Best AI Image Generators

Best AI Image Generators — hands-on reviews, top picks, pricing, pros and cons and a practical how-to guide on Aizhi.

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  Connected-component labeling

  Connected-component labeling (CCL), connected-component analysis (CCA), blob extraction, region labeling, blob discovery, or region extraction is an algorithmic application of graph theory, where subsets of connected components are uniquely labeled based on a given heuristic. Connected-component labeling is not to be confused with segmentation. Connected-component labeling is used in computer vision to detect connected regions in binary digital images, although color images and data with higher dimensionality can also be processed. When integrated into an image recognition system or human-computer interaction interface, connected component labeling can operate on a variety of information. Blob extraction is generally performed on the resulting binary image from a thresholding step, but it can be applicable to gray-scale and color images as well. Blobs may be counted, filtered, and tracked. Blob extraction is related to but distinct from blob detection. == Overview == A graph, containing vertices and connecting edges, is constructed from relevant input data. The vertices contain information required by the comparison heuristic, while the edges indicate connected 'neighbors'. An algorithm traverses the graph, labeling the vertices based on the connectivity and relative values of their neighbors. Connectivity is determined by the medium; image graphs, for example, can be 4-connected neighborhood or 8-connected neighborhood. Following the labeling stage, the graph may be partitioned into subsets, after which the original information can be recovered and processed . == Definition == The usage of the term connected-component labeling (CCL) and its definition is quite consistent in the academic literature, whereas connected-component analysis (CCA) varies both in terminology and in its definition of the problem. Rosenfeld et al. define connected components labeling as the “[c]reation of a labeled image in which the positions associated with the same connected component of the binary input image have a unique label.” Shapiro et al. define CCL as an operator whose “input is a binary image and [...] output is a symbolic image in which the label assigned to each pixel is an integer uniquely identifying the connected component to which that pixel belongs.” There is no consensus on the definition of CCA in the academic literature. It is often used interchangeably with CCL. A more extensive definition is given by Shapiro et al.: “Connected component analysis consists of connected component labeling of the black pixels followed by property measurement of the component regions and decision making.” The definition for connected-component analysis presented here is more general, taking the thoughts expressed in into account. == Algorithms == The algorithms discussed can be generalised to arbitrary dimensions, albeit with increased time and space complexity. === One component at a time === This is a fast and very simple method to implement and understand. It is based on graph traversal methods in graph theory. In short, once the first pixel of a connected component is found, all the connected pixels of that connected component are labelled before going onto the next pixel in the image. This algorithm is part of Vincent and Soille's watershed segmentation algorithm, other implementations also exist. In order to do that a linked list is formed that will keep the indexes of the pixels that are connected to each other, steps (2) and (3) below. The method of defining the linked list specifies the use of a depth or a breadth first search. For this particular application, there is no difference which strategy to use. The simplest kind of a last in first out queue implemented as a singly linked list will result in a depth first search strategy. It is assumed that the input image is a binary image, with pixels being either background or foreground and that the connected components in the foreground pixels are desired. The algorithm steps can be written as: Start from the first pixel in the image. Set current label to 1. Go to (2). If this pixel is a foreground pixel and it is not already labelled, give it the current label and add it as the first element in a queue, then go to (3). If it is a background pixel or it was already labelled, then repeat (2) for the next pixel in the image. Pop out an element from the queue, and look at its neighbours (based on any type of connectivity). If a neighbour is a foreground pixel and is not already labelled, give it the current label and add it to the queue. Repeat (3) until there are no more elements in the queue. Go to (2) for the next pixel in the image and increment current label by 1. Note that the pixels are labelled before being put into the queue. The queue will only keep a pixel to check its neighbours and add them to the queue if necessary. This algorithm only needs to check the neighbours of each foreground pixel once and doesn't check the neighbours of background pixels. The pseudocode is: algorithm OneComponentAtATime(data) input : imageData[xDim][yDim] initialization : label = 0, labelArray[xDim][yDim] = 0, statusArray[xDim][yDim] = false, queue1, queue2; for i = 0 to xDim do for j = 0 to yDim do if imageData[i][j] has not been processed do if imageData[i][j] is a foreground pixel do check its four neighbors(north, south, east, west) : if neighbor is not processed do if neighbor is a foreground pixel do add it to queue1 else update its status to processed end if labelArray[i][j] = label (give label) statusArray[i][j] = true (update status) while queue1 is not empty do For each pixel in the queue do : check its four neighbors if neighbor is not processed do if neighbor is a foreground pixel do add it to queue2 else update its status to processed end if give it the current label update its status to processed remove the current element from queue1 copy queue2 into queue1 end While increase the label end if else update its status to processed end if end if end if end for end for === Two-pass === Relatively simple to implement and understand, the two-pass algorithm, (also known as the Hoshen–Kopelman algorithm) iterates through 2-dimensional binary data. The algorithm makes two passes over the image: the first pass to assign temporary labels and record equivalences, and the second pass to replace each temporary label by the smallest label of its equivalence class. The input data can be modified in situ (which carries the risk of data corruption), or labeling information can be maintained in an additional data structure. Connectivity checks are carried out by checking neighbor pixels' labels (neighbor elements whose labels are not assigned yet are ignored), or say, the north-east, the north, the north-west and the west of the current pixel (assuming 8-connectivity). 4-connectivity uses only north and west neighbors of the current pixel. The following conditions are checked to determine the value of the label to be assigned to the current pixel (4-connectivity is assumed) Conditions to check: Does the pixel to the left (west) have the same value as the current pixel? Yes – We are in the same region. Assign the same label to the current pixel No – Check next condition Do both pixels to the north and west of the current pixel have the same value as the current pixel but not the same label? Yes – We know that the north and west pixels belong to the same region and must be merged. Assign the current pixel the minimum of the north and west labels, and record their equivalence relationship No – Check next condition Does the pixel to the left (west) have a different value and the one to the north the same value as the current pixel? Yes – Assign the label of the north pixel to the current pixel No – Check next condition Do the pixel's north and west neighbors have different pixel values than current pixel? Yes – Create a new label id and assign it to the current pixel The algorithm continues this way, and creates new region labels whenever necessary. The key to a fast algorithm, however, is how this merging is done. This algorithm uses the union-find data structure which provides excellent performance for keeping track of equivalence relationships. Union-find essentially stores labels which correspond to the same blob in a disjoint-set data structure, making it easy to remember the equivalence of two labels by the use of an interface method E.g.: findSet(l). findSet(l) returns the minimum label value that is equivalent to the function argument 'l'. Once the initial labeling and equivalence recording is completed, the second pass merely replaces each pixel label with its equivalent disjoint-set representative element. A faster-scanning algorithm for connected-region extraction is presented below. On the first pass: Iterate through each element of the data by column, then by row (Raster Scanning) If the element is not the background Get the neighboring elements of the current element If there are no neighbors, uniquely

