AI Content Paraphrasing Tool

AI Content Paraphrasing Tool — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

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  Automated medical scribe

  Automated medical scribes (also called artificial intelligence scribes, AI scribes, digital scribes, virtual scribes, ambient AI scribes, AI documentation assistants, and digital/virtual/smart clinical assistants) are tools for transcribing medical speech, such as patient consultations and dictated medical notes. Many also produce summaries of consultations. Automated medical scribes based on large language models (LLMs, commonly called "AI", short for "artificial intelligence") increased drastically in popularity in 2024. There are privacy and antitrust concerns. Accuracy concerns also exist, and intensify in situations in which tools try to go beyond transcribing and summarizing, and are asked to format information by its meaning, since LLMs do not deal well with meaning (see weak artificial intelligence). Medics using these scribes are generally expected to understand the ethical and legal considerations, and supervise the outputs. The privacy protections of automated medical scribes vary widely. While it is possible to do all the transcription and summarizing locally, with no connection to the internet, most closed-source providers require that data be sent to their own servers over the internet, processed there, and the results sent back (as with digital voice assistants). Some retailers say their tools use zero-knowledge encryption (meaning that the service provider can't access the data). Others explicitly say that they use patient data to train their AIs, or rent or resell it to third parties; the nature of privacy protections used in such situations is unclear, and they are likely not to be fully effective. Most providers have not published any safety or utility data in academic journals, and are not responsive to requests from medical researchers studying their products. == Privacy == Some providers unclear about what happens to user data. Some may sell data to third parties. Some explicitly send user data to for-profit tech companies for secondary purposes, which may not be specified. Some require users to sign consents to such reuse of their data. Some ingest user data to train the software, promising to anonymize it; however, deanonymization may be possible (that is, it may become obvious who the patient is). It is intrinsically impossible to prevent an LLM from correlating its inputs; they work by finding similar patterns across very large data sets. Some information on the patient will be known from other sources (for instance, information that they were injured in an incident on a certain day might be available from the news media; information that they attended specific appointment locations at specific times is probably available to their cellphone provider/apps/data brokers; information about when they had a baby is probably implied by their online shopping records; and they might mention lifestyle changes to their doctor and on a forum or blog). The software may correlate such information with the "anonymized" clinical consultation record, and, asked about the named patient, provide information which they only told their doctor privately. Because a patient's record is all about the same patient, it is all unavoidably linked; in very many cases, medical histories are intrinsically identifiable. Depending on how common a condition and what other data is available, K-anonymity may be useless. Differential privacy could theoretically preserve privacy. Data broker companies like Google, Amazon, Apple and Microsoft have produced or bought up medical scribes, some of which use user data for secondary purposes, which has led to antitrust concerns. Transfer of patient records for AI training has, in the past, prompted legal action. Open-source programs typically do all the transcription locally, on the doctor's own computer. Open-source software is widely used in healthcare, with some national public healthcare bodies holding hack days. === Data resale and commercialization === Several AI medical scribe providers include terms in their service agreements that allow the reuse, sale, or commercialization of de-identified or user-submitted data. Although such data are generally described as anonymized or aggregated, these practices have raised ethical concerns among clinicians and privacy advocates regarding secondary uses of medical information beyond clinical documentation. Freed, an AI transcription and scribe platform, states in its Terms of Use that it may "collect, use, publish, disseminate, sell, transfer, and otherwise exploit" de-identified and aggregated data derived from user inputs. OpenEvidence similarly states that it may "collect, use, transfer, sell, and disclose non-personal information and customer usage data for any purpose including commercial uses." Doximity, which offers an AI-enabled medical scribe as part of its physician platform, grants itself a "nonexclusive, irrevocable, worldwide, perpetual, unlimited, assignable, sublicensable, royalty-free" license to "copy, prepare derivative works from, improve, distribute, publish, ... analyze, index, tag, [and] commercialize" content submitted by users, subject to its privacy policy. Because these terms allow broad secondary use—including sale, licensing, model-training, derivative works, and commercial exploitation of de-identified or user-submitted data—some commentators have recommended that clinicians review data-handling provisions carefully when adopting AI-scribe tools, particularly in clinical environments where patient privacy and regulatory compliance are critical. === Encryption === Multifactor authentication for access to the data is expected practice. Typically, Diffie–Hellman key exchange is used for encryption; this is the standard method commonly used for things like online banking. This encryption is expensive but not impossible to break; it is not generally considered safe against eavesdroppers with the resources of a nation-state. If content is encrypted between the client and the service provider's remote server (transport cryptography), then the server has an unencrypted copy. This is necessary if the data is used by the service provider (for instance, to train the software). Zero-knowledge encryption implies that the only unencrypted copy is at the client, and the server cannot decrypt the data any more easily than a monster-in-the-middle attacker. == Platforms == Scribes may operate on desktops, laptop, or mobile computers, under a variety of operating systems. These vary in their risks; for instance, mobiles can be lost. The underlying mobile or desktop operating systems are also part of the trusted computing base, and if they are not secure, the software relying on them cannot be secure either. Some AI medical scribe platforms are designed to operate as cloud-based applications that generate structured clinical documentation from clinician–patient conversations. These systems may offer features such as real-time transcription, document generation, and integration with electronic health record (EHR) systems. == Confabulation, omissions, and other errors == Like other LLMs, medical-scribe LLMs are prone to hallucinations, where they make up content based on statistically associations between their training data and the transcription audio. LLMs do not distinguish between trying to transcribe the audio and guessing what words will come next, but perform both processes mixed together. They are especially likely to take short silences or non-speech noises and invent some sort of speech to transcribe them as. LLM medical scribes have been known to confabulate racist and otherwise prejudiced content; this is partly because the training datasets of many LLMs contain pseudoscientific texts about medical racism. They may misgender patients. A survey found that most doctors preferred, in principle, that scribes be trained on data reviewed by medical subject experts. Relevant, accurate training data increases the probability of an accurate transcription, but does not guarantee accuracy. Software trained on thousands of real clinical conversations generated transcripts with lower word error rates. Software trained on manually-transcribed training data did better than software trained with automatically transcribed training data such as YouTube captions. Autoscribes omit parts of the conversation classes as irrelevant. The may wrongly classify pertinent information as irrelevant and omit it. They may also confuse historic and current symptoms, or otherwise misclassify information. They may also simply wrongly transcribe the speech, writing something incorrect instead. If clinicians do not carefully check the recording, such mistakes could make their way into their medical records and cause patient harms. == Patient consent == Professional organizations generally require that scribes be used only with patient consent; some bodies may require written consent. Medics must also abide by local surveillance laws, which may criminalize recording pri

