AI Tools

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

Semantic analysis (machine learning)

In machine learning, semantic analysis of a text corpus is the task of building structures that approximate concepts from a large set of documents. It generally does not involve prior semantic understanding of the documents. Semantic analysis strategies include: Metalanguages based on first-order logic, which can analyze the speech of humans. Understanding the semantics of a text is symbol grounding: if language is grounded, it is equal to recognizing a machine-readable meaning. For the restricted domain of spatial analysis, a computer-based language understanding system was demonstrated. Latent semantic analysis (LSA), a class of techniques where documents are represented as vectors in a term space. A prominent example is probabilistic latent semantic analysis (PLSA). Latent Dirichlet allocation, which involves attributing document terms to topics. n-grams and hidden Markov models, which work by representing the term stream as a Markov chain, in which each term is derived from preceding terms. == Stochastic semantic analysis ==
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Content management

Content management (CM) are a set of processes and technologies that support the collection, managing, and publishing of information in any form or medium. When stored and accessed via computers, this information may be more specifically referred to as digital content, or simply as content. Digital content may take the form of text (such as electronic documents), images, multimedia files (such as audio or video files), or any other file type that follows a content lifecycle requiring management. The process of content development and management is complex enough that various commercial software vendors (large and small), such as Interwoven and Microsoft, offer content management software to control and automate significant aspects of the content lifecycle. == Process == Content management practices and goals vary by mission and by organizational governance structure. News organizations, e-commerce websites, and educational institutions all use content management, but in different ways. This leads to differences in terminology and in the names and number of steps in the process. For example, some digital content is created by one or more authors. Over time that content may be edited. One or more individuals may provide some editorial oversight, approving the content for publication. Publishing may take many forms: it may be the act of "pushing" content out to others, or simply granting digital access rights to certain content to one or more individuals. Later that content may be superseded by another version of the content and thus retired or removed from use (as when this wiki page is modified). Content management is an inherently collaborative process. It often consists of the following basic roles and responsibilities: Creator – responsible for creating and editing content. Editor – responsible for tuning the content message and the style of delivery, including translation and localization. Publisher – responsible for releasing the content for use. Administrator – responsible for managing access permissions to folders, collections and files, usually accomplished by assigning access rights to user groups or roles. Admins may also assist and support users in various ways. Consumer, viewer or guest – the person who reads or otherwise consumes the content after it is published or shared. A critical aspect of content management is the ability to manage versions of content as it evolves (see also version control). Authors and editors often need to restore older versions of edited products due to a process failure or an undesirable series of edits. Time-sensitive content may also require updates as the subject matter evolves over time. Another equally important aspect of content management involves the creation, maintenance, and application of review standards. Each member of the content creation and review process has a unique role and set of responsibilities in the development or publication of the content. Each review team member requires clear and concise review standards. These must be maintained on an ongoing basis to ensure the long-term consistency and health of the knowledge base. A content management system is a set of automated processes that may support the following features: Import and creation of documents and multimedia material Identification of all key users and their roles The ability to assign roles and responsibilities to different instances of content categories or types Definition of workflow tasks often coupled with messaging so that content managers are alerted to changes in content The ability to track and manage multiple versions of a single instance of content The ability to publish the content to a repository to support access The ability to personalize content based on a set of rules Increasingly, the repository is an inherent part of the system, and incorporates enterprise search and retrieval. Content management systems take the following forms: Web content management system—software for web site management (often what content management implicitly means) Output of a newspaper editorial staff organization Workflow for article publication Document management systems Knowledge management software Single source content management system—content stored in chunks within a relational database Variant management system—where personnel tag source content (usually text and graphics) to represent variants stored as single source "master" content modules, resolved to the desired variant at publication (for example: automobile owners manual content for 12 model years stored as single master content files and "called" by model year as needed)—often used in concert with database chunk storage (see above) for large content objects == Governance structures == Content management expert Marc Feldman defines three primary content management governance structures: localized, centralized, and federated—each having its unique strengths and weaknesses. === Localized governance === By putting control in the hands of those closest to the content, the context experts, localized governance models empower and unleash creativity. These benefits come, however, at the cost of a partial-to-total loss of managerial control and oversight. === Centralized governance === When the levers of control are strongly centralized, content management systems are capable of delivering an exceptionally clear and unified brand message. Moreover, centralized content management governance structures allow for a large number of cost-savings opportunities in large enterprises, realized, for example, through (1) the avoidance of duplicated efforts in creating, editing, formatting, repurposing and archiving content; (2) process management and the streamlining of all content related labor; and/or (3) an orderly deployment or updating of the content management system. === Federated governance === Federated governance models potentially realize the benefits of both localized and centralized control while avoiding the weaknesses of both. While content management software systems are inherently structured to enable federated governance models, realizing these benefits can be difficult because it requires, for example, negotiating the boundaries of control with local managers and content creators. In the case of larger enterprises, in particular, the failure to fully implement or realize a federated governance structure equates to a failure to realize the full return on investment and cost savings that content management systems enable. == Implementation == Content management implementations must be able to manage content distributions and digital rights in content life cycle. Content management systems are usually involved with digital rights management in order to control user access and digital rights. In this step, the read-only structures of digital rights management systems force some limitations on content management, as they do not allow authors to change protected content in their life cycle. Creating new content using managed (protected) content is also an issue that gets protected contents out of management controlling systems. A few content management implementations cover all these issues.
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Omni-Path

Omni-Path Architecture (OPA) is a high-performance communication architecture developed by Intel. It aims for low communication latency, low power consumption and a high throughput. It directly competes with InfiniBand. Intel planned to develop technology based on this architecture for exascale computing. The current owner of Omni-Path is Cornelis Networks. == History == Production of Omni-Path products started in 2015 and delivery of these products started in the first quarter of 2016. In November 2015, adapters based on the 2-port "Wolf River" ASIC were announced, using QSFP28 connectors with channel speeds up to 100 Gbit/s. Simultaneously, switches based on the 48-port "Prairie River" ASIC were announced. First models of that series were available starting in 2015. In April 2016, implementation of the InfiniBand "verbs" interface for the Omni-Path fabric was discussed. In October 2016, IBM, Hewlett Packard Enterprise, Dell, Lenovo, Samsung, Seagate Technology, Micron Technology, Western Digital and SK Hynix announced a joint consortium called Gen-Z to develop an open specification and architecture for non-volatile storage and memory products—including Intel's 3D Xpoint technology—which might in part compete against Omni-Path. Intel offered their Omni-Path products and components via other (hardware) vendors. For example, Dell EMC offered Intel Omni-Path as Dell Networking H-series, following the naming-standard of Dell Networking in 2017. In July 2019, Intel announced it would not continue development of Omni-Path networks and canceled OPA 200 series (200-Gbps variant of Omni-Path). In September 2020, Intel announced that the Omni-Path network products and technology would be spun out into a new venture with Cornelis Networks. Intel would continue to maintain support for legacy Omni-Path products, while Cornelis Networks continues the product line, leveraging existing Intel intellectual property related to Omni-Path architecture. In 2021, Cornelis announced Omni-Path Express, which replaces PSM2-based drivers and middleware, which trace back to PathScale's PSM created in 2003, for the existing Omni-Path hardware, with a native libfabric provider.
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Short Weather Cipher

