AI App How To Use

AI App How To Use — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

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  Abdul Majid Bhurgri Institute of Language Engineering

  Abdul Majid Bhurgri Institute of Language Engineering (Sindhi: عبدالماجد ڀرڳڙي انسٽيٽيوٽ آف لئنگئيج انجنيئرنگ) is an autonomous body under the administrative control of the Culture, Tourism and Antiquities Department, Government of Sindh established for bringing Sindhi language at par with national and international languages in all computational process and Natural language processing. == Establishment == In recognition to services of Abdul-Majid Bhurgri, who is the founder of Sindhi computing, Government of Sindh has established the institute after his name. The institute was primarily initiated on the concept given by a language engineer and linguist Amar Fayaz Buriro in briefing to the Minister, Culture, Tourism and Antiquities, Government of Sindh, Syed Sardar Ali Shah on 21 February 2017 on celebration of International Mother Language Day in Sindhi Language Authority, Hyderabad, Sindh. After the presentation and concept given by Amar Fayaz Buriro, the minister Syed Sardar Ali Shah had announced the Institute. Then, Government of Sindh added the development scheme in the Budget of fiscal year 2017-2018. == Projects == The Institute has developed several projects aimed at advancing the Sindhi language and promoting linguistic research. Notable initiatives include the AMBILE Hamiz Ali Sindhi Optical character recognition, which allows for the accurate digitization of Sindhi text, and the ongoing Sindhi WordNet System, a project to build a comprehensive lexical database for Natural language processing. The institute has also created the Font, which integrates symbols from the Indus script, Khudabadi script, and modern Perso-Arabic Script Code for Information Interchange into a single resource for researchers]. Additionally, institute has developed online converter tools that automatically transliterate between the Arabic-Perso script and Devanagari script, improving linguistic accessibility. Another key project is Bhittaipedia, a digital platform dedicated to the preservation and dissemination of the poetry of Shah Abdul Latif Bhittai, one of Sindh's most renowned poet. == Location == The institute is established behind Sindh Museum and Sindhi Language Authority, N-5 National Highway, Qasimabad, Hyderabad, Sindh.

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  Dropbox Carousel

  Dropbox Carousel was a photo and video management app offered by Dropbox. The third-party native app, available on Android and iOS, allowed users to store, manage, and organize photos. Photos were organized by date, time and event and backed up on Dropbox. It competed in this space against other online photo storage services such as Google's Google Photos, Apple's iCloud, and Yahoo's Flickr. Chris Lee, Dropbox's head of product development for Carousel described the app as an add-on to Dropbox, a “dedicated experience for photos and videos” and a space for “reliving personal memories”. == History == Mailbox founder, Gentry Underwood unveiled Carousel at a gathering in San Francisco on April 9, 2014. Much of the features in Carousel come from Snapjoy, a photo start-up, that Dropbox acquired on December 19, 2012. When Carousel was launched, it marked amongst many others, a series of acquisitions made by Dropbox to prep up before opening its stock for public offering. The acquisitions would help demonstrate its expansive product offerings pitching potential profitability to investors. In December 2015, Dropbox announced that Carousel would be shut down and some Carousel features would be integrated into the primary Dropbox application. On March 31, 2016, Carousel was deactivated. == Features == Carousel prompted users to free local storage once it had synced and backed-up local photos to the cloud. Flashback was a feature (enabled by default) that showed past photos or videos taken the same day, a year, or some years back. Flashback used an algorithm designed to identify human faces - resulting in greater likelihood of the user's picture or people in the user's close circle appearing. A scrollable timeline, which was earlier a scroll wheel, at the bottom let the user scroll to photo(s) at a specific date with a finger swipe.

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  Cloud printing

  There are, in essence, three kinds of Cloud printing. == Benefits == 76% of IT teams have moved, or plan to move, their print workflows to the cloud due to its simplicity. Consumers can print easily to any printer from their PC, tablet or smartphone, while the Cloud print service monitors the supplies level. Many printer vendors such as Lexmark propose an automatic supplies shipment based on the real-time analysis of the printer supplies and user behavior to ensure printing will always be possible. For IT department, Cloud Printing eliminates the need for print servers and represents the only way to print from Cloud virtual desktops and servers. For consumers, cloud ready printers eliminate the need for PC connections and print drivers, enabling them to print from mobile devices. As for publishers and content owners, cloud printing allows them to "avoid the cost and complexity of buying and managing the underlying hardware, software and processes" required for the production of professional print products. Leveraging cloud print for print on demand also allows businesses to cut down on the costs associated with mass production. Moreover, cloud printing can be considered more eco-friendly, as it significantly reduces the amount of paper used (13% reduction in print jobs yearly) and lowers carbon emissions from transportation. As many companies move their IT to the Cloud, some adopting the Windows 365 and Azure Virtual Desktop services from Microsoft, the connection from the Cloud environment to the on-premise printers become an issue as opening ports for incoming print flow traffic is not an option. In 2020, at the exact same time Google discontinued its Google Print offer, Microsoft has announced its Universal Print service offer, aimed at making printing compatible with Cloud Desktop environments, making printing driver-free and simple with no client to install on PC. With Universal Print Microsoft has built a disrupting architecture with a value proposition commodifying printers, removing print servers and drivers, allowing to move printers to VLAN for security purpose and printing from anywhere. Clients are free to use any printer from any model as they all work the same, clients are not tied anymore to any printer brand and that gave a significant boost to the Cloud print market. That Microsoft Universal Print architecture provides APIs to third-party developers who can develop add-ons such as Celiveo 365 to extend Microsoft Cloud Print with added features such as access control on printers and copiers, follow-me pull print, data encryption, advanced usage reporting or charge back. == Providers of Consumer Cloud Printing Solutions == Before 2020 only a handful of providers used to work towards a professional cloud print solution, operating in their own niche or focus on mobile devices. In 2020 Microsoft has boosted that market by announcing its Universal Print Cloud printing service and since then many publishers have started to propose solutions for that growing market. The Covid pandemic also created the need for employees to be able to print at home when using the corporate IT software. Closed VPN often prevent accessing home network printers from corporate laptops and Full Public Cloud solutions are meant to be a solution to that problem. After the decision by Google to terminate Google Cloud Print service on 31 December 2020, most printer vendors released their own mobile cloud solution to fill the gap, while Hewlett-Packard implemented its own cloud print with their ePrint solution. Those solutions are often proprietary, only working on printers proposed by the vendor. Google has decided to let third-party developers develop Cloud Print solutions and to limit its scope to certifying the best Print Management offers compatible with its Chrome Enterprise Cloud ecosystem. == Providers of Corporate Cloud Printing solutions == While many print solutions claim to be "Cloud Printing", there are actually three categories: full Private Cloud, full Public Cloud, and Hybrid Cloud. Their differences are real and have an impact on the overall TCO as the more software there is on-site, the more hidden cost there are. In the Full Public Cloud category, independent SaaS vendors like Celiveo, ezeep , Printix , and Y Soft support a wide range of printer brands and models, allowing clients to buy the best printer without being locked on any brand. They are leveraging cloud computing technology to offer cloud-based print infrastructure and cloud-based printing software as a Service (SaaS). These solutions have integrations to cloud enabled printers or provide embedded printer agents. They feature allow users to print to any printer in any network, isolated network or not, even if that printer is otherwise not reachable from the user's computer. This also allows IT departments to move printers to VLAN for maximum security, like what they are doing with IP phones. Google Chrome Enterprise Cloud ecosystem has its own technical particularities and Google certifies Print Management solutions, ensuring they comply with Google technical requirement, yet letting each solution differentiate from others with specific features or security. Many of solutions for Chrome Enterprise are Hybrid, a few are Full Public Cloud. Industry experts believe that as these services become more popular, users will no longer consider printers as necessary assets but rather as devices that they can access on demand when the need to generate a printed page presents itself. == Caveats of Cloud Printing == == Security == Print jobs flow through Public Internet. It is therefore important to verify no Man-in-the-Middle attack can be performed. The only technical solution is to ensure each printer and PC uses a non-self-generated cryptographic token or certificate allowing TLS mutual authentication and specific data encryption. Self-generated printer certificates are unknown from the Cloud and prevent trusted authentication. Microsoft has implemented its Zero Trust Access security in its Universal Print service, it generates a unique certificate on printers compatible with its service. Other Cloud Printing SaaS providers have followed Microsoft on that High Security path. Print jobs data stored on the Cloud is sensitive as it contains user information as well as all information appearing on pages. Good practices require such data is encrypted at rest and in motion, using asymmetric PKI keys instead of fixed encryption keys. Some solutions require to open incoming traffic ports on the firewall to let Cloud services communicate with printers attached behind that firewall (most of the time for IPP/IPPS flows), some other solutions use a pull model where the communication is always initiated by the printer and no firewall port needs to be open. In terms of security the later is to be preferred.