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  Graphics

  Graphics (from Ancient Greek γραφικός (graphikós) 'pertaining to drawing, painting, writing, etc.') are visual images or designs on some surface, such as a wall, canvas, screen, paper, or stone, to inform, illustrate, or entertain. In contemporary usage, it includes a pictorial representation of data, as in design and manufacture, in typesetting and the graphic arts, and in educational and recreational software. Images that are generated by a computer are called computer graphics. Examples are photographs, drawings, line art, mathematical graphs, line graphs, charts, diagrams, typography, numbers, symbols, geometric designs, maps, engineering drawings, or other images. Graphics often combine text, illustration, and color. Graphic design may consist of the deliberate selection, creation, or arrangement of typography alone, as in a brochure, flyer, poster, web site, or book without any other element. The objective can be clarity or effective communication, association with other cultural elements, or merely the creation of a distinctive style. Graphics can be functional or artistic. The latter can be a recorded version, such as a photograph, or an interpretation by a scientist to highlight essential features, or an artist, in which case the distinction with imaginary graphics may become blurred. It can also be used for architecture. == History == The earliest graphics known to anthropologists studying prehistoric periods are cave paintings and markings on boulders, bone, ivory, and antlers, which were created during the Upper Palaeolithic period from 40,000 to 10,000 B.C. or earlier. Many of these were found to record astronomical, seasonal, and chronological details. Some of the earliest graphics and drawings are known to the modern world, from almost 6,000 years ago, are that of engraved stone tablets and ceramic cylinder seals, marking the beginning of the historical periods and the keeping of records for accounting and inventory purposes. Records from Egypt predate these and papyrus was used by the Egyptians as a material on which to plan the building of pyramids; they also used slabs of limestone and wood. From 600 to 250 BC, the Greeks played a major role in geometry. They used graphics to represent their mathematical theories such as the Circle Theorem and the Pythagorean theorem. In art, "graphics" is often used to distinguish work in a monotone and made up of lines, as opposed to painting. === Drawing === Drawing generally involves making marks on a surface by applying pressure from a tool or moving a tool across a surface. In which a tool is always used as if there were no tools it would be art. Graphical drawing is an instrumental guided drawing. === Printmaking === Woodblock printing, including images is first seen in China after paper was invented (about A.D. 105). In the West, the main techniques have been woodcut, engraving and etching, but there are many others. ==== Etching ==== Etching is an intaglio method of printmaking in which the image is incised into the surface of a metal plate using an acid. The acid eats the metal, leaving behind roughened areas, or, if the surface exposed to the acid is very thin, burning a line into the plate. The use of the process in printmaking is believed to have been invented by Daniel Hopfer (c. 1470–1536) of Augsburg, Germany, who decorated armour in this way. Etching is also used in the manufacturing of printed circuit boards and semiconductor devices. === Line art === Line art is a rather non-specific term sometimes used for any image that consists of distinct straight and curved lines placed against a (usually plain) background, without gradations in shade (darkness) or hue (color) to represent two-dimensional or three-dimensional objects. Line art is usually monochromatic, although lines may be of different colors. === Illustration === An illustration is a visual representation such as a drawing, painting, photograph or other work of art that stresses the subject more than form. The aim of an illustration is to elucidate or decorate a story, poem or piece of textual information (such as a newspaper article), traditionally by providing a visual representation of something described in the text. The editorial cartoon, also known as a political cartoon, is an illustration containing a political or social message. Illustrations can be used to display a wide range of subject matter and serve a variety of functions, such as: giving faces to characters in a story displaying a number of examples of an item described in an academic textbook (e.g. A Typology) visualizing step-wise sets of instructions in a technical manual communicating subtle thematic tone in a narrative linking brands to the ideas of human expression, individuality, and creativity making a reader laugh or smile for fun (to make laugh) funny === Graphs === A graph or chart is a graphic that represents tabular or numeric data. Charts are often used to make it easier to understand large quantities of data and the relationships between different parts of the data. === Diagrams === A diagram is a simplified and structured visual representation of concepts, ideas, constructions, relations, statistical data, etc., used to visualize and clarify the topic. === Symbols === A symbol, in its basic sense, is a representation of a concept or quantity; i.e., an idea, object, concept, quality, etc. In more psychological and philosophical terms, all concepts are symbolic in nature, and representations for these concepts are simply token artifacts that are allegorical to (but do not directly codify) a symbolic meaning, or symbolism. === Maps === A map is a simplified depiction of a space, a navigational aid which highlights relations between objects within that space. Usually, a map is a two-dimensional, geometrically accurate representation of a three-dimensional space. One of the first 'modern' maps was made by Waldseemüller. === Photography === One difference between photography and other forms of graphics is that a photographer, in principle, just records a single moment in reality, with seemingly no interpretation. However, a photographer can choose the field of view and angle, and may also use other techniques, such as various lenses to choose the view or filters to change the colors. In recent times, digital photography has opened the way to an infinite number of fast, but strong, manipulations. Even in the early days of photography, there was controversy over photographs of enacted scenes that were presented as 'real life' (especially in war photography, where it can be very difficult to record the original events). Shifting the viewer's eyes ever so slightly with simple pinpricks in the negative could have a dramatic effect. The choice of the field of view can have a strong effect, effectively 'censoring out' other parts of the scene, accomplished by cropping them out or simply not including them in the photograph. This even touches on the philosophical question of what reality is. The human brain processes information based on previous experience, making us see what we want to see or what we were taught to see. Photography does the same, although the photographer interprets the scene for their viewer. === Engineering drawings === An engineering drawing is a type of drawing and is technical in nature, used to fully and clearly define requirements for engineered items. It is usually created in accordance with standardized conventions for layout, nomenclature, interpretation, appearance (such as typefaces and line styles), size, etc. === Computer graphics === There are two types of computer graphics: raster graphics, where each pixel is separately defined (as in a digital photograph), and vector graphics, where mathematical formulas are used to draw lines and shapes, which are then interpreted at the viewer's end to produce the graphic. Using vectors results in infinitely sharp graphics and often smaller files, but, when complex, like vectors take time to render and may have larger file sizes than a raster equivalent. In 1950, the first computer-driven display was attached to MIT's Whirlwind I computer to generate simple pictures. This was followed by MIT's TX-0 and TX-2, interactive computing which increased interest in computer graphics during the late 1950s. In 1962, Ivan Sutherland invented Sketchpad, an innovative program that influenced alternative forms of interaction with computers. In the mid-1960s, large computer graphics research projects were begun at MIT, General Motors, Bell Labs, and Lockheed Corporation. Douglas T. Ross of MIT developed an advanced compiler language for graphics programming. S.A.Coons, also at MIT, and J. C. Ferguson at Boeing, began work in sculptured surfaces. GM developed their DAC-1 system, and other companies, such as Douglas, Lockheed, and McDonnell, also made significant developments. In 1968, ray tracing was first described by Arthur Appel of the IBM Research Center, Yorktown Heights, N