The Short Weather Cipher (German: Wetterkurzschlüssel, abbreviated WKS), also known as the weather short signal book, was a cipher, presented as a codebook, that was used by the radio telegraphists aboard U-boats of the German Navy (Kriegsmarine) during World War II. It was used to condense weather reports into a short 7-letter message, which was enciphered by using the naval Enigma and transmitted by radiomen to intercept stations on shore, where it was deciphered by Enigma and the 7-letter weather report was reconstructed. == History == During World War II, during various times, different versions of the cipher were in operation. The first issue carried the codename Weimar. It was replaced by the edition Eisenach on 20 January 1942. On 10 March 1943, the third edition of the weather key, bearing the codename Naumburg, entered into force. On May 9, 1941, during Operation Primrose, the operation to occupy Åndalsnes and create a diversion south of Trondheim in Norway as part of the Norwegian Campaign, an intact Naval Enigma (M3) cipher machine, a copy of the "Weimar" version of the short weather cipher and a copy of the short signal book (German: Kurzsignalbuch or Kurzsignale for short) was recovered from the submarine U-110, that was captured in the North Atlantic east of Cape Farewell, Greenland. This enabled the cryptanalysts in Bletchley Park to break the encryption of the M3 and to decipher the German submarine radio messages. The Short Weather Cipher was critical in the cryptanalysis of the Naval Enigma M4 and yielded excellent cribs. On 30 October 1942, a copy of the Wetterkurzschlüssel, the short weather cipher, and of the short signal book, the Kurzsignale, were recovered as part of a daring raid on the U-boat U-559, when three Royal Navy sailors, Lieutenant Anthony Fasson, Able Seaman Colin Grazier and NAAFI canteen assistant Tommy Brown, then boarded the abandoned submarine, and recovered the documents after a 90-minute search. They reached the Government Code and Cypher at Bletchley Park after a three-week delay, on 24 November 1942. The documents which cost the lives of Fasson and Grazier proved to be particularly important in breaking the Naval Enigma M4. The version of the short weather cipher recovered was the Eisenach version. Unlike the first version Weimar, the Eisenach did not list the 26 rotor positions that were indicated by a letter, to be used in enciphering weather reports. Thus, Hut 8 cryptanalysts thought that all four rotors were used to encipher weather reports. Testing on the Bombes began to surface weather kisses (identical messages in two cryptosystems). On 13 December 1942, a crib obtained using the Short Weather Cipher gave a key with the Naval Enigma M4 rotatable Umkehrwalze (reversing roller or reflector) in the neutral position, making it equivalent to a standard Enigma and thus making B-Dienst messages potentially breakable on existing bombes. Hut 8 learned that the 4-letter indicators for regular U-boat messages were the same as 3-letter indicators for weather messages the same day, except for one extra letter. This meant that once the key was found for a weather message on any day, the fourth rotor had to be only tested in 26 positions to find the full 4-letter key. By the end of the day on Sunday 13 December, Rodger Winn of the Submarine Tracking Room at Bletchley Park knew that Shark Enigma Cipher was broken. When the third edition of the short signal book was introduced on 10 March 1943, Hut 8 was immediately deprived of cribs. However, by the 19 March, cribs were again being used by Hut 8 personnel, using the method of employing short signal sighting reports. These were reports made by U-boats when contact was made with Kurzsignalheft code book. Hut 8 managed to solve Shark for 90 out of 112 days before the end of June. Kurzsignalheft short sighting reports also used M4 in M3 mode. By the end of June, four-rotor bombes had entered service at Bletchley Park, and by August had been introduced by the US Navy. From September onwards, Shark was generally solved within 24 hours. == Operation == The U-boat encoded weather reports using the Short Weather Cipher, before being enciphered on the Naval Enigma. The shore patrol of the Kriegsmarine, deciphered the message and decoded it, then forwarding it to a central meteorological station, which rebroadcast the data as ship synoptics, after enciphering it with additive tables using a cipher, which was called Germet 3 by Hut 8 personnel. The short weather cipher coded weather reports using a polyphonic single-letter code with X missing. A = +28° ◦ B = +27° ◦ C = +26° ◦ D = +25° ◦ . . . ◦ W = +6° ◦ Y= +5° ◦ Z = +4° ◦ A = +3° ◦ B = +2° ◦ C = +1° ◦ D = 0° ◦ E =−1° ◦ F =−2° ◦ . . . ◦ Z = −21° ◦ In a similar way, water temperature, atmospheric pressure, humidity, wind direction, wind velocity, visibility, degree of cloudiness, geographic latitude, and geographic longitude had to be coded in a prescribed order with the weather report consisted of a single short word. Based on the approximate knowledge of the position of the submarine, the Kriegsmarine telegraphist who received the message could translate the letter "S", according to the above table, which could mean 10 °C or −15 °C, back to the correct temperature. Similarly, the direction and the type of swell was also coded with only a single letter: ----------------------------------------------------- Direction from which | Type of swell the swell comes | low | middle high | high | ----------------------------------------------------- N | a | i | q | NE | b | j | r | E | c | k | s | SE | d | l | t | S | e | m | u | SW | f | n | v | W | g | o | w | NW | h | p | x | No swelling | | | | y Intermittent | | | | z As an example of the cipher, a weather report for 68° North latitude, 20° West longitude (north of Iceland) with atmospheric pressure 972 millibars, temperature minus 5 °C, wind northwest Force 6 (on the Beaufort scale), 3/10 cirrus cloud cover, visibility 5 nautical miles, would be coded as MZNFPED. == Publications == Bauer, Arthur O. (1997), Funkpeilung als alliierte Waffe gegen deutsche U-Boote 1939–1945 [Direction finding as Allied weapon against German submarines from 1939 to 1945] (in German), Diemen, NL: Selbstverlag, ISBN 978-3-00-002142-8 Bauer, Friedrich L. (2007), Decrypted Secrets. Methods and Maxims of Cryptology (4., rev. and extended ed.), Berlin Heidelberg New York: Springer, ISBN 978-3-540-24502-5 Pfeiffer, Paul N. (October 1998), "Breaking the German Weather Ciphers in the Mediterranean Detachment, 849th Signal Intelligence Service", Cryptologia, 22 (4): 354–369, doi:10.1080/0161-119891886975, ISSN 0161-1194 Ulbricht, Heinz (2005), Die Chiffriermaschine Enigma – Trügerische Sicherheit. Ein Beitrag zur Geschichte der Nachrichtendienste [The Enigma cipher machine – Deceptive security. A contribution to the history of the intelligence services], Dissertation, Fachbereich Mathematik und Informatik, Technische Universität Braunschweig (in German)
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Structural synthesis of programs