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  Sunrise Calendar

  Sunrise is a discontinued electronic calendar application for mobile and desktop. The service was launched in 2013 by designers Pierre Valade and Jeremy Le Van. In October 2015, Microsoft announced that they had merged the Sunrise Calendar team into the larger Microsoft Outlook team where they will work closely with the Microsoft Outlook Mobile service. == History == The first iteration of Sunrise launched in 2012 and was a daily email digest of appointments, events and birthdays. Sunrise was launched initially as an iPhone application on February 19, 2013. In June 2013, Sunrise raised $2.2 million (~$2.91 million in 2024) in venture funding from Resolute.vc, NextView Ventures, Lerer Hippeau Ventures, SV Angel, and other angel investment firms like Loïc Le Meur, Dave Morin, Fabrice Grinda. In May 2014, Sunrise launched on Android as well as on the web via a web application. In July 2014, Sunrise announced it had raised $6 million (~$7.81 million in 2024) Series A from Balderton Capital. Bernard Liautaud joined the board. On February 11, 2015, Sunrise Atelier, Inc. was acquired by Microsoft for US$100 million (~$129 million in 2024). On October 28, 2015, Microsoft announced that Sunrise would be discontinued, and its functionality merged into Outlook Mobile. Microsoft later stated that the app would permanently cease functioning on August 31, 2016, but the shutdown was delayed to September 13, 2016, to coincide with an update to Outlook Mobile that incorporates aspects of Sunrise into its calendar interface. == Features == Sunrise allowed users to connect with Google Calendar, iCloud calendar and with Exchange Server. The following third-party services featured integration with Sunrise: Foursquare, GitHub, TripIt, Asana, Evernote, Google Tasks, Trello, Songkick, and Wunderlist. As a web app, users could sign-in and use Sunrise in a web browser, with no downloads required. A native Sunrise app could also be downloaded for OS X 10.9 and later, iOS 8.0 and later (both iPhone and iPad) as well as Android phones and tablets. In May 2015, Sunrise launched Meet, a keyboard for Android and iOS that lets users select available time slots in their calendar to schedule one-to-ones.