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  IPUMS

  IPUMS, originally the Integrated Public Use Microdata Series, is the world's largest individual-level population database. IPUMS consists of microdata samples from United States (IPUMS-USA) and international (IPUMS-International) census records, as well as data from U.S. and international surveys. The records are converted into a consistent format and made available to researchers through a web-based data dissemination and analysis system. IPUMS is housed at the Institute for Social Research and Data Innovation (ISRDI), an interdisciplinary research center at the University of Minnesota, under the direction of Professor Steven Ruggles. == Description == IPUMS includes all persons enumerated in the United States censuses from 1850 to 1950 (though, the 1890 census is missing because it was destroyed in a fire) and from the American Community Survey since 2000 and the Current Population Survey since 1962. IPUMS includes household-level data for United States Censuses from 1790 to 1840, due to the first six censuses only including the name of the head of household, with tallied household totals following. IPUMS provides consistent variable names, coding schemes, and documentation across all the samples, facilitating the analysis of long-term change. IPUMS-International includes countries from Africa, Asia, Europe, and Latin America for 1960 forward. The database currently includes more than a billion individuals enumerated in 365 censuses from 94 countries around the world. IPUMS-International converts census microdata for multiple countries into a consistent format, allowing for comparisons across countries and time periods. Special efforts are made to simplify use of the data while losing no meaningful information. Comprehensive documentation is provided in a coherent form to facilitate comparative analyses of social and economic change. Additional databases in the IPUMS family include the: North Atlantic Population Project (NAPP) IPUMS National Historical Geographic Information System (NHGIS) IPUMS Health Surveys IPUMS Global Health IPUMS Time Use The Journal of American History described the effort as "One of the great archival projects of the past two decades." Liens Socio, the French portal for the social sciences, gave IPUMS the only “best site” designation that has gone to any non-French website, writing “IPUMS est un projet absolument extraordinaire...époustouflante [mind-blowing]!” The official motto of IPUMS is "use it for good, never for evil." All public IPUMS data and documentation are available online free of charge.

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  Vinted

  Vinted Group UAB is a Lithuanian technology company best known for its online marketplace Vinted. Vinted is the leading second-hand fashion marketplace in Europe and a go-to destination for all kinds of second-hand items. According to the company, its mission is to make second-hand the first choice worldwide. The company operates as an ecosystem of businesses, including the Vinted Marketplace (its peer-to-peer resale platform), Vinted Go (logistics and shipping services), Vinted Pay (in-app payment solutions), and Vinted Ventures (an investment arm supporting the circular economy). Headquartered in Vilnius, Lithuania, it also has offices in Germany and the Netherlands and employs more than 2,200 people. == History == Vinted was co-founded in 2008 by Milda Mitkute and Justas Janauskas in Vilnius, Lithuania. The idea originated when Mitkute was moving house and wanted a way to sell clothes she no longer needed. Janauskas helped her create a website where users could trade clothing items. In 2016, Dutch entrepreneur Thomas Plantenga joined Vinted as a strategy consultant and later became Chief Executive Officer, leading the company through a period of international growth. In 2019, Vinted became Lithuania’s first technology unicorn after raising €128 million at a €1 billion valuation in a funding round led by Lightspeed Venture Partners. In October 2020, it acquired United Wardrobe, a Dutch competitor, and in November 2020 German Kleiderkreisel and Mamikreisel were officially merged into the Vinted platform. In 2024 it acquired Trendsales, a Danish resale platform. According to Vogue Business, Vinted’s revenue grew 61% between 2022 and 2023 and the company posted a net profit of €17.8 million in 2023. Usage of Vinted in the UK has grown from 1.2 million users in 2021, to 8 million in 2023. In 2024, the group reported consolidated revenue of €813.4 million (up 36% from 2023) and a net profit of €76.7 million, up 330% from 2023. As of 2024, Vinted was valued at approximately €5 billion, operating in more than 26 markets worldwide and announcing plans to launch in Ireland, Greece, Latvia, Slovenia, and Estonia in 2025. As of 2025 the company employed more than 2,200 people. In April 2026, Vinted completed a secondary share transaction of €880m, valuing the company at €8bn. == Products and operations == Vinted primarily resells clothing but now supports multiple categories including homeware, kidswear, electronics, books, collectibles, and high-value fashion. Vinted has worked with public figures such as Paul Mescal and Alexa Chung on exclusive wardrobe sales and has also partnered directly with charities including Oxfam on initiatives which promote the social and environmental value of second-hand fashion, such as the Style for Change fashion show at London Fashion Week. In 2025, Vinted produced its first television format, the second-hand fashion competition series RE/Style, hosted by Emma Willis. The show features emerging fashion designers from across Europe creating runway-ready looks from second-hand garments and aired on Prime Video UK. In 2025, Vinted was reported as France’s top clothing retailer by sales volume. == Criticism == Vinted has faced scrutiny from European data protection authorities in France, Lithuania, and Poland following complaints regarding GDPR compliance and account blocking practices. In July 2024, the Lithuanian authority fined the company €2,375,276. The case was coordinated by a dedicated Vinted Working Group under the European Data Protection Board. In early 2024, Swedish police reported around 300 fraud cases linked to the platform, in which users’ bank accounts were targeted by scammers. In October 2024, Channel 4 in the United Kingdom aired a documentary examining safety and privacy concerns related to the platform, including the sexualisation of underage users’ images and risks associated with second-hand baby products lacking safety certification. In November 2025, BBC News reported that Vinted’s update to its sizing system in the United Kingdom led to widespread user criticism. Vinted said the update was intended to standardise sizing across international brands.

