AI Art Krishna

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ACL Data Collection Initiative

The ACL Data Collection Initiative (ACL/DCI) was a project established in 1989 by the Association for Computational Linguistics (ACL) to create and distribute large text and speech corpora for computational linguistics research. The initiative aimed to address the growing need for substantial text databases that could support research in areas such as natural language processing, speech recognition, and computational linguistics. By 1993, the initiative’s activities had effectively ceased, with its functions and datasets absorbed by the Linguistic Data Consortium (LDC), which was founded in 1992. == Objectives == The ACL/DCI had several key objectives: To acquire a large and diverse text corpus from various sources To transform the collected texts into a common format based on the Standard Generalized Markup Language (SGML) To make the corpus available for scientific research at low cost with minimal restrictions To provide a common database that would allow researchers to replicate or extend published results To reduce duplication of effort among researchers in obtaining and preparing text data These objectives were designed to address the growing demand for very large amounts of text arising from applications in recognition and analysis of text and speech. Its core objective was to "oversee the acquisition and preparation of a large text corpus to be made available for scientific research at cost and without royalties". == History == By the late 1980s, researchers in computational linguistics and speech recognition faced a significant problem: the lack of large-scale, accessible text corpora for developing statistical models and testing algorithms. Existing generally available text databases were too small to meet the needs of developing applications in text and speech recognition. The initiative was formed to meet this need by collecting, standardizing, and distributing large quantities of text data with minimal restrictions for scientific research. As stated by Liberman (1990), "research workers have been severely hampered by the lack of appropriate materials, and specially by the lack of a large enough body of text on which published results can be replicated or extended by others." The ACL/DCI committee was established in February 1989. The committee included members from academic and industrial research laboratories in the United States and Europe. The initiative was chaired by Mark Liberman from the University of Pennsylvania (formerly of AT&T Bell Laboratories). Other committee members included representatives from organizations such as Bellcore, IBM T.J. Watson Research Center, Cambridge University, Virginia Polytechnic Institute & State University, Northeastern University, University of Pennsylvania, SRI International, MCC, Xerox PARC, ISSCO, and University of Pisa. The project operated initially without dedicated funding, relying on volunteer efforts from committee members and their affiliated institutions. Key supporters included AT&T Bell Labs, Bellcore, IBM, Xerox, and the University of Pennsylvania, which allowed the use of their computing facilities for ACL/DCI-related work. Previously running on volunteer effort pro bono, in 1991, it obtained funding from General Electric and the National Science Foundation (IRI-9113530). == Data == As of 1990, the ACL/DCI had collected hundreds of millions of words of diverse text. The collection included: Wall Street Journal articles (25 to 50 million words); Canadian Hansard (parliamentary records) in parallel English and French versions: cleaned-up English Hansard donated by the IBM alignment models group (100 million words), and original Bilingual Hansard (from a different time period) obtained directly (200 million words). Collins English Dictionary (1979 edition), both as fulltext (3 million words) and as various "database" versions, constructed using "typographers' tape" donated by Collins, which were computer tapes containing the structured digital data used to typeset and print the 1979 edition of the dictionary; Emails from ARPANET newsletters for the ACM Special Interest Group on Information Retrieval Forum (IRLIST) and AIList Digest issues distributed over the ARPANET (AILIST) (5 million words), both collected by Edward A. Fox at VIPSU; Articles on networking (2 million words); U.S. Department of Agriculture Extension Service Fact Sheets (>1 million words); 200,000 scientific abstracts of about 1,500 words each from the Department of Energy (25 million words); Archives of the Challenger Investigation Commission, including transcripts of depositions and hearings (2.5 million words); Books from the Library of America, including works by Mark Twain, Eugene O'Neill, Ralph Waldo Emerson, Herman Melville, W.E.B. DuBois, Willa Cather, and Benjamin Franklin (130 books, 20 million words); Public domain books like the King James Bible, Tristram Shandy, The Federalist Papers; Several million words of transcribed radiologists' reports, donated by Francis Ganong at Kurzweil Applied Intelligence Inc (about 5 million words); The Child Language Data Exchange corpus of child language acquisition transcripts; U.S. Department of Justice Justice Retrieval and Inquiry System (JURIS) materials; The Swiss Civil Code in parallel German, French and Italian; Economic reports from the Union Bank of Switzerland, in parallel English, German, French and Italian; About 12K words of administrative policy manuals and 14K words of administrative memos, contributed by Geoff Pullum of U.C.S.C.; Material from various ACM journals and the ACL journal Computational Linguistics; The CSLI publications series: 50-100 reports (8K words each) and 5-10 books (80K words each). The initiative started with North American English text but expanded to include Canadian French and planned to include Japanese, Chinese, and other Asian languages. At least 5 million words from the collection were tagged under the Penn Treebank project, and those tags were distributed by DCI as well. After DCI was absorbed by the LDC, the datasets were curated under LDC. == Format == The ACL/DCI corpus was coded in a standard form based on SGML (Standard Generalized Markup Language, ISO 8879), consistent with the recommendations of the Text Encoding Initiative (TEI), of which the DCI was an affiliated project. The TEI was a joint project of the ACL, the Association for Computers and the Humanities, and the Association for Literary and Linguistic Computing, aiming to provide a common interchange format for literary and linguistic data. The initiative planned to add annotations reflecting consensually approved linguistic features like part of speech and various aspects of syntactic and semantic structure over time. == Examples == As an example of the use of ACL/DCI, consider the Wall Street Journal (WSJ) corpus for speech recognition research. The WSJ corpus was used as the basis for the DARPA Spoken Language System (SLS) community's Continuous Speech Recognition (CSR) Corpus. The WSJ corpus became a standard benchmark for evaluating speech recognition systems and has been used in numerous research papers. The WSJ CSR Corpus provided DARPA with its first general-purpose English, large vocabulary, natural language, high perplexity corpus containing speech (400 hours) and text (47 million words) during 1987–89. The text corpus was 313 MB in size. The text was preprocessed to remove ambiguity in the word sequence that a reader might choose, ensuring that the unread text used to train language models was representative of the spoken test material. The preprocessing included converting numbers into orthographics, expanding abbreviations, resolving apostrophes and quotation marks, and marking punctuation. As another example, the Yarowsky algorithm used bitext data from DCI to train a simple word-sense disambiguation model that was competitive with advanced models trained on smaller datasets. == Distribution == Materials from the ACL/DCI collection were distributed to research groups on a non-commercial basis. By 1990, about 25 research groups and individual researchers had received tapes containing various portions of the collected material. To obtain the data, researchers had to sign an agreement not to redistribute the data or make direct commercial use of it. However, commercial application of "analytical materials" derived from the text, such as statistical tables or grammar rules, was explicitly permitted. The initiative first distributed data via 12-inch reels of 9-track tape, then via CD-ROMs. Each such tape could contain 30 million words compressed via the Lempel-Ziv algorithms. The first CD-ROM distribution was in 1991, funded by Dragon Systems Inc. It contained Collins English Dictionary, WSJ, scientific abstracts provided by the U.S. Department of Energy, and the Penn Treebank.
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Cryptee