Structural synthesis of programs (SSP) is a special form of (automatic) program synthesis that is based on propositional calculus. More precisely, it uses intuitionistic logic for describing the structure of a program in such a detail that the program can be automatically composed from pieces like subroutines or even computer commands. It is assumed that these pieces have been implemented correctly, hence no correctness verification of these pieces is needed. SSP is well suited for automatic composition of services for service-oriented architectures and for synthesis of large simulation programs. == History == Automatic program synthesis began in the artificial intelligence field, with software intended for automatic problem solving. The first program synthesizer was developed by Cordell Green in 1969. At about the same time, mathematicians including R. Constable, Z. Manna, and R. Waldinger explained the possible use of formal logic for automatic program synthesis. Practically applicable program synthesizers appeared considerably later. The idea of structural synthesis of programs was introduced at a conference on algorithms in modern mathematics and computer science organized by Andrey Ershov and Donald Knuth in 1979. The idea originated from G. Pólya’s well-known book on problem solving. The method for devising a plan for solving a problem in SSP was presented as a formal system. The inference rules of the system were restructured and justified in logic by G. Mints and E. Tyugu in 1982. A programming tool PRIZ that uses SSP was developed in the 1980s. A recent Integrated development environment that supports SSP is CoCoViLa — a model-based software development platform for implementing domain specific languages and developing large Java programs. == The logic of SSP == Structural synthesis of programs is a method for composing programs from already implemented components (e.g. from computer commands or software object methods) that can be considered as functions. A specification for synthesis is given in intuitionistic propositional logic by writing axioms about the applicability of functions. An axiom about the applicability of a function f is a logical implication X1 ∧ X2 ∧ ... ∧ Xm → Y1 ∧ Y2 ... Yn, where X1, X2, ... Xm are preconditions and Y1, Y2, ... Yn are postconditions of the application of the function f. In intuitionistic logic, the function f is called a realization of this formula. A precondition can be a proposition stating that input data exists, e.g. Xi may have the meaning “variable xi has received a value”, but it may denote also some other condition, e.g. that resources needed for using the function f are available, etc. A precondition may also be an implication of the same form as the axiom given above; then it is called a subtask. A subtask denotes a function that must be available as an input when the function f is applied. This function itself must be synthesized in the process of SSP. In this case, realization of the axiom is a higher order function, i.e., a function that uses another function as an input. For instance, the formula (state → nextState) ∧ initialState → result can specify a higher order function with two inputs and an output result. The first input is a function that has to be synthesized for computing nextState from state, and the second input is initialState. Higher order functions give generality to the SSP – any control structure needed in a synthesized program can be preprogrammed and used then automatically with a respective specification. In particular, the last axiom presented here is a specification of a complex program – a simulation engine for simulating dynamic systems on models where nextState can be computed from state of the system.
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Personal network

A personal network is a set of human contacts known to an individual, with whom that individual would expect to interact at intervals to support a given set of activities. In other words, a personal network is a group of caring, dedicated people who are committed to maintain a relationship with a person in order to support a given set of activities. Having a strong personal network requires being connected to a network of resources for mutual development and growth. Personal networks can be understood by: who knows you what you know about them what they know about you what are you learning together how you work at that Personal networks are intended to be mutually beneficial, extending the concept of teamwork beyond the immediate peer group. The term is usually encountered in the workplace, though it could apply equally to other pursuits outside work. Personal networking is the practice of developing and maintaining a personal network, which is usually undertaken over an extended period. The concept is related to business networking and is often encouraged by large organizations, in the hope of improving productivity, and so a number of tools exist to support the maintenance of networks. Many of these tools are IT-based, and use Web 2.0 technologies. == History of networking and business success == In the second half of the twentieth century, U.S. advocates for workplace equity popularized the term and concept of networking as part of a larger social capital lexicon—which also includes terms such as glass ceiling, role model, mentoring, and gatekeeper—serving to identify and address the problems barring non-dominant groups from professional success. Mainstream business literature subsequently adopted the terms and concepts, promoting them as pathways to success for all career climbers. In 1970 these terms were not in the general American vocabulary; by the mid-1990s they had become part of everyday speech. Before the mid-twentieth century, what we call networking today was framed in the language of family and friendship. These close personal relationships provided a range of opportunities to preferred subsets of people, such as access to job opportunities, information, credit, and partnerships. Family networks and nepotism have proven particularly strong throughout history. However, other common bonds—from ethnicity and religion to school ties and club memberships—can connect subsets of people as well. Of course people whom insiders consider undesirable have been barred from such networks, with important consequences. Those who tap into influential networks can be nurtured toward success. Those who are shut out from networks can lose hope of success. Numerous business heroes of the past—such as Benjamin Franklin, Andrew Carnegie, Henry Ford, and John D. Rockefeller—exploited networks to great effect. The business networks that seemed natural and transparent to these white men were a closed book to women and minorities for much of American history. Drawing on work from the social sciences, these outsider groups had to identify and then harness the mechanisms behind networking's power. A prominent early example of this process was the formation of corporate caucuses by black men at Xerox starting in 1969. Groups of black salesmen met regularly to share information about Xerox's culture and strategies for navigating it most effectively. Through confrontation and collaboration with a relatively accommodating upper management, the caucuses helped open opportunities for high-performing black employees. The popular and business press began using the terms "network" and "networking" in the mid-1970s in the context of businesswomen consciously pursuing this strategy. Authors encouraged female workers to recognize and exploit the informal workplace systems that provided advancement. They urged women to identify mentors, use social contacts, and build peer and authority networks. The push for networking drew on ideas and relationships from the era's feminist movement, and dictionaries of the time explicitly linked business networking to women's efforts to succeed in the workplace. Since the closing decades of the twentieth century, networking has become a pervasive term and concept in American society. People now invoke networking in relation to everything from business to child rearing to science. While ambitious careerists seek networks as an indispensable talisman, companies purposefully encourage networking among their employees to boost performance and gain competitive advantage. At the same time, Americans are forgetting the workplace activism that first illuminated the power of networking. Unfortunately, this loss of historical context can fuel a backlash against outsider groups who still seek to synthesize networks so they can access the same opportunities enjoyed by insiders. == Characteristics of networks == Broadly speaking, all networks have the following characteristics: Purpose – A network can be established for learning, mission, business, idea, and family or personal reasons. Structure – A network is a group of interlinked entities that form a cluster. Most social structures tend to be characterized by dense clusters of strong connections. Style – The place, space, pace and style of interaction of the networks give an understanding of the style of the networks. Namkee Park, Seungyoon Lee and Jang Hyun Kim examined the relations between personal network characteristics and Facebook use. According to their study, personal networks are investigated through several structural characteristics, which can be categorized into three major dimensions according to the level of analysis: Dyadic tie attributes which include the characteristics of ego-alter ties such as duration, multiplexity, and proximity. Ego-alter tie attributes represent various dimensions of relationships between the focal person and their close contacts. First, tie duration refers to the length of time since the tie was originally initiated, which indicates the duration of relationships. Second, multiplexity includes a focal individual's degree of involvement in various types of interactions with network members. The third dimension is the physical proximity between ego and alter. Theories of proximity suggest that physical proximity between people affects their interaction and subsequently, their formation of network ties. The characteristics of alter-alter ties including personal network density. When moving to ties at the alter-alter level, ego-network density, which refers to the extent to which one's alters are connected with each other, is an important dimension of personal networks. Dense personal network structure indicates close interpersonal contacts among alters, and consequently, is considered to promote the sharing of resources. On the other hand, loose connections, or structural holes in ego-networks, have been found to facilitate the flow of information and to provide advantages in searching and obtaining resources (e.g., getting a job). The composition of alter attributes centered on the heterogeneity of alters in one's personal network. The heterogeneity of alters in one's personal network is associated with access to diverse resources and information It is expected, thus, that the heterogeneity attributes may enhance the focal actor's social activities. Each of these characteristics represents unique aspects of individuals' network relationships. == Types of personal networks == Personal networks can be used for two main reasons: social and professional. In 2012, LinkedIn along with TNS conducted a survey of 6,000 social network users to understand the difference between personal social networks and personal professional networks. The "Mindset Divide" of users of these networks was compared as follows: Emotions: Personal social networks: Nostalgia, fun, distraction. Personal professional networks: Achievement, success, aspiration. Use: Personal social networks: Users are in a casual mindset often just passing time. They use social networks to socialize, stay in touch, be entertained and kill time. Personal professional networks: In this purposeful mindset, users invest time to improve themselves and their future. These networks are used to maintain professional identity, make useful contacts, search for opportunities and stay in touch. Content: Personal professional networks: These provide information about career, brand updates and current affairs. Professional development: Personal development networks: These provide access to those who can provide information, knowledge, advice, support, expertise, guidance, and concrete resources to learn and work effectively—thus those who support the continuing professional development. == Personal network management == Personal network management (PNM) is a crucial aspect of personal information management and can be understood as the practice of managing the links and connections for social and profession
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SocialIQ