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  Sentence embedding

  In natural language processing, a sentence embedding is a representation of a sentence as a vector of numbers which encodes meaningful semantic information. State of the art embeddings are based on the learned hidden layer representation of dedicated sentence transformer models. BERT pioneered an approach involving the use of a dedicated [CLS] token prepended to the beginning of each sentence inputted into the model; the final hidden state vector of this token encodes information about the sentence and can be fine-tuned for use in sentence classification tasks. In practice however, BERT's sentence embedding with the [CLS] token achieves poor performance, often worse than simply averaging non-contextual word embeddings. SBERT later achieved superior sentence embedding performance by fine tuning BERT's [CLS] token embeddings through the usage of a siamese neural network architecture on the SNLI dataset. Other approaches are loosely based on the idea of distributional semantics applied to sentences. Skip-Thought trains an encoder-decoder structure for the task of neighboring sentences predictions; this has been shown to achieve worse performance than approaches such as InferSent or SBERT. An alternative direction is to aggregate word embeddings, such as those returned by Word2vec, into sentence embeddings. The most straightforward approach is to simply compute the average of word vectors, known as continuous bag-of-words (CBOW). However, more elaborate solutions based on word vector quantization have also been proposed. One such approach is the vector of locally aggregated word embeddings (VLAWE), which demonstrated performance improvements in downstream text classification tasks. == Applications == In recent years, sentence embedding has seen a growing level of interest due to its applications in natural language queryable knowledge bases through the usage of vector indexing for semantic search. LangChain for instance utilizes sentence transformers for purposes of indexing documents. In particular, an indexing is generated by generating embeddings for chunks of documents and storing (document chunk, embedding) tuples. Then given a query in natural language, the embedding for the query can be generated. A top k similarity search algorithm is then used between the query embedding and the document chunk embeddings to retrieve the most relevant document chunks as context information for question answering tasks. This approach is also known formally as retrieval-augmented generation. Though not as predominant as BERTScore, sentence embeddings are commonly used for sentence similarity evaluation which sees common use for the task of optimizing a Large language model's generation parameters is often performed via comparing candidate sentences against reference sentences. By using the cosine-similarity of the sentence embeddings of candidate and reference sentences as the evaluation function, a grid-search algorithm can be utilized to automate hyperparameter optimization. == Evaluation == A way of testing sentence encodings is to apply them on Sentences Involving Compositional Knowledge (SICK) corpus for both entailment (SICK-E) and relatedness (SICK-R). In the best results are obtained using a BiLSTM network trained on the Stanford Natural Language Inference (SNLI) Corpus. The Pearson correlation coefficient for SICK-R is 0.885 and the result for SICK-E is 86.3. A slight improvement over previous scores is presented in: SICK-R: 0.888 and SICK-E: 87.8 using a concatenation of bidirectional Gated recurrent unit.

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  Unspent transaction output

  In cryptocurrencies, an unspent transaction output (UTXO, often capitalized as UTxO) is a distinctive element in a subset of digital currency models. A UTXO represents a certain amount of cryptocurrency that has been authorized by a sender and is available to be spent by a recipient. The utilization of UTXOs in transaction processes is a key feature of many cryptocurrencies, but it primarily characterizes those implementing the UTXO model. UTXOs employ public key cryptography to ascertain and transfer ownership. More specifically, the recipient's public key is formatted into the UTXO, thereby limiting the capability to spend the UTXO to the account that can demonstrate ownership of the corresponding private key. A valid digital signature associated with the public key must be included for the UTXO to be spent. In the UTXO model, each unit of currency is treated as a discrete object. The history of a UTXO is documented only within the blocks where it is transferred. To ascertain the total balance of an account, one must scan each block to find the latest UTXOs linked to that account. While all nodes within a blockchain network must consent to the block history, the blocks relevant to an account's balance are unique to that account. UTXOs constitute a chain of ownership depicted as a series of digital signatures dating back to the coin's inception, regardless of whether the coin was minted via mining, staking, or another procedure determined by the cryptocurrency protocol. The UTXO model was invented for Bitcoin. Cardano uses an extended version of the UTXO model known as EUTXO. == Origins == The conceptual framework of the UTXO model can be traced back to Hal Finney's Reusable Proofs of Work proposal, which itself was based on Adam Back's 1997 Hashcash proposal. Bitcoin, released in 2009, was the first widespread implementation of the UTXO model in practice. == UTXO model vs. account Model == Cryptocurrencies that utilize the UTXO model function differently compared to those using the account model. In the UTXO model, individual units of cryptocurrency, termed as unspent transaction outputs (UTXOs), are transferred between users, analogous to the exchange of physical cash. This model impacts how transactions and ownership are recorded and verified within the blockchain network. The account model preserves a record of each account and its corresponding balance for every block added to the network. This setup enables quicker balance verification without the need to scan historical blocks, but it increases the raw size of each block (though data compression techniques can be utilized to alleviate this). However, both models necessitate the inspection of past blocks to fully authenticate the origin of coins. In the UTXO model, each object is immutable - units of coins cannot be 'edited' in the same way an account balance is modified when a transaction occurs. Rather, the balance is computed from the transaction history dating back to when the coins were first minted. This simplicity enhances security, as a UTXO either exists in its anticipated form or it does not. In contrast, the account model requires meticulous verification of the account's status during transactions, which can lead to oversights if not conducted correctly. In valid blockchain transactions, only unspent outputs (UTXOs) are permissible for funding subsequent transactions. This requirement is critical to prevent double-spending and fraud. Accordingly, inputs in a transaction are removed from the UTXO set, while outputs create new UTXOs that are added to the set. The holders of private keys, such as those with cryptocurrency wallets, can utilize these UTXOs for future transactions.

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  Sherwood Applied Business Security Architecture

  SABSA (Sherwood Applied Business Security Architecture) is a model and methodology for developing a risk-driven enterprise information security architecture and service management, to support critical business processes. It was developed independently from the Zachman Framework, but has a similar structure. The primary characteristic of the SABSA model is that everything must be derived from an analysis of the business requirements for security, especially those in which security has an enabling function through which new business opportunities can be developed and exploited. The process analyzes the business requirements at the outset, and creates a chain of traceability through the strategy and concept, design, implementation, and ongoing ‘manage and measure’ phases of the lifecycle to ensure that the business mandate is preserved. Framework tools created from practical experience further support the whole methodology. The model is layered, with the top layer being the business requirements definition stage. At each lower layer a new level of abstraction and detail is developed, going through the definition of the conceptual architecture, logical services architecture, physical infrastructure architecture and finally at the lowest layer, the selection of technologies and products (component architecture). The SABSA model itself is generic and can be the starting point for any organization, but by going through the process of analysis and decision-making implied by its structure, it becomes specific to the enterprise, and is finally highly customized to a unique business model. It becomes in reality the enterprise security architecture, and it is central to the success of a strategic program of information security management within the organization. SABSA is a particular example of a methodology that can be used both for IT (information technology) and OT (operational technology) environments. == SABSA matrix == Note: The above is the original SABSA Matrix, which is still valid today, but it has been expanded by a comprehensive service management matrix and updated in some detail and terminology areas. In the words of David Lynas, SABSA author, "The SABSA Matrix and the SABSA Service Management Matrix have not been updated since the late 90s. We have redesigned them to deliver the improvements your feedback has requested over the years. We have not fundamentally changed the structure or principles of the matrices (very few elements have changed position) but have focused on terminology update and consistency." The new versions can be downloaded (along with the 2009 revision of the SABSA White Paper and other important documents like the SABSA Certification Roadmap) at the SABSA Members' Web Site.

