AI Content Humanizer

AI Content Humanizer — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

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  Voice activity detection

  Voice activity detection (VAD), also known as speech activity detection or speech detection, is the detection of the presence or absence of human speech, used in speech processing. The main uses of VAD are in speaker diarization, speech coding and speech recognition. It can facilitate speech processing, and can also be used to deactivate some processes during non-speech section of an audio session: it can avoid unnecessary coding/transmission of silence packets in Voice over Internet Protocol (VoIP) applications, saving on computation and on network bandwidth. VAD is an important enabling technology for a variety of speech-based applications. Therefore, various VAD algorithms have been developed that provide varying features and compromises between latency, sensitivity, accuracy and computational cost. Some VAD algorithms also provide further analysis, for example whether the speech is voiced, unvoiced or sustained. Voice activity detection is usually independent of language. It was first investigated for use on time-assignment speech interpolation (TASI) systems. == Algorithm overview == The typical design of a VAD algorithm is as follows: There may first be a noise reduction stage, e.g. via spectral subtraction. Then some features or quantities are calculated from a section of the input signal. A classification rule is applied to classify the section as speech or non-speech – often this classification rule finds when a value exceeds a certain threshold. There may be some feedback in this sequence, in which the VAD decision is used to improve the noise estimate in the noise reduction stage, or to adaptively vary the threshold(s). These feedback operations improve the VAD performance in non-stationary noise (i.e. when the noise varies a lot). A representative set of recently published VAD methods formulates the decision rule on a frame by frame basis using instantaneous measures of the divergence distance between speech and noise. The different measures which are used in VAD methods include spectral slope, correlation coefficients, log likelihood ratio, cepstral, weighted cepstral, and modified distance measures. Independently from the choice of VAD algorithm, a compromise must be made between having voice detected as noise, or noise detected as voice (between false positive and false negative). A VAD operating in a mobile phone must be able to detect speech in the presence of a range of very diverse types of acoustic background noise. In these difficult detection conditions it is often preferable that a VAD should fail-safe, indicating speech detected when the decision is in doubt, to lower the chance of losing speech segments. The biggest difficulty in the detection of speech in this environment is the very low signal-to-noise ratios (SNRs) that are encountered. It may be impossible to distinguish between speech and noise using simple level detection techniques when parts of the speech utterance are buried below the noise. == Applications == VAD is an integral part of different speech communication systems such as audio conferencing, echo cancellation, speech recognition, speech encoding, speaker recognition and hands-free telephony. In the field of multimedia applications, VAD allows simultaneous voice and data applications. Similarly, in Universal Mobile Telecommunications Systems (UMTS), it controls and reduces the average bit rate and enhances overall coding quality of speech. In cellular radio systems (for instance GSM and CDMA systems) based on Discontinuous Transmission (DTX) mode, VAD is essential for enhancing system capacity by reducing co-channel interference and power consumption in portable digital devices. In speech processing applications, voice activity detection plays an important role since non-speech frames are often discarded. For a wide range of applications such as digital mobile radio, Digital Simultaneous Voice and Data (DSVD) or speech storage, it is desirable to provide a discontinuous transmission of speech-coding parameters. Advantages can include lower average power consumption in mobile handsets, higher average bit rate for simultaneous services like data transmission, or a higher capacity on storage chips. However, the improvement depends mainly on the percentage of pauses during speech and the reliability of the VAD used to detect these intervals. On the one hand, it is advantageous to have a low percentage of speech activity. On the other hand, clipping, that is the loss of milliseconds of active speech, should be minimized to preserve quality. This is the crucial problem for a VAD algorithm under heavy noise conditions. === Use in telemarketing === One controversial application of VAD is in conjunction with predictive dialers used by telemarketing firms. In order to maximize agent productivity, telemarketing firms set up predictive dialers to call more numbers than they have agents available, knowing most calls will end up in either "Ring – No Answer" or answering machines. When a person answers, they typically speak briefly ("Hello", "Good evening", etc.) and then there is a brief period of silence. Answering machine messages are usually 3–15 seconds of continuous speech. By setting VAD parameters correctly, dialers can determine whether a person or a machine answered the call and, if it's a person, transfer the call to an available agent. If it detects an answering machine message, the dialer hangs up. Often, even when the system correctly detects a person answering the call, no agent may be available, resulting in a "silent call". Call screening with a multi-second message like "please say who you are, and I may pick up the phone" will frustrate such automated calls. == Performance evaluation == To evaluate a VAD, its output using test recordings is compared with those of an "ideal" VAD – created by hand-annotating the presence or absence of voice in the recordings. The performance of a VAD is commonly evaluated on the basis of the following four parameters: FEC (Front End Clipping): clipping introduced in passing from noise to speech activity; MSC (Mid Speech Clipping): clipping due to speech misclassified as noise; OVER: noise interpreted as speech due to the VAD flag remaining active in passing from speech activity to noise; NDS (Noise Detected as Speech): noise interpreted as speech within a silence period. Although the method described above provides useful objective information concerning the performance of a VAD, it is only an approximate measure of the subjective effect. For example, the effects of speech signal clipping can at times be hidden by the presence of background noise, depending on the model chosen for the comfort noise synthesis, so some of the clipping measured with objective tests is in reality not audible. It is therefore important to carry out subjective tests on VADs, the main aim of which is to ensure that the clipping perceived is acceptable. In VoIP applications, front-end clipping can be reduced by rewinding to shortly before the detection and sending very slightly delayed data. This kind of test requires a certain number of listeners to judge recordings containing the processing results of the VADs being tested, giving marks to several speech sequences on the following features: Quality; Comprehension difficulty; Audibility of clipping. These marks are then used to calculate average results for each of the features listed above, thus providing a global estimate of the behavior of the VAD being tested. To conclude, whereas objective methods are very useful in an initial stage to evaluate the quality of a VAD, subjective methods are more significant. As they require the participation of several people for a few days, increasing cost, they are generally only used when a proposal is about to be standardized. == Implementations == One early standard VAD is that developed by British Telecom for use in the Pan-European digital cellular mobile telephone service in 1991. It uses inverse filtering trained on non-speech segments to filter out background noise, so that it can then more reliably use a simple power-threshold to decide if a voice is present. The G.729 standard calculates the following features for its VAD: line spectral frequencies, full-band energy, low-band energy (<1 kHz), and zero-crossing rate. It applies a simple classification using a fixed decision boundary in the space defined by these features, and then applies smoothing and adaptive correction to improve the estimate. The GSM standard includes two VAD options developed by ETSI. Option 1 computes the SNR in nine bands and applies a threshold to these values. Option 2 calculates different parameters: channel power, voice metrics, and noise power. It then thresholds the voice metrics using a threshold that varies according to the estimated SNR. The Speex audio compression library uses a procedure named Improved Minima Controlled Recursive Averaging, which uses a smoothed representation of spectral pow

