AI Email Letter Generator

AI Email Letter Generator — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

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  Ave!Comics

  Ave!Comics Production is a privately owned French company editing comics on smartphones, tablets and computers. It was founded in 2008 and it is a subsidiary of Aquafadas, a software development company in digital publishing owned by Kobo Inc. AveComics is a comic book store for digital comic books that can be used on computers, tablets, and smartphones.(iOS, Android) Readers can buy and read comic books, manga and graphic novels in French, English and Spanish. AveComics uses a technology created by Aquafadas for comics transformation, distribution and reading, based around its AVE format. The AveComics application was also a finalist in the BlackBerry Innovation Awards 2009, in the "Entertainment" category. == Company history == Aquafadas, a company working on creative software for Flash, HTML5, photo, and video editing, created the application MyComics to allow the reading of comics on mobile in 2006. This application was made available in 2008, to enable the reading and storing of comics on iPhone and iPod Touch. A reading system adapted to low resolution screens was also available. In October of the same year, the company launched a comics library on both devices, in partnership with the Angoulême International Comics Festival, Fnac and SNCF. This library included the official selection of the festival, and was downloaded over 150 000 times. In December 2008 "The Adventures of Lucky Luke n°3", at Lucky Comics was published on both devices. The comic made a 50 000 € turnover. In April 2009, "Les Blondes" 10th volume was the top-selling comic for 10 months on the AppStore. After, in August 2009, the AveComics application was launched on iPhone, iPod Touch and BlackBerry. The company's website was launched in September when more than 100 titles were available on smartphones and computers. == Catalogue == AveComics works with over 80 international publishers including Glénat, Marsu Productions, Delcourt, Casterman, Soleil, Ubisoft, Les Humanoïdes Associés and Mad Fabrik. Comics such as "Assassin's Creed", "Talisman", "Titeuf", and "Seoul District" are sold by the company. == Award == Grand Prix Software Venture Capital - Senate 2008.

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  Forking lemma

  The forking lemma is any of a number of related lemmas in cryptography research. The lemma states that if an adversary (typically a probabilistic Turing machine), on inputs drawn from some distribution, produces an output that has some property with non-negligible probability, then with non-negligible probability, if the adversary is re-run on new inputs but with the same random tape, its second output will also have the property. This concept was first used by David Pointcheval and Jacques Stern in "Security proofs for signature schemes," published in the proceedings of Eurocrypt 1996. In their paper, the forking lemma is specified in terms of an adversary that attacks a digital signature scheme instantiated in the random oracle model. They show that if an adversary can forge a signature with non-negligible probability, then there is a non-negligible probability that the same adversary with the same random tape can create a second forgery in an attack with a different random oracle. The forking lemma was later generalized by Mihir Bellare and Gregory Neven. The forking lemma has been used and further generalized to prove the security of a variety of digital signature schemes and other random-oracle based cryptographic constructions. == Statement of the lemma == The generalized version of the lemma is stated as follows. Let A be a probabilistic algorithm, with inputs (x, h1, ..., hq; r) that outputs a pair (J, y), where r refers to the random tape of A (that is, the random choices A will make). Suppose further that IG is a probability distribution from which x is drawn, and that H is a set of size h from which each of the hi values are drawn according to the uniform distribution. Let acc be the probability that on inputs distributed as described, the J output by A is greater than or equal to 1. We can then define a "forking algorithm" FA that proceeds as follows, on input x: Pick a random tape r for A. Pick h1, ..., hq uniformly from H. Run A on input (x, h1, ..., hq; r) to produce (J, y). If J = 0, then return (0, 0, 0). Pick h'J, ..., h'q uniformly from H. Run A on input (x, h1, ..., hJ−1, h'J, ..., h'q; r) to produce (J', y'). If J' = J and hJ ≠ h'J then return (1, y, y'), otherwise, return (0, 0, 0). Let frk be the probability that FA outputs a triple starting with 1, given an input x chosen randomly from IG. Then frk ≥ acc ⋅ ( acc q − 1 h ) . {\displaystyle {\text{frk}}\geq {\text{acc}}\cdot \left({\frac {\text{acc}}{q}}-{\frac {1}{h}}\right).} === Intuition === The idea here is to think of A as running two times in related executions, where the process "forks" at a certain point, when some but not all of the input has been examined. In the alternate version, the remaining inputs are re-generated but are generated in the normal way. The point at which the process forks may be something we only want to decide later, possibly based on the behavior of A the first time around: this is why the lemma statement chooses the branching point (J) based on the output of A. The requirement that hJ ≠ h'J is a technical one required by many uses of the lemma. (Note that since both hJ and h'J are chosen randomly from H, then if h is large, as is usually the case, the probability of the two values not being distinct is extremely small.) === Example === For example, let A be an algorithm for breaking a digital signature scheme in the random oracle model. Then x would be the public parameters (including the public key) A is attacking, and hi would be the output of the random oracle on its ith distinct input. The forking lemma is of use when it would be possible, given two different random signatures of the same message, to solve some underlying hard problem. An adversary that forges once, however, gives rise to one that forges twice on the same message with non-negligible probability through the forking lemma. When A attempts to forge on a message m, we consider the output of A to be (J, y) where y is the forgery, and J is such that m was the Jth unique query to the random oracle (it may be assumed that A will query m at some point, if A is to be successful with non-negligible probability). (If A outputs an incorrect forgery, we consider the output to be (0, y).) By the forking lemma, the probability (frk) of obtaining two good forgeries y and y' on the same message but with different random oracle outputs (that is, with hJ ≠ h'J) is non-negligible when acc is also non-negligible. This allows us to prove that if the underlying hard problem is indeed hard, then no adversary can forge signatures. This is the essence of the proof given by Pointcheval and Stern for a modified ElGamal signature scheme against an adaptive adversary. == Known issues with application of forking lemma == The reduction provided by the forking lemma is not tight. Pointcheval and Stern proposed security arguments for Digital Signatures and Blind Signature using Forking Lemma. Claus P. Schnorr provided an attack on blind Schnorr signatures schemes, with more than p o l y l o g ( n ) {\displaystyle polylog(n)} concurrent executions (the case studied and proven secure by Pointcheval and Stern). A polynomial-time attack, for Ω ( n ) {\displaystyle \Omega (n)} concurrent executions, was shown in 2020 by Benhamouda, Lepoint, Raykova, and Orrù. Schnorr also suggested enhancements for securing blind signatures schemes based on discrete logarithm problem.