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  Truth discovery

  Truth discovery (also known as truth finding) is the process of choosing the actual true value for a data item when different data sources provide conflicting information on it. Several algorithms have been proposed to tackle this problem, ranging from simple methods like majority voting to more complex ones able to estimate the trustworthiness of data sources. Truth discovery problems can be divided into two sub-classes: single-truth and multi-truth. In the first case only one true value is allowed for a data item (e.g birthday of a person, capital city of a country). While in the second case multiple true values are allowed (e.g. cast of a movie, authors of a book). Typically, truth discovery is the last step of a data integration pipeline, when the schemas of different data sources have been unified and the records referring to the same data item have been detected. == General principles == The abundance of data available on the web makes more and more probable to find that different sources provide (partially or completely) different values for the same data item. This, together with the fact that we are increasing our reliance on data to derive important decisions, motivates the need of developing good truth discovery algorithms. Many currently available methods rely on a voting strategy to define the true value of a data item. Nevertheless, recent studies, have shown that, if we rely only on majority voting, we could get wrong results even in 30% of the data items. The solution to this problem is to assess the trustworthiness of the sources and give more importance to votes coming from trusted sources. Ideally, supervised learning techniques could be exploited to assign a reliability score to sources after hand-crafted labeling of the provided values; unfortunately, this is not feasible since the number of needed labeled examples should be proportional to the number of sources, and in many applications the number of sources can be prohibitive. == Single-truth vs multi-truth discovery == Single-truth and multi-truth discovery are two very different problems. Single-truth discovery is characterized by the following properties: only one true value is allowed for each data item; different values provided for a given data item oppose to each other; values and sources can either be correct or erroneous. While in the multi-truth case the following properties hold: the truth is composed by a set of values; different values could provide a partial truth; claiming one value for a given data item does not imply opposing to all the other values; the number of true values for each data item is not known a priori. Multi-truth discovery has unique features that make the problem more complex and should be taken into consideration when developing truth-discovery solutions. The examples below point out the main differences of the two methods. Knowing that in both examples the truth is provided by source 1, in the single truth case (first table) we can say that sources 2 and 3 oppose to the truth and as a result provide wrong values. On the other hand, in the second case (second table), sources 2 and 3 are neither correct nor erroneous, they instead provide a subset of the true values and at the same time they do not oppose the truth. == Source trustworthiness == The vast majority of truth discovery methods are based on a voting approach: each source votes for a value of a certain data item and, at the end, the value with the highest vote is select as the true one. In the more sophisticated methods, votes do not have the same weight for all the data sources, more importance is indeed given to votes coming from trusted sources. Source trustworthiness usually is not known a priori but estimated with an iterative approach. At each step of the truth discovery algorithm the trustworthiness score of each data source is refined, improving the assessment of the true values that in turn leads to a better estimation of the trustworthiness of the sources. This process usually ends when all the values reach a convergence state. Source trustworthiness can be based on different metrics, such as accuracy of provided values, copying values from other sources and domain coverage. Detecting copying behaviors is very important, in fact, copy allows to spread false values easily making truth discovery very hard, since many sources would vote for the wrong values. Usually systems decrease the weight of votes associated to copied values or even don’t count them at all. == Single-truth methods == Most of the currently available truth discovery methods have been designed to work well only in the single-truth case. Below are reported some of the characteristics of the most relevant typologies of single-truth methods and how different systems model source trustworthiness. === Majority voting === Majority voting is the simplest method, the most popular value is selected as the true one. Majority voting is commonly used as a baseline when assessing the performances of more complex methods. === Web-link based === These methods estimate source trustworthiness exploiting a similar technique to the one used to measure authority of web pages based on web links. The vote assigned to a value is computed as the sum of the trustworthiness of the sources that provide that particular value, while the trustworthiness of a source is computed as the sum of the votes assigned to the values that the source provides. === Information-retrieval based === These methods estimate source trustworthiness using similarity measures typically used in information retrieval. Source trustworthiness is computed as the cosine similarity (or other similarity measures) between the set of values provided by the source and the set of values considered true (either selected in a probabilistic way or obtained from a ground truth). === Bayesian based === These methods use Bayesian inference to define the probability of a value being true conditioned on the values provided by all the sources. P ( v ∣ ψ ( o ) ) = P ( ψ ( o ) ∣ v ) ⋅ P ( v ) P ( ψ ( o ) ) {\displaystyle P(v\mid \psi (o))={\frac {P(\psi (o)\mid v)\cdot P(v)}{P(\psi (o))}}} where v {\displaystyle \textstyle v} is a value provided for a data item o {\displaystyle \textstyle o} and ψ ( o ) {\displaystyle \textstyle \psi (o)} is the set of the observed values provided by all the sources for that specific data item. The trustworthiness of a source is then computed based on the accuracy of the values that provides. Other more complex methods exploit Bayesian inference to detect copying behaviors and use these insights to better assess source trustworthiness. == Multi-truth methods == Due to its complexity, less attention has been devoted to the study of the multi-truth discovery Below are reported two typologies of multi-truth methods and their characteristics. === Bayesian based === These methods use Bayesian inference to define the probability of a group of values being true conditioned on the values provided by all the data sources. In this case, since there could be multiple true values for each data item, and sources can provide multiple values for a single data item, it is not possible to consider values individually. An alternative is to consider mappings and relations between set of provided values and sources providing them. The trustworthiness of a source is then computed based on the accuracy of the values that provides. More sophisticated methods also consider domain coverage and copying behaviors to better estimate source trustworthiness. === Probabilistic Graphical Models based === These methods use probabilistic graphical models to automatically define the set of true values of given data item and also to assess source quality without need of any supervision. == Applications == Many real-world applications can benefit from the use of truth discovery algorithms. Typical domains of application include: healthcare, crowd/social sensing, crowdsourcing aggregation, information extraction and knowledge base construction. Truth discovery algorithms could be also used to revolutionize the way in which web pages are ranked in search engines, going from current methods based on link analysis like PageRank, to procedures that rank web pages based on the accuracy of the information they provide.