Social IQ (formerly Soovox Inc.) was a San Diego-based influencer marketing platform that measured users' online social influence and connected them with brands for word-of-mouth marketing campaigns. The company was founded in 2009 by Akram Benmbarek and was headquartered in San Diego, California. == History == Akram Benmbarek, who had previously worked in technology finance at Advanced Equities Financial Corp and in wealth management at Morgan Stanley, Merrill Lynch, and UBS, founded the company in mid-2009 under the name Soovox. In October 2011, Benmbarek rebranded the company as SocialIQ. At that time, the company was seeking a Series A round of venture capital, having raised under $1 million in angel seed funding. == Similar metrics == Klout PeerIndex
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Transparent decryption

Transparent decryption is a method of decrypting data which unavoidably produces evidence that the decryption operation has taken place. The idea is to prevent the covert decryption of data. In particular, transparent decryption protocols allow a user Alice to share with Bob the right to access data, in such a way that Bob may decrypt at a time of his choosing, but only while simultaneously leaving evidence for Alice of the fact that decryption occurred. Transparent decryption supports privacy, because this evidence alerts data subjects to the fact that information about them has been decrypted and disincentivises data misuse. Recent work further formalizes transparent decryption and explores practical implementations based on cryptographic protocols and blockchain systems. == Applications == Transparent decryption has been proposed for several systems where there is a need to simultaneously achieve accountability and secrecy. For example: In lawful interception, law enforcement agencies can access private messages and emails. Transparent decryption can make such accesses accountable, giving citizens guarantees about how their private information is accessed. Data arising from vehicles and IoT devices may contain personal information about the vehicle or device owners and their activities. Nevertheless, the data is typically processed in order to provide user functionality and also to investigate and fight crime. Transparent decryption can be used to help users monitor when and how data about them is being accessed and used. == Implementation == In transparent decryption, the decryption key is distributed among a set of agents (called trustees); they use their key share only if the required transparency conditions have been satisfied. Typically, the transparency condition can be formulated as the presence of the decryption request in a distributed ledger. == Alternative solutions == Besides transparent decryption, some other techniques have been proposed for achieving law enforcement while preserving privacy. Solutions that allow competing parties to unify their data access policies. Attribute-based encryption with oblivious attribute translation (OTABE) is an extension of attribute-based encryption that allows translation between proprietary attributes belonging to different organisations, and it has been applied to the problem of law-enforcement access to phone call metadata. Solutions that rely on sophisticated cryptography, such as zero-knowledge proofs that the actions of law enforcement is consistent with judge rulings and the actions of companies, and multi-party computation to compute results.
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Actor-critic algorithm