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  Airborne Networking

  An Airborne Network (AN) is the infrastructure owned by the United States Air Force that provides communication transport services through at least one node that is on a platform capable of flight. == Background == === Definition === The intent of the US Air Force's Airborne Network is to expand the Global Information Grid (GIG) to connect the three major domains of warfare: Air, Space, and Terrestrial. The Transformational Satellite Communications System network currently provides connectivity for all communication through space assets. The Combat Information Transport System and Theater Deployable Communications provide terrestrial connectivity for theatre based operations. The Airborne Network is engineered to utilize all airborne assets to connect with space and surface networks building a seamless communications platform across all domains. === Capabilities === The capabilities identified by this type of system are vastly beyond that of our current military. This system will enable the Air Force to provide a transportable network, flexible enough to communicate with any air, space, or ground asset in the area. The network will provide a beyond line-of-sight (LoS) communications infrastructure that can be packed up and moved in and out of the designated battlespace, enabling the military to have a reliable and secure communications network that extends globally. The network is designed to be flexible enough to provide the right communication and network packages for a specific region, mission, or technology. Operationally, The AN is designed to be self-forming, self-organizing, and self-generating, with nodes joining and leaving the network as they enter and exit a specific region. The network consists of dedicated tactical links, wideband air-to-air links, and ad hoc networks constructed by the Joint Tactical Radio System (JTRS) networking services. JTRS is a software-defined radio that will work with many existing military and civilian radios. It includes integrated encryption and Wideband Networking Software to create mobile ad hoc networks. It also provides system performance analysis and fault diagnostics automatically, reducing the demand for human intervention and network maintenance. === Intended Use === The AN was designed as the cornerstone for the new military doctrine known as Network Centric Warfare. This doctrine was developed to use information superiority to equip warfighters with more precise information enabling commanders and shooters to make smarter decisions faster. The AN contributes to Network Centric Warfare by enabling commanders to provide real-time information to warfighters in the air and on the ground. Warfighters can then utilize more information and make more educated decisions about how to act in a particular situation. Once the act has been carried out commanders will have immediate information about the result and can make judgments on how to continue. All-in-all the AN was designed to reduce the time necessary to identify a target, make clear and educated decisions to pull or not to pull the trigger, and assess battle == Topologies == There are four main network topologies that will be deployed and vary based on the placement of backbone and subnet class networks. === Space, Air, Ground Tether === Establishing a direct connection to another aircraft or ground node, via a point-to-point link for nodes within LOS or via a Satellite Communications (SATCOM) link for nodes that are beyond line-of-sight is known as tethering. SATCOM links provide connectivity to a network ground entry point. Strike aircraft that accompany C2 aircraft such as an AWACS are tethered via point-to-point links. Finally, C2 or intelligence, surveillance, and reconnaissnce (ISR) aircraft may connect via a LOS link directly to a network ground entry point. Each of these tethered alternatives works exactly like a hub or switch that has an entry point to a larger network and allows their connected users access to that network. === Flat Ad Hoc === A flat ad hoc topology refers to establishing nonpersistent network connections as needed among AN nodes that are present at a given time. With this network the nodes dynamically “discover” other nodes to which they can interconnect and form the network. The specific interconnections between the nodes are not planned in advance, but are made as opportunities arise. The nodes join and leave the network at will, continually changing connections to neighbor nodes based upon their location and mobility characteristics. === Tiered Ad Hoc === Ad hoc networks can be flat in the sense that all nodes are peers of each other in a single network, as discussed above, or they can dynamically organize themselves into hierarchical tiers such that higher tiers are used to move data between more localized subnets. This network topology can be compared to any conventional deployed network that utilizes routers, switches, and hubs to temporarily connect users. === Persistent Backbone === A network topology characterized by a persistent backbone is established using relatively persistent wideband connections among high-value platforms flying relatively stable orbits. It provides the connectivity between the tactical subnets which are considered edge networks relative to the backbone. This provides concentration points for connectivity to the space backbone as well as to terrestrial networks. This type of network topology is comparable to a conventional permanent network with established data trunks, routers, switches, and hubs to connect users. == Architecture == === Network Management === The platform management system enables operators to manage all on-board network elements. It interfaces and interoperates with the Airborne Network management system to enable operators to manage remote network elements in the airborne network. The network management system monitors the health of the network by passively testing the network for faults and latency. The system will also actively troubleshoot faults with probes to identify and isolate faulty connections, and enables operators to apply network parameters and security changes to all systems based on the status of the network. === Routing/Switching === Routing and switching enables data to be dynamically transmitted over the network to other nodes. Routing protocols must be able to identify nodes transmitted within their own platform and data to be sent to other platforms regardless of the current topology. The routing protocol must also provide seamless roaming by ensuring that no routed packets are lost when a node changes its point of attachment to the network. Maintaining scalability is important in routing as the network is constantly changing. The network must be able to function with numerous levels of platforms, varying numbers of fast moving platforms, and varying amounts of traffic per platform. Routers and switches will use metrics to determine the best paths to take when routing data. The routing protocol utilized for the AN will be an Adaptive Quality of Service routing protocol. === Gateways/Proxies === Gateways and proxies enable the connection numerous technology types regardless of age to communicate across the IP-based network. Gateways and proxies are essential in the operation of this network because so many different technologies are used to communicate in each domain. These systems will facilitate the transition of the legacy on-board infrastructure, transmission systems, tactical data link systems, and user applications to the objective airborne network systems. Therefore, they are only temporary until all platforms use a standardized IP radio for transmission. === Performance Enhancing Proxies === Performance Enhancing Proxies improve the performance of user applications running across the Airborne Network by countering wireless network impairments, such as limited bandwidth, long delays, high loss rates, and disruptions in network connections. Proxy systems are implemented between the user application and the network and can be used to improve performance at the application and transport functional layers of the OSI model. Some techniques that can be employed include: Compression: Data compression or header compression can be used to minimize the number of bits sent over the network. Data bundling: Smaller data packets can be combined (bundled) into a single large packet for transmission over the network. Caching: A local cache can be used to save and provide data objects that are requested multiple times, reducing transmissions over the network (and improving response times). Store and forward: Message queuing can be used to ensure message delivery to users who become disconnected from the network or are unable to connect to the network for a period of time. Once the platform connects, the stored messages are sent. Pipelining: Rather than opening several separate network connections pipelining can be used to share a single networ