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  Depop

  Depop Limited is a social e-commerce company based in London, with additional offices in Milan and New York City. The company allows users to buy and sell items, which are mostly used and vintage pieces of clothing. == History == Depop was founded in 2011 by entrepreneur Simon Beckerman at an Italian technological incubator and business start-up centre, H-Farm. Beckerman came up with the original outline of the application during his time working on PIG, a fashion magazine based in Italy that he co-founded. The idea was to create a platform where products shown in the magazine could be purchased by users online. This idea turned into a concept similar to a flea market but on the internet, where people could sell their items while also being in control of advertising, public relations, and the creative process behind their accounts. While being financially supported by H-Farm, Beckerman worked within a team to create and lay out the Depop application while exposing it to numerous investors. In 2013, Beckerman became a member of the company's board to help improve the application and business while concurrently ceding his role of CEO. Maria Raga, Depop's co-founder and former CEO, took on the role of vice president of operations in 2014, and in 2016, she became chief executive. According to Raga, the main goal while developing Depop was to become the next Airbnb or Spotify, but to make an impact on fashion. Paolo Barberis and Nana Bianca were two of the first investors in the platform in 2012 with a seed investment. Its headquarters were moved to London in 2012. Depop expanded and opened additional offices in Milan and New York City. Beckerman raised €1 million in funding in October 2013 from Red Circle Investment and brought on Faroese Runar Reistrup as new CEO. In 2015, Depop secured another investment of $8 million from Balderton Capital and HV Capital. In March 2016, former CEO, Runar Reistrup, stated that Depop's growth was achieved through word of mouth. During his time as CEO, this growth involved taking Depop as a startup and working to raise funds to eventually amass a significant user base within the United States. In June 2019, Depop raised $62 million in Series C from General Atlantic to fund its expansion. Previous investors HV Capital, Balderton Capital, Creandum, Octopus Ventures, TempoCap and Sebastian Siemiatkowski also participated. During this time, Depop held workshops and conversations as part of their Depop Live NY events, and the company also opened a London store through their partnership with Selfridges. In 2020, Depop's gross merchandise sales and revenue both more than doubled to $650 million and $70 million respectively. This may be attributed to Depop's responsiveness to user trends, its lack of issues regarding inventory management, and the increase in users looking to resell. As of 2024, Depop has over 35 million users, according to their website. Depop is popular for Gen Z and young millennials, it is the 10th most-visited shopping platform for Gen Z consumers in the US, and, in a poll conducted by The Strategist in 2019, Depop was voted by teenagers as their favorite resale website. === Acquisition by Etsy === In June 2021, Depop was acquired by Etsy for $1.6 billion in cash, making it Etsy's most expensive acquisition; however, Depop continues to operate as a standalone brand independent from Etsy. This means that in addition to Depop keeping its existing team, the company retained its London location. At the time of acquisition, Etsy CEO Josh Silverman’s goal was to counteract the influx of buyers starting to go back to physical shops for their purchases. He saw Depop for its potential as a platform supporting a variety of products and creating a greater community of users. According to Silverman, Depop may expand and improve its services for its significant Gen Z user base. For Etsy, this acquisition maintains the company's foothold in the clothing industry and allows the company to expand its customer base to a younger demographic; at the same time, Depop is now able to make use of Etsy's company operations. When Maria Raga relinquished her position as Depop's CEO in 2022, Etsy assigned the role to Kruti Patel Goyal, who was Etsy's former chief product officer and a leader there for eleven years. When Goyal was appointed president and chief growth officer for Etsy in May, Peter Semple, former chief marketing officer, was assigned CEO of Depop officially on August 1st. === Acquisition by eBay === In February 2026, Etsy announced a proposed sale of Depop to eBay for $1.2 billion that was estimated to close within the year. == Business model == === Selling === Depop operates as a marketplace and social platform, where users can follow friends and other influencers to view their buying and selling activities. Through the platform, users are able to sell branded and designer items, as well as vintage pieces. Depop users are also encouraged by the platform to use other social networking services such as Instagram to promote their shop profiles. Celebrities have resold their own items on Depop, with some donating proceeds to charitable causes. Depop's user interface is modeled after that of Instagram. According to Depop, users who list and sell items provide their own photos with item descriptions. Users also note their designer items' authenticity and if they include any labels, tags, and receipts. These listings will appear in users' feeds. The platform's "Explore" page features items picked out by Depop staff. According to Depop, purchases are made via Apple Pay, Google Pay, credit and debit cards, and Klarna. Depop payments stay in-app, allowing for the company to mediate disputes and process refunds. Depop payments allow sellers to directly receive their payments in their bank account. To get paid by Depop, a seller has to add a bank account and verify their identification by uploading an ID. On July 18, 2024, Depop CEO Kruti Patel Goyal announced the removal of selling fees for US sellers, while maintaining a payment processing fee. This policy adjustment aimed to enhance seller revenue and support the growth of the second-hand market. === Buying === A Depop transaction includes the agreed sale price of the item, shipping fees, VAT or other applicable taxes and duties, and the marketplace fee for buyers in the U.S. or U.K. For international deliveries, packages may be subject to import taxes, customs duties, or fees, payable upon arrival or at checkout if Depop collects the tax on behalf of the buyer. For domestic purchases, relevant taxes may be collected by the seller or charged by the platform at checkout, ensuring no additional taxes are due upon delivery. For users in Australia, the United Kingdom, and the United States, Depop allows users to receive a full refund if their item does not arrive, arrives damaged, or is considerably different from the original when the issue is reported within 30 days. === Competitors === As of June 2021, Depop's competitors include Vinted, a platform founded by Milda Mitkute and Justas Janauskas in 2008 and valued at €3.5 billion, as well as the U.S. resale site Poshmark, valued at $3.5 billion. Additional competitors include Grailed, a peer-to-peer e-commerce site founded in 2014 that is recognized for its high-end second-hand menswear and streetwear, and Vestiaire Collection, a European resale app established in 2009 which specializes in authenticated pre-owned luxury items. The popularity of Depop has negatively impacted traditional second-hand stores, which can struggle to compete due to high labor costs and quality demands. There is an oversupply of clothes with the rise of fast fashion; this has taken a toll on the revenue aspect of the second-hand clothing industry. == Criticism == In November 2019, Business of Fashion reported that users within the Depop app were receiving sexually suggestive messages. In February 2020, Jessica Hamilton, a Depop buyer, reported that she found many scammers on the platform. She noticed this issue after she attempted to purchase a Nintendo Switch from a seller who would suspiciously only accept payment through a direct bank transfer without buyer protection. Hamilton blamed the company for its lack of action and relaxed security measures compared to other e-commerce sites, which made the platform especially susceptible to hackers. Without a clear strategy for managing scams, Depop lost some users' trust because of its negligence. In October 2020, some Depop buyers were tricked into paying sellers directly to bypass Depop's buyer protections, and the Depop sellers then sold those users' information on the dark web. In response, Depop claimed that it would improve security through mandatory password updates and multi-factor authentication. Users have criticized Depop for belatedly taking action against this issue.