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  Consistency (database systems)

  In database systems, consistency (or correctness) refers to the requirement that any given database transaction must change affected data only in allowed ways. Any data written to the database must be valid according to all defined rules, including constraints, cascades, triggers, and any combination thereof. This does not guarantee correctness of the transaction in all ways the application programmer might have wanted (that is the responsibility of application-level code) but merely that any programming errors cannot result in the violation of any defined database constraints. In a distributed system, referencing CAP theorem, consistency can also be understood as after a successful write, update or delete of a Record, any read request immediately receives the latest value of the Record. == As an ACID guarantee == Consistency is one of the four guarantees that define ACID transactions; however, significant ambiguity exists about the nature of this guarantee. It is defined variously as: The guarantee that database constraints are not violated, particularly once a transaction commits. The guarantee that any transactions started in the future necessarily see the effects of other transactions committed in the past. As these various definitions are not mutually exclusive, it is possible to design a system that guarantees "consistency" in every sense of the word, as most relational database management systems in common use today arguably do. == As a CAP trade-off == The CAP theorem is based on three trade-offs, one of which is "atomic consistency" (shortened to "consistency" for the acronym), about which the authors note, "Discussing atomic consistency is somewhat different than talking about an ACID database, as database consistency refers to transactions, while atomic consistency refers only to a property of a single request/response operation sequence. And it has a different meaning than the Atomic in ACID, as it subsumes the database notions of both Atomic and Consistent." In the CAP theorem, you can only have two of the following three properties: consistency, availability, or partition tolerance. Therefore, consistency may have to be traded off in some database systems.

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  List of network buses

  List of electrical characteristics of single collision domain segment "slow speed" network buses: The number of nodes can be limited by either number of available addresses or bus capacitance. None of the above use any analog domain modulation techniques like MLT-3 encoding, PAM-5 etc. PSI5 designed with automation applications in mind is a bit unusual in that it uses Manchester code.

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  Pepper (cryptography)