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  Quantum machine learning

  Quantum machine learning (QML) is the study of quantum algorithms for machine learning. It often refers to quantum algorithms for machine learning tasks which analyze classical data, sometimes called quantum-enhanced machine learning. QML algorithms use qubits and quantum operations to try to improve the space and time complexity of classical machine learning algorithms. Hybrid QML methods involve both classical and quantum processing, where computationally difficult subroutines are outsourced to a quantum device. These routines can be more complex in nature and executed faster on a quantum computer. Furthermore, quantum algorithms can be used to analyze quantum states instead of classical data. The term "quantum machine learning" is sometimes used to refer classical machine learning methods applied to data generated from quantum experiments (i.e. machine learning of quantum systems), such as learning the phase transitions of a quantum system or creating new quantum experiments. QML also extends to a branch of research that explores methodological and structural similarities between certain physical systems and learning systems, in particular neural networks. For example, some mathematical and numerical techniques from quantum physics are applicable to classical deep learning and vice versa. Furthermore, researchers investigate more abstract notions of learning theory with respect to quantum information, sometimes referred to as "quantum learning theory". == Machine learning with quantum computers == Quantum-enhanced machine learning refers to quantum algorithms that solve tasks in machine learning, thereby improving and often expediting classical machine learning techniques. Such algorithms typically require one to encode the given classical data set into a quantum computer to make it accessible for quantum information processing. Subsequently, quantum information processing routines are applied and the result of the quantum computation is read out by measuring the quantum system. For example, the outcome of the measurement of a qubit reveals the result of a binary classification task. While many proposals of QML algorithms are still purely theoretical and require a full-scale universal quantum computer to be tested, others have been implemented on small-scale or special purpose quantum devices. === Quantum associative memories and quantum pattern recognition === Early work on quantum associative memories has been done by Dan Ventura and Tony Martinez and by Carlo A. Trugenberger in the late 1990s and early 2000s. Associative (or content-addressable) memories are able to recognize stored content on the basis of a similarity measure, while random access memories are accessed by the address of stored information and not its content. As such they must be able to retrieve both incomplete and corrupted patterns, the essential machine learning task of pattern recognition. Typical classical associative memories store p patterns in the O ( n 2 ) {\displaystyle O(n^{2})} interactions (synapses) of a real, symmetric energy matrix over a network of n artificial neurons. The encoding is such that the desired patterns are local minima of the energy functional and retrieval is done by minimizing the total energy, starting from an initial configuration. Unfortunately, classical associative memories are severely limited by the phenomenon of cross-talk. When too many patterns are stored, spurious memories appear which quickly proliferate, so that the energy landscape becomes disordered and no retrieval is anymore possible. The number of storable patterns is typically limited by a linear function of the number of neurons, p ≤ O ( n ) {\displaystyle p\leq O(n)} . Quantum associative memories (in their simplest realization) store patterns in a unitary matrix U acting on the Hilbert space of n qubits. Retrieval is realized by the unitary evolution of a fixed initial state to a quantum superposition of the desired patterns with probability distribution peaked on the most similar pattern to an input. By its very quantum nature, the retrieval process is thus probabilistic. Because quantum associative memories are free from cross-talk, however, spurious memories are never generated. Correspondingly, they have a superior capacity than classical ones. The number of parameters in the unitary matrix U is O ( p n ) {\displaystyle O(pn)} . One can thus have efficient, spurious-memory-free quantum associative memories for any polynomial number of patterns. If the matrix U is encoded as a unique operator (as opposed as to a sequence of gates as in the circuit model), e.g. by an optical interferometer, the retrieval becomes efficient even for an exponential number of patterns. === Linear algebra simulation with quantum amplitudes === A number of quantum algorithms for machine learning are based on the idea of amplitude encoding, that is, to associate the amplitudes of a quantum state with the inputs and outputs of computations. Since a state of n {\displaystyle n} qubits is described by 2 n {\displaystyle 2^{n}} complex amplitudes, this information encoding can allow for an exponentially compact representation. Intuitively, this corresponds to associating a discrete probability distribution over binary random variables with a classical vector. The goal of algorithms based on amplitude encoding is to formulate quantum algorithms whose resources grow polynomially in the number of qubits n {\displaystyle n} , which amounts to a logarithmic time complexity in the number of amplitudes and thereby the dimension of the input. Many QML algorithms in this category are based on variations of the quantum algorithm for linear systems of equations (colloquially called HHL, after the paper's authors) which, under specific conditions, performs a matrix inversion using an amount of physical resources growing only logarithmically in the dimensions of the matrix. One of these conditions is that a Hamiltonian which entry-wise corresponds to the matrix can be simulated efficiently, which is known to be possible if the matrix is sparse or low rank. For reference, any known classical algorithm for matrix inversion requires a number of operations that grows more than quadratically in the dimension of the matrix (e.g. O ( n 2.373 ) {\displaystyle O{\mathord {\left(n^{2.373}\right)}}} ), but they are not restricted to sparse matrices. Quantum matrix inversion can be applied to machine learning methods in which the training reduces to solving a linear system of equations, for example in least-squares linear regression, the least-squares version of support vector machines, and Gaussian processes. A crucial bottleneck of methods that simulate linear algebra computations with the amplitudes of quantum states is state preparation, which often requires one to initialise a quantum system in a state whose amplitudes reflect the features of the entire dataset. Although efficient methods for state preparation are known for specific cases, this step easily hides the complexity of the task. === Variational quantum algorithms (VQAs) === In a variational quantum algorithm, a classical computer optimizes the parameters used to prepare a quantum state, while a quantum computer is used to do the actual state preparation and measurement. VQAs are considered promising candidates for noisy intermediate-scale quantum computers. Variational quantum circuits (or parameterized quantum circuits) are a popular class of VQAs where the parameters are those used in a fixed quantum circuit. Researchers have studied VQCs to solve optimization problems and find the ground state energy of complex quantum systems, which were difficult to solve using a classical computer. === Quantum binary classifier === Pattern reorganization is one of the important tasks of machine learning, binary classification is one of the tools or algorithms to find patterns. Binary classification is used in supervised learning and in unsupervised learning. In QML, classical bits are converted to qubits and they are mapped to Hilbert space; complex value data are used in a quantum binary classifier to use the advantage of Hilbert space. By exploiting the quantum mechanic properties such as superposition, entanglement, interference the quantum binary classifier produces the accurate result in short period of time. === Quantum machine learning algorithms based on Grover search === Another approach to improving classical machine learning with quantum information processing uses amplitude amplification methods based on Grover's search algorithm, which has been shown to solve unstructured search problems with a quadratic speedup compared to classical algorithms. These quantum routines can be employed for learning algorithms that translate into an unstructured search task, as can be done, for instance, in the case of the k-medians and the k-nearest neighbors algorithms. Other applications include quadratic speedups in the training of perceptrons. An e

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  Twproject

  Twproject (say: T W Project) is a web-based project and groupware management tool created by Open Lab, an Italian software house founded in 2001. It won the 17th Jolt Productivity Award in 2007 in the project management category. In March 2019 it becomes property of Twproject company. It has widespread use in universities as a teaching tool in project management courses. It is used by Oracle Corporation, Prada, Calzedonia, General Electric and many other companies from corporations to small start-ups. == History == April 2001 - The idea of Teamwork came to Open-Lab founders from a need to overcome the PM tools used at that time. It was built in Microsoft ASP and Adobe Flash November 2002 - Open-Lab decide to move from Flash to HTML and from ASP to Java-JSP. Teamwork 2 development is started. June 2004 - Teamwork 2 released, using top open-source technologies like Hibernate, jBlooming, dynamic CSS, Ajax 7 January 2005 - Teamwork goes open source, under LGPL license; remains such until June 2006 (18 months): it is a hit application on SourceForge, with 38.000 downloads, covered by greeting but starving April 2005 - Open-Lab takes the decision to change commercial strategy to finance development of Teamwork version 3 6 June 2006 - Teamwork 3 is finally out (15 months development). New interface, many new features, agile support and much more 27 March 2007 - Teamwork wins the 2007 JOLT Productivity Awards for project management category July 2007 - Teamwork 4 development started: new interface, extended use of new HTML capabilities, JS-oriented interface, start using jQuery February 2009 - Teamwork 4.0 is out February 2010 - Teamwork 4.4: public project pages, Chinese interface. jQuery is getting more space in Teamwork December 2010 - Teamwork 4.6: released Mobile module available for iPhone, Android, BlackBerry. Intensive usage of jQuery June 2011 - Teamwork 4.7: released Issue Kanban / Organizer January 2012 - Teamwork 5.0 development started. Lighter interface, extensive usage of dynamic pages, easier installer and first time approach. Learning curve highly reduced. A jQuery Gantt editor included and released free for the community July 2012 - Teamwork 5 released and also the free online Gantt editor November 2012 - Teamwork 5.1 with new trees and improved model for staffing March 2013 - Teamwork 5.2 with stronger support for customizations and Japanese interface. April 2014 - Teamwork has changed its name in Twproject because the domain teamwork.com has been purchased by Teamwork. April 2013 - Twproject 5.4 with a redesigned more powerful Gantt chart. August 2015 - Twproject 5 finale release. September 2015 - Twproject 6 with a completely redesigned user interface. March 2019 - A new company Twproject srl has been spun off. September 2021 - Twproject 7 has been released introducing WBS based management and workload management. == Features == Project & task management (with Microsoft Project import/export), and JSON format Gantt editor. Uses jQuery Gantt components Time tracking. Several entry points: dashboard, weekly view, issues, start/stop buttons Resource planning with weekly/monthly view, work load overview, unavailability from agenda Issue tracking & planning(with Kanban), e-mail integration, task dedicated inboxes Dashboard configuration, with customizable portlets and layout Message boards Scrum module Meeting and minute management, attached documents Agenda (Integrates with iCal, Microsoft Outlook, Microsoft Entourage, and Google Calendar) Document management, remote file systems link with NTFS, FTP, SVN, S3 (Dropbox, Google drive) Mobile application for iPhone, iPad, Android, Blackberry, Windows phone == Integration == A complete JSON API is available for integrations. The applications runs in Java JDK 8+ on the Hibernate object/relational mapping. The standard distribution uses Apache Tomcat 9, but can run on any J2EE application server. Twproject is tested on these DB servers: MySQL, Oracle, SQL Server, PostgreSql, HSQLDB, but as uses Hibernate can run on many others. There is simple graphical step-by-step installer for Windows, Mac, Linux, .zip/.tar.gz/.rpm packages.

