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  Emergent algorithm

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

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  Media intelligence

  Media intelligence uses data mining and data science to analyze public, social and editorial media content. It refers to marketing systems that synthesize billions of online conversations into relevant information. This allow organizations to measure and manage content performance, understand trends, and drive communications and business strategy. Media intelligence can include software as a service using big data terminology. This includes questions about messaging efficiency, share of voice, audience geographical distribution, message amplification, influencer strategy, journalist outreach, creative resonance, and competitor performance in all these areas. Media intelligence differs from business intelligence in that it uses and analyzes data outside company firewalls. Examples of that data are user-generated content on social media sites, blogs, comment fields, and wikis etc. It may also include other public data sources like press releases, news, blogs, legal filings, reviews and job postings. Media intelligence may also include competitive intelligence, wherein information that is gathered from publicly available sources such as social media, press releases, and news announcements are used to better understand the strategies and tactics being deployed by competing businesses. Media intelligence is enhanced by means of emerging technologies like ambient intelligence, machine learning, semantic tagging, natural language processing, sentiment analysis and machine translation. == Technologies used == Different media intelligence platforms use different technologies for monitoring, curating content, engaging with content, data analysis and measurement of communications and marketing campaign success. These technology providers may obtain content by scraping content directly from websites or by connecting to the API provided by social media, or other content platforms that are created for 3rd party developers to develop their own applications and services that access data. Technology companies may also get data from a data reseller. Some social media monitoring and analytics companies use calls to data providers each time an end-user develops a query. Others archive and index social media posts to provide end users with on-demand access to historical data and enable methodologies and technologies leveraging network and relational data. Additional monitoring companies use crawlers and spidering technology to find keyword references, known as semantic analysis or natural language processing. Basic implementation involves curating data from social media on a large scale and analyzing the results to make sense out of it.

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  ESign (India)

  Aadhaar eSign is an online electronic signature service in India to facilitate an Aadhaar holder to digitally sign a document. The signature service is facilitated by authenticating the Aadhaar holder via the Aadhaar-based e-KYC (electronic Know Your Customer) service. To eSign a document, one has to have an Aadhaar card and a mobile number registered with Aadhaar. With these two things, an Indian citizen can sign a document remotely without being physically present. == Procedure == The notification issued by Government of India in this regard stipulates the following procedure for the e-authentication using Aadhaar e-KYC services. Authentication of an electronic record by e-authentication technique, which shall be done by the applicable use of e-authentication, hash function, and asymmetric cryptosystem techniques, leading to issuance of digital signature certificate by Certifying Authority, a trusted third party service by subscriber's key pair generation, storing of the key pairs on hardware security module and creation of digital signature provided that the trusted third party shall be offered by the certifying authority (the trusted third party shall send application form and certificate signing request to the Certifying Authority for issuing a digital signature certificate to the subscriber), issuance of digital signature certificate by Certifying Authority shall be based on e-authentication, particulars given in the prescribed format, digitally signed verified information from Aadhaar e-KYC services and electronic consent of digital signature certificate applicant, the manner and requirements for e-authentication shall be as issued by the Controller from time to time, the security procedure for creating the subscriber's key pair shall be in accordance with the e-authentication guidelines issued by the Controller, the standards referred to in rule 6 of the Information Technology (Certifying Authorities) Rules, 2000 shall be complied with, in so far as they relate to the certification function of public key of Digital Signature Certificate applicant, and the manner in which information is authenticated by means of digital signature shall comply with the standards specified in rule 6 of the Information Technology (Certifying Authorities) Rules, 2000 in so far as they relate to the creation, storage and transmission of Digital Signature. == eSign Service Providers == Organisations and individuals seeking to obtain the eSigning Service can utilize the services of various service providers. There are empanelled service providers with whom organisations can register as an Application Service Prover after submitting the requisite documents, getting UAT access, building the application around the service and going through an IT Audit by an CERT-IN empanelled auditor. However, the process of registering as an Application Service Provider is cumbersome, and requires huge investments of time, money and resources in complying with the regulations and building a suitable application. Most organisations prefer using services of plug-n-play gateway providers who take the responsibility of complying with the regulations, hence simplifying the process for the market.

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  Multistage interconnection networks