  In cryptography, a pepper is a secret added to an input such as a password during hashing with a cryptographic hash function. This value differs from a salt in that it is not stored alongside a password hash, but rather the pepper is kept separate using another meachanism, such as a Hardware Security Module. Note that the National Institute of Standards and Technology refers to this value as a secret key rather than a pepper. A pepper is similar in concept to a salt or an encryption key. It is like a salt in that it is a randomized value that is added to a password hash, and it is similar to an encryption key in that it should be kept secret. A pepper performs a comparable role to a salt or an encryption key, but while a salt is not secret (merely unique) and can be stored alongside the hashed output, a pepper is secret and must not be stored with the output. The hash and salt are usually stored in a database, but, if stored, a pepper must be stored separately to prevent it from being obtained by the attacker in case of a database breach. == History == The idea of a site- or service-specific salt (in addition to a per-user salt) has a long history, with Steven M. Bellovin proposing a local parameter in a Bugtraq post in 1995. In 1996 Udi Manber also described the advantages of such a scheme, terming it a secret salt. However, he suggested not storing the value of the secret salt, but instead rediscovering it by trial and error at password verification time. The term pepper has been used, by analogy to salt, but with a variety of meanings. For example, when discussing a challenge-response scheme, pepper has been used for a salt-like quantity, though not used for password storage; it has been used for a data transmission technique where a pepper must be guessed; and even as a part of jokes. The term pepper was proposed for a secret or local parameter stored separately from the password in a discussion of protecting passwords from rainbow table attacks. This usage did not immediately catch on: for example, Fred Wenzel added support to Django password hashing for storage based on a combination of bcrypt and HMAC with separately stored nonces, without using the term. Usage has since become more common. == Types == There are multiple different types of pepper: A shared secret that is common to all users. A randomly-selected number that must be re-discovered on every password input. These mechanisms could be combined with password salting, iterated hashing or even one another. == Shared-secret pepper == Bellovin and Webster suggest prepend a shared secret to the password before hashing, which allows easy use of existing hash functions. For example, consider two users to be added to a database. This table contains two combinations of username and password. The password is not saved, and the 8-byte (64-bit) 44534C70C6883DE2 pepper is saved in a safe place separate from the output values of the hash, in this case SHA256. Unlike the salt, the pepper does not provide protection to users who use the same password, but protects against dictionary attacks, unless the attacker has the pepper value available. Since the same pepper is not shared between different applications, an attacker is unable to reuse the hashes of one compromised database to another. A complete scheme for saving passwords may include both salt and pepper use. For example, it has been suggested to combine the pepper by encrypting salted password hashes, which allows rotation of the pepper. In the case of a shared-secret pepper, a single compromised password (via password reuse or other attack) along with a user's salt can lead to an attack to discover the pepper, rendering it ineffective. If an attacker knows a plaintext password and a user's salt, as well as the algorithm used to hash the password, then discovering the pepper can be a matter of brute forcing the values of the pepper. This is why NIST recommends the secret value be at least 112 bits, so that discovering it by exhaustive search is prohibitively expensive. The pepper must be generated anew for every application it is deployed in, otherwise a breach of one application would result in lowered security of another application. Without knowledge of the pepper, other passwords in the database will be far more difficult to extract from their hashed values, as the attacker would need to guess the password as well as the pepper. A pepper adds security to a database of salts and hashes because unless the attacker is able to obtain the pepper, cracking even a single hash is intractable, no matter how weak the original password. Even with a list of (salt, hash) pairs, an attacker must also guess the secret pepper in order to find the password which produces the hash. The NIST specification for a secret salt suggests using a Password-Based Key Derivation Function (PBKDF) with an approved Pseudorandom Function such as HMAC with SHA-3 as the hash function of the HMAC. The NIST recommendation is also to perform at least 1000 iterations of the PBKDF, and a further minimum 1000 iterations using the secret salt in place of the non-secret salt. == Randomly-selected pepper that must be re-discovered == The aim of this mechanism is to slow down password the password verification step, thus slowing attacks. The aim is similar increasing the iteration count on bcrypt or Argon2, but the mechanism is different. The secret salt or pepper must be rediscovered by the verifier or attacker each time by guessing. In this situation, the password hashing function is calculated using both the password and the pepper. At password storage time, the pepper is chosen randomly from a range between 1 and R, the hash output is calculated using the password and the pepper. The hash output is stored with the username. The pepper is then discarded. At password verification time, the verifier is provided with a username and password to verify. The originally calculated hash is retrieved for the given username, and then the hash of the password and each value between 1 and R is calculated. If any of these hash values match the stored password hash, the password is considered valid. Note, the possible values of the pepper should not be tested in a fixed order known to an attacker, otherwise a timing attack may reveal the pepper. If the password is correct, the correct pepper will be found in R/2 hash evaluations on average. If the password is incorrect, all R values must be tested before the password can be rejected.

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  Machine learning in video games