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  Waveform graphics

  Waveform graphics is a simple vector graphics system introduced by Digital Equipment Corporation (DEC) on the VT55 and VT105 terminals in the mid-1970s. It was used to produce graphics output from mainframes and minicomputers. DEC used the term "waveform graphics" to refer specifically to the hardware, but it was used more generally to describe the whole system. The system was designed to use as little computer memory as possible. At any given X location it could draw two dots at given Y locations, making it suitable for producing two superimposed waveforms, line charts or histograms. Text and graphics could be mixed, and there were additional tools for drawing axes and markers. The waveform graphics system was used only for a short period of time before it was replaced by the more sophisticated ReGIS system, first introduced on the VT125 in 1981. ReGIS allowed the construction of arbitrary vectors and other shapes. Whereas DEC normally provided a backward compatible solution in newer terminal models, they did not choose to do this when ReGIS was introduced, and waveform graphics disappeared from later terminals. == Description == Waveform graphics was introduced on the VT55 terminal in October 1975, an era when memory was extremely expensive. Although it was technically possible to produce a bitmap display using a framebuffer using technology of the era, the memory needed to do so at a reasonable resolution was typically beyond the price point that made it practical. All sorts of systems were used to replace computer memory with other concepts, like the storage tubes used in the Tektronix 4010 terminals, or the zero memory racing-the-beam system used in the Atari 2600. DEC chose to attack this problem through a clever use of a small buffer representing only the vertical positions on the screen. Such a system could not draw arbitrary shapes, but would allow the display of graph data. The system was based on a 512 by 236 pixel display, producing 512 vertical columns along the X-axis, and 236 horizontal rows on the Y-axis. Y locations were counted up from the bottom, so the coordinate 0,0 was in the lower left, and 511, 235 in the upper right. Had this been implemented using a framebuffer with each location represented by a single bit, 512 × 236 × 1 = 120,832 bits, or 15,104 bytes, would have been required. At the time, memory cost about $50 per kilobyte, so the buffer alone would cost over $700 (equivalent to $4,570 in 2025). Instead, the waveform graphic system used one byte of memory for each X axis location, with the byte's value representing the Y location. This required only 512 bytes for each graph, a total of 1024 bytes for the two graphs. Drawing a line required the programmer to construct a series of Y locations and send them as individual points, the terminal could not connect the dots itself. To make this easier, the terminal automatically incremented the X location every time an Y coordinate was received, so a graph line could be sent as a long string of numbers for subsequent Y locations instead of having to repeatedly send the X location every time. Drawing normally started by sending a single instruction to set the initial X location, often 0 on the left, and then sending in data for the entire curve. The system also included storage for up to 512 markers on both lines. These were always drawn centered on the Y value of the line they were associated with, meaning that a simple on/off indication for X locations was all that was needed, requiring only 1024 bits, or 128 bytes, in total. The markers extended 16 pixels vertically, and could only be aligned on 16-pixel boundaries, so they were not necessarily centered across the underlying graph. Markers were used to indicate important points on the graph, where a symbol of some sort would normally be used. The system also allowed a vertical line to be drawn for every horizontal location and a horizontal one at every vertical location. These were also stored as simple on/off bits, requiring another 128 bytes of memory. These lines were used to draw axes and scale lines, or could be used for a screen-spanning crosshair cursor. A separate set of two 7-bit registers held additional information about the drawing style and other settings. Although complex from the user's perspective, this system was easy to implement in hardware. A cathode ray tube produces a display by scanning the screen in a series of horizontal motions, moving down one vertical line after each horizontal scan. At any given instant during this process, the display hardware examines a few memory locations to see if anything needs to be displayed. For instance, it can determine whether to draw a marker on graph 0 by examining register 1 to see if markers are turned on, looking in the marker buffer to see if there is a 1 at the current X location, and then examining the Y location of graph 0 to see if it is within 16 pixels of the current scan line. If all of these are true, a spot is drawn to present that portion of the marker. As this will be true for 16 vertical locations during the scanning process, a 16-pixel high marker will be drawn. Sold alone, the VT55 was priced at $2,496 (equivalent to $16,295 in 2025),. Like other models of the VT50 series, the terminal could be equipped with an optional wet-paper printer in a panel on the right of the screen. This added $800 (equivalent to $5,223 in 2025) to the price. DEC also offered VT55 in a package with a small model of the PDP-11 to create one model of the DEClab 11/03 system. The DEClab normally sold for $14,000 (equivalent to $91,397 in 2025) with a DECwriter II (LA36) hard-copy terminal for $15,000 (equivalent to $97,925 in 2025), with the VT55. The system had I/O channels for up to 15 lab devices, and included libraries for FORTRAN and BASIC for reading the data and creating graphs. The fairly extensive VT55 Programmers Manual covered the latter in depth. == Commands and data == Data was sent to the terminal using an extended set of codes similar to those introduced on the VT52. VT52 codes generally started with the ESC character (octal 33, decimal 27) and was then followed by a single letter instruction. For instance, the string of four characters ESC H ESC J would reposition the cursor in the upper left (home) and then clear the screen from that point down. These codes were basically modeless; triggered by the ESC the resulting escape mode automatically exited again when the command was complete. Escape codes could be interspersed with display text anywhere in the stream of data. In contrast, the graphics system was entirely modal, with escape sequences being sent to cause the terminal to enter or exit graph drawing mode. Data sent between these two codes were interpreted by the graphics hardware, so text and graphics could not be mixed in a single stream of instructions. Graphics mode was entered by sending the string ESC 1, and exited again with the string ESC 2. Even the commands within the graphics mode were modal; characters were interpreted as being additional data for the previous load character (command) until another load character is seen. Ten load characters were available: @ - no operation, used to tell the terminal the last command is no longer active A - load data into register 0, selecting the drawing mode for the two graphs I - load data into register 1, selecting other drawing options H - load the starting X position (Horizontal) for the following commands B - load data for Y locations for graph 0 starting at the H position selected earlier J - load data for Y locations for graph 1 starting at the H position selected earlier C - store a marker on graph 0 at the following X location K - store a marker on graph 1 at the following X location D - draw a horizontal line at the given Y location L - draw a vertical line at the given X location X and Y locations were sent as 10-bit decimal numbers, encoded as ASCII characters, with 5 bits per character. This means that any number within the 1024 number space (210) can be stored as a string of two characters. To ensure the characters can be transmitted over 7-bit links, the pattern 01 is placed in front of both 5-bit numbers, producing 7-bit ASCII values that are always within the printable range. This results in a somewhat complex encoding algorithm. For instance, if one wanted to encode the decimal value 102, first you convert that to the 10-bit decimal pattern 0010010010. That is then split that into upper and lower 5-bit parts, 00100 and 10010. Then append 01 binary to produce 7-bit numbers 0100100 and 0110010. Individually convert back to decimal 40 and 50, and then look up those characters in an ASCII chart, finding ( and 2. These have to be sent to the terminal least significant character first. If these were being used to set the X coordinate, the complete string would be H2(. When used as X and Y locations for the graphs, extra digits were ignored. For instance, the 512 pixel X axis r