  Multistage interconnection networks (MINs) are a class of high-speed computer networks usually composed of processing elements (PEs) on one end of the network and memory elements (MEs) on the other end, connected by switching elements (SEs). The switching elements themselves are usually connected to each other in stages, hence the name. MINs are typically used in high-performance or parallel computing as a low-latency interconnection (as opposed to traditional packet switching networks), though they could be implemented on top of a packet switching network. Though the network is typically used for routing purposes, it could also be used as a co-processor to the actual processors for such uses as sorting; cyclic shifting, as in a perfect shuffle network; and bitonic sorting. == Background == Interconnection network are used to connect nodes, where nodes can be a single processor or group of processors, to other nodes. Interconnection networks can be categorized on the basis of their topology. Topology is the pattern in which one node is connected to other nodes. There are two main types of topology: static and dynamic. Static interconnect networks are hard-wired and cannot change their configurations. A regular static interconnect is mainly used in small networks made up of loosely couple nodes. The regular structure signifies that the nodes are arranged in specific shape and the shape is maintained throughout the networks. Some examples of static regular interconnections are: Completely connected network In a mesh network, multiple nodes are connected with each other. Each node in the network is connected to every other node in the network. This arrangement allows proper communication of the data between the nodes. But, there are a lot of communication overheads due to the increased number of node connections. Shared busThis network topology involves connection of the nodes with each other over a bus. Every node communicates with every other node using the bus. The bus utility ensures that no data is sent to the wrong node. But, the bus traffic is an important parameter which can affect the system. RingThis is one of the simplest ways of connecting nodes with each other. The nodes are connected with each other to form a ring. For a node to communicate with some other node, it has to send the messages to its neighbor. Therefore, the data message passes through a series of other nodes before reaching the destination. This involves increased latency in the system. TreeThis topology involves connection of the nodes to form a tree. The nodes are connected to form clusters and the clusters are in-turn connected to form the tree. This methodology causes increased complexity in the network. Hypercube This topology consists of connections of the nodes to form cubes. The nodes are also connected to the nodes on the other cubes. ButterflyThis is one of the most complex connections of the nodes. As the figure suggests, there are nodes which are connected and arranged in terms of their ranks. They are arranged in the form of a matrix. In dynamic interconnect networks, the nodes are interconnected via an array of simple switching elements. This interconnection can then be changed by use of routing algorithms, such that the path from one node to other nodes can be varied. Dynamic interconnections can be classified as: Single stage Interconnect Network Multistage interconnect Network Crossbar switch connections == Crossbar Switch Connections == In crossbar switch, there is a dedicated path from one processor to other processors. Thus, if there are n inputs and m outputs, we will need nm switches to realize a crossbar. As the number of outputs increases, the number of switches increases by factor of n. For large network this will be a problem. An alternative to this scheme is staged switching. == Single Stage Interconnect Network == In a single stage interconnect network, the input nodes are connected to output via a single stage of switches. The figure shows 88 single stage switch using shuffle exchange. As one can see, from a single shuffle, not all input can reach all output. Multiple shuffles are required for all inputs to be connected to all the outputs. == Multistage Interconnect Network == A multistage interconnect network is formed by cascading multiple single stage switches. The switches can then use their own routing algorithm, or be controlled by a centralized router, to form a completely interconnected network. Multistage Interconnect Network can be classified into three types: Non-blocking: A non-blocking network can connect any idle input to any idle output, regardless of the connections already established across the network. Crossbar is an example of this type of network. Rearrangeable non-blocking: This type of network can establish all possible connections between inputs and outputs by rearranging its existing connections. Blocking: This type of network cannot realize all possible connections between inputs and outputs. This is because a connection between one free input to another free output is blocked by an existing connection in the network. The number of switching elements required to realize a non-blocking network in highest, followed by rearrangeable non-blocking. Blocking network uses least switching elements. == Examples == Multiple types of multistage interconnection networks exist. === Omega network === An Omega network consists of multiple stages of 22 switching elements. Each input has a dedicated connection to an output. An NN omega network has log2(N) stages and N/2 switching elements in each stage for a perfect shuffle between stages. Thus the network has complexity of 0(N log(N)). Each switching element can employ its own switching algorithm. Consider an 88 omega network. There are 8! = 40320 1-to-1 mappings from input to output. There are 12 switching element for a total permutation of 2^12 = 4096. Thus, it is a blocking network. === Clos network === A Clos network uses 3 stages to switch from N inputs to N outputs. In the first stage, there are r= N/n crossbar switches and each switch is of size nm. In the second stage there are m switches of size rr and finally the last stage is a mirror of the first stage with r switches of size mn. A clos network will be completely non-blocking if m >= 2n-1. The number of connections, though more than omega network is much less than that of a crossbar network. === Beneš network === A Beneš network is a rearrangeably non-blocking network derived from the clos network by initializing n = m = 2. There are (2log2(N) - 1) stages, with each stage containing N/2 22 crossbar switches. An 88 Beneš network has 5 stages of switching elements, and each stage has 4 switching elements. The center three stages has two 44 benes network. The 44 Beneš network, can connect any input to any output recursively.

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  Inductive probability