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  Serge Belamant

  Serge Belamant (born 1953) is a French-born South African entrepreneur best known for designing the Universal Electronic Payment System (UEPS) and the Chip Offline Pre-authorised Card (COPAC). He founded the cash-payments company Net1 UEPS Technologies in 1989, led it through dual listings on the NASDAQ and the Johannesburg Stock Exchange, and oversaw the contentious welfare-payments contract with the South African Social Security Agency (SASSA) until his retirement in 2017. Since 2018 he has been non-executive chair of London-based buy-now-pay-later fintech Zilch. == Early life and education == Belamant moved from France to South Africa with his family in 1967 and matriculated from Highlands North Boys' High School, Johannesburg. In 1972 he entered the University of the Witwatersrand to study civil engineering but switched to computer science and applied mathematics in his second year. He left the university without a degree and later took short courses in information systems at the University of South Africa (UNISA). == Early career and SASWITCH (1981–1989) == Belamant worked for Control Data Corporation as a systems analyst for a decade before joining SASWITCH Ltd in 1985. Economic sanctions had left the consortium's national ATM network dependent on unsupported Christian Rovsing computers. Belamant led a rebuild on fault-tolerant Stratus hardware and wrote protocol-translation software that allowed fourteen banks to connect without altering their host systems. By 1988 SASWITCH was handling about three million ATM transactions a month, according to the Competition Commission. The switch—now run by BankservAfrica—remains the backbone of South Africa's shared ATM network. == Net1 UEPS Technologies (1989–2017) == === Founding and UEPS === In 1989, Serge Belamant developed the Universal Electronic Payment System (UEPS), enabling secure, real-time transactions even in areas with limited connectivity. In the same year, he founded NET1 UEPS Technologies Inc., serving as its CEO and Director. === COPAC for VISA === In 1995, VISA tasked Belamant with designing the Chip Offline Pre-authorized Card (COPAC), a technology still widely used in chip-enabled credit and debit cards. A year later, he listed his company APLITEC (Applied Technology Holdings Limited) on the Johannesburg Stock Exchange. === Listings and acquisitions === In 1999, Belamant acquired Cash Payment Services (CPS) from First National Bank of South Africa, modernizing its welfare payment system to serve millions in rural areas. In 2005, he led NET1 Technologies to an IPO, listing it as NET1 UEPS Technologies Inc. on the Nasdaq. A secondary listing on the Johannesburg Stock Exchange (JSE) followed in 2008. === SASSA contract === Under Belamant's leadership, NET1 managed welfare payments for the South African Social Security Agency (SASSA), handling payments for over 10 million beneficiaries monthly. Despite criticism over handling the SASSA contract, investigations by the U.S. Department of Justice and the South African Constitutional Court found no wrongdoing. == Zilch (2018–present) == Belamant co-founded London-based "buy-now-pay-later" firm Zilch Technology in 2018 and serves as non-executive chair. Zilch reported £145 million in annual-recurring revenue and 4.5 million customers in January 2025. == Patents == Belamant is listed as inventor on more than a dozen payment-security patents, including: "Funds transfer system" (US RE36,788, 2000) – the basis for UEPS. "Financial transactions with a varying PIN" (WO 2014/037869, 2014).

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  Cloud Native Computing Foundation