Cryptee is a privacy focused client-side encrypted and cross-platform productivity suite and data storage service. == History == Cryptee was founded in 2017, by John Ozbay, a cybersecurity researcher, commenter, and activist, to exclusively focus on providing a secure document editing service similar to Google Docs and Photos for everyone, with a particular focus on victims and survivors of domestic abuse, journalists and reporters. == Software == Users can write personal documents, notes, journals, store images, videos, and various kinds of other files. The source code of Cryptee is open source and publicly available to allow anyone to audit the service with ease, and help identify errors or potential vulnerabilities in a public and transparent manner. Cryptee has a few key features that differentiate it from other services in the industry, such as its Ghost Folders and Ghost Albums features, built specifically with victims and survivors of domestic abuse, journalists and reporters in mind. Cryptee allows users to hide (ghost) folders for plausible deniability also as known as deniable encryption in the field of cryptography and steganography, and ensure privacy even under coercion. === Features === Cryptee Docs' features include: To-do lists, Markdown support, KaTeX math and file attachments. cross-platform accessible, as it is a progressive web app. Bulk transfer from other note taking apps such as Evernote. Encrypted PDF and print-accurate (A4 and U.S. Letter paper-sized) text editing. Ability to edit docx files Cryptee Photos' features include: Ability to create slideshows. Ability to store original quality of photos. Ability to tag photos for organization. === Commercial strategy === The company's commercial strategy is focused on offering to its users an open source and transparent Photo Storage, Document Editor and Cloud Storage services without trackers or advertisements as it seeks to compete with Google Docs, Google Photos and similar services through its offerings. === Privacy === Cryptee utilizes zero-access storage to safe-keep all users' sensitive digital belongings. == Advocacy == === Lockdown mode === In July 2022, to fortify iPhones against the Pegasus Spyware, Apple announced a new, upcoming Lockdown Mode feature in iOS 16, welcomed by many experts. In the following weeks after Apple's announcement, in August 2022, the Founder and CEO of Cryptee, and privacy activist John Ozbay published their research detailing shortcoming of Apple's Lockdown Mode. They demonstrated that enabling Lockdown Mode makes it possible for all websites and online ads to be able to detect if users have Lockdown Mode enabled or not. This was due to the fact that disabling web fonts (an attack surface) was detectable by websites. === Confrontations against Apple === ==== On PWAs ==== In February 2024, Apple announced plans to kill progressive web apps on iOS devices in the EU, claiming it was to comply with the Digital Markets Act (DMA). The announcement was criticized as anti-competitive by many in the tech industry, including by Tim Sweeney, the CEO of Epic Games. In response, Cryptee started working together with Open Web Advocacy (OWA), an international not-for-profit digital rights group to advocate for the future of the open web, promote web browser choice on mobile operating systems through challenging Apple's anti-competitive third party browser engine ban, and to champion the use and equality of progressive web apps over native apps, by reaching out to the European Union's Digital Markets Act (DMA) team. To better understand the consequences of Apple's decision to kill web apps, the EU announced that they "seek to investigate Apple over cutting off web apps", and that they sent "requests for information to Apple and to app developers, who can provide useful information for our assessment". Apart from sending a response to the EU, Cryptee, along with the OWA, launched an open letter to Tim Cook, which in 48 hours, got thousands of signatories including European Parliament Members Karen Melchior and Patrick Breyer; and thousands of other developers and organizations from over 100 countries. Consequently, 24 hours later, Apple backed off, and reversed course on its plan to cut off progressive web apps in the EU. ==== Ozbay's representations ==== Following the events, eventually on March 18, 2024, Founder and CEO of Cryptee John Ozbay represented the Open Web Advocacy group in European Union's Digital Markets Act (DMA) hearing for Apple. At the hearing, OWA confronted Apple, accused Apple of "maliciously intending to undermine user choice", and stated that there was no defense for Apple's behavior. In response, according to the tech news outlet Ars Technica, Apple's spokesperson "seemed to dodge Ozbay's question". ==== Cooperation with the EU ==== Within a week of the hearing, the European Union announced a DMA non-compliance investigation against Apple and United States' Department of Justice filed an antitrust lawsuit against Apple. A few months later, on June 27, 2024, Cryptee, in cooperation with EDRi — an international advocacy group, along with Article 19 — a British international human rights organization, Privacy International, F-Droid, Free Software Foundation Europe, Guardian Project and others have submitted a comprehensive analysis to the European Commission about how Apple's plans to comply with the Digital Markets Act are insufficient. == Reviews == In a 2018 article, Wall Street Journal's MarketWatch reviewed Cryptee, articulating the fact that Cryptee offers zero-access storage for photos, files, documents and notes, and pointed out that: "Being based in Estonia puts Cryptee outside the “14 eyes jurisdiction,” an international surveillance alliance of European Union and North American countries, making it less likely it will be targeted with demands for data". In addition, the review highlighted Cryptee's Ghost Folders feature which ensures privacy even under coercion. In a 2019 article, Reclaim The Net named Cryptee as one of the "5 great privacy-focused Evernote alternatives to keep your notes safe", underlining that: "When it comes to security, this app is state of the art." and that "When making this app, the developers thought about every aspect of security and have taken every precaution to make it as secure as possible.". The review further underscored Cryptee's open-source nature, its strong encryption, and easy migration features. In a 2021 article, The Verge reviewed Cryptee, pointing out that Cryptee, based out of Europe, is one of the main photo storage service alternatives to Google Photos, and that it's their recommendation for users who are "concerned about privacy and like the idea of encryption" as Cryptee "offers to keep all your photos encrypted using AES-256". In a 2024 article, Beebom, enlisted Cryptee as one of the "7 best iCloud Photos Alternatives for iPhone and iPad", complimenting Cryptee's simplicity, its use of encryption to safeguard users' photos against hacking by not storing any unencrypted data. The article also provided further attention to Cryptee's additional features such as such as Ghost Albums, slideshows, easy-to-use drag and drop uploads, tagging and users' ability to store original-quality photos on Cryptee, concluding that Cryptee is "a safe bet if you are on the lookout for a privacy-centric iCloud Photos alternative".
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WikiTok

WikiTok is a web application that shows random Wikipedia articles in an infinite scrolling format similar to TikTok. Created by New York-based civil and software engineer Isaac Gemal in February 2025, the application aims to provide an "anti-algorithmic" alternative to traditional social media platforms while combating doomscrolling. == Development == WikiTok was developed on February 3, 2025, after Gemal saw a viral tweet from developer Tyler Angert proposing "all of wikipedia on a single, scrollable page". Gemal created the initial prototype in approximately two hours, using the Claude LLM and Cursor coding editor, completing it by 2 a.m. that same night. The application was built with React 18, TypeScript, Tailwind CSS, and Vite, consisting of only a few hundred lines of code with no backend infrastructure. Gemal has made the project open-source, with the code publicly available on GitHub. == Functionality == WikiTok serves users completely random Wikipedia article snippets from Wikipedia's nearly 9.5 million entries. Each article preview includes a full-screen image from Wikipedia and a short text excerpt. Users can click "Read More" to access the full Wikipedia article, or continue scrolling to see the next random entry. The application functions as a progressive web app that can be downloaded as a hybrid between an app and a website. It works on both mobile and desktop browsers, and is designed to be responsive whether users scroll with their thumb on mobile devices or use a cursor on desktop computers. As of 2025, WikiTok supports 14 different languages for article translation. == Philosophy == Gemal has explicitly positioned WikiTok as "anti-algorithmic," resisting numerous requests to implement personalized content algorithms. In an interview with Business Insider, Gemal stated: "I have had plenty of people message me and even make issues on my GitHub asking for some insane crazy WikiTok algorithm... we're already ruled by ruthless, opaque algorithms in our everyday life; why can't we just have one little corner in the world without them?" The application aims to combat "doomscrolling" – the practice of mindlessly consuming negative news online that can cause anxiety, helplessness, and anger. Unlike traditional social media platforms, WikiTok deliberately excludes algorithms, advertisements, and user tracking.
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Convolution