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  Clesh

  Clesh (clip load edit share) is a cloud-based video editing platform, created by Forbidden Technologies plc, designed for the consumers, prosumers, and online communities to integrate user-generated content. The core technology is based on FORscene which is geared towards professionals working for example in broadcasting, news media, post production. Video, audio, and graphical content is uploaded to Clesh via a standard web browser, a mobile device such as a phone / tablet, or desktop software for DV capture over FireWire. The hosted material can then be reviewed, searched, edited, and published online by anyone with a standard web browser or compatible mobile device. Clesh supports storyboard shot selection, frame-accurate editing, transitions and various other functions such as; pan, zoom, colour and light correction, and audio levels. Content can be published in formats for example; Podcast, Mpeg2, HTML video or in a proprietary Java format. Cloud-based software provides greater scope for sharing information and collaborating compared to LAN or desktop based systems. Users of cloud-based software rely on the cloud's owner for adequate security, performance and resilience. Clesh does not assert any rights over uploaded content in contrast to other platforms (such as YouTube). All rights to any content uploaded to Clesh remain with the Author. == Features == Some of the services available to Clesh users: Access via Java enabled desktops or Android smartphones or tablets Real-time video rendering including effects and transitions Multiple audio tracks Secured log-on Frame accurate timeline for fine cut editing Logging / meta-data annotation assigns text to portions of video (usable by Clesh and web search engines) Storyboard assembles rough cuts using drag-and-drop Import, host, organise and search for media (DV tape and various video, audio, and still image formats) Publish content to in formats such as podcast, MPEG-2, web (Java Applet), Flash, Ogg, HTML and JPEG Chatrooms to talk to other Clesh users Showreel (a gallery for publishing material visible to internet users) Moderation for approval of material prior to distribution downstream Re-branding and integration support for white-label deployment == Technology == Clesh is based on the same technology as FORscene. An array of servers on the internet backbone provide the cloud computing platform to host Clesh. As a white-label solution Clesh would be branded and hosted per the client requirement. == User interface == End-users access Clesh on clients such as standard Java-enabled Web Browsers and / or Android enabled mobile devices such as tablets and smartphones. == History == Clesh was launched January 2006 and subject to several upgrades during the year to extend functionality including; storyboard, podcasting, moderation, chat and a showreel. During 2007 consumers are offered Clesh via a subscription model. Upgrades include Web Start and graphics upload. Mr Paparazzi selects Clesh as the platform to host its video offering and TrueTube does the same in 2008 by choosing to use Clesh to manage its video portal. Several further upgrades are applied and include; better audio quality, image enhancement controls, transitions, fades, titles, and additional publishing options such as JPEG. In 2010 a version of Clesh is demonstrated on an Android OS tablet device (Samsung Galaxy S Tab), and several upgrades are applied including; HTML publishing, pan, zoom, and overlays.

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  Clarizen

  Clarizen, Inc. is a project management software and collaborative work management company. Clarizen uses a software as a service business model. Clarizen's features include attaching CAD drawings to a project, moving between the project view and design view and an E-mail reporting feature. In May 2014 Clarizen raised $35 million in venture capital investment led by Goldman Sachs. The round brought investment to $90 million. Previous investors, including Benchmark Capital, Carmel Ventures, DAG Ventures, Opus Capital and Vintage Investment Partners participated. In April 2020, Clarizen appointed Matt Zilli as its new CEO, replacing Boaz Chalamish who is appointed as Executive Chairman. In January 2021 Clarizen was acquired by Planview.

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  Learning to rank

  Learning to rank (LTR) or machine-learned ranking (MLR) is the application of machine learning, often supervised, semi-supervised or reinforcement learning, in the construction of ranking models for information retrieval and recommender systems. Training data may, for example, consist of lists of items with some partial order specified between items in each list. This order is typically induced by giving a numerical or ordinal score or a binary judgment (e.g. "relevant" or "not relevant") for each item. The goal of constructing the ranking model is to rank new, unseen lists in a similar way to rankings in the training data. == Applications == === In information retrieval === Ranking is a central part of many information retrieval problems, such as document retrieval, collaborative filtering, sentiment analysis, and online advertising. A possible architecture of a machine-learned search engine is shown in the accompanying figure. Training data consists of queries and documents matching them together with the relevance degree of each match. It may be prepared manually by human assessors (or raters, as Google calls them), who check results for some queries and determine relevance of each result. It is not feasible to check the relevance of all documents, and so typically a technique called pooling is used — only the top few documents, retrieved by some existing ranking models are checked. This technique may introduce selection bias. Alternatively, training data may be derived automatically by analyzing clickthrough logs (i.e. search results which got clicks from users), query chains, or such search engines' features as Google's (since-replaced) SearchWiki. Clickthrough logs can be biased by the tendency of users to click on the top search results on the assumption that they are already well-ranked. Training data is used by a learning algorithm to produce a ranking model which computes the relevance of documents for actual queries. Typically, users expect a search query to complete in a short time (such as a few hundred milliseconds for web search), which makes it impossible to evaluate a complex ranking model on each document in the corpus, and so a two-phase scheme is used. First, a small number of potentially relevant documents are identified using simpler retrieval models which permit fast query evaluation, such as the vector space model, Boolean model, weighted AND, or BM25. This phase is called top- k {\displaystyle k} document retrieval and many heuristics were proposed in the literature to accelerate it, such as using a document's static quality score and tiered indexes. In the second phase, a more accurate but computationally expensive machine-learned model is used to re-rank these documents. === In other areas === Learning to rank algorithms have been applied in areas other than information retrieval: In machine translation for ranking a set of hypothesized translations; In computational biology for ranking candidate 3-D structures in protein structure prediction problems; In recommender systems for identifying a ranked list of related news articles to recommend to a user after he or she has read a current news article. == Feature vectors == For the convenience of MLR algorithms, query-document pairs are usually represented by numerical vectors, which are called feature vectors. Such an approach is sometimes called bag of features and is analogous to the bag of words model and vector space model used in information retrieval for representation of documents. Components of such vectors are called features, factors or ranking signals. They may be divided into three groups (features from document retrieval are shown as examples): Query-independent or static features — those features, which depend only on the document, but not on the query. For example, PageRank or document's length. Such features can be precomputed in off-line mode during indexing. They may be used to compute document's static quality score (or static rank), which is often used to speed up search query evaluation. Query-dependent or dynamic features — those features, which depend both on the contents of the document and the query, such as TF-IDF score or other non-machine-learned ranking functions. Query-level features or query features, which depend only on the query. For example, the number of words in a query. Some examples of features, which were used in the well-known LETOR dataset: TF, TF-IDF, BM25, and language modeling scores of document's zones (title, body, anchors text, URL) for a given query; Lengths and IDF sums of document's zones; Document's PageRank, HITS ranks and their variants. Selecting and designing good features is an important area in machine learning, which is called feature engineering. == Evaluation measures == There are several measures (metrics) which are commonly used to judge how well an algorithm is doing on training data and to compare the performance of different MLR algorithms. Often a learning-to-rank problem is reformulated as an optimization problem with respect to one of these metrics. Examples of ranking quality measures: Mean average precision (MAP); DCG and NDCG; Precision@n, NDCG@n, where "@n" denotes that the metrics are evaluated only on top n documents; Mean reciprocal rank; Kendall's tau; Spearman's rho. DCG and its normalized variant NDCG are usually preferred in academic research when multiple levels of relevance are used. Other metrics such as MAP, MRR and precision, are defined only for binary judgments. Recently, there have been proposed several new evaluation metrics which claim to model user's satisfaction with search results better than the DCG metric: Expected reciprocal rank (ERR); Yandex's pfound. Both of these metrics are based on the assumption that the user is more likely to stop looking at search results after examining a more relevant document, than after a less relevant document. == Approaches == Learning to Rank approaches are often categorized using one of three approaches: pointwise (where individual documents are ranked), pairwise (where pairs of documents are ranked into a relative order), and listwise (where an entire list of documents are ordered). Tie-Yan Liu of Microsoft Research Asia has analyzed existing algorithms for learning to rank problems in his book Learning to Rank for Information Retrieval. He categorized them into three groups by their input spaces, output spaces, hypothesis spaces (the core function of the model) and loss functions: the pointwise, pairwise, and listwise approach. In practice, listwise approaches often outperform pairwise approaches and pointwise approaches. This statement was further supported by a large scale experiment on the performance of different learning-to-rank methods on a large collection of benchmark data sets. In this section, without further notice, x {\displaystyle x} denotes an object to be evaluated, for example, a document or an image, f ( x ) {\displaystyle f(x)} denotes a single-value hypothesis, h ( ⋅ ) {\displaystyle h(\cdot )} denotes a bi-variate or multi-variate function and L ( ⋅ ) {\displaystyle L(\cdot )} denotes the loss function. === Pointwise approach === In this case, it is assumed that each query-document pair in the training data has a numerical or ordinal score. Then the learning-to-rank problem can be approximated by a regression problem — given a single query-document pair, predict its score. Formally speaking, the pointwise approach aims at learning a function f ( x ) {\displaystyle f(x)} predicting the real-value or ordinal score of a document x {\displaystyle x} using the loss function L ( f ; x j , y j ) {\displaystyle L(f;x_{j},y_{j})} . A number of existing supervised machine learning algorithms can be readily used for this purpose. Ordinal regression and classification algorithms can also be used in pointwise approach when they are used to predict the score of a single query-document pair, and it takes a small, finite number of values. === Pairwise approach === In this case, the learning-to-rank problem is approximated by a classification problem — learning a binary classifier h ( x u , x v ) {\displaystyle h(x_{u},x_{v})} that can tell which document is better in a given pair of documents. The classifier shall take two documents as its input and the goal is to minimize a loss function L ( h ; x u , x v , y u , v ) {\displaystyle L(h;x_{u},x_{v},y_{u,v})} . The loss function typically reflects the number and magnitude of inversions in the induced ranking. In many cases, the binary classifier h ( x u , x v ) {\displaystyle h(x_{u},x_{v})} is implemented with a scoring function f ( x ) {\displaystyle f(x)} . As an example, RankNet adapts a probability model and defines h ( x u , x v ) {\displaystyle h(x_{u},x_{v})} as the estimated probability of the document x u {\displaystyle x_{u}} has higher quality than x v {\displaystyle x_{v}} : P u , v ( f ) = CDF ( f ( x u ) − f ( x v ) ) , {\displaystyle P_{u,v}(f)={\text{CDF}