  The Cloud Native Computing Foundation (CNCF) is a subsidiary of the Linux Foundation founded in 2015 to support cloud-native computing. == History == It was announced alongside Kubernetes 1.0, an open source container cluster manager, which was contributed to the Linux Foundation by Google as a seed technology. Founding members include Google, CoreOS, Mesosphere, Red Hat, Twitter, Huawei, Intel, RX-M, Cisco, IBM, Docker, Univa, and VMware. Today, CNCF is supported by over 450 members. In August 2018 Google announced that it was handing over operational control of Kubernetes to the community. == Projects == Argo is a collection of tools for getting work done with Kubernetes. Among its main features are Workflows and Events. It was accepted to CNCF on March 26, 2020 at the Incubating maturity level and then moved to the Graduated maturity level on December 6, 2022. cert-manager provisions and manages TLS certificates in Kubernetes. It was accepted to CNCF on November 10, 2020, moved to the Incubating maturity level on September 19, 2022, and then moved to the Graduated maturity level on September 29, 2024. Cilium provides networking, security, and observability for Kubernetes deployments using eBPF technology. It joined the CNCF at incubation level in October 2021 and the CNCF announced its graduation in October 2023. containerd is an industry-standard core container runtime. It is currently available as a daemon for Linux and Windows, which can manage the complete container lifecycle of its host system. In 2015, Docker donated the OCI Specification to The Linux Foundation with a reference implementation called runc. Since February 28, 2019 it is an official CNCF project. Its general availability and intention to donate the project to CNCF was announced by Docker in 2017. CoreDNS is a DNS server that chains plugins. Its graduation was announced in 2019. Dapr, the distributed application runtime, provides APIs for building secure and reliable microservices and agentic AI systems. Dapr was donated to the CNCF in November 2021 and joined at incubation level. The CNCF announced its graduation in November 2024. Envoy: Originally built at Lyft to move their architecture away from a monolith, Envoy is a high-performance open source edge and service proxy that makes the network transparent to applications. Lyft contributed Envoy to Cloud Native Computing Foundation in September 2017. etcd is a distributed key value store, providing a method of storing data across a cluster of machines. It became a CNCF incubating project in 2018 at KubeCon+CloudNativeCon North America in Seattle that year. Falco is an open source and cloud native runtime security initiative. It is the "de facto Kubernetes threat detection engine". It became an incubating project in January 2020 and graduated in February 2024. Flux is an open source project for powering GitOps in Kubernetes clusters. It provides the GitOps Toolkit, a set of Kubernetes APIs that allow you to define how configuration source code is securely pulled into your cluster and deployed by popular Kubernetes manifests rendering engines like Kustomize and Helm. The most recommended source mechanism is the OCIRepository API, which provides enhanced security and benefits from container image tooling out there. Flux has also notification integrations with popular services like Prometheus Alertmanager, PagerDuty, Slack and so on. Flux has graduated in CNCF in 2022. Harbor is an "open source trusted cloud native registry project that stores, signs, and scans content." It became an incubating project in September 2019 and graduated in June 2020. Helm is a package manager that helps developers "easily manage and deploy applications onto the Kubernetes cluster." It joined the incubating level in June 2018 and graduated in April 2020. Istio is a service mesh technology. It was accepted by CNCF in September 2022 and graduated on July 12, 2023. Jaeger, Created by Uber Engineering, Jaeger is an open source distributed tracing system inspired by Google Dapper paper and OpenZipkin community. It can be used for tracing microservice-based architectures, including distributed context propagation, distributed transaction monitoring, root cause analysis, service dependency analysis, and performance/latency optimization. The Cloud Native Computing Foundation Technical Oversight Committee voted to accept Jaeger as the 12th hosted project in September 2017 and became a graduated project in 2019. In 2020 it became an approved and fully integrated part of the CNCF ecosystem. Kubernetes is an open source framework for automating deployment and managing applications in a containerized and clustered environment. "It aims to provide better ways of managing related, distributed components across the varied infrastructure." It was originally designed by Google and donated to The Linux Foundation to form the Cloud Native Computing Foundation with Kubernetes as the seed technology. The "large and diverse" community supporting the project has made its staying power more robust than other, older technologies of the same ilk. In January 2020, the CNCF annual report showed significant growth in interest, training, event attendance and investment related to Kubernetes. Linkerd is CNCF's fifth member project, and the project that coined the term "service mesh". Linkerd adds observability, security, and reliability features to applications by adding them to the platform rather than the application layer, and features a "micro-proxy" to maximize speed and security of its data plane. Linkerd graduated from CNCF in July 2021. Open Policy Agent (OPA) is "an open source general-purpose policy engine and language for cloud infrastructure." It became a CNCF incubating project in April 2019. OPA graduated from CNCF in February 2021. Prometheus is a cloud monitoring tool sponsored by SoundCloud in early iterations. In August 2018, the tool was designated a graduated project by the Cloud Native Computing Foundation. It is now a Cloud Native Computing Foundation member project. Rook is CNCF's first cloud native storage project. It became an incubation level project in 2018 and graduated in October 2020. SPIFFE is an open standard and framework for workload identity, much the same way that OAuth is an open standard and framework for human identity. It is built from the ground up to accommodate modern computing environments, which operate with systems scale and velocity (as opposed to human scale and velocity), while still maintaining interoperability with existing technologies like OAuth and X.509 Public key infrastructure. Unlike other identity standards, SPIFFE supports multiple credential types for a single identity, ensuring that the highly varied needs of production environments are consistently met without compromise. SPIFFE joined the CNCF as a sandbox project in 2018, was accepted to incubation in 2020, and graduated in 2022. SPIRE is an open source identity provider for workloads based on the SPIFFE framework. It is highly pluggable, and fills the attestation and issuance needs required by any workload identity solution. The plugin interfaces it exposes allows users to write integrations with in-house systems, build internal self-service portals, and more. It is a very powerful building block for issuing short-lived identity credentials to dynamic cloud workloads. SPIRE became a CNCF Graduated project in 2022. The Update Framework (TUF) helps developers to secure new or existing software update systems, which are often found to be vulnerable to many known attacks. TUF addresses this widespread problem by providing a comprehensive, flexible security framework that developers can integrate with any software update system. TUF was CNCF's first security-focused project and the ninth project overall to graduate from the foundation's hosting program. TiKV provides a distributed key–value database. Vitess is a database clustering system for horizontal scaling of MySQL, first created for internal use by YouTube. It became a CNCF project in 2018 and graduated in November 2019. Contour is a management server for Envoy that can direct the management of Kubernetes' traffic. Contour also provides routing features that are more advanced than Kubernetes' out-of-the-box Ingress specification. VMWare contributed the project to CNCF in July 2020. Cortex offers horizontally scalable, multi-tenant, long-term storage for Prometheus and works alongside Amazon DynamoDB, Google Bigtable, Cassandra, S3, GCS, and Microsoft Azure. It was introduced into the ecosystem incubator alongside Thanos in August 2020. CRI-O is an Open Container Initiative (OCI) based "implementation of Kubernetes Container Runtime Interface". CRI-O allows Kubernetes to be container runtime-agnostic. It became an incubating project in 2019. gRPC is a "modern open source high performance RPC framework that can run in any environment." The project was formed in 2015 when Google decided to open sou

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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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  Social media intelligence