  Artificial intelligence and machine learning techniques are used in video games for a wide variety of applications such as non-player character (NPC) control, procedural content generation (PCG) and deep learning-based content generation. Machine learning is a subset of artificial intelligence that uses historical data to build predictive and analytical models. This is in sharp contrast to traditional methods of artificial intelligence such as search trees and expert systems. Information on machine learning techniques in the field of games is mostly known to public through research projects as most gaming companies choose not to publish specific information about their intellectual property. The most publicly known application of machine learning in games is likely the use of deep learning agents that compete with professional human players in complex strategy games. There has been a significant application of machine learning on games such as Atari/ALE, Doom, Minecraft, StarCraft, and car racing. Other games that did not originally exists as video games, such as chess and Go have also been affected by the machine learning. == Overview of relevant machine learning techniques == === Deep learning === Deep learning is a subset of machine learning which focuses heavily on the use of artificial neural networks (ANN) that learn to solve complex tasks. Deep learning uses multiple layers of ANN and other techniques to progressively extract information from an input. Due to this complex layered approach, deep learning models often require powerful machines to train and run on. ==== Convolutional neural networks ==== Convolutional neural networks (CNN) are specialized ANNs that are often used to analyze image data. These types of networks are able to learn translation invariant patterns, which are patterns that are not dependent on location. CNNs are able to learn these patterns in a hierarchy, meaning that earlier convolutional layers will learn smaller local patterns while later layers will learn larger patterns based on the previous patterns. A CNN's ability to learn visual data has made it a commonly used tool for deep learning in games. === Recurrent neural network === Recurrent neural networks are a type of ANN that are designed to process sequences of data in order, one part at a time rather than all at once. An RNN runs over each part of a sequence, using the current part of the sequence along with memory of previous parts of the current sequence to produce an output. These types of ANN are highly effective at tasks such as speech recognition and other problems that depend heavily on temporal order. There are several types of RNNs with different internal configurations; the basic implementation suffers from a lack of long term memory due to the vanishing gradient problem, thus it is rarely used over newer implementations. ==== Long short-term memory ==== A long short-term memory (LSTM) network is a specific implementation of a RNN that is designed to deal with the vanishing gradient problem seen in simple RNNs, which would lead to them gradually "forgetting" about previous parts of an inputted sequence when calculating the output of a current part. LSTMs solve this problem with the addition of an elaborate system that uses an additional input/output to keep track of long term data. LSTMs have achieved very strong results across various fields, and were used by several monumental deep learning agents in games. === Reinforcement learning === Reinforcement learning is the process of training an agent using rewards and/or punishments. The way an agent is rewarded or punished depends heavily on the problem; such as giving an agent a positive reward for winning a game or a negative one for losing. Reinforcement learning is used heavily in the field of machine learning and can be seen in methods such as Q-learning, policy search, Deep Q-networks and others. It has seen strong performance in both the field of games and robotics. === Neuroevolution === Neuroevolution involves the use of both neural networks and evolutionary algorithms. Instead of using gradient descent like most neural networks, neuroevolution models make use of evolutionary algorithms to update neurons in the network. Researchers claim that this process is less likely to get stuck in a local minimum and is potentially faster than state of the art deep learning techniques. == Deep learning agents == Machine learning agents have been used to take the place of a human player rather than function as NPCs, which are deliberately added into video games as part of designed gameplay. Deep learning agents have achieved impressive results when used in competition with both humans and other artificial intelligence agents. === Chess === Chess is a turn-based strategy game that is considered a difficult AI problem due to the computational complexity of its board space. Similar strategy games are often solved with some form of a Minimax Tree Search. These types of AI agents have been known to beat professional human players, such as the historic 1997 Deep Blue versus Garry Kasparov match. Since then, machine learning agents have shown ever greater success than previous AI agents. === Go === Go is another turn-based strategy game which is considered an even more difficult AI problem than chess. The state space of is Go is around 10^170 possible board states compared to the 10^120 board states for Chess. Prior to recent deep learning models, AI Go agents were only able to play at the level of a human amateur. ==== AlphaGo ==== Google's 2015 AlphaGo was the first AI agent to beat a professional Go player. AlphaGo used a deep learning model to train the weights of a Monte Carlo tree search (MCTS). The deep learning model consisted of 2 ANN, a policy network to predict the probabilities of potential moves by opponents, and a value network to predict the win chance of a given state. The deep learning model allows the agent to explore potential game states more efficiently than a vanilla MCTS. The network were initially trained on games of humans players and then were further trained by games against itself. ==== AlphaGo Zero ==== AlphaGo Zero, another implementation of AlphaGo, was able to train entirely by playing against itself. It was able to quickly train up to the capabilities of the previous agent. === StarCraft series === StarCraft and its sequel StarCraft II are real-time strategy (RTS) video games that have become popular environments for AI research. Blizzard and DeepMind have worked together to release a public StarCraft 2 environment for AI research to be done on. Various deep learning methods have been tested on both games, though most agents usually have trouble outperforming the default AI with cheats enabled or skilled players of the game. ==== Alphastar ==== Alphastar was the first AI agent to beat professional StarCraft 2 players without any in-game advantages. The deep learning network of the agent initially received input from a simplified zoomed out version of the gamestate, but was later updated to play using a camera like other human players. The developers have not publicly released the code or architecture of their model, but have listed several state of the art machine learning techniques such as relational deep reinforcement learning, long short-term memory, auto-regressive policy heads, pointer networks, and centralized value baseline. Alphastar was initially trained with supervised learning, it watched replays of many human games in order to learn basic strategies. It then trained against different versions of itself and was improved through reinforcement learning. The final version was hugely successful, but only trained to play on a specific map in a protoss mirror matchup. === Dota 2 === Dota 2 is a multiplayer online battle arena (MOBA) game. Like other complex games, traditional AI agents have not been able to compete on the same level as professional human player. The only widely published information on AI agents attempted on Dota 2 is OpenAI's deep learning Five agent. ==== OpenAI Five ==== OpenAI Five utilized separate long short-term memory networks to learn each hero. It trained using a reinforcement learning technique known as Proximal Policy Learning running on a system containing 256 GPUs and 128,000 CPU cores. Five trained for months, accumulating 180 years of game experience each day, before facing off with professional players. It was eventually able to beat the 2018 Dota 2 esports champion team in a 2019 series of games. === Planetary Annihilation === Planetary Annihilation is a real-time strategy game which focuses on massive scale war. The developers use ANNs in their default AI agent. === Supreme Commander 2 === Supreme Commander 2 is a real-time strategy (RTS) video game. The game uses Multilayer Perceptrons (MLPs) to control a platoon’s reaction to encountered enemy units. Total of four MLPs are used, one for each platoon type: land, naval