The actor-critic algorithm (AC) is a family of reinforcement learning (RL) algorithms that combine policy-based RL algorithms such as policy gradient methods, and value-based RL algorithms such as value iteration, Q-learning, SARSA, and TD learning. An AC algorithm consists of two main components: an "actor" that determines which actions to take according to a policy function, and a "critic" that evaluates those actions according to a value function. Some AC algorithms are on-policy, some are off-policy. Some apply to either continuous or discrete action spaces. Some work in both cases. == Overview == The actor-critic methods can be understood as an improvement over pure policy gradient methods like REINFORCE via introducing a baseline. === Actor === The actor uses a policy function π ( a | s ) {\displaystyle \pi (a|s)} , while the critic estimates either the value function V ( s ) {\displaystyle V(s)} , the action-value Q-function Q ( s , a ) , {\displaystyle Q(s,a),} the advantage function A ( s , a ) {\displaystyle A(s,a)} , or any combination thereof. The actor is a parameterized function π θ {\displaystyle \pi _{\theta }} , where θ {\displaystyle \theta } are the parameters of the actor. The actor takes as argument the state of the environment s {\displaystyle s} and produces a probability distribution π θ ( ⋅ | s ) {\displaystyle \pi _{\theta }(\cdot |s)} . If the action space is discrete, then ∑ a π θ ( a | s ) = 1 {\displaystyle \sum _{a}\pi _{\theta }(a|s)=1} . If the action space is continuous, then ∫ a π θ ( a | s ) d a = 1 {\displaystyle \int _{a}\pi _{\theta }(a|s)da=1} . The goal of policy optimization is to improve the actor. That is, to find some θ {\displaystyle \theta } that maximizes the expected episodic reward J ( θ ) {\displaystyle J(\theta )} : J ( θ ) = E π θ [ ∑ t = 0 T γ t r t ] {\displaystyle J(\theta )=\mathbb {E} _{\pi _{\theta }}\left[\sum _{t=0}^{T}\gamma ^{t}r_{t}\right]} where γ {\displaystyle \gamma } is the discount factor, r t {\displaystyle r_{t}} is the reward at step t {\displaystyle t} , and T {\displaystyle T} is the time-horizon (which can be infinite). The goal of policy gradient method is to optimize J ( θ ) {\displaystyle J(\theta )} by gradient ascent on the policy gradient ∇ J ( θ ) {\displaystyle \nabla J(\theta )} . As detailed on the policy gradient method page, there are many unbiased estimators of the policy gradient: ∇ θ J ( θ ) = E π θ [ ∑ 0 ≤ j ≤ T ∇ θ ln ⁡ π θ ( A j | S j ) ⋅ Ψ j | S 0 = s 0 ] {\displaystyle \nabla _{\theta }J(\theta )=\mathbb {E} _{\pi _{\theta }}\left[\sum _{0\leq j\leq T}\nabla _{\theta }\ln \pi _{\theta }(A_{j}|S_{j})\cdot \Psi _{j}{\Big |}S_{0}=s_{0}\right]} where Ψ j {\textstyle \Psi _{j}} is a linear sum of the following: ∑ 0 ≤ i ≤ T ( γ i R i ) {\textstyle \sum _{0\leq i\leq T}(\gamma ^{i}R_{i})} . γ j ∑ j ≤ i ≤ T ( γ i − j R i ) {\textstyle \gamma ^{j}\sum _{j\leq i\leq T}(\gamma ^{i-j}R_{i})} : the REINFORCE algorithm. γ j ∑ j ≤ i ≤ T ( γ i − j R i ) − b ( S j ) {\textstyle \gamma ^{j}\sum _{j\leq i\leq T}(\gamma ^{i-j}R_{i})-b(S_{j})} : the REINFORCE with baseline algorithm. Here b {\displaystyle b} is an arbitrary function. γ j ( R j + γ V π θ ( S j + 1 ) − V π θ ( S j ) ) {\textstyle \gamma ^{j}\left(R_{j}+\gamma V^{\pi _{\theta }}(S_{j+1})-V^{\pi _{\theta }}(S_{j})\right)} : TD(1) learning. γ j Q π θ ( S j , A j ) {\textstyle \gamma ^{j}Q^{\pi _{\theta }}(S_{j},A_{j})} . γ j A π θ ( S j , A j ) {\textstyle \gamma ^{j}A^{\pi _{\theta }}(S_{j},A_{j})} : Advantage Actor-Critic (A2C). γ j ( R j + γ R j + 1 + γ 2 V π θ ( S j + 2 ) − V π θ ( S j ) ) {\textstyle \gamma ^{j}\left(R_{j}+\gamma R_{j+1}+\gamma ^{2}V^{\pi _{\theta }}(S_{j+2})-V^{\pi _{\theta }}(S_{j})\right)} : TD(2) learning. γ j ( ∑ k = 0 n − 1 γ k R j + k + γ n V π θ ( S j + n ) − V π θ ( S j ) ) {\textstyle \gamma ^{j}\left(\sum _{k=0}^{n-1}\gamma ^{k}R_{j+k}+\gamma ^{n}V^{\pi _{\theta }}(S_{j+n})-V^{\pi _{\theta }}(S_{j})\right)} : TD(n) learning. γ j ∑ n = 1 ∞ λ n − 1 1 − λ ⋅ ( ∑ k = 0 n − 1 γ k R j + k + γ n V π θ ( S j + n ) − V π θ ( S j ) ) {\textstyle \gamma ^{j}\sum _{n=1}^{\infty }{\frac {\lambda ^{n-1}}{1-\lambda }}\cdot \left(\sum _{k=0}^{n-1}\gamma ^{k}R_{j+k}+\gamma ^{n}V^{\pi _{\theta }}(S_{j+n})-V^{\pi _{\theta }}(S_{j})\right)} : TD(λ) learning, also known as GAE (generalized advantage estimate). This is obtained by an exponentially decaying sum of the TD(n) learning terms. === Critic === In the unbiased estimators given above, certain functions such as V π θ , Q π θ , A π θ {\displaystyle V^{\pi _{\theta }},Q^{\pi _{\theta }},A^{\pi _{\theta }}} appear. These are approximated by the critic. Since these functions all depend on the actor, the critic must learn alongside the actor. The critic is learned by value-based RL algorithms. For example, if the critic is estimating the state-value function V π θ ( s ) {\displaystyle V^{\pi _{\theta }}(s)} , then it can be learned by any value function approximation method. Let the critic be a function approximator V ϕ ( s ) {\displaystyle V_{\phi }(s)} with parameters ϕ {\displaystyle \phi } . The simplest example is TD(1) learning, which trains the critic to minimize the TD(1) error: δ i = R i + γ V ϕ ( S i + 1 ) − V ϕ ( S i ) {\displaystyle \delta _{i}=R_{i}+\gamma V_{\phi }(S_{i+1})-V_{\phi }(S_{i})} The critic parameters are updated by gradient descent on the squared TD error: ϕ ← ϕ − α ∇ ϕ ( δ i ) 2 = ϕ + α δ i ∇ ϕ V ϕ ( S i ) {\displaystyle \phi \leftarrow \phi -\alpha \nabla _{\phi }(\delta _{i})^{2}=\phi +\alpha \delta _{i}\nabla _{\phi }V_{\phi }(S_{i})} where α {\displaystyle \alpha } is the learning rate. Note that the gradient is taken with respect to the ϕ {\displaystyle \phi } in V ϕ ( S i ) {\displaystyle V_{\phi }(S_{i})} only, since the ϕ {\displaystyle \phi } in γ V ϕ ( S i + 1 ) {\displaystyle \gamma V_{\phi }(S_{i+1})} constitutes a moving target, and the gradient is not taken with respect to that. This is a common source of error in implementations that use automatic differentiation, and requires "stopping the gradient" at that point. Similarly, if the critic is estimating the action-value function Q π θ {\displaystyle Q^{\pi _{\theta }}} , then it can be learned by Q-learning or SARSA. In SARSA, the critic maintains an estimate of the Q-function, parameterized by ϕ {\displaystyle \phi } , denoted as Q ϕ ( s , a ) {\displaystyle Q_{\phi }(s,a)} . The temporal difference error is then calculated as δ i = R i + γ Q θ ( S i + 1 , A i + 1 ) − Q θ ( S i , A i ) {\displaystyle \delta _{i}=R_{i}+\gamma Q_{\theta }(S_{i+1},A_{i+1})-Q_{\theta }(S_{i},A_{i})} . The critic is then updated by θ ← θ + α δ i ∇ θ Q θ ( S i , A i ) {\displaystyle \theta \leftarrow \theta +\alpha \delta _{i}\nabla _{\theta }Q_{\theta }(S_{i},A_{i})} The advantage critic can be trained by training both a Q-function Q ϕ ( s , a ) {\displaystyle Q_{\phi }(s,a)} and a state-value function V ϕ ( s ) {\displaystyle V_{\phi }(s)} , then let A ϕ ( s , a ) = Q ϕ ( s , a ) − V ϕ ( s ) {\displaystyle A_{\phi }(s,a)=Q_{\phi }(s,a)-V_{\phi }(s)} . Although, it is more common to train just a state-value function V ϕ ( s ) {\displaystyle V_{\phi }(s)} , then estimate the advantage by A ϕ ( S i , A i ) ≈ ∑ j ∈ 0 : n − 1 γ j R i + j + γ n V ϕ ( S i + n ) − V ϕ ( S i ) {\displaystyle A_{\phi }(S_{i},A_{i})\approx \sum _{j\in 0:n-1}\gamma ^{j}R_{i+j}+\gamma ^{n}V_{\phi }(S_{i+n})-V_{\phi }(S_{i})} Here, n {\displaystyle n} is a positive integer. The higher n {\displaystyle n} is, the more lower is the bias in the advantage estimation, but at the price of higher variance. The Generalized Advantage Estimation (GAE) introduces a hyperparameter λ {\displaystyle \lambda } that smoothly interpolates between Monte Carlo returns ( λ = 1 {\displaystyle \lambda =1} , high variance, no bias) and 1-step TD learning ( λ = 0 {\displaystyle \lambda =0} , low variance, high bias). This hyperparameter can be adjusted to pick the optimal bias-variance trade-off in advantage estimation. It uses an exponentially decaying average of n-step returns with λ {\displaystyle \lambda } being the decay strength. == Variants == Asynchronous Advantage Actor-Critic (A3C): Parallel and asynchronous version of A2C. Soft Actor-Critic (SAC): Incorporates entropy maximization for improved exploration. Deep Deterministic Policy Gradient (DDPG): Specialized for continuous action spaces.
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Messaging Layer Security