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  Torus interconnect

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

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  Social media reach

  Social media reach is a media analytics metric that refers to the number of users who have come across a particular content on a particular social media platform. Social media platforms have their own individual ways of tracking, analyzing and reporting the traffic on each of the individual platforms. As these platforms are a main source of communication between companies and their target audiences, by conducting research, companies are able to utilize analytical information, such as the reach of their posts, to better understand the interactions between the users and their content. There are multiple underlying factors that will determine what shows up on a newsfeed or timeline. Algorithms, for example, are a type of factor that can alter the reach of a post due to the way the algorithm is coded, which can affect who sees a post and when. Other examples of factors that can impede the reach can include the time at which posts are made, as well as how frequent the posts are between one another. In comparison, an impression is the total number of circumstances where content has been shown on a social timeline, meanwhile, engagement looks at how people interact with the content that they see on a social platform such as like, share or retweet. == Reach on Facebook == Facebook has their own analytic platform which allows the user to see how other users are interacting with their posts, with the use of multiple metrics. This is not something the average user uses, but rather a tool that is used by pages or public figures. For example, Facebook pages that represent a business often look at the activity their posts have generated. There are three types of reach that can be looked at on the Facebook analytic platform. === Types of reach === ==== Organic Reach ==== This type of reach regards the number of distinct users that have seen a specific post on their feed. Organic reach, in other words is the number of people who have seen the post being analyzed on their Facebook newsfeed. Data gathered from this type of reach can give intel to those doing the analysis, such as the demographics of those who have seen the post. ==== Paid Reach ==== This type of reach regards the number of times that distinct users have come across sponsored posts, ads or content. In other words, paid reach is the number of times Facebook users have seen a post that has been paid for by a company. Data collected can give insight, to advertisers or marketers for example, on the activity based around the reach of their post. ==== Viral Reach ==== This type of reach regards the number of views by distinct users on posts that have been commented on or shared by their friends on Facebook. In other words, viral reach looks at the number of people who have seen a post after a friend of theirs commented or shared the original post, therefore it showed on their timeline. Viral reach can be looked at in terms of a collective number of times that the post has been on individual user's timelines. Data collected from viral reach can be used in multiple ways, for example, it can be used to analyze the type of content that gets shared or commented on and can be further used to compare to other posts. === Engaged users === This refers to the number of individual users who have clicked and interacted with a post on Facebook. == Reach on Twitter == Twitter gives access to any of their users to analytics of their tweets as well as their followers. Their dashboard is user friendly, which allows anyone to take a look at the analytics behind their Twitter account. This open access is useful for both the average user and companies as it can provide a quick glance or general outlook of who has seen their tweets. The way that Twitter works is slightly different than the way of Facebook in terms of the reach. On Twitter, especially for users with a higher profile, they are not only engaging with the people who follow them, but also with the followers of their own followers. The reach metric on Twitter looks at the quantity of Twitter users who have been engaged, but also the number of users that follow them as well. This metric is useful to see the if the tweets/content being shared on Twitter are contributing to the growth of audience on this platform. == Reach on Instagram == Instagram gives their users access to their reach, in the Instagram Insights section. Instagram insights can be used to learn more about an account's followers and performance. Reach indicates the total number of unique Instagram accounts that have seen your Instagram post or story. You can find this data by looking at each individual post insights. == Uses of reach == The reach can be a useful metric to analyze for marketers and advertisers. Social media is a platform that is used by marketers to directly target their intended audience with ease. These platforms not only allow marketers to get a better understanding of their audience, but also allow advertisers to insert their ads onto the timelines of specific users to later be able to conduct research to see the reach of their posts/content. The basic goal of marketers is to increase their reach as much as possible to impact bigger audiences of their dream customers and, in the end, make more sales. When doing organic social media marketing, using paid methods like ads or doing influencer marketing whether it is paid or free, it allows marketers to track the performance of their strategy and tweak it based on what works and what does not. == Analytics and reach == Social analytics looks at the data collected based on the interactions of users on social media platforms. A lot of information can be gathered which can provide intel based on user activities on social media. When looking into analytics in regard to social media, each company or group has a different goal in mind to engage their audience. At a glance, the three might seem as if they are very similar, however the differences between them are significant. There are many aspects that can be analyzed from the data gathered from social media platforms, depending on what is being observed, the correct metric would then be selected to further analyze. One example of the many metrics that can be used through social analytics is the reach. == Reach formula == To calculate social media reach one can use the following formula: R = I f ¯ {\displaystyle R={\frac {I}{\bar {f}}}} where R {\displaystyle R} — is social media reach, I {\displaystyle I} stands for the number of impressions, f ¯ {\displaystyle {\bar {f}}} is the average frequency of impressions per user. f ¯ {\displaystyle {\bar {f}}} represents the number of events when the ad is shown to a particular user. The average value should be calculated over the time period with stable settings of advertisement campaign. == Commenting For Better Reach == Commenting For Better Reach also known as "CFBR" is a widely used strategy for organically boosting post reach on social media platforms. Algorithms tend to favor posts with substantial likes and comments, granting them broader exposure compared to less engaging content. Primarily seen on LinkedIn, a platform geared toward professional networking and business connections, the use of CFBR signals active engagement aimed at enhancing post visibility. It is important to note that genuine and meaningful comments are key to effective engagement. Spammy or irrelevant comments not only detract from the conversation but may also limit a post's potential reach and impact.