  Inductive probability attempts to give the probability of future events based on past events. It is the basis for inductive reasoning, and gives the mathematical basis for learning and the perception of patterns. It is a source of knowledge about the world. There are three sources of knowledge: inference, communication, and deduction. Communication relays information found using other methods. Deduction establishes new facts based on existing facts. Inference establishes new facts from data. Its basis is Bayes' theorem. Information describing the world is written in a language. For example, a simple mathematical language of propositions may be chosen. Sentences may be written down in this language as strings of characters. But in the computer it is possible to encode these sentences as strings of bits (1s and 0s). Then the language may be encoded so that the most commonly used sentences are the shortest. This internal language implicitly represents probabilities of statements. Occam's razor says the "simplest theory, consistent with the data is most likely to be correct". The "simplest theory" is interpreted as the representation of the theory written in this internal language. The theory with the shortest encoding in this internal language is most likely to be correct. == History == Probability and statistics was focused on probability distributions and tests of significance. Probability was formal, well defined, but limited in scope. In particular its application was limited to situations that could be defined as an experiment or trial, with a well defined population. Bayes's theorem is named after Rev. Thomas Bayes 1701–1761. Bayesian inference broadened the application of probability to many situations where a population was not well defined. But Bayes' theorem always depended on prior probabilities, to generate new probabilities. It was unclear where these prior probabilities should come from. Ray Solomonoff developed algorithmic probability which gave an explanation for what randomness is and how patterns in the data may be represented by computer programs, that give shorter representations of the data circa 1964. Chris Wallace and D. M. Boulton developed minimum message length circa 1968. Later Jorma Rissanen developed the minimum description length circa 1978. These methods allow information theory to be related to probability, in a way that can be compared to the application of Bayes' theorem, but which give a source and explanation for the role of prior probabilities. Marcus Hutter combined decision theory with the work of Ray Solomonoff and Andrey Kolmogorov to give a theory for the Pareto optimal behavior for an Intelligent agent, circa 1998. === Minimum description/message length === The program with the shortest length that matches the data is the most likely to predict future data. This is the thesis behind the minimum message length and minimum description length methods. At first sight Bayes' theorem appears different from the minimimum message/description length principle. At closer inspection it turns out to be the same. Bayes' theorem is about conditional probabilities, and states the probability that event B happens if firstly event A happens: P ( A ∧ B ) = P ( B ) ⋅ P ( A | B ) = P ( A ) ⋅ P ( B | A ) {\displaystyle P(A\land B)=P(B)\cdot P(A|B)=P(A)\cdot P(B|A)} becomes in terms of message length L, L ( A ∧ B ) = L ( B ) + L ( A | B ) = L ( A ) + L ( B | A ) . {\displaystyle L(A\land B)=L(B)+L(A|B)=L(A)+L(B|A).} This means that if all the information is given describing an event then the length of the information may be used to give the raw probability of the event. So if the information describing the occurrence of A is given, along with the information describing B given A, then all the information describing A and B has been given. ==== Overfitting ==== Overfitting occurs when the model matches the random noise and not the pattern in the data. For example, take the situation where a curve is fitted to a set of points. If a polynomial with many terms is fitted then it can more closely represent the data. Then the fit will be better, and the information needed to describe the deviations from the fitted curve will be smaller. Smaller information length means higher probability. However, the information needed to describe the curve must also be considered. The total information for a curve with many terms may be greater than for a curve with fewer terms, that has not as good a fit, but needs less information to describe the polynomial. === Inference based on program complexity === Solomonoff's theory of inductive inference is also inductive inference. A bit string x is observed. Then consider all programs that generate strings starting with x. Cast in the form of inductive inference, the programs are theories that imply the observation of the bit string x. The method used here to give probabilities for inductive inference is based on Solomonoff's theory of inductive inference. ==== Detecting patterns in the data ==== If all the bits are 1, then people infer that there is a bias in the coin and that it is more likely also that the next bit is 1 also. This is described as learning from, or detecting a pattern in the data. Such a pattern may be represented by a computer program. A short computer program may be written that produces a series of bits which are all 1. If the length of the program K is L ( K ) {\displaystyle L(K)} bits then its prior probability is, P ( K ) = 2 − L ( K ) {\displaystyle P(K)=2^{-L(K)}} The length of the shortest program that represents the string of bits is called the Kolmogorov complexity. Kolmogorov complexity is not computable. This is related to the halting problem. When searching for the shortest program some programs may go into an infinite loop. ==== Considering all theories ==== The Greek philosopher Epicurus is quoted as saying "If more than one theory is consistent with the observations, keep all theories". As in a crime novel all theories must be considered in determining the likely murderer, so with inductive probability all programs must be considered in determining the likely future bits arising from the stream of bits. Programs that are already longer than n have no predictive power. The raw (or prior) probability that the pattern of bits is random (has no pattern) is 2 − n {\displaystyle 2^{-n}} . Each program that produces the sequence of bits, but is shorter than the n is a theory/pattern about the bits with a probability of 2 − k {\displaystyle 2^{-k}} where k is the length of the program. The probability of receiving a sequence of bits y after receiving a series of bits x is then the conditional probability of receiving y given x, which is the probability of x with y appended, divided by the probability of x. ==== Universal priors ==== The programming language affects the predictions of the next bit in the string. The language acts as a prior probability. This is particularly a problem where the programming language codes for numbers and other data types. Intuitively we think that 0 and 1 are simple numbers, and that prime numbers are somehow more complex than numbers that may be composite. Using the Kolmogorov complexity gives an unbiased estimate (a universal prior) of the prior probability of a number. As a thought experiment an intelligent agent may be fitted with a data input device giving a series of numbers, after applying some transformation function to the raw numbers. Another agent might have the same input device with a different transformation function. The agents do not see or know about these transformation functions. Then there appears no rational basis for preferring one function over another. A universal prior insures that although two agents may have different initial probability distributions for the data input, the difference will be bounded by a constant. So universal priors do not eliminate an initial bias, but they reduce and limit it. Whenever we describe an event in a language, either using a natural language or other, the language has encoded in it our prior expectations. So some reliance on prior probabilities are inevitable. A problem arises where an intelligent agent's prior expectations interact with the environment to form a self reinforcing feed back loop. This is the problem of bias or prejudice. Universal priors reduce but do not eliminate this problem. === Universal artificial intelligence === The theory of universal artificial intelligence applies decision theory to inductive probabilities. The theory shows how the best actions to optimize a reward function may be chosen. The result is a theoretical model of intelligence. It is a fundamental theory of intelligence, which optimizes the agents behavior in, Exploring the environment; performing actions to get responses that broaden the agents knowledge. Competing or co-operating with another agent; games. Balancing short and long term rewards. In general no agent will always provi