In mathematics (in particular, functional analysis), convolution is a mathematical operation on two functions f {\displaystyle f} and g {\displaystyle g} that produces a third function f ∗ g {\displaystyle fg} , as the integral of the product of the two functions after one is reflected about the y-axis and shifted. The term convolution refers to both the resulting function and to the process of computing it. The integral is evaluated for all values of shift, producing the convolution function. The choice of which function is reflected and shifted before the integral does not change the integral result (see commutativity). Graphically, it expresses how the 'shape' of one function is modified by the other. Some features of convolution are similar to cross-correlation: for real-valued functions, of a continuous or discrete variable, convolution f ∗ g {\displaystyle fg} differs from cross-correlation f ⋆ g {\displaystyle f\star g} only in that either f ( x ) {\displaystyle f(x)} or g ( x ) {\displaystyle g(x)} is reflected about the y-axis in convolution; thus it is a cross-correlation of g ( − x ) {\displaystyle g(-x)} and f ( x ) {\displaystyle f(x)} , or f ( − x ) {\displaystyle f(-x)} and g ( x ) {\displaystyle g(x)} . For complex-valued functions, the cross-correlation operator is the adjoint of the convolution operator. Convolution has applications that include probability, statistics, acoustics, spectroscopy, signal processing and image processing, computer vision and human vision, geophysics, engineering, physics, and differential equations. The convolution can be defined for functions on Euclidean space and other groups (as algebraic structures). For example, periodic functions, such as the discrete-time Fourier transform, can be defined on a circle and convolved by periodic convolution. (See row 18 at DTFT § Properties.) A discrete convolution can be defined for functions on the set of integers. Generalizations of convolution have applications in the field of numerical analysis and numerical linear algebra, and in the design and implementation of finite impulse response filters in signal processing. Computing the inverse of the convolution operation is known as deconvolution. == Definition == The convolution of f {\displaystyle f} and g {\displaystyle g} is written f ∗ g {\displaystyle fg} , denoting the operator with the symbol ∗ {\displaystyle } . It is defined as the integral of the product of the two functions after one is reflected about the y-axis and shifted. As such, it is a particular kind of integral transform: ( f ∗ g ) ( t ) := ∫ − ∞ ∞ f ( τ ) g ( t − τ ) d τ . {\displaystyle (fg)(t):=\int _{-\infty }^{\infty }f(\tau )g(t-\tau )\,d\tau .} An equivalent definition is (see commutativity): ( f ∗ g ) ( t ) := ∫ − ∞ ∞ f ( t − τ ) g ( τ ) d τ . {\displaystyle (fg)(t):=\int _{-\infty }^{\infty }f(t-\tau )g(\tau )\,d\tau .} While the symbol t {\displaystyle t} is used above, it need not represent the time domain. At each t {\displaystyle t} , the convolution formula can be described as the area under the function f ( τ ) {\displaystyle f(\tau )} weighted by the function g ( − τ ) {\displaystyle g(-\tau )} shifted by the amount t {\displaystyle t} . As t {\displaystyle t} changes, the weighting function g ( t − τ ) {\displaystyle g(t-\tau )} emphasizes different parts of the input function f ( τ ) {\displaystyle f(\tau )} ; If t {\displaystyle t} is a positive value, then g ( t − τ ) {\displaystyle g(t-\tau )} is equal to g ( − τ ) {\displaystyle g(-\tau )} that slides or is shifted along the τ {\displaystyle \tau } -axis toward the right (toward + ∞ {\displaystyle +\infty } ) by the amount of t {\displaystyle t} , while if t {\displaystyle t} is a negative value, then g ( t − τ ) {\displaystyle g(t-\tau )} is equal to g ( − τ ) {\displaystyle g(-\tau )} that slides or is shifted toward the left (toward − ∞ {\displaystyle -\infty } ) by the amount of | t | {\displaystyle |t|} . For functions f {\displaystyle f} , g {\displaystyle g} supported on only [ 0 , ∞ ) {\displaystyle [0,\infty )} (i.e., zero for negative arguments), the integration limits can be truncated, resulting in: ( f ∗ g ) ( t ) = ∫ 0 t f ( τ ) g ( t − τ ) d τ for f , g : [ 0 , ∞ ) → R . {\displaystyle (fg)(t)=\int _{0}^{t}f(\tau )g(t-\tau )\,d\tau \quad \ {\text{for }}f,g:[0,\infty )\to \mathbb {R} .} For the multi-dimensional formulation of convolution, see domain of definition (below). === Notation === A common engineering notational convention is: f ( t ) ∗ g ( t ) := ∫ − ∞ ∞ f ( τ ) g ( t − τ ) d τ ⏟ ( f ∗ g ) ( t ) , {\displaystyle f(t)g(t)\mathrel {:=} \underbrace {\int _{-\infty }^{\infty }f(\tau )g(t-\tau )\,d\tau } _{(fg)(t)},} which has to be interpreted carefully to avoid confusion. For instance, f ( t ) ∗ g ( t − t 0 ) {\displaystyle f(t)g(t-t_{0})} is equivalent to ( f ∗ g ) ( t − t 0 ) {\displaystyle (fg)(t-t_{0})} , but f ( t − t 0 ) ∗ g ( t − t 0 ) {\displaystyle f(t-t_{0})g(t-t_{0})} is in fact equivalent to ( f ∗ g ) ( t − 2 t 0 ) {\displaystyle (fg)(t-2t_{0})} . === Relations with other transforms === Given two functions f ( t ) {\displaystyle f(t)} and g ( t ) {\displaystyle g(t)} with bilateral Laplace transforms (two-sided Laplace transform) F ( s ) = ∫ − ∞ ∞ e − s u f ( u ) d u {\displaystyle F(s)=\int _{-\infty }^{\infty }e^{-su}\ f(u)\ {\text{d}}u} and G ( s ) = ∫ − ∞ ∞ e − s v g ( v ) d v {\displaystyle G(s)=\int _{-\infty }^{\infty }e^{-sv}\ g(v)\ {\text{d}}v} respectively, the convolution operation ( f ∗ g ) ( t ) {\displaystyle (fg)(t)} can be defined as the inverse Laplace transform of the product of F ( s ) {\displaystyle F(s)} and G ( s ) {\displaystyle G(s)} . More precisely, F ( s ) ⋅ G ( s ) = ∫ − ∞ ∞ e − s u f ( u ) d u ⋅ ∫ − ∞ ∞ e − s v g ( v ) d v = ∫ − ∞ ∞ ∫ − ∞ ∞ e − s ( u + v ) f ( u ) g ( v ) d u d v {\displaystyle {\begin{aligned}F(s)\cdot G(s)&=\int _{-\infty }^{\infty }e^{-su}\ f(u)\ {\text{d}}u\cdot \int _{-\infty }^{\infty }e^{-sv}\ g(v)\ {\text{d}}v\\&=\int _{-\infty }^{\infty }\int _{-\infty }^{\infty }e^{-s(u+v)}\ f(u)\ g(v)\ {\text{d}}u\ {\text{d}}v\end{aligned}}} Let t = u + v {\displaystyle t=u+v} , then F ( s ) ⋅ G ( s ) = ∫ − ∞ ∞ ∫ − ∞ ∞ e − s t f ( u ) g ( t − u ) d u d t = ∫ − ∞ ∞ e − s t ∫ − ∞ ∞ f ( u ) g ( t − u ) d u ⏟ ( f ∗ g ) ( t ) d t = ∫ − ∞ ∞ e − s t ( f ∗ g ) ( t ) d t . {\displaystyle {\begin{aligned}F(s)\cdot G(s)&=\int _{-\infty }^{\infty }\int _{-\infty }^{\infty }e^{-st}\ f(u)\ g(t-u)\ {\text{d}}u\ {\text{d}}t\\&=\int _{-\infty }^{\infty }e^{-st}\underbrace {\int _{-\infty }^{\infty }f(u)\ g(t-u)\ {\text{d}}u} _{(fg)(t)}\ {\text{d}}t\\&=\int _{-\infty }^{\infty }e^{-st}(fg)(t)\ {\text{d}}t.\end{aligned}}} Note that F ( s ) ⋅ G ( s ) {\displaystyle F(s)\cdot G(s)} is the bilateral Laplace transform of ( f ∗ g ) ( t ) {\displaystyle (fg)(t)} . A similar derivation can be done using the unilateral Laplace transform (one-sided Laplace transform). The convolution operation also describes the output (in terms of the input) of an important class of operations known as linear time-invariant (LTI). See LTI system theory for a derivation of convolution as the result of LTI constraints. In terms of the Fourier transforms of the input and output of an LTI operation, no new frequency components are created. The existing ones are only modified (amplitude and/or phase). In other words, the output transform is the pointwise product of the input transform with a third transform (known as a transfer function). See Convolution theorem for a derivation of that property of convolution. Conversely, convolution can be derived as the inverse Fourier transform of the pointwise product of two Fourier transforms. == Visual explanation == == Historical developments == One of the earliest uses of the convolution integral appeared in D'Alembert's derivation of Taylor's theorem in Recherches sur différents points importants du système du monde, published in 1754. Also, an expression of the type: ∫ f ( u ) ⋅ g ( x − u ) d u {\displaystyle \int f(u)\cdot g(x-u)\,du} is used by Sylvestre François Lacroix on page 505 of his book entitled Treatise on differences and series, which is the last of 3 volumes of the encyclopedic series: Traité du calcul différentiel et du calcul intégral, Chez Courcier, Paris, 1797–1800. Soon thereafter, convolution operations appear in the works of Pierre Simon Laplace, Jean-Baptiste Joseph Fourier, Siméon Denis Poisson, and others. The term itself did not come into wide use until the 1950s or 1960s. Prior to that it was sometimes known as Faltung (which means folding in German), composition product, superposition integral, and Carson's integral. Yet it appears as early as 1903, though the definition is rather unfamiliar in older uses. The operation: ∫ 0 t φ ( s ) ψ ( t − s ) d s , 0 ≤ t < ∞ , {\displaystyle \int _{0}^{t}\varphi (s)\psi (t-s)\,ds,\quad 0\leq t<\infty ,} is a particular case of composition products considered by the Italian mathematician Vito Volterra in 1913. == Circular c
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Geometric hashing