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  Bitcoin Satoshi Vision

  Bitcoin Satoshi Vision (BSV) is a cryptocurrency that is a hard fork of Bitcoin Cash. Bitcoin Satoshi Vision was created in November 2018 by a group of individuals led by Craig Steven Wright, who has claimed since 2015 to be Satoshi Nakamoto, the creator of the original bitcoin. == History == === 2018 split from Bitcoin Cash === On 15 November 2018, a hard fork chain split of Bitcoin Cash occurred between two rival factions called Bitcoin Cash and Bitcoin SV. On 15 November 2018 Bitcoin Cash traded at about $289, and Bitcoin SV traded at about $96.50, down from $425.01 on 14 November for the un-split Bitcoin Cash. The split originated from what was described as a "civil war" in two competing Bitcoin Cash camps. The first camp, supported by entrepreneur Roger Ver and Jihan Wu of Bitmain, promoted the software entitled Bitcoin ABC (short for Adjustable Blocksize Cap), which would maintain the block size at 32 MB. The second camp led by Craig Steven Wright and billionaire Calvin Ayre put forth a competing software version Bitcoin SV, short for "Bitcoin Satoshi Vision", which would increase the block size limit to 128 MB. === 2019 de-listing from Binance === In April 2019, an online feud broke out between those who supported the claims of Bitcoin SV supporter Craig Wright that he was Satoshi Nakamoto, and those who did not. The feud resulted in cryptocurrency exchange Binance de-listing Bitcoin SV from their platform, stating that: At Binance, we periodically review each digital asset we list to ensure that it continues to meet the high level of standard we expect. When a coin or token no longer meets this standard, or the industry changes, we conduct a more in-depth review and potentially delist it. We believe this best protects all of our users. When we conduct these reviews, we consider a variety of factors. Here are some that drive whether we decide to delist a digital asset: Commitment of team to project Level and quality of development activity Network / smart contract stability Level of public communication Responsiveness to our periodic due diligence requests Evidence of unethical / fraudulent conduct Contribution to a healthy and sustainable crypto ecosystem === 2021 network attack === In August 2021, Bitcoin SV suffered a 51% attack, after previously suffering attacks in June and July of the same year. Such an attack involves cryptocurrency miners gaining control of more than half of a network's computing power; these kinds of network attacks have the goal of preventing new transactions from gaining confirmations, allowing the attackers to double-spend coins. Adam James, senior editor at OKEx Insights claimed that "In the intermediate term, the attack has seemingly somewhat-negligible impact on its current price action," however "Faith in [Bitcoin SV] will likely be reduced following the incident." === 2024 high court ruling === In March 2024, Mr Justice James Mellor in the British High Court ruled that Wright is not Satoshi Nakamoto.

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  DABUS

  DABUS (Device for the Autonomous Bootstrapping of Unified Sentience) is an artificial intelligence (AI) system created by Stephen Thaler. It reportedly conceived of two novel products — a food container constructed using fractal geometry, which enables rapid reheating, and a flashing beacon for attracting attention in an emergency. The filing of patent applications designating DABUS as inventor has led to decisions by patent offices and courts on whether a patent can be granted for an invention reportedly made by an AI system. == History in different jurisdictions == === Australia === On 17 September 2019, Thaler filed an application to patent a "Food container and devices and methods for attracting enhanced attention," naming DABUS as the inventor. On 21 September 2020, IP Australia found that section 15(1) of the Patents Act 1990 (Cth) is inconsistent with an artificial intelligence machine being treated as an inventor, and Thaler's application had lapsed. Thaler sought judicial review, and on 30 July 2021, the Federal Court set aside IP Australia's decision and ordered IP Australia to reconsider the application. On 13 April 2022, the Full Court of the Federal Court set aside that decision, holding that only a natural person can be an inventor for the purposes of the Patents Act 1990 (Cth) and the Patents Regulations 1991 (Cth), and that such an inventor must be identified for any person to be entitled to a grant of a patent. On 11 November 2022, Thaler was refused special leave to appeal to the High Court. === European Patent Office === On 17 October 2018 and 7 November 2018, Thaler filed two European patent applications with the European Patent Office. The first claimed invention was a "Food Container" and the second was "Devices and Methods for Attracting Enhanced Attention." On 27 January 2020, the EPO rejected the applications on the grounds that the application listed an AI system named DABUS, and not a human, as the inventor, based on Article 81 and Rule 19(1) of the European Patent Convention (EPC). On 21 December 2021, the Board of Appeal of the EPO dismissed Thaler's appeal from the EPO's primary decision. The Board of Appeal confirmed that "under the EPC the designated inventor has to be a person with legal capacity. This is not merely an assumption on which the EPC was drafted. It is the ordinary meaning of the term inventor." === United Kingdom === Similar applications were filed by Thaler to the United Kingdom Intellectual Property Office on 17 October and 7 November 2018. The Office asked Thaler to file statements of inventorship and of right of grant to a patent (Patent Form 7) in respect of each invention within 16 months of the filing date. Thaler filed those forms naming DABUS as the inventor and explaining in some detail why he believed that machines should be regarded as inventors in the circumstances. His application was rejected on the grounds that: (1) naming a machine as inventor did not meet the requirements of the Patents Act 1977; and (2) the IPO was not satisfied as to the manner in which Thaler had acquired rights that would otherwise vest in the inventor. Thaler was not satisfied with the decision and asked for a hearing before an official known as the "hearing officer". By a decision dated 4 December 2019 the hearing officer rejected Thaler's appeal. Thaler appealed against the hearing officer's decision to the Patents Court (a specialist court within the Chancery Division of the High Court of England and Wales that determines patent disputes). On 21 September 2020, Mr Justice Marcus Smith upheld the decision of the hearing officer. On 21 September 2021, Thaler's further appeal to the Court of Appeal was dismissed by Arnold LJ and Laing LJ (Birss LJ dissenting). On 20 December 2023, the UK Supreme Court dismissed a further appeal by Thaler. In its judgment, the court held that an "inventor" under the Patents Act 1977 must be a natural person. === United States === The patent applications on the inventions were refused by the USPTO, which held that only natural persons can be named as inventors in a patent application. Thaler first fought this result by filing a complaint under the Administrative Procedure Act alleging that the decision was "arbitrary, capricious, an abuse of discretion and not in accordance with the law; unsupported by substantial evidence, and in excess of Defendants’ statutory authority." A month later on August 19, 2019, Thaler filed a petition with the USPTO as allowed in 37 C.F.R. § 1.181 stating that DABUS should be the inventor. The judge and Thaler agreed in this case that Thaler himself is unable to receive the patent on behalf of DABUS. In their August 5, 2022, Thaler decision, the US Court of Appeals for the Federal Circuit affirmed that only a natural person could be an inventor, which means that the AI that invents any other type of invention is not addressed by the "who" mentioned in the legislation. === New Zealand === On January 31, 2022, the Intellectual Property Office of New Zealand (IPONZ) decided that a patent application (776029) filed by Stephen Thaler was void, on the basis that no inventor was identified on the patent application. IPONZ determined that DABUS could not be "an actual devisor of the invention" as required by the Patents Act 2013, and that this must be a natural person as held by the previous patent offices above. The High Court of New Zealand confirmed the decision in 2023. === South Africa === On 24 June 2021, the South African Companies and Intellectual Property Commission (CIPC) accepted Dr Thaler's Patent Cooperation Treaty, for a patent in respect of inventions generated by DABUS. In July 2021, the CIPC released a notice of issuance for the patent. It is the first patent granted for an AI invention. === Switzerland === On June 26, 2025, the Swiss Federal Administrative Court ruled that artificial intelligence systems such as DABUS cannot be listed as inventors in patent applications. The court upheld the existing practice of the Swiss Federal Institute of Intellectual Property (IPI), which requires that only natural persons can be recognized as inventors under Swiss patent law. The case concerned a patent application, which sought to designate DABUS as the sole inventor of a food container designed with a fractal geometry to enhance heat distribution. The IPI had rejected the application, arguing that both the absence of a human inventor and the attribution of inventorship to an AI system were inadmissible. While the court dismissed Thaler's main request, it accepted a subsidiary request: if a human applicant recognizes and files a patent based on an AI-generated invention, that person may be considered the inventor. As a result, the application may proceed with Thaler listed as the inventor. The decision (B-2532/2024) can still be appealed to the Swiss Federal Supreme Court.