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  HKDF

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

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  Format-transforming encryption

  In cryptography, format-transforming encryption (FTE) refers to encryption where the format of the input plaintext and output ciphertext are configurable. Descriptions of formats can vary, but are typically compact set descriptors, such as a regular expression. Format-transforming encryption is closely related to, and a generalization of, format-preserving encryption. == Applications of FTE == === Restricted fields or formats === Similar to format-preserving encryption, FTE can be used to control the format of ciphertexts. The canonical example is a credit card number, such as 1234567812345670 (16 bytes long, digits only). However, FTE does not enforce that the input format must be the same as the output format. === Censorship circumvention === FTE is used by the Tor Project to circumvent deep packet inspection by pretending to be some other protocols. The implementation is fteproxy; it was written by the authors who came up with the FTE concept.

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  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

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  2024–present global memory supply shortage

  A global computer memory supply shortage started in 2024 due to supply constraints and rapid price escalation in the semiconductor memory market, particularly affecting DRAM and NAND flash memory. This shortage is sometimes labelled by tech media outlets as "RAMmageddon" or the "RAMpocalypse". Unlike the 2020–2023 global chip shortage, which stemmed primarily from pandemic-related supply chain disruptions from COVID-19, this shortage is driven by a structural reallocation of manufacturing capacity toward high-margin products for artificial intelligence infrastructure, creating scarcity of computer memory in consumer and enterprise PC markets. According to a 2026 Kearney's PERLab analysis, the shortage is expected to last at least until 2030, with CEOs agreeing with the timelines. == Background == Following a severe market downturn in 2022–2023, major memory manufacturers—Samsung Electronics, SK Hynix, and Micron Technology—implemented strategic production cuts to stabilize pricing. By mid-2024, the rapid expansion of generative AI services triggered unprecedented demand for specialized memory products, particularly High Bandwidth Memory (HBM) used in AI accelerators and data center GPUs. Specialized components of semiconductor technology are also experiencing supply constraints due to high demand in AI application. For example, glass cloth, a high-performance glass fiber substrate used for power efficient high speed data transfer and a crucial component of semiconductor manufacturing, is experiencing a supply crisis. Nitto Boseki, a Japanese firm having overwhelming monopoly in its production, is not able to meet increased demands, making chip-makers such as Qualcomm, Apple, Nvidia and AMD compete for securing supply. There are also reports of smaller electronics companies struggling to find suppliers for components such as NAND flash. Memory suppliers are adapting to increased demands and market unpredictability by requiring prepayment or shorter time-frame of payment, which makes it more difficult for smaller firms to acquire capital to survive. By 2026, due to steadily increased demand on resources, CPUs are also experiencing shortage issues due to low fabrication capacity, prioritisation of server CPUs, and increased demand, with CPU prices also being forecast to increase by as much as 15%. The demand on memory has also increased strain on other electronic components such as hard disk devices, with reports such as Western Digital's hard disk supply for 2026 being booked for enterprise applications before February 2026. A 2024 McKinsey analysis projected that global demand for AI-ready data center capacity would grow at approximately 33% annually through 2030, with AI workloads consuming roughly 70% of total data center capacity by the decade's end. In addition, according to Kearney's State of Semiconductor 2025 Report, executives were already expecting a shortage in the <8nm wafer size with memory chips being mentioned as an acute source of concern. Multiple companies mentioned being prepared for it through long-term agreements with RAM suppliers or amassing additional inventory. On 24 March 2026, Google announced TurboQuant, a memory compression technology focused on large language models (LLM) and vector search engines, which it claimed achieves 6x lower memory consumption in tested local LLMs and 8x performance enhancement in tests running on H100 accelerators. The technology is also a drop in enhancement for existing inference pipeline. Amid speculation about memory demand trends, memory manufacturers, SanDisk, Micron, Western Digital and Seagate, among other companies involved in memory manufacture experienced stock price declines. Prices of memory kits also reduced in the following months, although still at inflated prices. == Causes == === HBM production displacement === HBM manufacturing requires significantly more wafer capacity per bit than standard DRAM modules. Industry sources reported that as manufacturers allocated increasing wafer capacity to HBM production to meet contracts with AI infrastructure providers, the supply of conventional DDR4 and DDR5 modules for consumer PCs and smartphones contracted sharply. By September 2025, Samsung Electronics had reportedly expanded its 1c DRAM capacity to target 60,000 wafers per month specifically for HBM4 production, further diverting resources from consumer memory lines. === Geopolitical and trade barriers === The supply chain was further constrained by escalating trade tensions between the United States and China. Throughout 2025, fears of U.S. regulatory backlash and new tariff structures led major manufacturers like Samsung and SK Hynix to halt sales of older semiconductor manufacturing equipment to Chinese entities, effectively capping production capacity in the region. Additionally, proposed tariff policies by the U.S. administration in late 2025 prompted supply chain realignments, with Apple reportedly accelerating plans to source all U.S.-bound iPhones from India to avoid potential levies. === NAND flash capacity constraints === In the NAND flash segment, manufacturers prioritized higher-margin enterprise SSDs for data center applications while phasing out older process nodes more rapidly than anticipated. In November 2025, contract prices for NAND wafers increased by more than 60% month-over-month for certain product categories, with 512GB TLC experiencing the steepest rise as legacy manufacturing capacity was retired. == Impact on industry and consumers == === Manufacturer responses === Major PC manufacturers responded to component cost increases with significant price adjustments and supply chain strategies. Dell Technologies Chief Operating Officer Jeff Clarke stated during a November 2025 analyst call that the company had "never witnessed costs escalating at the current pace," describing tighter availability across DRAM, hard drives, and NAND flash memory. Analysts at Morgan Stanley downgraded Dell Technologies stock from "Overweight" to "Underweight" in late 2025, citing the company's heavy exposure to rising server memory costs. The firm warned that skyrocketing memory prices could significantly erode margins for server and PC OEMs. Conversely, Apple Inc. was reportedly less affected than its competitors, having secured long-term supply agreements for DRAM through the first quarter of 2026. Lenovo Chief Financial Officer Winston Cheng described the cost surge as "unprecedented" and disclosed that the company's memory inventories were approximately 50% above normal levels in anticipation of further price increases. === Consumer electronics sector === The shortage particularly affected smartphone manufacturers and other consumer electronics producers. DRAM prices reportedly rose by 172% throughout 2025, leading manufacturers like Samsung to halt new orders for DDR5 modules to reassess pricing structures and Micron to exit its 'Crucial' brand of consumer products. In Tokyo's Akihabara electronics district, retailers began limiting purchases of memory products to prevent hoarding, with prices for popular DDR5 memory modules more than doubling in some cases. Despite the broad trend of rising hardware costs, some companies engaged in aggressive pricing strategies to maintain market share; for example, Sony reduced the price of the PlayStation 5 by $100 for Black Friday 2025, potentially absorbing increased component costs to stimulate software ecosystem growth. Due to memory prices more than doubling in a single quarter, HP revealed in its Q1 2026 earnings call that memory costs account for 35% of PC build materials up from 15-18% previous quarter. Despite showing strong Q1 2026 earning driven by Windows 11 upgrade cycle and AI PC adoption, HP warned investors of low operating margins and up to double digit percentage decline for coming quarter. Trendforce, an IT analytics company, updated its forecast from 1.7% year-over-year growth in PC market to 2.6% year-over-year decline for 2026, amid backdrop of steadily increasing prices and supply crisis. Research and analytics firms, Gartner and IDC expect worldwide PC market to decline 10-11% and smartphone market to decline 8-9% in 2026. Gartner also projects that rising memory prices will make low-margin entry level laptops under 500 USD financially unviable in two years. The RAM shortage has delayed the release of Valve's second Steam Machine due to increased memory prices. The device was originally set to launch in early 2026. === AI infrastructure competition === Technology companies including Google, Amazon, Microsoft, and Meta Platforms placed open-ended orders with memory suppliers, indicating they would accept as much supply as available regardless of cost, according to Reuters sources. The limited supply of AI chips has been cited as a reason for the slow down in compute growth. In October 2025, OpenAI formally announced a strategic partnership using letters of intent with Samsung Electronics and SK Hynix