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  Cloud-computing comparison

  The following is a comparison of cloud-computing software and providers. == IaaS (Infrastructure as a service) == === Providers === ==== General ==== == SaaS (Software as a Service) == === General === === Supported hosts === === Supported guests === == PaaS (Platform as a service) == === Providers === === Providers on IaaS === PaaS providers which can run on IaaS providers ("itself" means the provider is both PaaS and IaaS):

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  TAPPS2

  TAPPS2 (Technische Alternative Planungs- und Programmier-System) is a tool used for developing the program logic for the universal, heating and solar thermal controllers by Austrian manufacturer Technische Alternative. Its primary usecase is defining the exact reaction of the controller to a certain event. Other than its predecessor, TAPPS, which could only be used to program controllers of type UVR1611, TAPPS2 is mainly used to program the UVR16x2 and RSM610 controllers, as well as several extension modules. == Development == Development in TAPPS2 is done on a vector-based drawing surface using components that can be placed via drag and drop. The components, which can be separated into inputs, functions and outputs are then being connected according to their individual features. Available components vary according to the current solar thermal control unit.

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  List of Ada software and tools

  This is a list of software and programming tools for the Ada programming language, including IDEs, compilers, libraries, verification and debugging tools, numerical and scientific computing libraries, and related projects. == Compilers == GNAT — GCC Ada compiler and toolchain, maintained by AdaCore AdaCore GNAT Pro — commercial Ada compiler with advanced tooling for high-integrity and real-time systems Green Hills compiler for Ada — Ada compiler for embedded and safety-critical systems ObjectAda — Ada development environment for safety-critical and embedded systems == Integrated development environments (IDEs) and editors == GNAT Studio — IDE developed by AdaCore Emacs — supports Ada editing with Ada mode and syntax checking Eclipse — supports Ada through GNATbench plugin Visual Studio Code — Ada support via Ada Language Server extensions == Libraries and frameworks == See also: Ada Libraries on Wikibooks Ada.Calendar — date and time library Ada Web Services (AWS) — support for RESTful and SOAP web services Ada.Text_IO — standard library for text input/output Florist (POSIX Ada binding) – open-source implementation of the POSIX Ada bindings GNAT – Ada compiler part of GCC, which also provides an extensive runtime and library package hierarchy. GtkAda – Ada bindings for the GTK+ graphical user interface toolkit Matreshka – multipurpose Ada framework supporting Unicode, XML, JSON, and more. XML/Ada – XML and Unicode processing library == Real-time and embedded systems == Ada tasking — built-in concurrency support with tasks, protected objects, and rendezvous. Ada.Real_Time — real-time clocks, delays, and scheduling. ARINC 653 Ada profiles — for avionics real-time applications OpenMP Ada bindings — parallel programming for multi-core embedded systems Ravenscar profile — subset of Ada tasking for real-time and deterministic execution == Numerical and scientific computing == Ada.Numerics — libraries for numerical methods, linear algebra, and mathematical functions. SPARK math libraries — formal-methods-compliant numerical routines == Verification, debugging, and analysis == GNATprove — formal verification and static analysis tool for Ada and SPARK GNATstack — runtime stack analysis and checking GNATcoverage — code coverage measurement for Ada projects AdaControl — style checking and metrics for Ada == Testing frameworks == AUnit — unit testing framework for Ada GNATtest — automated testing framework for Ada == Documentation and code generation == GNATdoc — generates HTML documentation from Ada source code

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  Automated storage and retrieval system