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  SIGINT Activity Designator

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

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  Talim (textiles)

  Talim (Kashmiri: تعليم, Kashmiri pronunciation: [t̪əːliːm], Urdu: تَعْلِیم, Arabic: تعليم, pronounced [taʕ.liːm] ) in textiles is a symbolic code and system of notation that facilitates the creation of intricate patterns in fabrics, such as shawls and carpets, and the written coded plans that include colour schemes and weaving instructions. The term is used in traditional hand-weaving in the Indian subcontinent. Talim was initially used to create certain types of patterns in Kashmiri shawls, and later came to be applied in the production of carpets. == Etymology and origin == The term talim, which refers to a symbolic code and system of notation used by shawl and carpet artisans in their weaving processes, came to the Urdu language from the Arabic noun taʻlim (تعليم), which means "authoritative instruction", "teaching", or "edification". It means the same in Urdu and Kashmiri. The Arabic noun originated from the second form of the Arabic root verb ʻalima (علم), which means "to know". According to a local belief in Kashmir, talim was introduced to them by Persian scholar and Sufi Muslim saint Mir Sayyid Ali Hamadani. The belief notwithstanding, talim might have originated from Kashmir; Amritsar was the only place outside of Srinagar where talim was used, by migrated Kashmiri artisans. == Technique == Whereas carpets are generally woven horizontally, providing weavers with a clear view of the progress they are making in creating designs, in Kashmir, carpets are woven vertically, so the weaver is reliant on the talim. The talim technique forms fabrics by passing the weft thread as per a given script that has design codes. Weavers use talim to weave the desired pattern with planned colours. Talim involves teamwork when applying the technique, as the process of creating intricate fabric designs in weaving begins with the Naqash (designer, who designs using pencils on graphs) meticulously crafting the design on a blank sheet of paper called a naska, and the master, Talim guru, making the colour codes and symbols for weft yarns that would interlace the warp to construct the desired design. He writes on a long strip of paper, in specific symbols, the colour codes, and the number of knots to be woven with each colour. Taraha guru collaborates with talim guru and is known as the artisan responsible for determining the colours. Talim uthana is a process or the act of "picking the codes" from the graph. A clerk called the Talim Navis would record the step-by-step instructions for these numbers and colours, and thousands of low-paid and interchangeable weavers would read or recite the record to carry out the design. Afterward, a talim copyist makes copies, which are needed when multiple looms weave the same product. The script, which has been encoded, is deciphered and translated according to the specific guidelines of weavers in order to incorporate the design that is included within it. Talim has been compared to "hieroglyphics" or as a "notational-cum-cryptographic system", as it is challenging to decipher and is unique to the shawls of Kashmir, which requires expertise to comprehend. According to researcher Gagan Deep Kaur, "The talim is widely held to be a trade secret of the community and has always been fiercely guarded by the owners." Those who use talim for shawl-making do not assign important tasks to women, because of the fear that the technique and knowledge may be divulged to other communities when the women are sent there to be married. The coded cards known as talim in the Kashmiri language were used for creating certain types of patterns in shawl weaving. The talim technique is employed in the creation of kani shawls, which originated from the Kanihama region of the Kashmir valley. Carpet weaving adapted the technique from shawl making. When Kashmiri artisans started to create carpets, they chose to continue using the talim rather than switching to a different method. The resurgence of the carpet industry in Amritsar during the last century resulted in the prevalent use of the talim technique among the local weavers, a majority of whom hailed from the region of Kashmir. === Recitation of codes === Talim was also communicated through recitation accompanied by a melodic chant or song. In traditional weaving practices, the use of chanting was common. The movement of the shuttles was synchronised with the song of the weaver, adding a musical rhythm to the instructions represented through hieroglyphics. The weaver's chant, "Two blue, one red, three yellow, two blue," served as a guide as they wove and replicated the designated pattern. == Usage == The first factories established in Amritsar around 1860 utilised Bokhara designs. However, Kashmiri weavers maintained their traditional techniques and employed the talim, instead of a cartoon, for tying knots. As a result, Amritsar became the second location in the Indian subcontinent to use the talim. The traditional weaving practices are still carried out in some parts of the Indian subcontinent. The exact date when talim was last used in the subcontinent varies depending on the region and the specific weaving community. Indian textile historian Jasleen Dhamija wrote in her 1989 book Handwoven Fabrics of India that there were still some weavers in the Kashmiri village of Kanihama who applied talim in weaving shawls. As of 2022, the carpet weavers in Kashmir were the only remaining users of talim in carpets, according to Zubair Ahmed, director of the Indian Institute of Carpet Technology. The institute aims to preserve traditional Kashmiri carpet designs by digitising talim and training weavers in the technique. == Gallery ==