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  Hike Messenger

  Hike Messenger, aka Hike Sticker Chat, is a multifunctional Indian social media and social networking service offering instant messaging (IM) and Voice over IP (VoIP) services that was launched on December 11, 2012, by Kavin Bharti Mittal. Hike functioned through SMS. The app registration used a s‍tandard, one-time password (OTP) based authentication process. It was estimated to be worth $1.4 billion and had more than 100 million registered users. It went defunct on January 6, 2021, as they were unable to compete with global messaging platforms. The app re-appeared on google play store and apple app store on 19 September 2025. == History == Hike Messenger was launched on December 12, 2012, by its founder, Kavin Bharti Mittal. The majority of users were from India, with 80% under the age of 25. The company purchased startups like TinyMogul and Hoppr in 2015. After buying US-based free voice calling company Zip Phones, Hike provided VoIP calling services. On March 5, 2015, Hike launched the 'Great Indian Sticker Challenge' to create more stickers. In February 2017, Hike acquired the social networking app Pulse. From version 5.0, it became the first social messaging app to start a mobile payment service in India. The timeline feature came back after multiple user requests and the introduction of a personalized digital envelope called Blue Packets for sending monetary gifts through a built-in wallet. In 2017, the acquisition of Bengaluru-based startup Creo was announced to enable third-party developers to build services on top of the Hike platform. In 2018, Hike provided 1 billion users with internet access by targeting smaller cities. In January 2019, the company discarded the previous super-app approach, and began launching specialized apps for specific use-cases. In May 2019, Hike announced a collaboration with Indraprastha Institute of Information Technology, Delhi (IIIT-D) to develop a variety of machine learning models. In April 2019, the company launched its first standalone app, Hike Sticker Chat. A separate content app Hike News & Content was also launched. In 2021, Hike shut down its messaging service and shifted focus to gaming and community platforms. It launched Rush, a real-money gaming app featuring casual titles like ludo and carrom, which scaled to over 10 million users and generated more than US$500 million in gross revenue over four years. The company also introduced Vibe, an approval-only community app, as part of its pivot away from the super-app and messaging model. In September 2025, following the passage of the Promotion and Regulation of Online Gaming Act, which banned real-money gaming in India, Hike announced its complete closure. Founder Kavin Bharti Mittal stated that while the company had begun international expansion, scaling globally under the new regulatory regime would require a full reset that was not a viable use of capital or resources. On 19 September 2025, hike was relaunched on play store and app store by the name hike messenger. == Application == === Timeline of Features === On 15 April 2014, Hike introduced unlimited free SMS via a service called Hike Offline, through credits earned by users from regular chatting, as connectivity is still a major issue in many parts of India. In an attempt to appeal to its younger users, Hike introduced features that find resonance with the local market, such as Last Seen Privacy and localized sticker packs. It also introduced a two-way chat theme, allowing users to change the chat background for themselves and for their friends simultaneously. The app also started showing live Cricket scores in collaboration with Cricbuzz, as well as news, casual games, and social media feeds. Hike also added a file transfer service, allowing files less than 100MB of all formats, with a view on further increasing the size limit to 1 GB. With the launch of version 2.9.2.0 in January 2015, Hike implemented support for sending uncompressed images and a "quick upload" feature optimized for 2G speed. Later that month, Hike introduced a voice calling feature for its users. In September 2015, Hike launched free group call support with up to 100 people in a simultaneous conference call environment. In November 2016, Hike announced the launch of a feature called Stories that allows people to share real-life moments using fun live filters which automatically get deleted after 48 hours, and a new camera design with localized filters. Hike 4.0 launched on 26 August 2015 with the tagline 'Got a Gang? Get on Hike'. Hike 4.0 was an optimization-focused update, increasing the performance of the app on poor networks. It supported photo filters, doodles, and bite-sized news updates in under 100 characters. Hike launched News Feed with Hindi language support on 29 September 2015 to cater for the needs of the non-English population. Hike launched version 3.5 as the biggest update for Windows Phone 8.1 during December 2015 which changed the user interface for more simpler navigation, supported sending unlimited non-media files and documents of any format and better group admin settings. It also included ten brand new chat themes. Hike launched a microapp feature which was live for two days on 8 May 2016, as a Mother's Day special in which users could add images, quotes or messages as a token of love with customized e-cards and stickers on their timeline not only on Hike, but also on other platforms. On 26 October 2016, Hike Messenger rolled out the beta version of a video calling feature ahead of WhatsApp starting with the Android users which also lets recipients preview a video call before deciding to take it and is optimized to even work under 2G conditions. On 24 December 2016, Hike rolled out a short 20-second Video Stories feature that can be directly shared with friends or posted on a public timeline with different filters in collaboration with content creators with the same 48-hour time limit before being automatically deleted. The Stories feature continues to receive constant future updates to include and enable content, public story option, private user messaging and geo-tagging. In September 2017, Hike launched personalized sticker packs with 20,000+ graphical stickers for over 500 colleges that covered around 1,000 colleges by December 2018 across India which can be used across different geographies, and are highly customized for users with availability in 40+ local languages that support automatic sticker suggestions where the application suggests the best reply for any sticker message and also allows users to "nudge", a feature used to ping the receiver. Hike started supporting user comments on friend's posts, added a specific message reply function, a redesigned camera interface to support front flash and user mentions with the help of the @ symbol. In December, 2017, Hike launched group voting, bill splitting, checklists and event reminders for group chat that supports up to 1,000 users both on iOS and Android platform. Hike launched another feature called Hike Land, which is a virtual world with beta trial to start from March 2020, that will use Hike Moji where online users with their digital avatar can hang out with other users and will be built inside the Hike Sticker Chat application. It is mainly targeted but not restricted towards 16 to 21 years age group of people. Without unveiling much about Hike Land, a separate website has been created with option to reserve spots by giving details like name, gender and phone number that will link the user profile from the Hike Sticker Chat account though it is not a necessity. ==== Hike Direct ==== The Hike Direct feature is based on the technology known as WiFi Direct, which initially was also called WiFi P2P and got introduced to users by October 2015, which enables sharing of files such as music, apps, videos without a live internet connection within a 100-meter radius by creating a wireless network between two or more devices with a transfer speed of 100MB per minute. For privacy and security reasons, Hike didn't show the recipient's location or proximity and works only when two users are connected in the same room by adding one another into the contact list. ==== Hike Wallet ==== In June 2017, Hike announced the launch of version 5.0 with multiple new features like User Chat Themes, Night Mode and Magic Selfie. along with a built-in Wallet partnered with Yes Bank. This feature was first rolled out to Android users followed by iOS users at a later stage. Hike collaborated with Airtel Payment Bank to power its digital payment wallet by November 2017 where Hike users have access to Airtel Payments Bank's merchant & utility payment services and know your customer (KYC) infrastructure with 5 million transactions happening from services like recharge and P2P. Hike formed a partnership with Ola Cabs to bring a taxi and auto-rickshaw booking facility from 14 February 2018. With Hike Wallet facility users could now book bus tickets with 3