  Social media intelligence (SMI or SOCMINT) comprises the collective tools and solutions that allow organizations to analyze conversations, respond to synchronize social signals, and synthesize social data points into meaningful trends and analysis, based on the user's needs. Social media intelligence allows one to utilize intelligence gathering from social media sites, using both intrusive or non-intrusive means, from open and closed social networks. This type of intelligence gathering is one element of OSINT (Open- Source Intelligence). To support both the sensing and seizing of social signals at scale, organisations increasingly rely on dedicated audience intelligence platforms which combine data aggregation, NLP-driven analysis, and cross-platform monitoring. The term 'Social Media Intelligence' was coined in a 2012 paper written by Sir David Omand, Jamie Bartlett and Carl Miller for the Centre for the Analysis of Social Media, at the London-based think tank, Demos. The authors argued that social media is now an important part of intelligence and security work, but that technological, analytical, and regulatory changes are needed before it can be considered a powerful new form of intelligence, including amendments to the United Kingdom Regulation of Investigatory Powers Act 2000. Given the dynamic evolution of social media and social media monitoring, our current understanding of how social media monitoring can help organizations create business value is inadequate. As a result, there is a need to study how organizations can (a) extract and analyze social media data related to their business (Sensing), and (b) utilize external intelligence gained from social media monitoring for specific business initiatives (Seizing). == Governmental use == In Thailand, the Technology Crime Suppression Division not only employs a 30-person team to scrutinize social media for content deemed disrespectful to the monarchy, known as lèse-majesté but also encourages citizens to report such content. Particularly targeting the youth, they run a "Cyber Scout" program where participants are rewarded for reporting individuals posting material perceived as detrimental to the monarchy. Instances in Israel involve the arrest of Palestinians by the police for their social media posts. An example includes a 15-year-old girl who posted a Facebook status with the words "forgive me," raising suspicions among Israeli authorities that she might be planning an attack. In Egypt, a leaked 2014 call for tender from the Ministry of Interior reveals efforts to procure a social media monitoring system to identify leading figures and prevent protests before they occur. In the United States, ZeroFOX faced criticism for sharing a report with Baltimore officials showcasing how their social media monitoring tool could track riots following Freddie Gray's funeral. The report labeled 19 individuals, including two prominent figures from the #BlackLivesMatter movement, as "threat actors." In the UK, the Association of Chief Police Officers of England, Wales, and Northern Ireland emphasized the significance of social media in intelligence gathering during anti-fracking protests in 2011. Social media analysis closely monitored protests against the badger cull in 2013, with a 2013 report revealing a team of 17 officers in the National Domestic Extremism Unit scanning public tweets, YouTube videos, Facebook profiles, and other online content from UK citizens. == Effects on political opinion == During the 2016 United States presidential election, the Senate Intelligence Committee released reports containing information about Russia’s use of troll farms to mislead black voters about voting. Also, German researchers in 2010 analyzed Twitter messages regarding the German federal election concluding that Twitter played a role in leading users to a specific political opinion. In a broad sense, social media refers to a conversational, distributed mode of content generation, dissemination, and communication among communities. Different from broadcast-based traditional and industrial media, social media has torn down the boundaries between authorship and readership, while the information consumption and dissemination process is becoming intrinsically intertwined with the process of generating and sharing information. An example of how SOCMINT is used to affect political opinions is the Cambridge Analytica Scandal. Cambridge Analytica was a company that purchased data from Facebook about its users without the consent or knowledge of Americans. They used this data to build a "psychological warfare tool" to persuade US voters to elect Donald Trump as president in the 2016 election. Christopher Wylie, the whistleblower, reported that personal information was taken in early 2014, and used to build a system that could target US voters with personalized pollical advertisements. More than 50 million individuals' data was exploited and manipulated. == Law enforcement == In September of 2023, the Philadelphia Police Department began using social media to track and stay one step ahead of criminal activity to stop meetups and potential robberies. This new approach has made officers utilize another tool in their field by being able to find new information as quickly as possible. Law enforcement agencies worldwide are increasingly employing social media intelligence to enhance their capabilities in both crime prevention and investigation. By analyzing publicly available data from social platforms such as Facebook, Twitter, and Instagram, police can track criminal activities, identify suspects, and even prevent potential crimes before they occur. For instance, the FBI utilizes SOCMINT to monitor threats and investigate criminal activities, including analyzing posts, images, and videos that might signal illegal activities or security concerns. == Marketing == SOCMINT collects data from both organizations and people on an individual level. It has a variety of different purposes, and though its main goal is to improve national security advancements, there are several other benefits as well. This intelligence can identify patterns, predict trends, gather information in current time, etc. In addition, these aspects have allowed for both improvement within businesses and help for law enforcement. Artificial Social Networking Intelligence (ASNI) refers to the application of artificial intelligence within social networking services and social media platforms. It encompasses various technologies and techniques used to automate, personalize, enhance, improve, and synchronize user's interactions and experiences within social networks. ASNI is expected to evolve rapidly, influencing how we interact online and shaping their digital experiences. Transparency, ethical considerations, media influence bias, and user control over data will be crucial to ensure responsible development and positive impact. Google provides many free services and has built an entire media brand with its vast variety of products. Along with data collection, Google also owns two advertising services, Google Ads, and Google AdSense. Surprisingly, most of its revenue comes from advertising, not direct sales of its services or products. Google makes money by selling advertising services to advertisers. They provide ad space to websites on Google, and target ads to consumers of Google services and products. Google can market ads using SOCMINT to collect data from its users and generate revenue. Research shows that various social media platforms on the Internet such as Twitter, Tumblr (micro-blogging websites), Facebook (a popular social networking website), YouTube (largest video sharing and hosting website), Blogs and discussion forums are being misused by extremist groups for spreading their beliefs and ideologies, promoting radicalization, recruiting members and creating online virtual communities sharing a common agenda. Popular microblogging websites such as Twitter are being used as a real-time platform for information sharing and communication during the planning and mobilization of civil unrest-related events.