In computer science, geometric hashing is a method for efficiently finding two-dimensional objects represented by discrete points that have undergone an affine transformation, though extensions exist to other object representations and transformations. In an off-line step, the objects are encoded by treating each pair of points as a geometric basis. The remaining points can be represented in an invariant fashion with respect to this basis using two parameters. For each point, its quantized transformed coordinates are stored in the hash table as a key, and indices of the basis points as a value. Then a new pair of basis points is selected, and the process is repeated. In the on-line (recognition) step, randomly selected pairs of data points are considered as candidate bases. For each candidate basis, the remaining data points are encoded according to the basis and possible correspondences from the object are found in the previously constructed table. The candidate basis is accepted if a sufficiently large number of the data points index a consistent object basis. Geometric hashing was originally suggested in computer vision for object recognition in 2D and 3D, but later was applied to different problems such as structural alignment of proteins. == Geometric hashing in computer vision == Geometric hashing is a method used for object recognition. Let’s say that we want to check if a model image can be seen in an input image. This can be accomplished with geometric hashing. The method could be used to recognize one of the multiple objects in a base, in this case the hash table should store not only the pose information but also the index of object model in the base. === Example === For simplicity, this example will not use too many point features and assume that their descriptors are given by their coordinates only (in practice local descriptors such as SIFT could be used for indexing). ==== Training Phase ==== Find the model's feature points. Assume that 5 feature points are found in the model image with the coordinates ( 12 , 17 ) ; {\displaystyle (12,17);} ( 45 , 13 ) ; {\displaystyle (45,13);} ( 40 , 46 ) ; {\displaystyle (40,46);} ( 20 , 35 ) ; {\displaystyle (20,35);} ( 35 , 25 ) {\displaystyle (35,25)} , see the picture. Introduce a basis to describe the locations of the feature points. For 2D space and similarity transformation the basis is defined by a pair of points. The point of origin is placed in the middle of the segment connecting the two points (P2, P4 in our example), the x ′ {\displaystyle x'} axis is directed towards one of them, the y ′ {\displaystyle y'} is orthogonal and goes through the origin. The scale is selected such that absolute value of x ′ {\displaystyle x'} for both basis points is 1. Describe feature locations with respect to that basis, i.e. compute the projections to the new coordinate axes. The coordinates should be discretised to make recognition robust to noise, we take the bin size 0.25. We thus get the coordinates ( − 0.75 , − 1.25 ) ; {\displaystyle (-0.75,-1.25);} ( 1.00 , 0.00 ) ; {\displaystyle (1.00,0.00);} ( − 0.50 , 1.25 ) ; {\displaystyle (-0.50,1.25);} ( − 1.00 , 0.00 ) ; {\displaystyle (-1.00,0.00);} ( 0.00 , 0.25 ) {\displaystyle (0.00,0.25)} Store the basis in a hash table indexed by the features (only transformed coordinates in this case). If there were more objects to match with, we should also store the object number along with the basis pair. Repeat the process for a different basis pair (Step 2). It is needed to handle occlusions. Ideally, all the non-colinear pairs should be enumerated. We provide the hash table after two iterations, the pair (P1, P3) is selected for the second one. Hash Table: Most hash tables cannot have identical keys mapped to different values. So in real life one won’t encode basis keys (1.0, 0.0) and (-1.0, 0.0) in a hash table. ==== Recognition Phase ==== Find interesting feature points in the input image. Choose an arbitrary basis. If there isn't a suitable arbitrary basis, then it is likely that the input image does not contain the target object. Describe coordinates of the feature points in the new basis. Quantize obtained coordinates as it was done before. Compare all the transformed point features in the input image with the hash table. If the point features are identical or similar, then increase the count for the corresponding basis (and the type of object, if any). For each basis such that the count exceeds a certain threshold, verify the hypothesis that it corresponds to an image basis chosen in Step 2. Transfer the image coordinate system to the model one (for the supposed object) and try to match them. If successful, the object is found. Otherwise, go back to Step 2. === Finding mirrored pattern === It seems that this method is only capable of handling scaling, translation, and rotation. However, the input image may contain the object in mirror transform. Therefore, geometric hashing should be able to find the object, too. There are two ways to detect mirrored objects. For the vector graph, make the left side positive, and the right side negative. Multiplying the x position by -1 will give the same result. Use 3 points for the basis. This allows detecting mirror images (or objects). Actually, using 3 points for the basis is another approach for geometric hashing. === Geometric hashing in higher-dimensions === Similar to the example above, hashing applies to higher-dimensional data. For three-dimensional data points, three points are also needed for the basis. The first two points define the x-axis, and the third point defines the y-axis (with the first point). The z-axis is perpendicular to the created axis using the right-hand rule. Notice that the order of the points affects the resulting basis
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Digital omnivore