Messaging Layer Security (MLS) is a security layer for end-to-end encrypted messages. It is maintained by the MLS working group of the Internet Engineering Task Force (IETF), and is designed to provide an efficient and practical security mechanism for groups as large as 50,000 and for those who access chat systems from multiple devices. == Security properties == Security properties of MLS include message confidentiality, message integrity and authentication, membership authentication, asynchronicity, forward secrecy, post-compromise security, and scalability. == History == The idea was born in 2016 and first discussed in an unofficial meeting during IETF 96 in Berlin with attendees from Wire, Mozilla and Cisco. Initial ideas were based on pairwise encryption for secure 1:1 and group communication. In 2017, an academic paper introducing Asynchronous Ratcheting Trees was published by the University of Oxford and Facebook setting the focus on more efficient encryption schemes. The first BoF took place in February 2018 at IETF 101 in London. The founding members are Mozilla, Facebook, Wire, Google, Twitter, University of Oxford, and INRIA. On March 29, 2023, the IETF approved publication of Messaging Layer Security (MLS) as a new standard. It was officially published on July 19, 2023. At that time, Google announced it intended to add MLS to the end to end encryption used by Google Messages over Rich Communication Services (RCS). In March 2025, the GSMA announced the Universal Profile 3.0 standard of RCS would support MLS and Apple announced it would support this RCS standard on Apple Messages. Both Google Messages and Apple Messages began the rollout of MLS E2EE over RCS in May 2026. Matrix is one of the protocols declaring migration to MLS. In 2026, Discord rolled out end-to-end encryption on voice and video calls, using MLS for scalable group key exchanges. Research on adding post-quantum cryptography (PQC) to MLS is ongoing. The IETF has prepared an Internet-Draft using PQC algorithms in MLS. == Implementations ==
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Social media use in African politics