  An automated storage and retrieval system (ASRS or AS/RS) consists of a variety of computer-controlled systems for automatically placing and retrieving loads from defined storage locations. Automated storage and retrieval systems (AS/RS) are typically used in applications where: There is a very high volume of loads being moved into and out of storage Storage density is important because of space constraints No value is added in this process (no processing, only storage and transport) Accuracy is critical because of potential expensive damages to the load An AS/RS can be used with standard loads as well as nonstandard loads, meaning that each standard load can fit in a uniformly-sized volume; for example, the film canisters in the image of the Defense Visual Information Center are each stored as part of the contents of the uniformly sized metal boxes, which are shown in the image. Standard loads simplify the handling of a request of an item. In addition, audits of the accuracy of the inventory of contents can be restricted to the contents of an individual metal box, rather than undergoing a top-to-bottom search of the entire facility, for a single item. They can also be used in self storage places. == Overview == AS/RS systems are designed for automated storage and retrieval of parts and items in manufacturing, distribution, retail, wholesale and institutions. They first originated in the 1960s, initially focusing on heavy pallet loads but with the evolution of the technology the handled loads have become smaller. The systems operate under computerized control, maintaining an inventory of stored items. Retrieval of items is accomplished by specifying the item type and quantity to be retrieved. The computer determines where in the storage area the item can be retrieved from and schedules the retrieval. It directs the proper automated storage and retrieval machine (SRM) to the location where the item is stored and directs the machine to deposit the item at a location where it is to be picked up. A system of conveyors and or automated guided vehicles is sometimes part of the AS/RS system. These take loads into and out of the storage area and move them to the manufacturing floor or loading docks. To store items, the pallet or tray is placed at an input station for the system, the information for inventory is entered into a computer terminal and the AS/RS system moves the load to the storage area, determines a suitable location for the item, and stores the load. As items are stored into or retrieved from the racks, the computer updates its inventory accordingly. The benefits of an AS/RS system include reduced labor for transporting items into and out of inventory, reduced inventory levels, more accurate tracking of inventory, and space savings. Items are often stored more densely than in systems where items are stored and retrieved manually. Within the storage, items can be placed on trays or hang from bars, which are attached to chains/drives in order to move up and down. The equipment required for an AS/RS include a storage & retrieval machine (SRM) that is used for rapid storage and retrieval of material. SRMs are used to move loads vertically or horizontally, and can also move laterally to place objects in the correct storage location. The trend towards Just In Time production often requires sub-pallet level availability of production inputs, and AS/RS is a much faster way of organizing the storage of smaller items next to production lines. The Material Handling Institute of America (MHIA), the non-profit trade association for the material handling world, and its members have categorised AS/RS into two primary segments: Fixed Aisle and Carousels/Vertical Lift Modules (VLMs). Both sets of technologies provide automated storage and retrieval for parts and items, but use different technologies. Each technology has its unique set of benefits and disadvantages. Fixed Aisle systems are characteristically larger systems whereas carousels and Vertical Lift Modules are used individually or grouped, but in small to medium-sized applications. A fixed-aisle AS/R machine (stacker crane) is one of two main designs: single-masted or double masted. Most are supported on a track and ceiling guided at the top by guide rails or channels to ensure accurate vertical alignment, although some are suspended from the ceiling. The 'shuttles' that make up the system travel between fixed storage shelves to deposit or retrieve a requested load (ranging from a single book in a library system to a several ton pallet of goods in a warehouse system). The entire unit moves horizontally within an aisle, while the shuttles are able to elevate up to the necessary height to reach the load, and can extend and retract to store or retrieve loads that are several positions deep in the shelving. A semi-automated system can be achieved by utilizing only specialized shuttles within an existing rack system. Another AS/RS technology is known as shuttle technology. In this technology the horizontal movement is made by independent shuttles each operating on one level of the rack while a lift at a fixed position within the rack is responsible for the vertical movement. By using two separate machines for these two axes the shuttle technology is able to provide higher throughput rates than stacker cranes. Storage and Retrieval Machines pick up or drop off loads to the rest of the supporting transportation system at specific stations, where inbound and outbound loads are precisely positioned for proper handling. In addition, there are several types of Automated Storage & Retrieval Systems (AS/RS) devices called Unit-load AS/RS, Mini-load AS/RS, Mid-Load AS/RS, Vertical Lift Modules (VLMs), Horizontal Carousels and Vertical Carousels. These systems are used either as stand-alone units or in integrated workstations called pods or systems. These units are usually integrated with various types of pick to light systems and use either a microprocessor controller for basic usage or inventory management software. These systems are ideal for increasing space utilization up to 90%, productivity levels by 90%, accuracy to 99.9%+ levels and throughput up to 750 lines per hour/per operator or more depending on the configuration of the system. == Horizontal carousels == Robotic Inserter/Extractor devices can be used for horizontal carousels. The robotic device is positioned in the front or rear of up to three horizontal carousels tiered high. The robot grabs the tote required in the order and often replenishes at the same time to speed up throughput. The tote(s) are then delivered to a conveyor, which routes it to a work station for picking or replenishing. Up to eight transactions per minute per unit can be done. Totes or containers up to 36" x 36" x 36" can be used in a system. On a simplistic level, horizontal carousels are also often used as "rotating shelving". With simple "fetch" command, items are brought to the operator and otherwise wasted space is eliminated. AS/RS Applications: Most applications of AS/RS technology have been associated with warehousing and distribution operations. An AS/RS can also be used to store raw materials and work in process in manufacturing. Three application areas can be distinguished for AS/RS: (1) Unit load storage and handling, (2) Order picking, and (3) Work in process storage. Unit load storage and retrieval applications are represented by unit load AS/RS and deep-lane storage systems. These kinds of applications are commonly found in warehousing for finishing goods in a distribution center, rarely in manufacturing. Deep-lane systems are used in the food industry. As described above, order picking involves retrieving materials in less than full unit load quantities. Minilpass, man-on board, and items retrieval systems are used for this second application area. Work in process storage is a more recent application of automated storage technology. While it is desirable to minimize the amount of work in process, WIP is unavoidable and must be effectively managed. Automated storage systems, either automated storage/retrieval systems or carousel systems, represent an efficient way to store materials between processing steps, particularly in batch and job shop production. In high production, work in process is often carried between operations by conveyor system, which this serve both storage and transport functions. === Inventory Category-specific AS/RS === Each inventory category—raw materials, work-in-process, and finished goods—requires its own specialized Automated Storage and Retrieval System (AS/RS). Particularly for work-in-process (WIP) inventories, due to variations in manufacturing processes, the AS/RS systems are significantly different in design and function, tailored specifically to match unique handling, storage, and retrieval requirements === Installed applications === Installed applications of this technology can be wide-ranging. In some librarie