A digital omnivore is a person who uses multiple modalities (devices) to access the Internet and other media content in their daily life. As people increasingly own mobile devices, cross-platform multimedia consumption has continued to shape the digital landscape, both in terms of the type of media content they consume and how they consume it. As of 2021, at least half of all global digital traffic is generated by mobile devices. == Connected devices and digital consumption == A 2015 study of digital media consumption showed that smartphones were primarily used for communication, and tablets were primarily used for entertainment – additionally, both were frequently used in conjuncture with other devices, like televisions. An earlier 2011 analysis of the way consumers in the U.S. viewed news content on their devices throughout the day demonstrated how people use different mobile devices for different functions. On a typical weekend morning, digital omnivores accessed their news using their tablet, favored their computer during the working day, and returned to tablet use in the evening, peaking between the hours of 9pm and midnight. Mobile phones were used for web-browsing throughout the day when users were away from their personal computer. Increased Wi-Fi availability and mobile broadband adoption have changed the way people are going online. In August 2011, more than a third (37.2%) of U.S. digital traffic coming from mobile phones occurred via a Wi-Fi connection while tablets, which traditionally required a Wi-Fi connection to access the Internet, are increasingly driving traffic using mobile broadband access. As of 2021, LTE, 5G, and other forms of mobile broadband access are available on the majority of mobile devices. Tablets contributed nearly 2% of all web browsing traffic in the United States in 2011. During this period, iPads also began to account for a higher share of Internet traffic than iPhones (46.8% vs. 42.6% of all iOS device traffic. == Implications for marketing, advertisers and publishers == As of 2021, the average amount of time spent daily consuming digital media was eight hours, an increase from 2020 and a further increase from 2019, partially as a result of the COVID-19 pandemic. Social media platforms such as Instagram, Facebook, Twitter, and TikTok, as well as other online platforms like YouTube, incorporate advertisements into the in-app or online experience, with some offering the ability to shop for and sell items through the app or website.
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Multicloud

Multicloud (also written as multi-cloud or multi cloud) is a term with varying interpretations, generally referring to a system using multiple cloud computing providers. According to ISO/IEC 22123-1: "multi-cloud is a cloud deployment model in which a customer uses public cloud services provided by two or more cloud service providers". Multi-cloud can involve various deployment models, including public, private, and hybrid clouds, and multiple service models, such as Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS). Multicloud incorporates workload, data, traffic, and workflow portability options, which can result in varying implementation complexity. When effectively implemented, multicloud solutions can enhance architectural resilience, reduce dependence on a single vendor, and improve flexibility by leveraging services from different providers. However, multicloud strategies also present challenges, including increased operational complexity, security risks, higher costs, and integration difficulties. According to the 2024 State of the Cloud Report by Flexera, multi-cloud adoption has continued to rise in 2024. Enterprises increasingly silo applications into specific clouds and select best-fit services. Key use cases include data analysis in separate clouds and cross-cloud disaster recovery. == Advantages and challenges == There are several advantages to using a multicloud approach, including the ability to negotiate better pricing with cloud providers, the ability to quickly switch to another provider if needed, and the ability to avoid vendor lock-in. Multicloud can also be a good way to hedge against the risks of obsolescence, as it allows you to rely on multiple vendors and open standards, which can prolong the life of your systems. Additional benefits of the multicloud architecture include adherence to local policies that require certain data to be physically present within the area/country, geographical distribution of processing requests from physically closer cloud unit which in turn reduces latency and protect against disasters. Various issues and challenges also present themselves in a multicloud environment. Security and governance is more complicated, and more "moving parts" may create resiliency issues. == Difference between multicloud and hybrid cloud == Multicloud differs from hybrid cloud in that it refers to multiple cloud services from different vendors rather than multiple deployment modes (on-premises hardware, and public and private, cloud hosting). However, when considering a broad definition of multi-cloud, hybrid cloud can still be regarded as a special form of multi-cloud.
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Curvelet

