystyle \mu _{x}} the pixel sample mean of x {\displaystyle x} ; μ y {\displaystyle \mu _{y}} the pixel sample mean of y {\displaystyle y} ; σ x 2 {\displaystyle \sigma _{x}^{2}} the sample variance of x {\displaystyle x} ; σ y 2 {\displaystyle \sigma _{y}^{2}} the sample variance of y {\displaystyle y} ; σ x y {\displaystyle \sigma _{xy}} the sample covariance of x {\displaystyle x} and y {\displaystyle y} ; c 1 = ( k 1 L ) 2 {\displaystyle c_{1}=(k_{1}L)^{2}} , c 2 = ( k 2 L ) 2 {\displaystyle c_{2}=(k_{2}L)^{2}} two variables to stabilize the division with weak denominator; L {\displaystyle L} the dynamic range of the pixel-values (typically this is 2 # b i t s p e r p i x e l − 1 {\displaystyle 2^{\#bits\ per\ pixel}-1} ); k 1 = 0.01 {\displaystyle k_{1}=0.01} and k 2 = 0.03 {\displaystyle k_{2}=0.03} by default. === General formula and components === The SSIM formula is based on three comparison measurements between the samples of x {\displaystyle x} and y {\displaystyle y} : luminance ( l {\displaystyle l} ), contrast ( c {\displaystyle c} ), and structure ( s {\displaystyle s} ). The individual comparison functions are: l ( x , y ) = 2 μ x μ y + c 1 μ x 2 + μ y 2 + c 1 {\displaystyle l(x,y)={\frac {2\mu _{x}\mu _{y}+c_{1}}{\mu _{x}^{2}+\mu _{y}^{2}+c_{1}}}} c ( x , y ) = 2 σ x σ y + c 2 σ x 2 + σ y 2 + c 2 {\displaystyle c(x,y)={\frac {2\sigma _{x}\sigma _{y}+c_{2}}{\sigma _{x}^{2}+\sigma _{y}^{2}+c_{2}}}} s ( x , y ) = σ x y + c 3 σ x σ y + c 3 {\displaystyle s(x,y)={\frac {\sigma _{xy}+c_{3}}{\sigma _{x}\sigma _{y}+c_{3}}}} The SSIM for each block is then a weighted combination of those comparative measures: SSIM ( x , y ) = l ( x , y ) α ⋅ c ( x , y ) β ⋅ s ( x , y ) γ {\displaystyle {\text{SSIM}}(x,y)=l(x,y)^{\alpha }\cdot c(x,y)^{\beta }\cdot s(x,y)^{\gamma }} Choosing the third denominator stabilizing constant as: c 3 = c 2 / 2 {\displaystyle c_{3}=c_{2}/2} leads to a simplification when combining the c and s components with equal exponents ( β = γ {\displaystyle \beta =\gamma } ), as the numerator of c is then twice the denominator of s, leading to a cancellation leaving just a 2. Setting the weights (exponents) α , β , γ {\displaystyle \alpha ,\beta ,\gamma } to 1, the formula can then be reduced to the special case shown above. === Mathematical properties === SSIM satisfies the identity of indiscernibles, and symmetry properties, but not the triangle inequality or non-negativity, and thus is not a distance function. However, under certain conditions, SSIM may be converted to a normalized root MSE measure, which is a distance function. The square of such a function is not convex, but is locally convex and quasiconvex, making SSIM a feasible target for optimization. === Application of the formula === In order to evaluate the image quality, this formula is usually applied only on luma, although it may also be applied on color (e.g., RGB) values or chromatic (e.g. YCbCr) values. The resultant SSIM index is a decimal value between -1 and 1, where 1 indicates perfect similarity, 0 indicates no similarity, and -1 indicates perfect anti-correlation. For an image, it is typically calculated using a sliding Gaussian window of size 11×11 or a block window of size 8×8. The window can be displaced pixel-by-pixel on the image to create an SSIM quality map of the image. In the case of video quality assessment, the authors propose to use only a subgroup of the possible windows to reduce the complexity of the calculation. === Variants === ==== Multi-scale SSIM ==== A more advanced form of SSIM, called Multiscale SSIM (MS-SSIM) is conducted over multiple scales through a process of multiple stages of sub-sampling, reminiscent of multiscale processing in the early vision system. It has been shown to perform equally well or better than SSIM on different subjective image and video databases. ==== Multi-component SSIM ==== Three-component SSIM (3-SSIM) is a form of SSIM that takes into account the fact that the human eye can see differences more precisely on textured or edge regions than on smooth regions. The resulting metric is calculated as a weighted average of SSIM for three categories of regions: edges, textures, and smooth regions. The proposed weighting is 0.5 for edges, 0.25 for the textured and smooth regions. The authors mention that a 1/0/0 weighting (ignoring anything but edge distortions) leads to results that are closer to subjective ratings. This suggests that edge regions play a dominant role in image quality perception. The authors of 3-SSIM have also extended the model into four-component SSIM (4-SSIM). The edge types are further subdivided into preserved and changed edges by their distortion status. The proposed weighting is 0.25 for all four components. ==== Structural dissimilarity ==== Structural dissimilarity (DSSIM) may be derived from SSIM, though it does not constitute a distance function as the triangle inequality is not necessarily satisfied. DSSIM ( x , y ) = 1 − SSIM ( x , y ) 2 {\displaystyle {\hbox{DSSIM}}(x,y)={\frac {1-{\hbox{SSIM}}(x,y)}{2}}} ==== Video quality metrics and temporal variants ==== It is worth noting that the original vers