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  Cryptographic nonce

  In cryptography, a nonce is an arbitrary number that can be used just once in a cryptographic communication. It is often a random or pseudo-random number issued in an authentication protocol to ensure that each communication session is unique, and therefore that old communications cannot be reused in replay attacks. Nonces can also be useful as initialization vectors and in cryptographic hash functions. == Definition == A nonce is an arbitrary number used only once in a cryptographic communication, in the spirit of a nonce word. They are often random or pseudo-random numbers. Many nonces also include a timestamp to ensure exact timeliness, though this requires clock synchronisation between organisations. The addition of a client nonce ("cnonce") helps to improve the security in some ways as implemented in digest access authentication. To ensure that a nonce is used only once, it should be time-variant (including a suitably fine-grained timestamp in its value), or generated with enough random bits to ensure an insignificantly low chance of repeating a previously generated value. Some authors define pseudo-randomness (or unpredictability) as a requirement for a nonce. Nonce is a word dating back to Middle English for something only used once or temporarily (often with the construction "for the nonce"). It descends from the construction "then anes" ("the one [purpose]"). A false etymology claiming it to stand for "number used once" or similar is incorrect. == Usage == === Authentication === Authentication protocols may use nonces to ensure that old communications cannot be reused in replay attacks. For instance, nonces are used in HTTP digest access authentication to calculate an MD5 digest of the password. The nonces are different each time the 401 authentication challenge response code is presented, thus making replay attacks virtually impossible. The scenario of ordering products over the Internet can provide an example of the usefulness of nonces in replay attacks. An attacker could take the encrypted information and—without needing to decrypt—could continue to send a particular order to the supplier, thereby ordering products over and over again under the same name and purchase information. The nonce is used to give 'originality' to a given message so that if the company receives any other orders from the same person with the same nonce, it will discard those as invalid orders. A nonce may be used to ensure security for a stream cipher. Where the same key is used for more than one message and then a different nonce is used to ensure that the keystream is different for different messages encrypted with that key; often the message number is used. Secret nonce values are used by the Lamport signature scheme as a signer-side secret which can be selectively revealed for comparison to public hashes for signature creation and verification. === Hashing === Nonces are used in proof-of-work systems to vary the input to a cryptographic hash function so as to obtain a hash for a certain input that fulfils certain arbitrary conditions. In doing so, it becomes far more difficult to create a "desirable" hash than to verify it, shifting the burden of work onto one side of a transaction or system. For example, proof of work, using hash functions, was considered as a means to combat email spam by forcing email senders to find a hash value for the email (which included a timestamp to prevent pre-computation of useful hashes for later use) that had an arbitrary number of leading zeroes, by hashing the same input with a large number of values until a "desirable" hash was obtained. Similarly, the Bitcoin blockchain hashing algorithm can be tuned to an arbitrary difficulty by changing the required minimum/maximum value of the hash so that the number of bitcoins awarded for new blocks does not increase linearly with increased network computation power as new users join. This is likewise achieved by forcing Bitcoin miners to add nonce values to the value being hashed to change the hash algorithm output. As cryptographic hash algorithms cannot easily be predicted based on their inputs, this makes the act of blockchain hashing and the possibility of being awarded bitcoins something of a lottery, where the first "miner" to find a nonce that delivers a desirable hash is awarded bitcoins.