Curvelets are a non-adaptive technique for multi-scale object representation. Being an extension of the wavelet concept, they are becoming popular in similar fields, namely in image processing and scientific computing. Wavelets generalize the Fourier transform by using a basis that represents both location and spatial frequency. For 2D or 3D signals, directional wavelet transforms go further, by using basis functions that are also localized in orientation. A curvelet transform differs from other directional wavelet transforms in that the degree of localisation in orientation varies with scale. In particular, fine-scale basis functions are long ridges; the shape of the basis functions at scale j is 2 − j {\displaystyle 2^{-j}} by 2 − j / 2 {\displaystyle 2^{-j/2}} so the fine-scale bases are skinny ridges with a precisely determined orientation. Curvelets are an appropriate basis for representing images (or other functions) which are smooth apart from singularities along smooth curves, where the curves have bounded curvature, i.e. where objects in the image have a minimum length scale. This property holds for cartoons, geometrical diagrams, and text. As one zooms in on such images, the edges they contain appear increasingly straight. Curvelets take advantage of this property, by defining the higher resolution curvelets to be more elongated than the lower resolution curvelets. However, natural images (photographs) do not have this property; they have detail at every scale. Therefore, for natural images, it is preferable to use some sort of directional wavelet transform whose wavelets have the same aspect ratio at every scale. When the image is of the right type, curvelets provide a representation that is considerably sparser than other wavelet transforms. This can be quantified by considering the best approximation of a geometrical test image that can be represented using only n {\displaystyle n} wavelets, and analysing the approximation error as a function of n {\displaystyle n} . For a Fourier transform, the squared error decreases only as O ( 1 / n ) {\displaystyle O(1/{\sqrt {n}})} . For a wide variety of wavelet transforms, including both directional and non-directional variants, the squared error decreases as O ( 1 / n ) {\displaystyle O(1/n)} . The extra assumption underlying the curvelet transform allows it to achieve O ( ( log ⁡ n ) 3 / n 2 ) {\displaystyle O({(\log n)}^{3}/{n^{2}})} . Efficient numerical algorithms exist for computing the curvelet transform of discrete data. The computational cost of the discrete curvelet transforms proposed by Candès et al. (Discrete curvelet transform based on unequally-spaced fast Fourier transforms and based on the wrapping of specially selected Fourier samples) is approximately 6–10 times that of an FFT, and has the same dependence of O ( n 2 log ⁡ n ) {\displaystyle O(n^{2}\log n)} for an image of size n × n {\displaystyle n\times n} . == Curvelet construction == To construct a basic curvelet ϕ {\displaystyle \phi } and provide a tiling of the 2-D frequency space, two main ideas should be followed: Consider polar coordinates in frequency domain Construct curvelet elements being locally supported near wedges The number of wedges is N j = 4 ⋅ 2 ⌈ j 2 ⌉ {\displaystyle N_{j}=4\cdot 2^{\left\lceil {\frac {j}{2}}\right\rceil }} at the scale 2 − j {\displaystyle 2^{-j}} , i.e., it doubles in each second circular ring. Let ξ = ( ξ 1 , ξ 2 ) T {\displaystyle {\boldsymbol {\xi }}=\left(\xi _{1},\xi _{2}\right)^{T}} be the variable in frequency domain, and r = ξ 1 2 + ξ 2 2 , ω = arctan ⁡ ξ 1 ξ 2 {\displaystyle r={\sqrt {\xi _{1}^{2}+\xi _{2}^{2}}},\omega =\arctan {\frac {\xi _{1}}{\xi _{2}}}} be the polar coordinates in the frequency domain. We use the ansatz for the dilated basic curvelets in polar coordinates: ϕ ^ j , 0 , 0 := 2 − 3 j 4 W ( 2 − j r ) V ~ N j ( ω ) , r ≥ 0 , ω ∈ [ 0 , 2 π ) , j ∈ N 0 {\displaystyle {\hat {\phi }}_{j,0,0}:=2^{\frac {-3j}{4}}W(2^{-j}r){\tilde {V}}_{N_{j}}(\omega ),r\geq 0,\omega \in [0,2\pi ),j\in N_{0}} To construct a basic curvelet with compact support near a ″basic wedge″, the two windows W {\displaystyle W} and V ~ N j {\displaystyle {\tilde {V}}_{N_{j}}} need to have compact support. Here, we can simply take W ( r ) {\displaystyle W(r)} to cover ( 0 , ∞ ) {\displaystyle (0,\infty )} with dilated curvelets and V ~ N j {\displaystyle {\tilde {V}}_{N_{j}}} such that each circular ring is covered by the translations of V ~ N j {\displaystyle {\tilde {V}}_{N_{j}}} . Then the admissibility yields ∑ j = − ∞ ∞ | W ( 2 − j r ) | 2 = 1 , r ∈ ( 0 , ∞ ) . {\displaystyle \sum _{j=-\infty }^{\infty }\left|W(2^{-j}r)\right|^{2}=1,r\in (0,\infty ).} see Window Functions for more information For tiling a circular ring into N {\displaystyle N} wedges, where N {\displaystyle N} is an arbitrary positive integer, we need a 2 π {\displaystyle 2\pi } -periodic nonnegative window V ~ N {\displaystyle {\tilde {V}}_{N}} with support inside [ − 2 π N , 2 π N ] {\displaystyle \left[{\frac {-2\pi }{N}},{\frac {2\pi }{N}}\right]} such that ∑ l = 0 N − 1 V ~ N 2 ( ω − 2 π l N ) = 1 {\displaystyle \sum _{l=0}^{N-1}{\tilde {V}}_{N}^{2}\left(\omega -{\frac {2\pi l}{N}}\right)=1} , for all ω ∈ [ 0 , 2 π ) {\displaystyle \omega \in \left[0,2\pi \right)} , V ~ N {\displaystyle {\tilde {V}}_{N}} can be simply constructed as 2 π {\displaystyle 2\pi } -periodizations of a scaled window V ( N ω 2 π ) {\displaystyle V\left({\frac {N\omega }{2\pi }}\right)} . Then, it follows that ∑ l = 0 N j − 1 | 2 3 j 4 ϕ ^ j , 0 , 0 ( r , ω − 2 π l N j ) | 2 = | W ( 2 − j r ) | 2 ∑ l = 0 N j − 1 V ~ N j 2 ( ω − 2 π l N ) = | W ( 2 − j r ) | 2 {\displaystyle \sum _{l=0}^{N_{j}-1}\left|2^{\frac {3j}{4}}{\hat {\phi }}_{j,0,0}\left(r,\omega -{\frac {2\pi l}{N_{j}}}\right)\right|^{2}=\left|W(2^{-j}r)\right|^{2}\sum _{l=0}^{N_{j}-1}{\tilde {V}}_{N_{j}}^{2}\left(\omega -{\frac {2\pi l}{N}}\right)=\left|W(2^{-j}r)\right|^{2}} For a complete covering of the frequency plane including the region around zero, we need to define a low pass element ϕ ^ − 1 := W 0 ( | ξ | ) {\displaystyle {\hat {\phi }}_{-1}:=W_{0}(\left|\xi \right|)} with W 0 2 ( r ) 2 := 1 − ∑ j = 0 ∞ W ( 2 − j r ) 2 {\displaystyle W_{0}^{2}(r)^{2}:=1-\sum _{j=0}^{\infty }W(2^{-j}r)^{2}} that is supported on the unit circle, and where we do not consider any rotation. == Applications == Image processing Seismic exploration Fluid mechanics PDEs solving Compressed sensing
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International Road Traffic and Accident Database

The International Road Traffic and Accident Database (IRTAD) is an initiative dedicated to compiling and analyzing global road crash data. It is managed by the International Transport Forum (ITF) under the auspices of its permanent working group, which specializes in road safety, commonly referred to as the IRTAD Group. The primary objective of IRTAD is to provide a robust empirical basis for international comparisons in the field of road safety and to offer data to support the formulation of effective road safety policies. == Data availability == A portion of the data gathered by IRTAD is accessible for free through the OECD statistics website, however the remaining data requires a subscription for access. == History == The IRTAD database was originally started in 1988 by Germany's Federal Institution for Roads (BASt) in response to demands for international comparative data. It was later taken over and expanded by the International Transport Forum and has grown to be an important resource for comparing road safety metrics between countries worldwide, although mostly in the developed world. Every year, the ITF publishes comparative and country-by-country road safety data gathered for the IRTAD database and analysed by the IRTAD Group in the ITF Road Safety Annual Report, informally known as "IRTAD Report". Over the years, the IRTAD acronym has come to stand not only for the database, but also for the Traffic Safety Data and Analysis Group (usually referred to as IRTAD Group). The IRTAD Group is the International Transport Forum's permanent working group on road safety. It consists of a group of international road safety experts drawn from national road administrations, road safety research institutes, International organizations, automobile associations, insurance companies, car manufacturers and other road safety stakeholders. The IRTAD Group is a major forum for international road safety collaboration and exchange of best practices. Its focus is on improving road safety data as a basis for targeting interventions that are effective in reducing the number of road deaths and serious traffic injuries. The work of IRTAD, among that of others, has spawned the creation of road safety observatories for different world regions: the Ibero-American Road Safety Observatory Archived 2020-06-28 at the Wayback Machine (OISEVI), the African Road Safety Observatory Archived 2020-06-10 at the Wayback Machine, and the South-East Asian Road Safety Observatory. The ITF supports OISEVI through the Spanish-language IRTAD-LAC database and is actively involved in the implementation of the African and South East-Asian observatories. The genesis of the road safety observatory movement dates back to 2008, when the ITF, via IRTAD, began to facilitate twinning between countries striving to improve their road safety record and countries with high road safety performance. The initial twinning was between Jamaica and the United Kingdom. This work was supported by the World Bank, the Inter-American Development Bank (IADB) and the FIA Foundation. The twinning between Argentina and Spain in 2011 led to the creation of OISEVI. To this day, the ITF supports OISEVI through the Spanish-language IRTAD-LAC database. In 2006, the ITF set up Safer City Streets, a global traffic safety network for cities that replicates the successful IRTAD approach for urban road safety.
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JDoodle