Since the Egyptian Revolution in 2011 and the Tunisian Revolution, social media, especially Facebook, Twitter, and YouTube, began to gain traction as a political tool in Africa. Various political actors have used social media to pursue a wide range of political objectives. State actors can use social media to encourage political discourse, campaign, or implement censorship and surveillance. Non-state actors, such as civil society organizations and opposition movements, can use social media to address political concerns and to organize widespread uprisings, such as the 2014 Burkinabé uprising. Meanwhile, extremist organizations can use social media to further their propaganda and recruitment. However, social media has been criticized for its limited accessibility and for facilitating the spread of misinformation, causing some skepticism about its effectiveness. Due to low entry barriers and user-generated content, social media provides a platform where people from different social classes can engage and interact with one another. Under traditional media, the public had limited opportunities to voice their political opinions. Social media enables people to both create and consume content. The public has become increasingly comfortable and confident in expressing political opinions online, often away from government scrutiny. Scholars argue that social media use has democratizing effects in African countries. == State actors == === Promoting political discourse === Through social media, the government and its citizens can discuss policy ideas, policy implementation, and political actions. Regardless of geographical location and distance, people are able to voice their opinions to the government. Social media includes citizens who were previously not able to express their discontent or share their ideas to the government. As state actors keep the public informed, social media can increase civic engagement. With more civic engagement, policies can be discussed without politicization. Before the commonplace use of social media, African countries faced weak feedback mechanisms that effectively excluded the average African citizen from policy discourse. In South Africa, the government uses social media to connect with constituencies. The South African president runs an official Twitter, Facebook, YouTube, and Flickr accounts to engage with the public. === Campaigning === Political parties also use social media for political campaigns during election periods. In South Africa, the ANC (African National Congress) and DA (Democratic Alliance) use social media for political purposes. These parties specifically use Facebook as a tool for campaigning and engaging with the public to improve their relationship with citizens. Nigerian President Goodluck Jonathan employed social media to campaign for the presidential election in 2011, which he won. When President Goodluck Jonathan announced his bid for the presidency on social media in 2010, it reached about 217,000 people. As his campaign progressed, President Goodluck Jonathan was able to increase his followers to half a million by early 2011. === Censorship & Surveillance === While state actors can use social media to encourage their party or discourse, social media can be used to censor and surveil citizens. For example, the ANC and DA use Facebook to monitor South Africans. The government is able to track down people who have spoken against the government and translate this information into physical action to stop any possibility of a revolution. Social media platforms can be shut down to manipulate the flow of information. In Chad, citizens cannot access information through online platforms. This censorship blocked "Facebook, Twitter, WhatsApp and Viber". In the Democratic Republic of Congo, the government shut down the internet before contested elections. In Zimbabwe, the government shut down the internet to hide civilian protests against fuel price increases. == Non-state actors == === Civil society organizations (CSOs) === Civil society organizations have also used social media networks in an effort to recruit supporters and communicate with the public. CSOs can use social media to mobilize people to support their cause, such as the Ghanaian Committee for Joint Action (CJA). In 2005 and 2006, the CJA gathered support to protest against the 50% fuel price increase. CSOs can play the role of a counterforce against state actors and state propaganda during times of crises, such as protests and military clashes. In some cases, CSOs release their own videos and photos on social media which challenges traditional forms of media. CSOs have also served to monitor elections to reduce corruption and violence during election day. For instance, the Zambian Bantu Watch started the #bantuwatch social media campaign to monitor the 2011 presidential election. Zambians used Facebook and Twitter to report polling station results to mitigate election fraud and election violence. In South Africa, CSOs created 'amandla.mobi' to campaign for public policies by creating petitions. Through 'amandla.mobi', CSOs are able to circulate petitions on social media to collect signatures. South African CSOs reported how social media helped their organizations to gain support and share ideas. However, CSOs struggle to attract media attention and often have to pay for media coverage. === Opposition forces against the government === Social media is also used by the public or opposition forces against the government. Through horizontal social media, organizing can lead to street protests and revolutions, some of which are successful. For instance, during the Egyptian revolution of 2011, "The Day of the Revolution Against Torture, Poverty, Corruption, and Unemployment" and "We Are All Khaled Said" gathered support against President Hosni Mubarak. In particular, "We Are All Khaled Said" had Egyptian citizens gather around the death of Khaled Said who was brutally tortured and killed by the Egyptian government because Said wanted to uncover government corruption. As unrest erupted into public demonstrations, President Hosni Mubarak was forced to resign. Witnessing the success of social media during the Egyptian revolution, the Tunisian Revolution, or the Jasmine Revolution, mobilized through Facebook and Twitter. Likewise, in South Africa, Malawi, and Mozambique, these countries have used social media as "new protest drums." Due to social media's low entry barrier, opposition forces against the government can facilitate political discourse that can lead to accountability. Whistleblowers and opposition forces are able to expose corruption through social media, where they face less repression while reaching a larger audience. For example, the youth of Zimbabwe and South Africa use Facebook to discuss politics without judgment. Specifically, in Zimbabwe, political youth used Facebook to avoid state surveillance. Social media is used as a supplemental tool for activism. In 2015, South African student activists started the hashtag #RhodesMustFall to push the issue of colonialism and racism at the forefront of the public. === Extremist organizations === Social media is easily accessible and created by user-based content. Therefore, marginalized groups are able to use social media to spread extremist ideas. For instance, Boko Haram created the Media Office of West Africa Province and perpetuated propaganda through Twitter and YouTube. Boko Haram's online propaganda campaign targets and persuades young dissuaded Nigerians to join their cause. It is important to note that social media has also been used against Boko Haram. In April 2014, Boko Haram kidnapped 276 schoolgirls and an international campaign fought for their return through #BringBackOurGirls. Another extremist group, Al-Shabaab, has created an online presence through Twitter and YouTube. Through these social media networks, Al-Shabaab recruits new members to their extremist group through their propaganda which emphasizes the group's successes. Albeit their efforts, Al-Shabaab has not been very successful in coordinating their members but they are successful in financing their group. Furthermore, the Islamic State of Iraq and the Levant (ISIL) use social media to target and recruit individuals to their cause. ISIL's social media usage is more diverse compared to Boko Haram and Al-Shabaab; ISIL uses "Facebook, Twitter, YouTube, WhatsApp, Telegram, JustPaste.it, Kik and Ask.fm." Since ISIL's Twitter accounts kept getting shut down, ISIL uses Telegram and WhatsApp chat rooms to privately conduct meetings. Due to the spread of extremist ideology, Zhuravskaya et al. acknowledge social media's potential to be misused. == Challenges == Although social media can be used as a political tool, it faces challenges in Africa. Due to low literacy rates in Africa, social media networks exclude many of the population members. In addition, lack of access to electricity and the internet can fur
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Instapoetry

Instapoetry is a style of poetry that emerged after the advent of social media, especially on Instagram. The term has been used to describe poems written specifically for being shared online, most commonly on Instagram, but also other platforms including Twitter, Tumblr, and TikTok. The style usually consists of short, direct lines in aesthetically pleasing fonts that are sometimes accompanied by an image or drawing, often without rhyme schemes or meter, and dealing with commonplace themes. Literary critics, poets, and writers have contended with Instapoetry's focus on brevity and plainness compared to traditional poetry, criticizing it for reproducing rather than subverting normative ideas on social media platforms that favor popularity and accessibility over craft and depth. == History == Instapoetry developed as a result of young, predominantly women, amateur poets sharing their output to expand their readership, who began using social media as their preferred method of distribution rather than traditional publishing methods. The term "Instapoetry" is a portmanteau of the words "Instagram" and "poetry," and was created by other writers trying to define and understand the new extension of "instant poetry" shared via social media, most prominently Instagram. In its most basic form, Instapoetry usually consists of bite-sized verses that consider political and social subjects such as immigration, domestic violence, sexual assault, love, culture, feminism, gun violence, war, racism, LGBTQ rights, and other social justice topics. All of these elements are usually made to fit social media feeds that are easily accessible through applications on smartphones. == Scholarship == Despite the diversity of poetry on Instagram, the Brazilian linguist Bruna Osaki Fazano found that shared "aspects of the compositional form, theme and style" mean that it can be understood as a specific genre. Camilla Holm Soelseth argues that taking on the platform-specific tasks of a social media creator is a prerequisite for being an Instapoet. Writing in Poetics Today, JuEunhae Knox combined quantitative and qualitative analysis to show that Instapoetry is a cohesive genre, in part because "the sheer volume and rapidity of content production in turn encourages posts that are not only visually appealing but also immediately recognizable as Instapoems". Instapoetry has been seen as a practice that serves as a form of self-staging for poets and "[crafts] authenticity". Eirik Vassenden describes the work of Norwegian poet Trygve Skaug as appearing to offer a "simple, almost direct access to the inner self". Vassenden writes that poems such as Rupi Kaur's "if you are not enough for yourself / you will never be enough / for someone else" are "authentic" to such an extent that they are not literary. Kiera Obbard describes how Rupi Kaur uses humour as a rhetorical device in her poetry performances to tell personal stories of trauma and challenge social inequalities. Scholars have also studied the work of specific Instapoets, such as Rupi Kaur, R.M. Drake, Aja Monet, Yrsa Daley-Ward, Nayyirah Waheed, Atticus, Nikita Gill and Trygve Skaug. == Overview == Academics have shown appreciation for the way in which Instapoetry has stimulated interest in poetry in general. Meanwhile, it has been argued that since Instapoets avoid critical evaluations, academics, and the publishing industry, Instapoets qualify more as online celebrities than literary figures. Additionally, although Instapoetry has been characterized as anti-establishment, Alyson Miller noted traditional or even conservative views in the online posts of Instapoets in contrast with the activist views the style is associated with, and that there is a contradiction between "the extra-textual commentary surrounding Instapoetry, particularly by way of interviews and artistic statements, and the content of works which repeatedly reinscribe conservative, patriarchal, and heteronormative worldviews". Thom Young, a poet and high school English teacher, created a parody Instagram page as a way to mock Instapoets and their work, describing it as "fidget-spinner poetry. Like they're just scrolling on their devices, to read something instantly, while the libraries are empty. I think people today don't want to read anything that causes a whole lot of critical thinking." According to Johnathan Ford's piece in the Financial Times, as Instagram's algorithms have limited prospective Instapoets' reach-per-post, it has pushed them to pay to promote their material. Popular Instagram accounts will be promoted to the front of users' feeds, with the app's algorithm, in the view of critics, favoring the spread of bland, inauthentic, or clichéd content while preventing disciplined poetry from reaching new audiences. == Writers described as Instapoets == Rupi Kaur Atticus Amanda Lovelace Tyler Knott Gregson Najwa Zebian Lang Leav Nikita Gill Upile Chisala Tendai M. Shaba Donna Ashworth Trista Mateer
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Content as a service