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  List of cryptography journals

  List of cryptography journals includes notable peer-reviewed academic journals that focus on cryptography, cryptanalysis, information security, and related areas in computer science and mathematics. == Notable journals == Cryptologia Designs, Codes and Cryptography IEEE Transactions on Information Theory International Journal of Information Security Journal of Cryptology Journal of Computer Security ACM Transactions on Privacy and Security Information Processing Letters Information and Computation

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  IBM 37xx

  IBM 37xx (or 37x5) is a family of IBM Systems Network Architecture (SNA) programmable front-end processors used mainly in mainframe environments. All members of the family ran one of three IBM-supplied programs. Emulation Program (EP) mimicked the operation of the older IBM 270x non-programmable controllers. Network Control Program (NCP) supported Systems Network Architecture devices. Partitioned Emulation Program (PEP) combined the functions of the two. == Models == === 370x series === 3705 — the oldest of the family, introduced in 1972 to replace the non-programmable IBM 270x family. The 3705 could control up to 352 communications lines. 3704 was a smaller version, introduced in 1973. It supported up to 32 lines. === 371x === The 3710 communications controller was introduced in 1984. === 372x series === The 3725 and the 3720 systems were announced in 1983. The 3725 replaced the hardware line scanners used on previous 370x machines with multiple microcoded processors. The 3725 was a large-scale node and front end processor. The 3720 was a smaller version of the 3725, which was sometimes used as a remote concentrator. The 3726 was an expansion unit for the 3725. With the expansion unit, the 3725 could support up to 256 lines at data rates up to 256 kbit/s, and connect to up to eight mainframe channels. Marketing of the 372x machines was discontinued in 1989. IBM discontinued support for the 3705, 3720, 3725 in 1999. === 374x series === The 3745, announced in 1988, provides up to eight T1 circuits. At the time of the announcement, IBM was estimated to have nearly 85% of the over US$825 million market for communications controllers over rivals such as NCR Comten and Amdahl Corporation. The 3745 is no longer marketed, but still supported and used. The 3746 "Nways Controller" model 900, unveiled in 1992, was an expansion unit for the 3745 supporting additional Token Ring and ESCON connections. A stand-alone model 950 appeared in 1995. == Successors == IBM no longer manufactures 37xx processors. The last models, the 3745/46, were withdrawn from marketing in 2002. Replacement software products are Communications Controller for Linux on System z and Enterprise Extender. == Clones == Several companies produced clones of 37xx controllers, including NCR COMTEN and Amdahl Corporation.

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  Fabric Connect

  Fabric Connect, in computer networking usage, is the name used by Extreme Networks to market an extended implementation of the IEEE 802.1aq and IEEE 802.1ah-2008 standards. The Fabric Connect technology was originally developed by the Enterprise Solutions R&D department within Nortel Networks. In 2009, Avaya, Inc acquired Nortel Networks Enterprise Business Solutions; this transaction included the Fabric Connect intellectual property together with all of the Ethernet Switching platforms that supported it. Subsequently, the Fabric Connect technology became part of the Extreme Networks portfolio by virtue of their 2017 purchase of the Avaya Networking business and assets. It was during the Avaya era that this technology was promoted as the lead element of the Virtual Enterprise Network Architecture (VENA). == Technologies == === Fabric Connect === Fabric Connect's provides network-wide, end-to-end, multi-layer virtualization. A network virtualization capability, based on an enhanced implementation of the IEEE 802.1aq Shortest Path Bridging (SPB) standard, Fabric Connect offers the ability to create a simplified network that can dynamically virtualize elements to efficiently provision and utilize resources, thus reducing the strain on the network and personnel. Extreme Networks base the Fabric Connect technology on the SPB standard, including support for RFC 6329, and have integrated IP Routing and IP Multicast support; this unified technology allows for the replacement of multiple conventional protocols such as Spanning Tree, RIP and/or OSPF, ECMP, and PIM. === Fabric Attach === An adjunct to the Fabric Connect technology, Fabric Attach allows network operators to extend network virtualization directly into conventional wiring closets (using existing non-Fabric Ethernet switches) and automate the provisioning of devices to their appropriate virtual network. This is particularly relevant for the mass of unattended network end-point that are now appearing, such as IP Phones, Wireless Access Points, and IP Cameras. Fabric Attach standardized protocols such as 802.1AB LLDP to exchange credentials and obtain provisioning information that allows "Client" Switches to be automatically re-configured on the fly with parameters that let Traffic Flows Map through to Fabric Connect Edge Switches (aka "Backbone Edge Bridge" in SPB definition) functioning as a Fabric Attach "Server" Switch. This method is described by an IETF "Internet Draft", pending further standardization activity. Fabric Attach is typically used to automate Wiring Closet connectivity, but has the potential to be extensible for use in the Data Center, with Virtual Machines being able to dynamically request VLAN/VSN (Virtual Service Network) assignment based upon application requirements. == Hardware products == === Virtual Services Platform 9000 Series === A range of modular chassis-based products, featuring a carrier-grade Linux operation system, and designed for high-performance deployment scenarios that need to scale to multiple terabits of switching capacity and support 10 and 40 gigabit Ethernet connections, and is designed eventually to support 100 gigabit Ethernet. === Virtual Services Platform 8000 Series === A compact form-factor platform delivering high-density 10/40 gigabit Ethernet connectivity, and targeted at mid-market through to mid-size enterprise core switch applications. === Virtual Services Platform 7000 Series === A range of high-end 10 gigabit Ethernet stackable switches that extend fabric-based networking to the data center top-of-rack. They support 40 gigabit Ethernet via the MDA Slot. === Virtual Services Platform 4000 Series === A range of high-end gigabit Ethernet stackable switches that extend Fabric-based networking to branch and metro locations. === Ethernet Routing Switch 5000 Series === A range of high-end gigabit Ethernet stackable switches that provides enterprise-class desktop features, including PoE, and offers 10 Gbit/s uplink connections. Each Switch supports up to 144 Gbit/s of virtual backplane capacity, delivering up to 1.152 Tbit/s for a system of eight, creating a virtual backplane through a stacking configuration. === Ethernet Routing Switch 4000 Series === A range of gigabit Ethernet stackable switches that provide enterprise-class desktop features, including PoE/PoE+, and offer 1/10 Gbit/s uplink connections. Each switch supports up to 48 Gbit/s of virtual backplane capacity, delivering up to 384 Gbit/s for a system of 8, creating a virtual backplane through a stacking configuration. === Ethernet Routing Switch 3500 Series === These entry-level gigabit Ethernet stackable switches provide enterprise-class desktop features, including PoE/PoE+, and 1 Gbit/s uplink connections.