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  Factorization of polynomials over finite fields

  In mathematics and computer algebra the factorization of a polynomial consists of decomposing it into a product of irreducible factors. This decomposition is theoretically possible and is unique for polynomials with coefficients in any field, but rather strong restrictions on the field of the coefficients are needed to allow the computation of the factorization by means of an algorithm. In practice, algorithms have been designed only for polynomials with coefficients in a finite field, in the field of rationals or in a finitely generated field extension of one of them. All factorization algorithms, including the case of multivariate polynomials over the rational numbers, reduce the problem to this case; see polynomial factorization. It is also used for various applications of finite fields, such as coding theory (cyclic redundancy codes and BCH codes), cryptography (public key cryptography by the means of elliptic curves), and computational number theory. As the reduction of the factorization of multivariate polynomials to that of univariate polynomials does not have any specificity in the case of coefficients in a finite field, only polynomials with one variable are considered in this article. == Background == === Finite field === The theory of finite fields, whose origins can be traced back to the works of Gauss and Galois, has played a part in various branches of mathematics. Due to the applicability of the concept in other topics of mathematics and sciences like computer science there has been a resurgence of interest in finite fields and this is partly due to important applications in coding theory and cryptography. Applications of finite fields introduce some of these developments in cryptography, computer algebra and coding theory. A finite field or Galois field is a field with a finite order (number of elements). The order of a finite field is always a prime or a power of prime. For each prime power q = pr, there exists exactly one finite field with q elements, up to isomorphism. This field is denoted GF(q) or Fq. If p is prime, GF(p) is the prime field of order p; it is the field of residue classes modulo p, and its p elements are denoted 0, 1, ..., p−1. Thus a = b in GF(p) means the same as a ≡ b (mod p). === Irreducible polynomials === Let F be a finite field. As for general fields, a non-constant polynomial f in F[x] is said to be irreducible over F if it is not the product of two polynomials of positive degree. A polynomial of positive degree that is not irreducible over F is called reducible over F. Irreducible polynomials allow us to construct the finite fields of non-prime order. In fact, for a prime power q, let Fq be the finite field with q elements, unique up to isomorphism. A polynomial f of degree n greater than one, which is irreducible over Fq, defines a field extension of degree n which is isomorphic to the field with qn elements: the elements of this extension are the polynomials of degree lower than n; addition, subtraction and multiplication by an element of Fq are those of the polynomials; the product of two elements is the remainder of the division by f of their product as polynomials; the inverse of an element may be computed by the extended GCD algorithm (see Arithmetic of algebraic extensions). It follows that, to compute in a finite field of non prime order, one needs to generate an irreducible polynomial. For this, the common method is to take a polynomial at random and test it for irreducibility. For sake of efficiency of the multiplication in the field, it is usual to search for polynomials of the shape xn + ax + b. Irreducible polynomials over finite fields are also useful for pseudorandom number generators using feedback shift registers and discrete logarithm over F2n. The number of irreducible monic polynomials of degree n over Fq is the number of aperiodic necklaces, given by Moreau's necklace-counting function Mq(n). The closely related necklace function Nq(n) counts monic polynomials of degree n which are primary (a power of an irreducible); or alternatively irreducible polynomials of all degrees d which divide n. === Example === The polynomial P = x4 + 1 is irreducible over Q but not over any finite field. On any field extension of F2, P = (x + 1)4. On every other finite field, at least one of −1, 2 and −2 is a square, because the product of two non-squares is a square and so we have If − 1 = a 2 , {\displaystyle -1=a^{2},} then P = ( x 2 + a ) ( x 2 − a ) . {\displaystyle P=(x^{2}+a)(x^{2}-a).} If 2 = b 2 , {\displaystyle 2=b^{2},} then P = ( x 2 + b x + 1 ) ( x 2 − b x + 1 ) . {\displaystyle P=(x^{2}+bx+1)(x^{2}-bx+1).} If − 2 = c 2 , {\displaystyle -2=c^{2},} then P = ( x 2 + c x − 1 ) ( x 2 − c x − 1 ) . {\displaystyle P=(x^{2}+cx-1)(x^{2}-cx-1).} === Complexity === Polynomial factoring algorithms use basic polynomial operations such as products, divisions, gcd, powers of one polynomial modulo another, etc. A multiplication of two polynomials of degree at most n can be done in O(n2) operations in Fq using "classical" arithmetic, or in O(nlog(n) log(log(n)) ) operations in Fq using "fast" arithmetic. A Euclidean division (division with remainder) can be performed within the same time bounds. The cost of a polynomial greatest common divisor between two polynomials of degree at most n can be taken as O(n2) operations in Fq using classical methods, or as O(nlog2(n) log(log(n)) ) operations in Fq using fast methods. For polynomials h, g of degree at most n, the exponentiation hq mod g can be done with O(log(q)) polynomial products, using exponentiation by squaring method, that is O(n2log(q)) operations in Fq using classical methods, or O(nlog(q)log(n) log(log(n))) operations in Fq using fast methods. In the algorithms that follow, the complexities are expressed in terms of number of arithmetic operations in Fq, using classical algorithms for the arithmetic of polynomials. == Factoring algorithms == Many algorithms for factoring polynomials over finite fields include the following three stages: Square-free factorization Distinct-degree factorization Equal-degree factorization An important exception is Berlekamp's algorithm, which combines stages 2 and 3. === Berlekamp's algorithm === Berlekamp's algorithm is historically important as being the first factorization algorithm which works well in practice. However, it contains a loop on the elements of the ground field, which implies that it is practicable only over small finite fields. For a fixed ground field, its time complexity is polynomial, but, for general ground fields, the complexity is exponential in the size of the ground field. === Square-free factorization === The algorithm determines a square-free factorization for polynomials whose coefficients come from the finite field Fq of order q = pm with p a prime. This algorithm firstly determines the derivative and then computes the gcd of the polynomial and its derivative. If it is not one then the gcd is again divided into the original polynomial, provided that the derivative is not zero (a case that exists for non-constant polynomials defined over finite fields). This algorithm uses the fact that, if the derivative of a polynomial is zero, then it is a polynomial in xp, which is, if the coefficients belong to Fp, the pth power of the polynomial obtained by substituting x by x1/p. If the coefficients do not belong to Fp, the pth root of a polynomial with zero derivative is obtained by the same substitution on x, completed by applying the inverse of the Frobenius automorphism to the coefficients. This algorithm works also over a field of characteristic zero, with the only difference that it never enters in the blocks of instructions where pth roots are computed. However, in this case, Yun's algorithm is much more efficient because it computes the greatest common divisors of polynomials of lower degrees. A consequence is that, when factoring a polynomial over the integers, the algorithm which follows is not used: one first computes the square-free factorization over the integers, and to factor the resulting polynomials, one chooses a p such that they remain square-free modulo p. Algorithm: SFF (Square-Free Factorization) Input: A monic polynomial f in Fq[x] where q = pm Output: Square-free factorization of f R ← 1 # Make w be the product (without multiplicity) of all factors of f that have # multiplicity not divisible by p c ← gcd(f, f′) w ← f/c # Step 1: Identify all factors in w i ← 1 while w ≠ 1 do y ← gcd(w, c) fac ← w / y R ← R · faci w ← y; c ← c / y; i ← i + 1 end while # c is now the product (with multiplicity) of the remaining factors of f # Step 2: Identify all remaining factors using recursion # Note that these are the factors of f that have multiplicity divisible by p if c ≠ 1 then c ← c1/p R ← R·SFF(c)p end if Output(R) The idea is to identify the product of all irreducible factors of f with the same multiplicity. This is done in two steps. The first step uses the formal d