Content as a service (CaaS) or managed content as a service (MCaaS) is a service-oriented model, where the service provider delivers the content on demand to the service consumer via web services that are licensed under subscription. The content is hosted by the service provider centrally in the cloud and offered to a number of consumers that need the content delivered into any applications or system, hence content can be demanded by the consumers as and when required. Content as a Service is a way to provide raw content (in other words, without the need for a specific human compatible representation, such as HTML) in a way that other systems can make use of it. Content as a Service is not meant for direct human consumption, but rather for other platforms to consume and make use of the content according to their particular needs. This happens usually on the cloud, with a centralized platform which can be globally accessible and provides a standard format for your content. With Content as a Service, you centralize your content into a single repository, where you can manage it, categorize it, make it available to others, search for it, or do whatever you wish with it. == Overview == The content delivered typically could be one or more of the following The technical terminology related to equipment or spares that is required to procure or design the materials The industrial terminology of the equipment or spares Technical values pertaining to various types, specifications, applications, characteristics of equipment or spares Sourcing information which will help in procurement or supply-chain management of equipment or spares Descriptive specifications of equipment or spares based on the product reference number or identifier UNSPSC codes or industry practiced classifications ISO, IEC compliant terminology Ontology or Technical Dictionary of products & services Predefined content for specific business needs The term "Content as a service" (CaaS) is considered to be part of the nomenclature of cloud computing service models & Service-oriented architecture along with Software as a service (SaaS), Infrastructure as a service (IaaS), and Platform as a service (PaaS).
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G.9963

Recommendation G.9963 is a home networking standard under development at the International Telecommunication Union standards sector, the ITU-T. It was begun in 2010 by ITU-T to add multiple-input and multiple-output (known as MIMO) capabilities to the G.hn standard originally defined in Recommendation G.9960. The standard is also known as "G.hn-mimo". As part of the family of G.hn standards, G.9963 was endorsed by the HomeGrid Forum.
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Majal (organization)

Majal is a regional not-for-profit organization focused on "amplifying voices of dissent" throughout the Middle East and North Africa via digital media. Founded in Bahrain, the organization "creates platforms and web applications that promote freedom of expression and social justice." Majal, which relies on open source platforms, like WordPress and Ruby on Rails, was launched in 2006 by Esra'a Al Shafei as a simple group-blogging idea. However, it has changed course to focus on the development of unique applications and tools. == Objectives and means == Majal's content, in addition to its projects and applications, is free open source content to ensure right to access information for everyone. The organization uses a broad spectrum of social media tools, ranging from written blogs, podcasts, vlogs, comics, video animation and pictures to live broadcasting through radio. == Projects and applications == Majal runs various active projects that include Alliance for Kurdish Rights, The Muslim Network for Baháʼí Rights, a discussion tool for Arab LGBT youth and various Mobile apps. == Funding == Majal is funded through private donations and grants from non-governmental organizations, as well as any potential revenues earned through freelance development. Its primary funders are the Shuttleworth Foundation and the Omidyar Network. In 2008, Majal won the Berkman Award from the Berkman Klein Center for Internet & Society at Harvard University in the Human Rights/Global Advocacy category. This $10,000 award was Majal’s first source of funding. This award is presented to “people or institutions that have made a significant contribution to the Internet and its impact on society over the past decade.” In 2009, the March 18 Movement, a project of Majal, received the Think Social Award, which demonstrates how social media can be used to solve the world’s problems. Esra'a Al-Shafei was named a 2009 Echoing Green Fellow for Civil and Human Rights, a seed funding award for young entrepreneurs engaged in social change. Financially, the fellowship consists of a $60,000 stipend paid over two years. Most recently, MEY has received a grant from the Arab Fund for Arts and Culture for its Mideast Tunes website. == Awards == Winner of Human Rights Tulip 2014 Human Rights Tulip - Human rights - Government.nl Ashoka Changemakers Citizen Media competition in 2011 for their CrowdVoice project. Monaco Media Prize 2011 for Majal founder and director Esra'a Al Shafei in 2011. The BOBS Special Topic Human Rights award in 2011 for the Majal website Migrant Rights. ThinkSocial Award in 2009, as powerful model for how social media can be used to address global problems. Echoing Green, 2009 Fellowship. TEDGlobal 2009 Fellowship. Berkman Award for Internet Innovation from Berkman Klein Center for Internet & Society at Harvard Law School in 2008 for the outstanding contributions to the internet and its impact on society. The Global Journal selected Majal as one of the Top 100 NGOs in 2013. 2013-2014 Shuttleworth Foundation Fellowship. == Leadership == Majal team is led primarily by women. The organization was founded by Esra'a Al Shafei, a blogger from Bahrain in 2006. Ahmed Zidan of Egypt has served for over three years as the Editor-in-Chief of Majal Arabic, and is the co-founder of Ahwaa, and is also a podcaster. Other team members include Mona Kareem, Rima Kalush, Abir Ghattas, Namita Malhotra, and Vani Saraswathi. == 2011 Middle East and North Africa protests == Blogs and video played a role in the documentation of protests throughout the Middle East and North Africa during 2010-2011, also known as the Arab Spring. During this period, MEY's project, CrowdVoice (launched in 2010) helped curate and archive the large amounts of videos, images, and eye-witness reports being aggregated and crowdsourced from across the region. As a result, it had been censored temporarily in Yemen and is still censored in Bahrain. == Media coverage == Majal claims to have received various coverage from news agencies, TV satellite channels, radio stations, newspapers, magazines. For instance, Sky News, CNN, New York Times, BBC, The Guardian, NPR, Time, MTV political blog "Act", VH1, Daily Telegraph, Die Zeit, Frankfurter Rundschau FR-online, Toronto Star, TechCrunch, Rolling Stone Middle East, Abu Dhabi TV, Gulf News, Al-Hasnaa' magazine, ReadWriteWeb, Mashable, The Next Web, Radio Sawt Beirut International, Radio Farda among many others.
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