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  Model-based clustering

  In statistics, cluster analysis is the algorithmic grouping of objects into homogeneous groups based on numerical measurements. Model-based clustering based on a statistical model for the data, usually a mixture model. This has several advantages, including a principled statistical basis for clustering, and ways to choose the number of clusters, to choose the best clustering model, to assess the uncertainty of the clustering, and to identify outliers that do not belong to any group. == Model-based clustering == Suppose that for each of n {\displaystyle n} observations we have data on d {\displaystyle d} variables, denoted by y i = ( y i , 1 , … , y i , d ) {\displaystyle y_{i}=(y_{i,1},\ldots ,y_{i,d})} for observation i {\displaystyle i} . Then model-based clustering expresses the probability density function of y i {\displaystyle y_{i}} as a finite mixture, or weighted average of G {\displaystyle G} component probability density functions: p ( y i ) = ∑ g = 1 G τ g f g ( y i ∣ θ g ) , {\displaystyle p(y_{i})=\sum _{g=1}^{G}\tau _{g}f_{g}(y_{i}\mid \theta _{g}),} where f g {\displaystyle f_{g}} is a probability density function with parameter θ g {\displaystyle \theta _{g}} , τ g {\displaystyle \tau _{g}} is the corresponding mixture probability where ∑ g = 1 G τ g = 1 {\displaystyle \sum _{g=1}^{G}\tau _{g}=1} . Then in its simplest form, model-based clustering views each component of the mixture model as a cluster, estimates the model parameters, and assigns each observation to cluster corresponding to its most likely mixture component. === Gaussian mixture model === The most common model for continuous data is that f g {\displaystyle f_{g}} is a multivariate normal distribution with mean vector μ g {\displaystyle \mu _{g}} and covariance matrix Σ g {\displaystyle \Sigma _{g}} , so that θ g = ( μ g , Σ g ) {\displaystyle \theta _{g}=(\mu _{g},\Sigma _{g})} . This defines a Gaussian mixture model. The parameters of the model, τ g {\displaystyle \tau _{g}} and θ g {\displaystyle \theta _{g}} for g = 1 , … , G {\displaystyle g=1,\ldots ,G} , are typically estimated by maximum likelihood estimation using the expectation-maximization algorithm (EM); see also EM algorithm and GMM model. Bayesian inference is also often used for inference about finite mixture models. The Bayesian approach also allows for the case where the number of components, G {\displaystyle G} , is infinite, using a Dirichlet process prior, yielding a Dirichlet process mixture model for clustering. === Choosing the number of clusters === An advantage of model-based clustering is that it provides statistically principled ways to choose the number of clusters. Each different choice of the number of groups G {\displaystyle G} corresponds to a different mixture model. Then standard statistical model selection criteria such as the Bayesian information criterion (BIC) can be used to choose G {\displaystyle G} . The integrated completed likelihood (ICL) is a different criterion designed to choose the number of clusters rather than the number of mixture components in the model; these will often be different if highly non-Gaussian clusters are present. === Parsimonious Gaussian mixture model === For data with high dimension, d {\displaystyle d} , using a full covariance matrix for each mixture component requires estimation of many parameters, which can result in a loss of precision, generalizabity and interpretability. Thus it is common to use more parsimonious component covariance matrices exploiting their geometric interpretation. Gaussian clusters are ellipsoidal, with their volume, shape and orientation determined by the covariance matrix. Consider the eigendecomposition of a matrix Σ g = λ g D g A g D g T , {\displaystyle \Sigma _{g}=\lambda _{g}D_{g}A_{g}D_{g}^{T},} where D g {\displaystyle D_{g}} is the matrix of eigenvectors of Σ g {\displaystyle \Sigma _{g}} , A g = diag { A 1 , g , … , A d , g } {\displaystyle A_{g}={\mbox{diag}}\{A_{1,g},\ldots ,A_{d,g}\}} is a diagonal matrix whose elements are proportional to the eigenvalues of Σ g {\displaystyle \Sigma _{g}} in descending order, and λ g {\displaystyle \lambda _{g}} is the associated constant of proportionality. Then λ g {\displaystyle \lambda _{g}} controls the volume of the ellipsoid, A g {\displaystyle A_{g}} its shape, and D g {\displaystyle D_{g}} its orientation. Each of the volume, shape and orientation of the clusters can be constrained to be equal (E) or allowed to vary (V); the orientation can also be spherical, with identical eigenvalues (I). This yields 14 possible clustering models, shown in this table: It can be seen that many of these models are more parsimonious, with far fewer parameters than the unconstrained model that has 90 parameters when G = 4 {\displaystyle G=4} and d = 9 {\displaystyle d=9} . Several of these models correspond to well-known heuristic clustering methods. For example, k-means clustering is equivalent to estimation of the EII clustering model using the classification EM algorithm. The Bayesian information criterion (BIC) can be used to choose the best clustering model as well as the number of clusters. It can also be used as the basis for a method to choose the variables in the clustering model, eliminating variables that are not useful for clustering. Different Gaussian model-based clustering methods have been developed with an eye to handling high-dimensional data. These include the pgmm method, which is based on the mixture of factor analyzers model, and the HDclassif method, based on the idea of subspace clustering. The mixture-of-experts framework extends model-based clustering to include covariates. == Example == We illustrate the method with a dateset consisting of three measurements (glucose, insulin, sspg) on 145 subjects for the purpose of diagnosing diabetes and the type of diabetes present. The subjects were clinically classified into three groups: normal, chemical diabetes and overt diabetes, but we use this information only for evaluating clustering methods, not for classifying subjects. The BIC plot shows the BIC values for each combination of the number of clusters, G {\displaystyle G} , and the clustering model from the Table. Each curve corresponds to a different clustering model. The BIC favors 3 groups, which corresponds to the clinical assessment. It also favors the unconstrained covariance model, VVV. This fits the data well, because the normal patients have low values of both sspg and insulin, while the distributions of the chemical and overt diabetes groups are elongated, but in different directions. Thus the volumes, shapes and orientations of the three groups are clearly different, and so the unconstrained model is appropriate, as selected by the model-based clustering method. The classification plot shows the classification of the subjects by model-based clustering. The classification was quite accurate, with a 12% error rate as defined by the clinical classification. Other well-known clustering methods performed worse with higher error rates, such as single-linkage clustering with 46%, average link clustering with 30%, complete-linkage clustering also with 30%, and k-means clustering with 28%. == Outliers in clustering == An outlier in clustering is a data point that does not belong to any of the clusters. One way of modeling outliers in model-based clustering is to include an additional mixture component that is very dispersed, with for example a uniform distribution. Another approach is to replace the multivariate normal densities by t {\displaystyle t} -distributions, with the idea that the long tails of the t {\displaystyle t} -distribution would ensure robustness to outliers. However, this is not breakdown-robust. A third approach is the "tclust" or data trimming approach which excludes observations identified as outliers when estimating the model parameters. == Non-Gaussian clusters and merging == Sometimes one or more clusters deviate strongly from the Gaussian assumption. If a Gaussian mixture is fitted to such data, a strongly non-Gaussian cluster will often be represented by several mixture components rather than a single one. In that case, cluster merging can be used to find a better clustering. A different approach is to use mixtures of complex component densities to represent non-Gaussian clusters. == Non-continuous data == === Categorical data === Clustering multivariate categorical data is most often done using the latent class model. This assumes that the data arise from a finite mixture model, where within each cluster the variables are independent. === Mixed data === These arise when variables are of different types, such as continuous, categorical or ordinal data. A latent class model for mixed data assumes local independence between the variable. The location model relaxes the local independence assumption. The clustMD approach assumes that the observed variables are manifestations of underlying continuous Gaussian latent