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  Kerckhoffs's principle

  Kerckhoffs's principle (also called Kerckhoffs's desideratum, assumption, axiom, doctrine or law) of cryptography was stated by the Dutch cryptographer Auguste Kerckhoffs in the 19th century. The principle holds that a cryptosystem should be secure, even if everything about the system, except the key, is public knowledge. This concept is widely embraced by cryptographers, in contrast to security through obscurity, which is not. Kerckhoffs's principle was phrased by the American mathematician Claude Shannon as "the enemy knows the system", i.e., "one ought to design systems under the assumption that the enemy will immediately gain full familiarity with them". In that form, it is called Shannon's maxim. Another formulation by American researcher and professor Steven M. Bellovin is: In other words—design your system assuming that your opponents know it in detail. (A former official at NSA's National Computer Security Center told me that the standard assumption there was that serial number 1 of any new device was delivered to the Kremlin.) == Origins == The invention of telegraphy radically changed military communications and increased the number of messages that needed to be protected from the enemy dramatically, leading to the development of field ciphers which had to be easy to use without large confidential codebooks prone to capture on the battlefield. It was this environment which led to the development of Kerckhoffs's requirements. Auguste Kerckhoffs was a professor of German language at Ecole des Hautes Etudes Commerciales (HEC) in Paris. In early 1883, Kerckhoffs's article, La Cryptographie Militaire, was published in two parts in the Journal of Military Science, in which he stated six design rules for military ciphers. Translated from French, they are: The system must be practically, if not mathematically, indecipherable; It should not require secrecy, and it should not be a problem if it falls into enemy hands; It must be possible to communicate and remember the key without using written notes, and correspondents must be able to change or modify it at will; It must be applicable to telegraph communications; It must be portable, and should not require several persons to handle or operate; Lastly, given the circumstances in which it is to be used, the system must be easy to use and should not be stressful to use or require its users to know and comply with a long list of rules. Some are no longer relevant given the ability of computers to perform complex encryption. The second rule, now known as Kerckhoffs's principle, is still critically important. == Explanation of the principle == Kerckhoffs viewed cryptography as a rival to, and a better alternative than, steganographic encoding, which was common in the nineteenth century for hiding the meaning of military messages. One problem with encoding schemes is that they rely on humanly-held secrets such as "dictionaries" which disclose for example, the secret meaning of words. Steganographic-like dictionaries, once revealed, permanently compromise a corresponding encoding system. Another problem is that the risk of exposure increases as the number of users holding the secrets increases. Nineteenth century cryptography, in contrast, used simple tables which provided for the transposition of alphanumeric characters, generally given row-column intersections which could be modified by keys which were generally short, numeric, and could be committed to human memory. The system was considered "indecipherable" because tables and keys do not convey meaning by themselves. Secret messages can be compromised only if a matching set of table, key, and message falls into enemy hands in a relevant time frame. Kerckhoffs viewed tactical messages as only having a few hours of relevance. Systems are not necessarily compromised, because their components (i.e. alphanumeric character tables and keys) can be easily changed. === Advantage of secret keys === Using secure cryptography is supposed to replace the difficult problem of keeping messages secure with a much more manageable one, keeping relatively small keys secure. A system that requires long-term secrecy for something as large and complex as the whole design of a cryptographic system obviously cannot achieve that goal. It only replaces one hard problem with another. However, if a system is secure even when the enemy knows everything except the key, then all that is needed is to manage keeping the keys secret. There are a large number of ways the internal details of a widely used system could be discovered. The most obvious is that someone could bribe, blackmail, or otherwise threaten staff or customers into explaining the system. In war, for example, one side will probably capture some equipment and people from the other side. Each side will also use spies to gather information. If a method involves software, someone could do memory dumps or run the software under the control of a debugger in order to understand the method. If hardware is being used, someone could buy or steal some of the hardware and build whatever programs or gadgets needed to test it. Hardware can also be dismantled so that the chip details can be examined under the microscope. === Maintaining security === A generalization some make from Kerckhoffs's principle is: "The fewer and simpler the secrets that one must keep to ensure system security, the easier it is to maintain system security." Bruce Schneier ties it in with a belief that all security systems must be designed to fail as gracefully as possible: Kerckhoffs's principle applies beyond codes and ciphers to security systems in general: every secret creates a potential failure point. Secrecy, in other words, is a prime cause of brittleness—and therefore something likely to make a system prone to catastrophic collapse. Conversely, openness provides ductility. Any security system depends crucially on keeping some things secret. However, Kerckhoffs's principle points out that the things kept secret ought to be those least costly to change if inadvertently disclosed. For example, a cryptographic algorithm may be implemented by hardware and software that is widely distributed among users. If security depends on keeping that secret, then disclosure leads to major logistic difficulties in developing, testing, and distributing implementations of a new algorithm – it is "brittle". On the other hand, if keeping the algorithm secret is not important, but only the keys used with the algorithm must be secret, then disclosure of the keys simply requires the simpler, less costly process of generating and distributing new keys. == Applications == In accordance with Kerckhoffs's principle, the majority of civilian cryptography makes use of publicly known algorithms. By contrast, ciphers used to protect classified government or military information are often kept secret (see Type 1 encryption). However, it should not be assumed that government/military ciphers must be kept secret to maintain security. It is possible that they are intended to be as cryptographically sound as public algorithms, and the decision to keep them secret is in keeping with a layered security posture. == Security through obscurity == It is moderately common for companies to keep the inner workings of a system secret. Some argue this "security by obscurity" makes the product safer and less vulnerable to attack. A counter-argument is that keeping the innards secret may improve security in the short term, but in the long run, only systems that have been published and analyzed should be trusted. Steven Bellovin and Randy Bush commented: Security Through Obscurity Considered Dangerous Hiding security vulnerabilities in algorithms, software, and/or hardware decreases the likelihood they will be repaired and increases the likelihood that they can and will be exploited. Discouraging or outlawing discussion of weaknesses and vulnerabilities is extremely dangerous and deleterious to the security of computer systems, the network, and its citizens. Open Discussion Encourages Better Security The long history of cryptography and cryptoanalysis has shown time and time again that open discussion and analysis of algorithms exposes weaknesses not thought of by the original authors, and thereby leads to better and more secure algorithms. As Kerckhoffs noted about cipher systems in 1883 [Kerc83], "Il faut qu'il n'exige pas le secret, et qu'il puisse sans inconvénient tomber entre les mains de l'ennemi." (Roughly, "the system must not require secrecy and must be able to be stolen by the enemy without causing trouble.")

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  Amália (LLM)

  Amália is a Portuguese large language model (LLM) announced in November 2024 by the Portuguese Prime-Minister Luís Montenegro. Its final version is expected to be launched in 2026. It is being developed by Center for Responsible AI (Centro para a AI Responsável) and by the research centers of NOVA School of Science and Technology and Instituto Superior Técnico. == History == In 2024 it was announced that the Portuguese Agency for Administrative Modernization (Agência para a Modernização Administrativa) transpose this LLM to Portuguese Public Administration. According to Paulo Dimas (CEO of the Center for Responsible AI) the three fundamental points of this LLM project are the linguistic variant (European Portuguese), cultural representation and data protection. In April 2025 it was announced that Amália had entered beta phase with an improved version being expected to be launched in September 2025. The beta version released in September is available only to the Public Administration, but the website launched in October reiterates the final version will be an open model.

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  Brain Imaging Data Structure

  The Brain Imaging Data Structure (BIDS) is a standard for organizing, annotating, and describing data collected during neuroimaging experiments. It is based on a formalized file and directory structure and metadata files (based on JSON and TSV) with controlled vocabulary. This standard has been adopted by a multitude of labs around the world as well as databases such as OpenNeuro, SchizConnect, Developing Human Connectome Project, and FCP-INDI, and is seeing uptake in an increasing number of studies. While originally specified for MRI data, BIDS has been extended to several other imaging modalities such as MEG, EEG, and intracranial EEG (see also BIDS Extension Proposals). == History == The project is a community-driven effort. BIDS, originally OBIDS (Open Brain Imaging Data Structure), was initiated during an INCF sponsored data sharing working group meeting (January 2015) at Stanford University. It was subsequently spearheaded and maintained by Chris Gorgolewski. Since October 2019, the project is headed by a Steering Group and maintained by a separate team of maintainers, the Maintainers Group, according to a governance document that was approved of by the BIDS community in a vote. BIDS has advanced under the direction and effort of contributors, the community of researchers that appreciate the value of standardizing neuroimaging data to facilitate sharing and analysis. == BIDS Extension Proposals == BIDS can be extended in a backwards compatible way and is evolving over time. This is accomplished through BIDS Extension Proposals (BEPs), which are community-driven processes following agreed-upon guidelines. A full list of finalized BEPs and BEPs in progress can be found on the BIDS website