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  Textual entailment

  In natural language processing, textual entailment (TE), also known as natural language inference (NLI), is a directional relation between text fragments. The relation holds whenever the truth of one text fragment follows from another text. == Definition == In the TE framework, the entailing and entailed texts are termed text (t) and hypothesis (h), respectively. Textual entailment is not the same as pure logical entailment – it has a more relaxed definition: "t entails h" (t ⇒ h) if, typically, a human reading t would infer that h is most likely true. (Alternatively: t ⇒ h if and only if, typically, a human reading t would be justified in inferring the proposition expressed by h from the proposition expressed by t.) The relation is directional because even if "t entails h", the reverse "h entails t" is much less certain. Determining whether this relationship holds is an informal task, one which sometimes overlaps with the formal tasks of formal semantics (satisfying a strict condition will usually imply satisfaction of a less strict conditioned); additionally, textual entailment partially subsumes word entailment. == Examples == Textual entailment can be illustrated with examples of three different relations: An example of a positive TE (text entails hypothesis) is: text: If you help the needy, God will reward you. hypothesis: Giving money to a poor man has good consequences. An example of a negative TE (text contradicts hypothesis) is: text: If you help the needy, God will reward you. hypothesis: Giving money to a poor man has no consequences. An example of a non-TE (text does not entail nor contradict) is: text: If you help the needy, God will reward you. hypothesis: Giving money to a poor man will make you a better person. == Ambiguity of natural language == A characteristic of natural language is that there are many different ways to state what one wants to say: several meanings can be contained in a single text and the same meaning can be expressed by different texts. This variability of semantic expression can be seen as the dual problem of language ambiguity. Together, they result in a many-to-many mapping between language expressions and meanings. The task of paraphrasing involves recognizing when two texts have the same meaning and creating a similar or shorter text that conveys almost the same information. Textual entailment is similar but weakens the relationship to be unidirectional. Mathematical solutions to establish textual entailment can be based on the directional property of this relation, by making a comparison between some directional similarities of the texts involved. == Approaches == Textual entailment measures natural language understanding as it asks for a semantic interpretation of the text, and due to its generality remains an active area of research. Many approaches and refinements of approaches have been considered, such as word embedding, logical models, graphical models, rule systems, contextual focusing, and machine learning. Practical or large-scale solutions avoid these complex methods and instead use only surface syntax or lexical relationships, but are correspondingly less accurate. As of 2005, state-of-the-art systems are far from human performance; a study found humans to agree on the dataset 95.25% of the time. Algorithms from 2016 had not yet achieved 90%. == Applications == Many natural language processing applications, like question answering, information extraction, summarization, multi-document summarization, and evaluation of machine translation systems, need to recognize that a particular target meaning can be inferred from different text variants. Typically entailment is used as part of a larger system, for example in a prediction system to filter out trivial or obvious predictions. Textual entailment also has applications in adversarial stylometry, which has the objective of removing textual style without changing the overall meaning of communication. == Datasets == Some of available English NLI datasets include: SNLI MultiNLI SciTail SICK MedNLI QA-NLI In addition, there are several non-English NLI datasets, as follows: XNLI DACCORD, RTE3-FR, SICK-FR for French FarsTail for Farsi OCNLI for Chinese SICK-NL for Dutch IndoNLI for Indonesian