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  Cambridge Analytica

  Cambridge Analytica Ltd. (CA), previously known as SCL USA, was a British political consulting firm that came to prominence through the Facebook–Cambridge Analytica data scandal. It was founded in 2013, as a subsidiary of the private intelligence company and self-described "global election management agency" SCL Group by long-time SCL executives Nigel Oakes, Alexander Nix and Alexander Oakes, with Nix as CEO. Cambridge Analytica was hired by a variety of political actors, including the Trinidadian government in 2010 and the 2016 presidential campaigns of Ted Cruz and Donald Trump. The firm maintained offices in London, New York City, and Washington, D.C. The company closed operations in 2018 due to backlash from the scandal, although firms related to both Cambridge Analytica and its parent firm SCL still exist. == History == Cambridge Analytica was founded in 2013 as a subsidiary of the private intelligence company SCL Group, which describes itself as providing "data, analytics and strategy to governments and military organisations worldwide". The company was part of "an international web of companies" headed by the London-based SCL Group. Cambridge Analytica (SCL USA) was incorporated in January 2013 with its registered office being in Westferry Circus, London and consisting of just one staff member, director and CEO Alexander Nix (also appointed in January 2015). Nix was also the director of nine similar companies sharing the same registered offices in London, including Firecrest technologies, Emerdata and six SCL Group companies including "SCL elections limited". Nigel Oakes, known as the former boyfriend of Lady Helen Windsor, had founded the predecessor SCL Group in the 1990s, and in 2005 Oakes established SCL Group together with his brother Alexander Oakes and Alexander Nix; SCL Group was the parent company of Cambridge Analytica. Former Conservative minister and MP Sir Geoffrey Pattie was the founding chairman of SCL; Lord Ivar Mountbatten also joined Oakes as a director of the company. As a result of the Facebook–Cambridge Analytica data scandal, Nix was removed as CEO and replaced by Julian Wheatland before the company closed. Several of the company's executives were Old Etonians. The company's owners included several of the Conservative Party's largest donors such as billionaire Vincent Tchenguiz, former British Conservative minister Jonathan Marland, Baron Marland and the family of American hedge fund manager Robert Mercer. The company combined misappropriation of digital assets, data mining, data brokerage, and data analysis with strategic communication during electoral processes. While its parent SCL had focused on influencing elections in developing countries since the 1990s, Cambridge Analytica focused more on the western world, including the United Kingdom and the United States; CEO Alexander Nix has said CA was involved in 44 U.S. political races in 2014. In 2015, CA performed data analysis services for Ted Cruz's presidential campaign. In 2016, CA worked for Donald Trump's presidential campaign as well as for Leave.EU (one of the organisations campaigning in the United Kingdom's referendum on European Union membership). CA's role in those campaigns has been controversial and is the subject of ongoing inquiries in both countries. Political scientists question CA's claims about the effectiveness of its methods of targeting voters. == Data scandal == In March 2018, media outlets broke news of Cambridge Analytica's business practices. The New York Times and The Observer reported that the company had acquired and used personal data about Facebook users from an external researcher who had told Facebook he was collecting it for academic purposes. Shortly afterwards, Channel 4 News aired undercover investigative videos showing Nix boasting about using prostitutes, bribery sting operations, and honey traps to discredit politicians on whom it had conducted opposition research, and saying that the company "ran all of (Donald Trump's) digital campaign". In response to the media reports, the Information Commissioner's Office (ICO) of the UK pursued a warrant to search the company's servers. Facebook banned Cambridge Analytica from advertising on its platform, saying that it had been deceived. On 23 March 2018, the British High Court granted the ICO a warrant to search Cambridge Analytica's London offices. As a result, Nix was suspended as CEO, and replaced by Julian Wheatland. The personal data of up to 87 million Facebook users were acquired via the 270,000 Facebook users who used a Facebook app created by Aleksandr Kogan called "This Is Your Digital Life". This was a personality profiling app and asked simple personality questions similar to other Facebook quizzes. Kogan was a scientist and psychologist, also being an employed lecturer for the University of Cambridge from 2012 to 2018. Alexander Nix claimed they had close to five thousand data points on each person who participated. They also gathered information through other data brokers ending with them acquiring millions of data points from American citizens. Kogan's app exploited a feature of Facebook's Graph API (version 1.0), which permitted any third-party app to access not only the app user's data, but also the full