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  Menu hack

  A menu hack is a non-standard method of ordering food, usually at fast-food or fast casual restaurants, that offers a different result than what is explicitly stated on a menu. Menu hacks may range from a simple alternate flavor to "gaming the system" in order to obtain more food than normal. They are often spread on social media platforms such as TikTok, and are more popular with Generation Z, which has been known to customize their orders more than previous generations. Hacks are sometimes officially added to the menu after their popularity grows. However, in some cases, they have been criticized for overburdening fast food employees with outlandish requests, sparking debate as to whether certain menu hacks are unethical. The list of all possible menu hacks is called a secret menu. == History == The term "menu hack" stems from hacker culture and its tradition of overcoming previously imposed limitations. However, the tradition of ordering from a secret menu dates back to the early days of fast food. "Animal style" fries, a word of mouth menu item ordered from In-N-Out since the 1960s, was rumored to have been created by local surfers. In the Information Age, the rise of social media gave influencers the ability to communicate unique food combinations to their followers, which proved to go viral easily. Design mistakes in food ordering apps also proved to be easily exploitable. In some cases, these hacks boosted the profile of brands on social media, while in others, they caused financial harm when the company was unprepared to handle the sudden influx of unusual orders. One restaurant chain notable for the phenomenon is Chipotle Mexican Grill. A viral hack from Alexis Frost, suggesting a quesadilla with fajita vegetables inside, dipped in Chipotle vinaigrette mixed with sour cream, obtained 1.9 million views on TikTok, overloading the chain's workers, who had to work harder to prepare more vegetables and vinaigrette. Some restaurants began to deny the dish to customers, forcing them to only order meat and cheese on quesadillas. The company ultimately left the dish on the menu, but urged customers to stop ordering it via social media. When it later officially added the Fajita Quesadilla to the menu, digital sales nearly doubled. A method to order nachos, which are not officially on the menu, was also noted by customers. Starbucks is also famous for menu hacks, including the Pink Drink, a "Barbiecore" beverage in which coconut milk replaced the water in the strawberry açaí refresher. After it went viral, the company made it a permanent menu item and distributed it bottled in grocery stores. == Controversy == Menu hacks have been subject to a growing backlash, with employees stating that they "dread" younger customers due to the proliferation of unusual orders. Service industry workers, already overworked and underpaid, have called the rise of menu hacks and their difficulty to make an additional reason to unionize and demand higher wages.

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  Completeness (cryptography)

  In cryptography, a boolean function is said to be complete if the value of each output bit depends on all input bits. This is a desirable property to have in an encryption cipher, so that if one bit of the input (plaintext) is changed, every bit of the output (ciphertext) has an average of 50% probability of changing. The easiest way to show why this is good is the following: consider that if we changed our 8-byte plaintext's last byte, it would only have any effect on the 8th byte of the ciphertext. This would mean that if the attacker guessed 256 different plaintext-ciphertext pairs, he would always know the last byte of every 8byte sequence we send (effectively 12.5% of all our data). Finding out 256 plaintext-ciphertext pairs is not hard at all in the internet world, given that standard protocols are used, and standard protocols have standard headers and commands (e.g. "get", "put", "mail from:", etc.) which the attacker can safely guess. On the other hand, if our cipher has this property (and is generally secure in other ways, too), the attacker would need to collect 264 (~1020) plaintext-ciphertext pairs to crack the cipher in this way.

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  Multi-scale approaches

  The scale space representation of a signal obtained by Gaussian smoothing satisfies a number of special properties, scale-space axioms, which make it into a special form of multi-scale representation. There are, however, also other types of "multi-scale approaches" in the areas of computer vision, image processing and signal processing, in particular the notion of wavelets. The purpose of this article is to describe a few of these approaches: == Scale-space theory for one-dimensional signals == For one-dimensional signals, there exists quite a well-developed theory for continuous and discrete kernels that guarantee that new local extrema or zero-crossings cannot be created by a convolution operation. For continuous signals, it holds that all scale-space kernels can be decomposed into the following sets of primitive smoothing kernels: the Gaussian kernel : g ( x , t ) = 1 2 π t exp ⁡ ( − x 2 / 2 t ) {\displaystyle g(x,t)={\frac {1}{\sqrt {2\pi t}}}\exp({-x^{2}/2t})} where t > 0 {\displaystyle t>0} , truncated exponential kernels (filters with one real pole in the s-plane): h ( x ) = exp ⁡ ( − a x ) {\displaystyle h(x)=\exp({-ax})} if x ≥ 0 {\displaystyle x\geq 0} and 0 otherwise where a > 0 {\displaystyle a>0} h ( x ) = exp ⁡ ( b x ) {\displaystyle h(x)=\exp({bx})} if x ≤ 0 {\displaystyle x\leq 0} and 0 otherwise where b > 0 {\displaystyle b>0} , translations, rescalings. For discrete signals, we can, up to trivial translations and rescalings, decompose any discrete scale-space kernel into the following primitive operations: the discrete Gaussian kernel T ( n , t ) = I n ( α t ) {\displaystyle T(n,t)=I_{n}(\alpha t)} where α , t > 0 {\displaystyle \alpha ,t>0} where I n {\displaystyle I_{n}} are the modified Bessel functions of integer order, generalized binomial kernels corresponding to linear smoothing of the form f o u t ( x ) = p f i n ( x ) + q f i n ( x − 1 ) {\displaystyle f_{out}(x)=pf_{in}(x)+qf_{in}(x-1)} where p , q > 0 {\displaystyle p,q>0} f o u t ( x ) = p f i n ( x ) + q f i n ( x + 1 ) {\displaystyle f_{out}(x)=pf_{in}(x)+qf_{in}(x+1)} where p , q > 0 {\displaystyle p,q>0} , first-order recursive filters corresponding to linear smoothing of the form f o u t ( x ) = f i n ( x ) + α f o u t ( x − 1 ) {\displaystyle f_{out}(x)=f_{in}(x)+\alpha f_{out}(x-1)} where α > 0 {\displaystyle \alpha >0} f o u t ( x ) = f i n ( x ) + β f o u t ( x + 1 ) {\displaystyle f_{out}(x)=f_{in}(x)+\beta f_{out}(x+1)} where β > 0 {\displaystyle \beta >0} , the one-sided Poisson kernel p ( n , t ) = e − t t n n ! {\displaystyle p(n,t)=e^{-t}{\frac {t^{n}}{n!}}} for n ≥ 0 {\displaystyle n\geq 0} where t ≥ 0 {\displaystyle t\geq 0} p ( n , t ) = e − t t − n ( − n ) ! {\displaystyle p(n,t)=e^{-t}{\frac {t^{-n}}{(-n)!}}} for n ≤ 0 {\displaystyle n\leq 0} where t ≥ 0 {\displaystyle t\geq 0} . From this classification, it is apparent that we require a continuous semi-group structure, there are only three classes of scale-space kernels with a continuous scale parameter; the Gaussian kernel which forms the scale-space of continuous signals, the discrete Gaussian kernel which forms the scale-space of discrete signals and the time-causal Poisson kernel that forms a temporal scale-space over discrete time. If we on the other hand sacrifice the continuous semi-group structure, there are more options: For discrete signals, the use of generalized binomial kernels provides a formal basis for defining the smoothing operation in a pyramid. For temporal data, the one-sided truncated exponential kernels and the first-order recursive filters provide a way to define time-causal scale-spaces that allow for efficient numerical implementation and respect causality over time without access to the future. The first-order recursive filters also provide a framework for defining recursive approximations to the Gaussian kernel that in a weaker sense preserve some of the scale-space properties.