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  Merit Network

  Merit Network, Inc., is a nonprofit member-governed organization providing high-performance computer networking and related services to educational, government, health care, and nonprofit organizations, primarily in Michigan. Created in 1966, Merit operates the longest running regional computer network in the United States. == Organization == Created in 1966 as the Michigan Educational Research Information Triad by Michigan State University (MSU), the University of Michigan (U-M), and Wayne State University (WSU), Merit was created to investigate resource sharing by connecting the mainframe computers at these three Michigan public research universities. Merit's initial three node packet-switched computer network was operational in October 1972 using custom hardware based on DEC PDP-11 minicomputers and software developed by the Merit staff and the staffs at the three universities. Over the next dozen years the initial network grew as new services such as dial-in terminal support, remote job submission, remote printing, and file transfer were added; as gateways to the national and international Tymnet, Telenet, and Datapac networks were established, as support for the X.25 and TCP/IP protocols was added; as additional computers such as WSU's MVS system and the UM's electrical engineering's VAX running UNIX were attached; and as new universities became Merit members. Merit's involvement in national networking activities started in the mid-1980s with connections to the national supercomputing centers and work on the 56 kbit/s National Science Foundation Network (NSFNET), the forerunner of today's Internet. From 1987 until April 1995, Merit re-engineered and managed the NSFNET backbone service. MichNet, Merit's regional network in Michigan was attached to NSFNET and in the early 1990s Merit began extending "the Internet" throughout Michigan, offering both direct connect and dial-in services, and upgrading the statewide network from 56 kbit/s to 1.5 Mbit/s, and on to 45, 155, 622 Mbit/s, and eventually 1 and 10 Gbit/s. In 2003 Merit began its transition to a facilities based network, using fiber optic facilities that it shares with its members, that it purchases or leases under long-term agreements, or that it builds. In addition to network connectivity services, Merit offers a number of related services within Michigan and beyond, including: Internet2 connectivity, VPN, Network monitoring, Voice over IP (VOIP), Cloud storage, E-mail, Domain Name, Network Time, VMware and Zimbra software licensing, Colocation, and professional development seminars, workshops, classes, conferences, and meetings. == History == === Creating the network: 1966 to 1973 === The Michigan Educational Research Information Triad (MERIT) was formed in the fall of 1966 by Michigan State University (MSU), University of Michigan (U-M), and Wayne State University (WSU). More often known as the Merit Computer Network or simply Merit, it was created to design and implement a computer network connecting the mainframe computers at the universities. In the fall of 1969, after funding for the initial development of the network had been secured, Bertram Herzog was named director for MERIT. Eric Aupperle was hired as senior engineer, and was charged with finding hardware to make the network operational. The National Science Foundation (NSF) and the State of Michigan provided the initial funding for the network. In June 1970, the Applied Dynamics Division of Reliance Electric in Saline, Michigan was contracted to build three Communication Computers or CCs. Each would consist of a Digital Equipment Corporation (DEC) PDP-11 computer, dataphone interfaces, and interfaces that would attach them directly to the mainframe computers. The cost was to be slightly less than the $300,000 ($2,487,100, adjusted for inflation) originally budgeted. Merit staff wrote the software that ran on the CCs, while staff at each of the universities wrote the mainframe software to interface to the CCs. The first completed connection linked the IBM S/360-67 mainframe computers running the Michigan Terminal System at WSU and U-M, and was publicly demonstrated on December 14, 1971. The MSU node was completed in October 1972, adding a CDC 6500 mainframe running Scope/Hustler. The network was officially dedicated on May 15, 1973. === Expanding the network: 1974 to 1985 === In 1974, Herzog returned to teaching in the University of Michigan's Industrial Engineering Department, and Aupperle was appointed as director. Use of the all uppercase name "MERIT" was abandoned in favor of the mixed case "Merit". The first network connections were host to host interactive connections which allowed person to remote computer or local computer to remote computer interactions. To this, terminal to host connections, batch connections (remote job submission, remote printing, batch file transfer), and interactive file copy were added. And, in addition to connecting to host computers over custom hardware interfaces, the ability to connect to hosts or other networks over groups of asynchronous ports and via X.25 were added. Merit interconnected with Telenet (later SprintNet) in 1976 to give Merit users dial-in access from locations around the United States. Dial-in access within the U.S. and internationally was further expanded via Merit's interconnections to Tymnet, ADP's Autonet, and later still the IBM Global Network as well as Merit's own expanding network of dial-in sites in Michigan, New York City, and Washington, D.C. In 1978, Western Michigan University (WMU) became the fourth member of Merit (prompting a name change, as the acronym Merit no longer made sense as the group was no longer a triad). To expand the network, the Merit staff developed new hardware interfaces for the Digital PDP-11 based on printed circuit technology. The new system became known as the Primary Communications Processor (PCP), with the earliest PCPs connecting a PDP-10 located at WMU and a DEC VAX running UNIX at U-M's Electrical Engineering department. A second hardware technology initiative in 1983 produced the smaller Secondary Communication Processors (SCP) based on DEC LSI-11 processors. The first SCP was installed at the Michigan Union in Ann Arbor, creating UMnet, which extended Merit's network connectivity deeply into the U-M campus. In 1983 Merit's PCP and SCP software was enhanced to support TCP/IP and Merit interconnected with the ARPANET. === National networking, NSFNET, and the Internet: 1986 to 1995 === In 1986 Merit engineered and operated leased lines and satellite links that allowed the University of Michigan to access the supercomputing facilities at Pittsburgh, San Diego, and NCAR. In 1987, Merit, IBM and MCI submitted a winning proposal to NSF to implement a new NSFNET backbone network. The new NSFNET backbone network service began July 1, 1988. It interconnected supercomputing centers around the country at 1.5 megabits per second (T1), 24 times faster than the 56 kilobits-per-second speed of the previous network. The NSFNET backbone grew to link scientists and educators on university campuses nationwide and connect them to their counterparts around the world. The NSFNET project caused substantial growth at Merit, nearly tripling the staff and leading to the establishment of a new 24-hour Network Operations Center at the U-M Computer Center. In September 1990 in anticipation of the NSFNET T3 upgrade and the approaching end of the 5-year NSFNET cooperative agreement, Merit, IBM, and MCI formed Advanced Network and Services (ANS), a new non-profit corporation with a more broadly based Board of Directors than the Michigan-based Merit Network. Under its cooperative agreement with NSF, Merit remained ultimately responsible for the operation of NSFNET, but subcontracted much of the engineering and operations work to ANS. In 1991 the NSFNET backbone service was expanded to additional sites and upgraded to a more robust 45 Mbit/s (T3) based network. The new T3 backbone was named ANSNet and provided the physical infrastructure used by Merit to deliver the NSFNET Backbone Service. On April 30, 1995, the NSFNET project came to an end, when the NSFNET backbone service was decommissioned and replaced by a new Internet architecture with commercial Internet service providers (ISPs) interconnected at Network Access Points provided by multiple providers across the country. === Bringing the Internet to Michigan: 1985 to 2001 === During the 1980s, Merit Network grew to serve eight member universities, with Oakland University joining in 1985 and Central Michigan University, Eastern Michigan University, and Michigan Technological University joining in 1987. In 1990, Merit's board of directors formally changed the organization's name to Merit Network, Inc., and created the name MichNet to refer to Merit's statewide network. The board also approved a staff proposal to allow organizations other than publicly supported universities, referred to as aff