JDoodle is a cloud-based online integrated development environment and compiler platform that supports execution of source code in 70+ programming languages including Java, Python, C/C++, PHP, Ruby, Perl, HTML, and more. It provides zero‑setup code for compilation, execution, and sharing via a web browser interface. == Features == Provides real‑time collaboration and code embedding via shareable URLs and APIs Offers an integrated terminal interface supporting database engines such as MySQL and MongoDB. JDroid — AI‑assistant to generate code snippets, optimize code, and assist debugging. == Languages and frameworks supported ==
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Human visual system model

A human visual system model (HVS model) is used by image processing, video processing and computer vision experts to deal with biological and psychological processes that are not yet fully understood. Such a model is used to simplify the behaviors of what is a very complex system. As our knowledge of the true visual system improves, the model is updated. Psychovisual study is the study of the psychology of vision. The human visual system model can produce desired effects in perception and vision. Examples of using an HVS model include color television, lossy compression, and Cathode-ray tube (CRT) television. Originally, it was thought that color television required too high a bandwidth for the then available technology. Then it was noticed that the color resolution of the HVS was much lower than the brightness resolution; this allowed color to be squeezed into the signal by chroma subsampling. Another example is lossy image compression, like JPEG. Our HVS model says we cannot see high frequency detail, so in JPEG we can quantize these components without a perceptible loss of quality. Similar concepts are applied in audio compression, where sound frequencies inaudible to humans are band-stop filtered. Several HVS features are derived from evolution when we needed to defend ourselves or hunt for food. We often see demonstrations of HVS features when we are looking at optical illusions. == Block diagram of HVS == == Assumptions about the HVS == Low-pass filter characteristic (limited number of rods in human eye): see Mach bands Lack of color resolution (fewer cones in human eye than rods) Motion sensitivity More sensitive in peripheral vision Stronger than texture sensitivity, e.g. viewing a camouflaged animal Texture stronger than disparity – 3D depth resolution does not need to be so accurate Integral Face recognition (babies smile at faces) Depth inverted face looks normal (facial features overrule depth information) Upside down face with inverted mouth and eyes looks normal == Examples of taking advantage of an HVS model == Flicker frequency of film and television using persistence of vision to fool viewer into seeing a continuous image Interlaced television painting half images to give the impression of a higher flicker frequency Color television (chrominance at half resolution of luminance corresponding to proportions of rods and cones in eye) Image compression (difficult to see higher frequencies more harshly quantized) Motion estimation (use luminance and ignore color) Watermarking and Steganography
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Outlook on the web

Outlook on the web (formerly Outlook Web App and Outlook Web Access) is a personal information manager web app from Microsoft. It is a web-based version of Microsoft Outlook, and is included in Exchange Server and Exchange Online (a component of Microsoft 365). It can be freely accessed from any web browser whether inside or outside an organization's network, and includes a web email client, a calendar tool, a contact manager, and a task manager. It also includes add-in integration, Skype on the web, and alerts as well as unified themes that span across all the web apps. == Purpose == Outlook on the web is available to Microsoft 365 (formerly Office 365) and Exchange Online subscribers, and is included with the on-premises Exchange Server, to enable users to connect to their email accounts via a web browser, without requiring the installation of Microsoft Outlook or other email clients. In case of Exchange Server, it is hosted on a local intranet and requires a network connection to the Exchange Server for users to work with e-mail, address book, calendars and task. The Exchange Online version, which can be bought either independently or through Office 365 licensing program, is hosted on Microsoft servers on the World Wide Web. == History == Outlook Web Access was created in 1995 by Microsoft Program Manager Thom McCann on the Exchange Server team. An early working version was demonstrated by Microsoft Vice President Paul Maritz at Microsoft's famous Internet summit in Seattle on December 27, 1995. The first customer version was shipped as part of the Exchange Server 5.0 release in early 1997. The first component to allow client-side scripts to issue HTTP requests (XMLHTTP) was originally written by the Outlook Web Access team. It soon became a part of Internet Explorer 5. Renamed XMLHttpRequest and standardized by the World Wide Web Consortium, it has since become one of the cornerstones of the Ajax technology used to build advanced web apps. Outlook Web Access was later renamed Outlook Web App in 2010. An update on August 4, 2015, renamed OWA to "Outlook on the web", often referred to in brief as simply "Outlook". == Components == === Mail === Mail is the webmail component of Outlook on the web. The default view is a three column view with folders and groups on the left, an email message list in the middle, and the selected message on the right. With the 2015 update, Microsoft introduced the ability to pin, sweep and archive messages, and undo the last action, as well as richer image editing features. It can connect to other services such as GitHub and Twitter through Office 365 Connectors. Actionable Messages in emails allows a user to complete a task from within the email, such as retweeting a Tweet on Twitter or setting a meeting date on a calendar. Outlook on the web supports S/MIME and includes features for managing calendars, contacts, tasks, documents (used with SharePoint or Office Web Apps), and other mailbox content. In the Exchange 2007 release, Outlook on the web (still called Outlook Web App at the time) also offers read-only access to documents stored in SharePoint sites and network UNC shares. === Calendar === Calendar is the calendaring component of Outlook on the web. With the update, Microsoft added a weather forecast directly in the Calendar, as well as icons (or "charms") as visual cues for an event. In addition, email reminders came to all events, and a special Birthday and Holiday event calendars are created automatically. Calendars can be shared and there are multiple views such as day, week, month, and today. Another view is work week which includes Mondays through Fridays in the calendar view. Calendar's "Board View" feature allows for a customizable calendar with widgets such as Goal, Calendar, Tasks and Tips. Calendar details can be added with HTML and rich-text editing, and files can be attached to calendar events and appointments. === People === People is the contact manager component of Outlook on the web. A user can search and edit existing contacts, as well as create new ones. Contacts can be placed into folders and duplicate contacts can be linked from multiple sources such as LinkedIn or Twitter. In Outlook Mail, a contact can be created by clicking on an email address sender, which pulls down a contact card with an add button to add to Outlook People. Contacts can be imported as well as placed into a list that can be utilized when composing an email in Outlook Mail. People can also sync with friends and connections lists on LinkedIn, Facebook, and Twitter. === To Do === To Do was originally launched as Tasks for Outlook Web App. Microsoft was slowly rolling out a preview of Tasks to its consumer-based Outlook.com service that in May 2015, was announced to be moving to the Office 365 infrastructure. It was initially a part of Calendar as a view. Microsoft has separated the services into its own web app in Outlook on the web. In a post on the Office Blogs in 2015, Microsoft announced that Outlook Web App would be renamed Outlook on the web and that Tasks would move under that brand. A user can create tasks, put them into categories, and move them to another folder. A feature added was the ability to set due days and sort and filter the tasks according to those criteria. The app provides the user with fields such as subject, start and end dates, percent complete, priority, and how much work was put into each task. Rich editing features like bold, italic, underline, numbering, and bullet points were also introduced. Tasks can be edited and categorized according to how the user wishes them to be sorted. == Removed features == Outlook on the web has had two interfaces available: one with a complete feature set (known as Premium) and one with reduced functionality (known as Light or sometimes Lite). Prior to Exchange 2010, the Premium client required Internet Explorer. Exchange 2000 and 2003 require Internet Explorer 5 and later, and Exchange 2007 requires Internet Explorer 6 and later. Exchange 2010 supports a wider range of web browsers: Internet Explorer 7 or later, Firefox 3.01 or later, Chrome, or Safari 3.1 or later. However, Exchange 2010 restricts its Firefox and Safari support to macOS and Linux. In Exchange 2013, these browser restrictions were lifted. In Exchange 2010 and earlier, the Light user interface is rendered for browsers other than Internet Explorer. The basic interface did not support search on Exchange Server 2003. In Exchange Server 2007, the Light interface supported searching mail items; managing contacts and the calendar was also improved. The 2010 version can connect to an external email account. The ability to add new accounts to Outlook on the web using the Connected accounts feature was removed in September 2018 and all connected accounts stopped synchronizing email the following month.
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Paprika (app)