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  Computer network engineering

  Computer network engineering is a technology discipline within engineering that deals with the design, implementation, and management of computer networks. These systems contain both physical components, such as routers, switches, cables, and some logical elements, such as protocols and network services. Computer network engineers attempt to ensure that the data is transmitted efficiently, securely, and reliably over both local area networks (LANs) and wide area networks (WANs), as well as across the Internet. Computer networks often play a large role in modern industries ranging from telecommunications to cloud computing, enabling processes such as email and file sharing, as well as complex real-time services like video conferencing and online gaming. == Background == The evolution of network engineering is marked by significant milestones that have greatly impacted communication methods. These milestones particularly highlight the progress made in developing communication protocols that are vital to contemporary networking. This discipline originated in the 1960s with projects like ARPANET, which initiated important advancements in reliable data transmission. The advent of protocols such as TCP/IP revolutionized networking by enabling interoperability among various systems, which, in turn, fueled the rapid growth of the Internet. Key developments include the standardization of protocols and the shift towards increasingly complex layered architectures. These advancements have profoundly changed the way devices interact across global networks. == Network infrastructure design == The foundation of computer network engineering lies in the design of the network infrastructure. This involves planning both the physical layout of the network and its logical topology to ensure optimal data flow, reliability, and scalability. === Physical infrastructure === The physical infrastructure consists of the hardware used to transmit data, which is represented by the first layer of the OSI model. ==== Cabling ==== Copper cables such as ethernet over twisted pair are commonly used for short-distance connections, especially in local area networks (LANs), while fiber optic cables are favored for long-distance communication due to their high-speed transmission capabilities and lower susceptibility to interference. Fiber optics play a significant role in the backbone of large-scale networks, such as those used in data centers and internet service provider (ISP) infrastructures. ==== Wireless networks ==== In addition to wired connections, wireless networks have become a common component of physical infrastructure. These networks facilitate communication between devices without the need for physical cables, providing flexibility and mobility. Wireless technologies use a range of transmission methods, including radio frequency (RF) waves, infrared signals, and laser-based communication, allowing devices to connect to the network. Wi-Fi based on IEEE 802.11 standards is the most widely used wireless technology in local area networks and relies on RF waves to transmit data between devices and access points. Wireless networks operate across various frequency bands, including 2.4 GHz and 5 GHz, each offering unique ranges and data rates; the 2.4 GHz band provides broader coverage, while the 5 GHz band supports faster data rates with reduced interference, ideal for densely populated environments. Beyond Wi-Fi, other wireless transmission methods, such as infrared and laser-based communication, are used in specific contexts, like short-range, line-of-sight links or secure point-to-point communication. In mobile networks, cellular technologies like 3G, 4G, and 5G enable wide-area wireless connectivity. 3G introduced faster data rates for mobile browsing, while 4G significantly improved speed and capacity, supporting advanced applications like video streaming. The latest evolution, 5G, operates across a range of frequencies, including millimeter-wave bands, and provides high data rates, low latency, and support for more device connectivity, useful for applications like the Internet of Things (IoT) and autonomous systems. Together, these wireless technologies allow networks to meet a variety of connectivity needs across local and wide areas. ==== Network devices ==== Routers and switches help direct data traffic and assist in maintaining network security; network engineers configure these devices to optimize traffic flow and prevent network congestion. In wireless networks, wireless access points (WAP) allow devices to connect to the network. To expand coverage, multiple access points can be placed to create a wireless infrastructure. Beyond Wi-Fi, cellular network components like base stations and repeaters support connectivity in wide-area networks, while network controllers and firewalls manage traffic and enforce security policies. Together, these devices enable a secure, flexible, and scalable network architecture suitable for both local and wide-area coverage. === Logical topology === Beyond the physical infrastructure, a network must be organized logically, which defines how data is routed between devices. Various topologies, such as star, mesh, and hierarchical designs, are employed depending on the network’s requirements. In a star topology, for example, all devices are connected to a central hub that directs traffic. This configuration is relatively easy to manage and troubleshoot but can create a single point of failure. In contrast, a mesh topology, where each device is interconnected with several others, offers high redundancy and reliability but requires a more complex design and larger hardware investment. Large networks, especially those in enterprises, often employ a hierarchical model, dividing the network into core, distribution, and access layers to enhance scalability and performance. == Network protocols and communication standards == Communication protocols dictate how data in a network is transmitted, routed, and delivered. Depending on the goals of the specific network, protocols are selected to ensure that the network functions efficiently and securely. The Transmission Control Protocol/Internet Protocol (TCP/IP) suite is fundamental to modern computer networks, including the Internet. It defines how data is divided into packets, addressed, routed, and reassembled. The Internet Protocol (IP) is critical for routing packets between different networks. In addition to traditional protocols, advanced protocols such as Multiprotocol Label Switching (MPLS) and Segment Routing (SR) enhance traffic management and routing efficiency. For intra-domain routing, protocols like Open Shortest Path First (OSPF) and Enhanced Interior Gateway Routing Protocol (EIGRP) provide dynamic routing capabilities. On the local area network (LAN) level, protocols like Virtual Extensible LAN (VXLAN) and Network Virtualization using Generic Routing Encapsulation (NVGRE) facilitate the creation of virtual networks. Furthermore, Internet Protocol Security (IPsec) and Transport Layer Security (TLS) secure communication channels, ensuring data integrity and confidentiality. For real-time applications, protocols such as Real-time Transport Protocol (RTP) and WebRTC provide low-latency communication, making them suitable for video conferencing and streaming services. Additionally, protocols like QUIC enhance web performance and security by establishing secure connections with reduced latency. == Network security == As networks have become essential for business operations and personal communication, the demand for robust security measures has increased. Network security is a critical component of computer network engineering, concentrating on the protection of networks against unauthorized access, data breaches, and various cyber threats. Engineers are responsible for designing and implementing security measures that ensure the integrity and confidentiality of data transmitted across networks. Firewalls serve as barriers between trusted internal networks and external environments, such as the Internet. Network engineers configure firewalls, including next-generation firewalls (NGFW), which incorporate advanced features such as deep packet inspection and application awareness, thereby enabling more refined control over network traffic and protection against sophisticated attacks. In addition to firewalls, engineers use encryption protocols, including Internet Protocol Security (IPsec) and Transport Layer Security (TLS), to secure data in transit. These protocols provide a means of safeguarding sensitive information from interception and tampering. For secure remote access, Virtual Private Networks (VPNs) are deployed, using technologies to create encrypted tunnels for data transmission over public networks. These VPNs are often used for maintaining security when remote users access corporate networks but are also used ion other settings. To enhance threat detection and r