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  ServerNet

  ServerNet is a switched fabric communications link primarily used in proprietary computers made by Tandem Computers, Compaq, and HP. Its features include good scalability, clean fault containment, error detection and failover. The ServerNet architecture specification defines a connection between nodes, either processor or high performance I/O nodes such as storage devices. == History == Tandem Computers developed the original ServerNet architecture and protocols for use in its own proprietary computer systems starting in 1992, and released the first ServerNet systems in 1995. Early attempts to license the technology and interface chips to other companies failed, due in part to a disconnect between the culture of selling complete hardware / software / middleware computer systems and that needed for selling and supporting chips and licensing technology. A follow-on development effort ported the Virtual Interface Architecture to ServerNet with PCI interface boards connecting personal computers. Infiniband directly inherited many ServerNet features. As of 2017, systems still ship based on the ServerNet architecture.

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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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  Software diagnosis

  Software diagnosis (also: software diagnostics) refers to concepts, techniques, and tools that allow for obtaining findings, conclusions, and evaluations about software systems and their implementation, composition, behaviour, and evolution. It serves as means to monitor, steer, observe and optimize software development, software maintenance, and software re-engineering in the sense of a business intelligence approach specific to software systems. It is generally based on the automatic extraction, analysis, and visualization of corresponding information sources of the software system. It can also be manually done and not automatic. == Applications == Software diagnosis supports all branches of software engineering, in particular project management, quality management, risk management as well as implementation and test. Its main strength is to support all stakeholders of software projects (in particular during software maintenance and for software re-engineering tasks) and to provide effective communication means for software development projects. For example, software diagnosis facilitates "bridging an essential information gap between management and development, improve awareness, and serve as early risk detection instrument". Software diagnosis includes assessment methods for "perfective maintenance" that, for example, apply "visual analysis techniques to combine multiple indicators for low maintainability, including code complexity and entanglement with other parts of the system, and recent changes applied to the code". == Characteristics == In contrast to manifold approaches and techniques in software engineering, software diagnosis does not depend on programming languages, modeling techniques, software development processes or the specific techniques used in the various stages of the software development process. Instead, software diagnosis aims at analyzing and evaluating the software system in its as-is state and based on system-generated information to bypass any subjective or potentially outdated information sources (e.g., initial software models). For it, software diagnosis combines and relates sources of information that are typically not directly linked. Examples: Source-code metrics are related with software developer activity to gain insight into developer-specific effects on software code quality. System structure and run-time execution traces are correlated to facilitate program comprehension through dynamic analysis in software maintenance tasks. == Principles == The core principle of software diagnosis is to automatically extract information from all available information sources of a given software projects such as source code base, project repository, code metrics, execution traces, test results, etc. To combine information, software-specific data mining, analysis, and visualization techniques are applied. Its strength results, among various reasons, from integrating decoupled information spaces in the scope of a typical software project, for example development and developer activities (recorded by the repository) and code and quality metrics (derived by analyzing source code) or key performance indicators (KPIs). == Examples == Examples of software diagnosis tools include software maps and software metrics. == Critics == Software diagnosis—in contrast to many approaches in software engineering—does not assume that developer capabilities, development methods, programming or modeling languages are right or wrong (or better or worse compared to each other): Software diagnosis aims at giving insight into a given software system and its status regardless of the methods, languages, or models used to create and maintain the system. === Related subjects === Cost estimation in software engineering Programming productivity Rapid application development Software design Software development Software documentation Software map Software release life cycle Systems design Systems Development Life Cycle

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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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  Open Data-Link Interface

  The Open Data-Link Interface (ODI) is an application programming interface (API) for network interface controllers (NICs) developed by Apple and Novell. The API serves the same function as Microsoft and 3COM's Network Driver Interface Specification (NDIS). Originally, ODI was written for NetWare and Macintosh environments. Like NDIS, ODI provides rules that establish a vendor-neutral interface between the protocol stack and the adapter driver. It resides in Layer 2, the Data Link layer, of the OSI model. This interface also enables one or more network drivers to support one or more protocol stacks.

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