Paprika is an app and website that helps users organize recipes, produce meal plans, and create grocery lists. The app is available for Android, iOS, macOS, and Windows devices. == Overview == The app allows users to import recipes from various sources, including websites and other apps. The app also allows users to automatically generate meal plans, which are also customizable, in order to achieve specific objectives such as weight loss, muscle gain, adherence to various dietary preferences, or personal taste. The app is also capable of generating grocery lists based on the daily or weekly meal plans chosen by the user. All the recipes, menus, and grocery lists of each user are accessible from smartphones, tablets, and computers. The app is part of a broader category of mobile apps focused on meal planning, recipe management, and shopping list automation, which have grown in popularity with the expansion of smartphone usage and digital cooking tools. == History == Paprika Recipe Manager for iPad version 1.0 was initially released in September 2010 by Hindsight LLC. Paprika 2.0 was released for iPhone and iPad in November 2013, and Paprika 3.0 was released for iOS and macOS in November 2017. == Reception == Paprika has been featured in technology and lifestyle publications as a recipe management and meal planning application. Coverage has noted features such as importing recipes from websites, ingredient scaling, and cross-platform synchronization. The app has also appeared in lists of cooking and meal planning tools published by outlets including The Verge and The Kitchn.
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Viaweb

Viaweb was a web-based application that allowed users to build and host their own online stores with little technical expertise using a web browser. The company was started in July 1995 by Paul Graham, Robert Morris (using the pseudonym "John McArtyem"), and Trevor Blackwell. Graham claims Viaweb was the first application service provider. Viaweb was also unusual for being partially written in the Lisp programming language. The software was originally called Webgen, but another company was using the same name, so the company renamed it to Viaweb, "because it worked via the Web". In 1998, Yahoo! Inc. bought Viaweb for 455,000 shares of Yahoo! capital stock, valued at about $49 million, and renamed it Yahoo! Store. Viaweb's example has been influential in Silicon Valley's entrepreneurial culture, largely due to Graham's widely read essays and his subsequent career as a successful venture capitalist.
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CEITON

CEITON is a web-based software system for facilitating and automating business processes such as planning, scheduling, and payroll using workflow technologies. The system is used by several media companies such as MDR, Yle, RAI and Red Bull Media House. In December 2018, the first CEITON User Group Meeting took place in Leipzig, Germany. == Architecture == The software runs on a server (on premises) or in the cloud and is scalable on parallel servers. Data security is warranted by role-based access control (RBAC). The software is used via web-browsers and not dependent on particular system software. == Structure and Features == CEITON combines the two classical approaches of production planning and control and workflow management. === Project Management === The scheduling system plans, manages, bills, and analyzes projects or tasks. It manages human and technical resources, material, and locations on a single GUI. The system uses a gantt chart to assign tasks to be done to available and eligible resources (i.e. staff), automatically or by drag-and-drop. The scheduling module includes material management, resource management/ human resource management, integration of freelancers, clients and suppliers, long-term budget planning, time-tracking, shift scheduling, quality management, delivery and logistics, document management, archive, analysis and controlling, business reporting, as well as all accounting and documentation processes. === Workflow === The workflow management system module coordinates business processes. Processes are defined once as a workflow and then repeatedly executed. Human resources are automatically assigned to steps (tasks) and integrated in workflow forms. Systems are integrated with an EAI/SOAP module, allowing data exchange with arbitrary external systems which are also involved in the business process. It also features a 3-D workflow overview in which the status of each project step can be determined by its color in the overview. === Process Management === For project and order processing management, business processes are designed as workflows, and coordinate communication automatically. Different user interfaces for staff, customers or suppliers can be created so each gets only relevant information. Different workflow forms are associated with different log-ins. The main application for the system is knowledge-based business processes, in which many people are involved and virtual results are produced, e.g. in research, or development of media products, such as TV and movies. Broadcasters and media companies such as MDR and Yle use CEITON to control their production processes for products and services and coordinate complex workflows with all kinds of resources. === Integrations === An integrated EAI module allows CEITON to integrate every external system in any business process without programming, using SOAP and similar technologies. Aspera and FileCatalyst were integrated for faster data transfer, yet complex ERP systems and numerous SAP modules have also been integrated, for example, to extract working times to payroll. === Mobile Working === Since Version 7, released in 2015, CEITON includes a time-tracking module allowing employees to enter their times from mobile devices such as tablets running Android, iPhones etc. == History == Ceiton Technologies (SME tech firm), the company developing CEITON, was founded in Leipzig, Germany in 2000, staffing solutions for the Bureau of Internal Revenue in Manila, Philippines, were implemented in 2000 together with the Deutsche Gesellschaft für Technische Zusammenarbeit of the German government. The first version (1.0) of the software was released in July 2001. The product was originally developed for German broadcasting companies. CEITON is named after the Japanese concept Seiton, one of the principles of Japanese workplace design methodology known as 5S. Since version 7, released in 2015, CEITON includes a time-tracking module allowing employees to enter their times from mobile devices such as tablets running Android, iPhones etc. In May 2005 CEITON won the IQ innovation award, sponsored by Siemens, in the category Excellent innovation in the IT-sector. Since 2007, CEITON has been present at the broadcast trade fairs NAB in Las Vegas and IBC in Amsterdam. In 2020, the company celebrated its 20th anniversary.
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