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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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  Contrast set learning

  Contrast set learning is a form of association rule learning that seeks to identify meaningful differences between separate groups by reverse-engineering the key predictors that identify for each particular group. For example, given a set of attributes for a pool of students (labeled by degree type), a contrast set learner would identify the contrasting features between students seeking bachelor's degrees and those working toward PhD degrees. == Overview == A common practice in data mining is to classify, to look at the attributes of an object or situation and make a guess at what category the observed item belongs to. As new evidence is examined (typically by feeding a training set to a learning algorithm), these guesses are refined and improved. Contrast set learning works in the opposite direction. While classifiers read a collection of data and collect information that is used to place new data into a series of discrete categories, contrast set learning takes the category that an item belongs to and attempts to reverse engineer the statistical evidence that identifies an item as a member of a class. That is, contrast set learners seek rules associating attribute values with changes to the class distribution. They seek to identify the key predictors that contrast one classification from another. For example, an aerospace engineer might record data on test launches of a new rocket. Measurements would be taken at regular intervals throughout the launch, noting factors such as the trajectory of the rocket, operating temperatures, external pressures, and so on. If the rocket launch fails after a number of successful tests, the engineer could use contrast set learning to distinguish between the successful and failed tests. A contrast set learner will produce a set of association rules that, when applied, will indicate the key predictors of each failed tests versus the successful ones (the temperature was too high, the wind pressure was too high, etc.). Contrast set learning is a form of association rule learning. Association rule learners typically offer rules linking attributes commonly occurring together in a training set (for instance, people who are enrolled in four-year programs and take a full course load tend to also live near campus). Instead of finding rules that describe the current situation, contrast set learners seek rules that differ meaningfully in their distribution across groups (and thus, can be used as predictors for those groups). For example, a contrast set learner could ask, “What are the key identifiers of a person with a bachelor's degree or a person with a PhD, and how do people with PhD's and bachelor’s degrees differ?” Standard classifier algorithms, such as C4.5, have no concept of class importance (that is, they do not know if a class is "good" or "bad"). Such learners cannot bias or filter their predictions towards certain desired classes. As the goal of contrast set learning is to discover meaningful differences between groups, it is useful to be able to target the learned rules towards certain classifications. Several contrast set learners, such as MINWAL or the family of TAR algorithms, assign weights to each class in order to focus the learned theories toward outcomes that are of interest to a particular audience. Thus, contrast set learning can be thought of as a form of weighted class learning. === Example: Supermarket Purchases === The differences between standard classification, association rule learning, and contrast set learning can be illustrated with a simple supermarket metaphor. In the following small dataset, each row is a supermarket transaction and each "1" indicates that the item was purchased (a "0" indicates that the item was not purchased): Given this data, Association rule learning may discover that customers that buy onions and potatoes together are likely to also purchase hamburger meat. Classification may discover that customers that bought onions, potatoes, and hamburger meats were purchasing items for a cookout. Contrast set learning may discover that the major difference between customers shopping for a cookout and those shopping for an anniversary dinner are that customers acquiring items for a cookout purchase onions, potatoes, and hamburger meat (and do not purchase foie gras or champagne). == Treatment learning == Treatment learning is a form of weighted contrast-set learning that takes a single desirable group and contrasts it against the remaining undesirable groups (the level of desirability is represented by weighted classes). The resulting "treatment" suggests a set of rules that, when applied, will lead to the desired outcome. Treatment learning differs from standard contrast set learning through the following constraints: Rather than seeking the differences between all groups, treatment learning specifies a particular group to focus on, applies a weight to this desired grouping, and lumps the remaining groups into one "undesired" category. Treatment learning has a stated focus on minimal theories. In practice, treatment are limited to a maximum of four constraints (i.e., rather than stating all of the reasons that a rocket differs from a skateboard, a treatment learner will state one to four major differences that predict for rockets at a high level of statistical significance). This focus on simplicity is an important goal for treatment learners. Treatment learning seeks the smallest change that has the greatest impact on the class distribution. Conceptually, treatment learners explore all possible subsets of the range of values for all attributes. Such a search is often infeasible in practice, so treatment learning often focuses instead on quickly pruning and ignoring attribute ranges that, when applied, lead to a class distribution where the desired class is in the minority. === Example: Boston housing data === The following example demonstrates the output of the treatment learner TAR3 on a dataset of housing data from the city of Boston (a nontrivial public dataset with over 500 examples). In this dataset, a number of factors are collected for each house, and each house is classified according to its quality (low, medium-low, medium-high, and high). The desired class is set to "high", and all other classes are lumped together as undesirable. The output of the treatment learner is as follows: Baseline class distribution: low: 29% medlow: 29% medhigh: 21% high: 21% Suggested Treatment: [PTRATIO=[12.6..16), RM=[6.7..9.78)] New class distribution: low: 0% medlow: 0% medhigh: 3% high: 97% With no applied treatments (rules), the desired class represents only 21% of the class distribution. However, if one filters the data set for houses with 6.7 to 9.78 rooms and a neighborhood parent-teacher ratio of 12.6 to 16, then 97% of the remaining examples fall into the desired class (high-quality houses). == Algorithms == There are a number of algorithms that perform contrast set learning. The following subsections describe two examples. === STUCCO === The STUCCO contrast set learner treats the task of learning from contrast sets as a tree search problem where the root node of the tree is an empty contrast set. Children are added by specializing the set with additional items picked through a canonical ordering of attributes (to avoid visiting the same nodes twice). Children are formed by appending terms that follow all existing terms in a given ordering. The formed tree is searched in a breadth-first manner. Given the nodes at each level, the dataset is scanned and the support is counted for each group. Each node is then examined to determine if it is significant and large, if it should be pruned, and if new children should be generated. After all significant contrast sets are located, a post-processor selects a subset to show to the user - the low order, simpler results are shown first, followed by the higher order results which are "surprising and significantly different." The support calculation comes from testing a null hypothesis that the contrast set support is equal across all groups (i.e., that contrast set support is independent of group membership). The support count for each group is a frequency value that can be analyzed in a contingency table where each row represents the truth value of the contrast set and each column variable indicates the group membership frequency. If there is a difference in proportions between the contrast set frequencies and those of the null hypothesis, the algorithm must then determine if the differences in proportions represent a relation between variables or if it can be attributed to random causes. This can be determined through a chi-square test comparing the observed frequency count to the expected count. Nodes are pruned from the tree when all specializations of the node can never lead to a significant and large contrast set. The decision to prune is based on: The minimum deviation size: The maximum difference between the support