Instant messaging
Instant messaging (IM) technology is a type of synchronous computer-mediated communication involving the immediate (real-time) transmission of messages between two or more parties over the Internet or another computer network. Originally involving simple text message exchanges, modern instant messaging applications and services (also variously known as instant messenger, messaging app, chat app, chat client, or simply a messenger) tend to also feature the exchange of multimedia, emojis, file transfer, VoIP (voice calling), and video chat capabilities. Instant messaging systems facilitate connections between specified known users (often using a contact list also known as a "buddy list" or "friend list") or in chat rooms, and can be standalone apps or integrated into a wider social media platform, or in a website where it can, for instance, be used for conversational commerce. Originally the term "instant messaging" was distinguished from "text messaging" by being run on a computer network instead of a cellular/mobile network, being able to write longer messages, real-time communication, presence ("status"), and being free (only cost of access instead of per SMS message sent). Instant messaging was pioneered in the early Internet era; the IRC protocol was the earliest to achieve wide adoption. Later in the 1990s, ICQ was among the first closed and commercialized instant messengers, and several rival services appeared afterwards as it became a popular use of the Internet. Beginning with its first introduction in 2005, BlackBerry Messenger became the first popular example of mobile-based IM, combining features of traditional IM and mobile SMS. Instant messaging remains very popular today; IM apps are the most widely used smartphone apps: in 2018 for instance there were 980 million monthly active users of WeChat and 1.3 billion monthly users of WhatsApp, the largest IM network. == Overview == Instant messaging (IM), sometimes also called "messaging" or "texting", consists of computer-based human communication between two users (private messaging) or more (chat room or "group") in real-time, allowing immediate receipt of acknowledgment or reply. This is in direct contrast to email, where conversations are not in real-time, and the perceived quasi-synchrony of the communications by the users (although many systems allow users to send offline messages that the other user receives when logging in). Earlier IM networks were limited to text-based communication, not dissimilar to mobile text messaging. As technology has moved forward, IM has expanded to include voice calling using a microphone, videotelephony using webcams, file transfer, location sharing, image and video transfer, voice notes, and other features. IM is conducted over the Internet or other types of networks (see also LAN messenger). Depending on the IM protocol, the technical architecture can be peer-to-peer (direct point-to-point transmission) or client–server (when all clients have to first connect to the central server). Primary IM services are controlled by their corresponding companies and usually follow the client-server model. At one point, the term "Instant Messenger" was a service mark of AOL Time Warner and could not be used in software not affiliated with AOL in the United States. For this reason, in April 2007, the instant messaging client formerly named Gaim (or gaim) announced that they would be renamed "Pidgin". === Clients === Modern IM services generally provide their own client, either a separately installed application or a browser-based client. They are normally centralised networks run by the servers of the platform's operators, unlike peer-to-peer protocols like XMPP. These usually only work within the same IM network, although some allow limited function with other services (see #Interoperability). Third-party client software applications exist that will connect with most of the major IM services. There is the class of instant messengers that uses the serverless model, which doesn't require servers, and the IM network consists only of clients. There are several serverless messengers: RetroShare, Tox, Bitmessage, Ricochet. See also: LAN messenger. Some examples of popular IM services today include Signal, Telegram, WhatsApp