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  Yahoo Mail

  Yahoo! Mail (also written as Yahoo Mail) is a mailbox provider by Yahoo. It is one of the largest email services worldwide, with 225 million users. It is accessible via a web browser (webmail), mobile app, or through third-party email clients via the POP, SMTP, and IMAP protocols. Users can also connect non-Yahoo e-mail accounts to their Yahoo Mail inbox. The service was launched on October 8, 1997. The service is free for personal use, with an optional monthly fee for additional features. It is also available in several languages other than English. == History == === 1997–2002 === On October 8, 1997, Yahoo announced its acquisition of online communications company Four11 for $92 million in stock. As part of the purchase, Yahoo received Four11's RocketMail webmail service. Yahoo Mail, based on the RocketMail technology, launched at the same time. Yahoo! chose acquisition rather than internal platform development, because, as Healy said, "Hotmail was growing at thousands and thousands users per week. We did an analysis. For us to build, it would have taken four to six months, and by then, so many users would have taken an email account. The speed of the market was critical." On March 21, 2002, Yahoo! eliminated free software client access and introduced the $29.99 per year Mail Forwarding Service. Mary Osako, a Yahoo! Spokeswoman, told CNET, "For-pay services on Yahoo!, originally launched in February 1999, have experienced great acceptance from our base of active registered users, and we expect this adoption to continue to grow." === 2002–2010 === During 2002, the Yahoo network was gradually redesigned, including the company website, Yahoo Mail and other services. Along with the new design, new features were implemented, including drop-down menus in DHTML and keyboard shortcuts. On July 9, 2004, Yahoo! acquired Oddpost, a webmail service which simulated a desktop email client. Oddpost had features such as drag-and-drop support, right-click menus, RSS feeds, a preview pane, and increased speed using email caching to shorten response time. Many of the features were incorporated into an updated Yahoo! Mail service. ==== Competition ==== On April 1, 2004, Google announced its Gmail service with 1 GB of storage, although Gmail's invitation-only accounts kept the other webmail services at the forefront. Most major webmail providers, including Yahoo! Mail, increased their mailbox storage in response. Yahoo! first announced 100 MB of storage for basic accounts and 2 GB of storage for premium users. However, soon Yahoo Mail increased its free storage quota to 1 GB, before eventually allowing unlimited storage from March 27, 2007, until October 8, 2013. === 2011–2021 === In May 2011, Yahoo Mail rolled out a new interface. It included updated design, enhanced performance, and improved Facebook integration. In 2013, Yahoo! redesigned the site and removed several features, such as simultaneously opening multiple emails in tabs, sorting by sender name, and dragging mails to folders. The new email interface was geared to give an improved user-experience for mobile devices, but was criticized for having an inferior desktop interface. Many users objected to the unannounced nature of the changes through an online post asking Yahoo! to bring back mail tabs with one hundred thousand voting and nearly ten thousand commenting. The redesign produced a problem that caused an unknown number of users to lose access to their accounts for several weeks. In December 2013, Yahoo! Mail suffered a major outage where approximately one million users, one percent of the site's total users, could not access their emails for several days. Yahoo!'s then-CEO Marissa Mayer publicly apologized to the site's users. China Yahoo Mail announced in April 2013 that it would shut down that August as part of Yahoo ceasing services in China since acquiring a stake in Alibaba in 2005. Users with email address suffixes @yahoo.com.cn and @yahoo.cn could transfer their accounts to AliCloud to continue receiving messages through the end of 2014. In January 2014, an undisclosed number of usernames and passwords were released to hackers, following a security breach that Yahoo! believed had occurred through a third-party website. Yahoo! contacted affected users and requested that passwords be changed. In October 2015, Yahoo! updated the mail service with a "more subtle" redesign, as well as improved mobile features. The same release introduced the Yahoo! Account Key, a smartphone-based replacement for password logins. The app also added support for third-party mail accounts. In 2017, Yahoo! again redesigned the web interface with a "more minimal" look, and introduced the option to customize it with different color themes and layouts. In 2019, Yahoo released a redesigned Yahoo Mail app to organize user inboxes, introducing features including a one-tap unsubscribe tool, package tracking, and travel updates. In 2020, Yahoo Mail users were able to fill Walmart shopping carts directly from their inboxes, an industry first. Yahoo! also added a feature to view NFL matches. === 2022–present === In 2022, updates to the Yahoo Mail mobile app added tools to help manage receipts, gift cards, and subscriptions. AI-based additions in 2023 included a feature that automates tracking coupon codes and credits for online shopping, as well as updates to search suggestions, message summaries and AI writing assistance. In 2024, updates to the desktop interface added more AI-based features, including a "priority inbox" tab with automatically generated summaries of important messages and automated suggestions of next actions based on message contents. In February 2025, Yahoo aired its first Super Bowl ad since 2002, in which Bill Murray invited viewers to contact him at his Yahoo Mail email address ([email protected]). The address received nearly 150,000 emails in the first two hours after broadcast. In June 2025, Yahoo Mail introduced a "Catch Up" feature that provides AI-generated summaries and email previews and prompts users to choose to delete or retain each one. As part of the feature's launch, Yahoo Mail collaborated with streetwear brand Anti Social Social Club on an apparel release. == User interface == As many as three web interfaces were available at any given time. The traditional "Yahoo! Mail Classic" preserved the availability of their original 1997 interface until July 2013 in North America. A 2005 version included a new Ajax interface, drag-and-drop, improved search, keyboard shortcuts, address auto-completion, and tabs. However, other features were removed, such as column widths and one click delete-move-to-next. In October 2010, Yahoo! released a beta version of Yahoo! Mail, which included improvements to performance, search, and Facebook integration. In May 2011, this became the default interface. Their current Webmail interface was introduced in 2017. == Spam policy == Yahoo! Mail is often used by spammers to provide a "remove me" email address. Often, these addresses are used to verify the recipient's address, thus opening the door for more spam. Yahoo! does not tolerate this practice and terminates accounts connected with spam-related activities without warning, causing spammers to lose access to any other Yahoo! services connected with their ID under the Terms of Service. Additionally, Yahoo! stresses that its servers are based in California and any spam-related activity which uses its servers could potentially violate that state's anti-spam laws. In February 2006, Yahoo! announced its decision (along with AOL) to give some organizations the option to "certify" mail by paying up to one cent for each outgoing message, allowing the mail in question to bypass inbound spam filters. Few mailers used it and, Goodmail, the company running the certification process, shut down in 2011. === Filters === In order to prevent abuse, in 2002 Yahoo! Mail activated filters which changed certain words (that could trigger unwanted JavaScript events) and word fragments into other words. "mocha" was changed to "espresso", "expression" became "statement", and "eval" (short for "evaluation") became "review". This resulted in many unintended corrections, such as "prevent" (prevalent), "revalidation" (evaluation) and "media review" (medieval). When asked about these changes, Yahoo! explained that the changed words were common terms used in their privacy dashboard and were blacklisted to prevent hackers from sending damaging commands via the program's HTML function. Starting before February 7, 2006, Yahoo! Mail ended the practice, and began to add an underscore as a prefix to certain suspicious words and word fragments. === Greylisting === Incoming mail to Yahoo! addresses can be subjected to deferred delivery as part of Yahoo's incoming spam controls. This can delay delivery of mail sent to Yahoo! addresses without the sender or recipients being aware of it. The deferral is typically of short duration, but

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