profile data of all of that user's Facebook friends, without those friends' knowledge or consent. This platform-wide design was available to all developers and was used by tens of thousands of apps; Facebook CEO Mark Zuckerberg later told the House Energy and Commerce Committee that the company was auditing "tens of thousands" of apps that had had access to large amounts of user data. Because the average Facebook user at the time had approximately 300 friends, the 270,000 users who installed Kogan's app yielded data on up to 87 million people. Facebook deprecated the friends-data API in April 2014 and shut it down entirely in April 2015, but data already collected by apps remained in developers' possession. Kogan passed this data to Cambridge Analytica, breaching Facebook's terms of service. On 1 May 2018, Cambridge Analytica and its parent company SCL filed for insolvency proceedings and closed operations. Alexander Tayler, a former director for Cambridge Analytica, was appointed director of Emerdata on 28 March 2018. Rebekah Mercer, Jennifer Mercer, Alexander Nix and Johnson Chun Shun Ko, who has links to American businessman Erik Prince, are in leadership positions at Emerdata. The Russo brothers are producing an upcoming film on Cambridge Analytica. In 2019 the Federal Trade Commission filed an administrative complaint against Cambridge Analytica for misuse of data. In 2020, the British Information Commissioner's Office closed a three-year inquiry into the company, concluded that Cambridge Analytica was "not involved" in the 2016 Brexit referendum and found no additional evidence for Russia's alleged interference during the campaign. US sensitive polling and election data, however, were passed to Russian Intelligence via a Cambridge Analytica contractor Sam Patten, Trump campaign manager Paul Manafort, and Russian agent Konstantin Kilimnik, who was indicted during the affair. Publicly, parent company SCL Group called itself a "global election management agency", Politico reported it was known for involvement "in military disinformation campaigns to social media branding and voter targeting". SCL gained work on a large number of campaigns for the US and UK governments' war on terror advancing their model of behavioral conflict during the 2000s. SCL's involvement in the political world has been primarily in the developing world where it has been used by the military and politicians to study and manipulate public opinion and political will. Slate writer Sharon Weinberger compared one of SCL's hypothetical test scenarios to fomenting a coup. Among the investors in Cambridge Analytica were some of the Conservative Party's largest donors such as billionaire Vincent Tchenguiz, former Conservative minister Jonathan Marland, Baron Marland, Roger Gabb, the family of American hedge fund manager Robert Mercer, and Steve Bannon. A minimum of 15 million dollars has been invested into the company by Mercer, according to The New York Times. Bannon's stake in the company was estimated at 1 to 5 million dollars, but he divested his holdings in April 2017 as required by his role as White House Chief Strategist. In March 2018, Jennifer Mercer and Rebekah Mercer became directors of Emerdata limited. In March 2018 it became public by Christopher Wylie, that Cambridge Analytica's first activities were founded on a data set, which its parent company SCL bought 2014 from a company named Global Science Research founded by Aleksandr Kogan and his team present across the world who worked as a psychologist at Cambridge. During Boris Johnson's tenure as foreign secretary, the Foreign Office sought advice from Cambridge Analytica and Boris Johnson had a meeting with Alexander N

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  Content inventory

  A content inventory is the process and the result of cataloging the entire contents of a website. An allied practice—a content audit—is the process of evaluating that content. A content inventory and a content audit are closely related concepts, and they are often conducted in tandem. == Description == A content inventory typically includes all information assets on a website, such as web pages (HTML), meta elements (e.g., keywords, description, page title), images, audio and video files, and document files (e.g., .pdf, .doc, .ppt). A content inventory is a quantitative analysis of a website. It simply logs what is on a website. The content inventory will answer the question: “What is there?” and can be the start of a website review. A related (and sometimes confused term) is a content audit, a qualitative analysis of information assets on a website. It is the assessment of that content and its place in relationship to surrounding Web pages and information assets. The content audit will answer the question: “Is it any good?” Over the years, techniques for creating and managing a content inventory have been developed and refined in the field of website content management. A spreadsheet application (e.g., Microsoft Excel or LibreOffice Calc) is the preferred tool for keeping a content inventory; the data can be easily configured and manipulated. Typical categories in a content inventory include the following: Link — The URL for the page Format — For example, .HTML, .pdf, .doc, .ppt Meta page title — Page title as it appears in the meta