Messenger, WeChat, QQ Messenger, Viber, Line, and Snapchat. The popularity of certain apps greatly differ between different countries. Certain apps have an emphasis on certain uses - for example, Skype focuses on video calling, Slack focuses on messaging and file sharing for work teams, and Snapchat focuses on image messages. Some social networking services offer messaging services as a component of their overall platform, such as Facebook's Facebook Messenger, who also own WhatsApp. Others have a direct IM function as an additional adjunct component of their social networking platforms, like Instagram, Reddit, Tumblr, TikTok, Clubhouse and Twitter; this also includes for example dating websites, such as OkCupid or Plenty of Fish, and online gaming chat platforms. === Features === ==== Private and group messaging ==== Private chat allows users to converse privately with another person or a group. Privacy can also be enhanced in several ways, such as end-to-end encryption by default. Public and group chat features allow users to communicate with multiple people simultaneously. ==== Calling ==== Many major IM services and applications offer a call feature for user-to-user voice calls, conference calls, and voice messages. The call functionality is useful for professionals who utilize the application for work purposes and as a hands-free method. Videotelephony using a webcam is also possible by some. ==== Games and entertainment ==== Some IM applications include in-app games for entertainment. Yahoo! Messenger, for example, introduced these where users could play a game and viewed by friends in real-time. MSN Messenger featured a number of playable games within the interface. Facebook's Messenger has had a built-in option to play games with people in a chat, including games like Tetris and Blackjack. Discord features multiple games built inside the "activities" tab in voice channels. ==== Payments ==== A relatively new feature to instant messaging, peer-to-peer payments are available for financial tasks on top of communication. The lack of a service fee also makes these advantageous to financial applications. IM services such as Facebook Messenger and the WeChat 'super-app' for example offer a payment feature. == History == === Early systems === Though the term dates from the 1990s, instant messaging predates the Internet, first appearing on multi-user operating systems like Compatible Time-Sharing System (CTSS) and Multiplexed Information and Computing Service (Multics) in the mid-1960s. Initially, some of these systems were used as notification systems for services like printing, but quickly were used to facilitate communication with other users logged into the same machine. CTSS facilitated communication via text message for up to 30 people. Parallel to instant messaging were early online chat facilities, the earliest of which was Talkomatic (1973) on the PLATO system, which allowed 5 people to chat simultaneously on a 512 x 512 plasma display (5 lines of text + 1 status line per person). During the bulletin board system (BBS) phenomenon that peaked during the 1980s, some systems incorporated chat features which were similar to instant messaging; Freelancin' Roundtable was one prime example. The first such general-availability commercial online chat service (as opposed to PLATO, which was educational) was the CompuServe CB Simulator in 1980, created by CompuServe executive Alexander "Sandy" Trevor in Columbus, Ohio. As networks developed, the protocols spread with the networks. Some of these used a peer-to-peer protocol (e.g. talk, ntalk and ytalk), while others required peers to connect to a server (see talker and IRC). The Zephyr Notification Service (still in use at some institutions) was invented at MIT's Project Athena in the 1980s to allow service providers to locate and send messages to users. Early instant messaging programs were primarily real-time text, where characters appeared as they were typed. This includes the Unix "talk" command line program, which was popular in the 1980s and early 1990s. Some BBS chat programs (i.e. Celerity BBS) also used a similar interface. Modern implementations of real-time text also exist in instant messengers, such as AOL's Real-Time IM as an optional feature. In the latter half of the 1980s and into the early 1990s, the Quantum Link online service for Commodore 64 computers offered user-to-user messages between concurrently connected customers, which they called "On-Line Messages" (or OLM for short), and later "FlashMail." Quantum Link later became America Online and made AOL Instant Messenger (AIM, discussed later). While the Quantum Link client software ran on a Commodore 64, using only