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  Information security

  Information security is the practice of protecting information by mitigating information risks. It is part of information risk management. It typically involves preventing or reducing the probability of unauthorized or inappropriate access to data or the unlawful use, disclosure, disruption, deletion, corruption, modification, inspection, recording, or devaluation of information. It also involves actions intended to reduce the adverse impacts of such incidents. Protected information may take any form, e.g., electronic or physical, tangible (e.g., paperwork), or intangible (e.g., knowledge). Information security's primary focus is the balanced protection of data confidentiality, integrity, and availability (known as the CIA triad, unrelated to the US government organization) while maintaining a focus on efficient policy implementation, all without hampering organization productivity. This is largely achieved through a structured risk management process. To standardize this discipline, academics and professionals collaborate to offer guidance, policies, and industry standards on passwords, antivirus software, firewalls, encryption software, legal liability, security awareness and training, and so forth. This standardization may be further driven by a wide variety of laws and regulations that affect how data is accessed, processed, stored, transferred, and destroyed. While paper-based business operations are still prevalent, requiring their own set of information security practices, enterprise digital initiatives are increasingly being emphasized, with information assurance now typically being dealt with by information technology (IT) security specialists. These specialists apply information security to technology (most often some form of computer system). IT security specialists are almost always found in any major enterprise/establishment due to the nature and value of the data within larger businesses. They are responsible for keeping all of the technology within the company secure from malicious attacks that often attempt to acquire critical private information or gain control of the internal systems. There are many specialist roles in Information Security including securing networks and allied infrastructure, securing applications and databases, security testing, information systems auditing, business continuity planning, electronic record discovery, and digital forensics. == Standards == Information security standards are guidelines generally outlined in published materials that aim to protect a user's or an organization's cyber environment from threats. This environment includes the users themselves, hardware such as devices and networks, software such as applications or services, and any information in storage or transit. These standards comprise security concepts, technologies, and guidelines to deal with an adverse event. They may also include assessment criteria and certification for organizations implementing a minimum level of security. These standards are developed by various international and national bodies to prevent or mitigate cyber-attacks, ensure consistency among developers, and establish a minimum standard in industries susceptible to an attack. The ISO/IEC 27000 family, published by the International Organization for Standardization (ISO) and the International Electrotechnical Commission (IEC), provides information about the guidelines and requirements for an Information Security Management System (ISMS). The Common Criteria (ISO/IEC 15408) provides guidelines on evaluating and certifying the security of a system. The IEC 62443 establishes security standards for automation and control systems. Similarly, the ISO/SAE 21434, ETSI EN 303 645, and EN 18031 provide standards for road vehicles, the Internet of Things, and radio-based systems respectively. The NIST Cybersecurity Framework (NIST CSF) is a set of guidelines developed by the U.S. National Institute of Standards and Technology to help organizations with risk management. NIST also publishes various Federal Information Processing Standards (FIPS) and Special Publications. The United Kingdom has introduced Cyber Essentials, which is a certification scheme to protect organizations against common security threats. The Australian Cyber Security Centre publishes the Essential Eight mitigation strategies. The Payment Card Industry Data Security Standard (PCI DSS) regulates handling of cardholder data in order to reduce credit card fraud. UL has published standards related to specific industries such as UL 2900-2-3 for security and life safety signaling systems and UL-2900-2-1 for healthcare and wellness systems. == Threats == Information security threats come in many different forms. Some of the most common threats today are software attacks, theft of intellectual property, theft of identity, theft of equipment or information, sabotage, and information extortion. Viruses, worms, phishing attacks, and Trojan horses are a few common examples of software attacks. The theft of intellectual property has also been an extensive issue for many businesses. Identity theft is the attempt to act as someone else usually to obtain that person's personal information or to take advantage of their access to vital information through social engineering. Sabotage usually consists of the destruction of an organization's website in an attempt to cause loss of confidence on the part of its customers. Information extortion consists of theft of a company's property or information as an attempt to receive a payment in exchange for returning the information or property back to its owner, as with ransomware. One of the most functional precautions against these attacks is to conduct periodical user awareness. Governments, military, corporations, financial institutions, hospitals, non-profit organizations, and private businesses amass a great deal of confidential information about their employees, customers, products, research, and financial status. Should confidential information about a business's customers or finances or new product line fall into the hands of a competitor or hacker, a business and its customers could suffer widespread, irreparable financial loss, as well as damage to the company's reputation. From a business perspective, information security must be balanced against cost; the Gordon-Loeb Model provides a mathematical economic approach for addressing this concern. For the individual, information security has a significant effect on privacy, which is viewed very differently in various cultures. == History == Since the early days of communication, diplomats and military commanders understood that it was necessary to provide some mechanism to protect the confidentiality of correspondence and to have some means of detecting tampering. Julius Caesar is credited with the invention of the Caesar cipher c. 50 B.C., which was created in order to prevent his secret messages from being read should a message fall into the wrong hands. However, for the most part protection was achieved through the application of procedural handling controls. Sensitive information was marked up to indicate that it should be protected and transported by trusted persons, guarded and stored in a secure environment or strong box. As postal services expanded, governments created official organizations to intercept, decipher, read, and reseal letters (e.g., the U.K.'s Secret Office, founded in 1653). In the mid-nineteenth century more complex classification systems were developed to allow governments to manage their information according to the degree of sensitivity. For example, the British Government codified this, to some extent, with the publication of the Official Secrets Act in 1889. Section 1 of the law concerned espionage and unlawful disclosures of information, while Section 2 dealt with breaches of official trust. A public interest defense was soon added to defend disclosures in the interest of the state. A similar law was passed in India in 1889, The Indian Official Secrets Act, which was associated with the British colonial era and used to crack down on newspapers that opposed the Raj's policies. A newer version was passed in 1923 that extended to all matters of confidential or secret information for governance. By the time of the First World War, multi-tier classification systems were used to communicate information to and from various fronts, which encouraged greater use of code making and breaking sections in diplomatic and military headquarters. Encoding became more sophisticated between the wars as machines were employed to scramble and unscramble information. The establishment of computer security inaugurated the history of information security. The need for such appeared during World War II. The volume of information shared by the Allied countries during the Second World War necessitated formal alignment of classification systems and procedural controls. An arcane range of markings evol

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