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  Contextual AI

  Contextual AI is an enterprise software company based in Mountain View, California. It develops a platform for building specialized Retrieval-Augmented Generation (RAG) agents for enterprise use. The company was founded in 2023 by Douwe Kiela and Amanpreet Singh, both former AI researchers at Facebook AI Research (FAIR) and Hugging Face. Douwe Kiela previously led the Meta research team that introduced the Retrieval-Augmented Generation (RAG) approach in 2020. Contextual AI focuses on enterprise generative AI applications using RAG 2.0 technology, with deployments primarily in the technology, banking, finance and media sectors. == History == In June 2023, Contextual AI announced it had raised $20 million in a seed funding round led by Bain Capital Ventures (BCV), with participation from Lightspeed Venture Partners, Greycroft, SV Angel, and several angel investors. In August 2024, the company raised $80 million in a Series A funding round led by Greycroft, with participation from previous investors including Bain Capital Ventures, Lightspeed, and Conviction Partners. The round also included new backers such as Bezos Expeditions, NVentures (Nvidia), HSBC Ventures, and Snowflake Ventures. == Features == Retrieval-Augmented Generation (RAG) is an artificial intelligence framework that integrates information retrieval with text generation to improve the performance of large language models (LLMs) on complex, knowledge-intensive tasks. It was introduced in 2020 by researchers at Meta AI, including Douwe Kiela, Patrick Lewis and others, in their paper Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. RAG enables language models to access and incorporate external information, such as proprietary databases or real-time web content, at query time, instead of relying solely on pre-trained, internal, static knowledge. This architecture addresses common limitations of standard LLMs, including hallucination, outdated information, and lack of attribution to source materials. RAG systems retrieve relevant context through a variety of techniques - including vector search, keyword search, text-to-SQL - and feeds this context into the language model to generate responses. The approach improves factual accuracy, supports domain-specific customization, enables citation of sources, and allows for more updated information without retraining the model itself. General Availability. In January 2025, Contextual AI announced the general availability of its enterprise platform for building specialized RAG agents. Early adopters included Qualcomm, which used the platform for their Customer Engineering team needs. Grounded Language Model. In March 2025, the company introduced a Grounded Language Model (GLM) for factual accuracy in enterprise AI applications. Reranker. In March 2025, Contextual AI released an instruction-following reranker that allows users to influence the ranking of retrieved documents through natural language instructions, such as prioritizing recent files, specific formats, or content from designated sources. == Applications == Contextual AI's platform has been adopted across a range of industries, including finance, technology, media and professional services. Clients include Fortune 500 companies such as Qualcomm and HSBC.

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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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  Atomicity (database systems)

  In database systems, atomicity (; from Ancient Greek: ἄτομος, romanized: átomos, lit. 'undividable') is the property of a database transaction consisting of an indivisible and irreducible series of database operations such that either all occur, or none occur. It is one of the ACID transaction properties: Atomicity, Consistency, Isolation, Durability. A guarantee of atomicity prevents partial database updates from occurring, because they can cause greater problems than rejecting the whole series outright. As a consequence, an atomic transaction cannot be observed to be in progress by another database client: at one moment in time, it has not yet happened, and at the next it has already occurred in whole (or nothing happened if the transaction was cancelled in progress). An example of transaction atomicity could be a digital monetary transfer from bank account A to account B. It consists of two operations, debiting the money from account A and crediting it to account B. Performing both of these operations inside of an atomic transaction ensures that the database remains in a consistent state, if either operation fails there will not be any unaccountable credits or debits affecting either account. The same term is also used in the definition of First normal form in database systems, where it instead refers to the concept that the values for fields may not consist of multiple smaller values to be decomposed, such as a string into which multiple names, numbers, dates, or other types may be packed. == Orthogonality == Atomicity does not behave completely orthogonally with regard to the other ACID properties of transactions. For example, isolation relies on atomicity to roll back the enclosing transaction in the event of an isolation violation such as a deadlock; consistency also relies on atomicity to roll back the enclosing transaction in the event of a consistency violation by an illegal transaction. As a result of this, a failure to detect a violation and roll back the enclosing transaction may cause an isolation or consistency failure. == Implementation == Typically, systems implement Atomicity by providing some mechanism to indicate which transactions have started and which finished; or by keeping a copy of the data before any changes occurred (Read-copy-update). Several filesystems have developed methods for avoiding the need to keep multiple copies of data, using journaling (see journaling file system). Databases usually implement this using some form of logging/journaling to track changes. The system synchronizes the logs (often the metadata) as necessary after changes have successfully taken place. Afterwards, crash recovery ignores incomplete entries. Although implementations vary depending on factors such as concurrency issues, the principle of atomicity – i.e. complete success or complete failure – remain. Ultimately, any application-level implementation relies on operating-system functionality. At the file-system level, POSIX-compliant systems provide system calls such as open(2) and flock(2) that allow applications to atomically open or lock a file. At the process level, POSIX Threads provide adequate synchronization primitives. The hardware level requires atomic operations such as Test-and-set, Fetch-and-add, Compare-and-swap, or Load-Link/Store-Conditional, together with memory barriers. Portable operating systems cannot simply block interrupts to implement synchronization, since hardware that lacks concurrent execution such as hyper-threading or multi-processing is now extremely rare. In distributed and sharded databases, atomicity is complicated by network latency and the potential for partial failures. While traditional distributed systems often employ locking protocols (like 2PC) to ensure cross-shard atomicity, these can introduce performance bottlenecks. Recent research into distributed ledger consensus suggests alternative models, such as "braided synchronization". This technique, utilized in protocols like Cerberus, intertwines the consensus phases of multiple shards to enforce atomic guarantees without a global ordering of all transactions.

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