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  CityEngine

  ArcGIS CityEngine is a commercial 3D modeling program. Developed by Esri R&D Center Zurich (formerly Procedural Inc.), it specializes in the generation of 3D urban environments to support the creation of detailed large-scale 3D city models. Unlike traditional 3D modeling methodology, which uses computer-aided design (CAD) tools and techniques, CityEngine takes a procedural modeling approach which shapes generation via a rules-based system. Due to its integration with the wider ArcGIS platform, CityEngine can also be used with geographic information system (GIS) datasets. CityEngine can be used for urban planning and architecture, graphics visualization, game development, entertainment, and archeology. CityEngine can be used to visualize the building information modeling (BIM) data of buildings in a larger urban context, making for more realistic construction projects. == History and releases == === Software history === ArcGIS CityEngine, originally named Esri CityEngine, was developed at Swiss technology university ETH Zurich by Pascal Mueller, the co-founder and CEO of Procedural Inc. While researching for his PhD at the ETH Computer Vision Lab, Mueller invented a number of techniques for procedural modeling of 3D architecture that make up the foundation of CityEngine. CityEngine publically debuted at the 2001 SIGGRAPH conference; since then, additional research papers have been published that have contributed to CityEngine and its features. The first commercial version of CityEngine was released in 2008. In 2007, Procedural Inc. was founded and separated from ETH Zurich, the top-ranking technology university in Switzerland. In the summer of 2011, Procedural Inc. was acquired by Esri Inc., becoming Esri R&D Center Zurich. Esri CityEngine was renamed to ArcGIS CityEngine in June 2020 to offically make it a part of the ArcGIS software suite. === Releases === === Licensing and pricing === ArcGIS CityEngine is included in the Professional and Professional Plus tiers of ArcGIS Online. Pricing may vary by region and distributors. In the US, the professional tier costs US$2,200 per year; in the UK, it is £4,200 per year (excluding VAT). CityEngine can be purchased elsewhere via a local Esri partner. . Once purchased, users can download and obtain license details from the MyEsri portal. == Features == CGA (computer generated architecture) parametric modeling rules to control mass, geometry assets, proportions, or texturing of buildings or streets on a citywide scale Select a target location and import geo-referenced satellite imagery and 3D terrain of the location to more quickly build accurate urban environments through OpenStreetMap integration Interactively control specific street or building parameters, such as height or age Import/export geo-spatial/vector data with industry-standard formats such as Esri Shapefile, File Geodatabase, and OpenStreetMap, as well as file formats for WebGL, KMZ, Collada, Autodesk FBX, Autodesk Maya, 3DS, Wavefront OBJ, RenderMan RIB, Alembic, e-on software's Vue, Universal Scene Description USD, Khronos Group GLTF, Unreal Engine, and Unreal Datasmith Script and generate rules-based reports to show socioeconomic figures (e.g., Gross Floor Area (GFA) and Floor Area Ratio (FAR)) to analyze their urban design proposals. VR viewing of modeled environments with Samsung Gear VR Use a variety of materials through the Esri materials library == Procedural modeling == ArcGIS CityEngine uses a procedural modeling approach to automatically generate models through a predefined rule set. The rules are defined through a CGA shape grammar system, enabling the creation of complex parametric models. Users can change or add the shape grammar as needed. Urban environments can be modeled within CityEngine by starting with creating a street network (either from the street drawing tool or with data imported from map data). Then, lots may be subdivided as many times as specified, resulting in a map of multiple lots and streets. CityEngine can then be instructed to start generating the buildings using defined procedural modeling rules. At this point, the city model can be re-designed and adjusted by changing the parameters or the shape grammar. === Geodesign === Though CityEngine is not an analytical tool like GIS, discussions about geodesign often mention the use of ArcGIS CityEngine. As it can be used to enhance 3D shape generation in ArcGIS, ArcGIS CityEngine is a critical product to improve the applicability of geodesign by using geospatial information to design or analyze a city. == Applications == === Urban design and planning === Garsdale Design used ArcGIS CityEngine in the creation of city master plans in Iraq before 2013, both to model existing historic areas and also model future plans. Larger companies like Foster+Partners and HOK Architects have also used CityEngine in their urban planning projects. === Urban and environmental studies === Because its primary feature is building informative city models, some urban researchers use CityEngine to compare land-use planning schemes, for example in very dense global cities such as Hong Kong and Seoul. Environmental scientists can also utilize the instant 3D model generation in CityEngine, which can make for more convenient informative research than modeling a city by creating each building individually. === Game development === CityEngine can be used as a tool in the creation of video games that require detailed 3D environments to assign interactive scripts. === Movie industry === Zootopia (also known outside of the US as Zootopolis), which won the 2016 Academy Award for Best Animated Feature Film, used CityEngine to model the city in its movie. multi-scaling city, the designers used CityEngine due to its rule-based system. CityEngine was also used to create Big Hero 6's San-Fransokyo. === Military === Due to its integration with the Esri product suite and its ability to process geospatial data to create 3D scenes/maps, CityEngine can be used within military/defense organizations. == List of movies and TV shows using CityEngine == Studios and companies rarely state what software they use in their pipelines. When CityEngine is mentioned as a tool in production, it's often in a small reference in a larger article. Movies only claimed to use CityEngine by a single Esri employee Presented at FMX 2025 workshop == Ports == ArcGIS CityEngine is built on top of Eclipse IDE, and has therefore able to be used on Windows and Linux operating systems. Support for macOS was stopped in March 2021. == Plugins and extensions == ArcGIS CityEngine currently works with a number of third party 3D modeling, rendering, and analytical software products via its SDK and API; these currently are: ArcGIS CityEngine for ArcGIS Urban: ArcGIS Urban Suite Puma: ArcGIS CityEngine for Rhinoceros 3D Palladio: ArcGIS CityEngine for Houdini Serlio: ArcGIS CityEngine for Maya PyPRT: ArcGIS CityEngine for Python ArcGIS CityEngine provides a Python scripting interface built on Jython (current version 2.7.0) which allows users to create their own tools and functionality. == Publications ==

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  UpScrolled

  UpScrolled is an Australian social media platform for microblogging and short-form online video sharing that was launched in June 2025 by Recursive Methods Pty Ltd. It was founded by Issam Hijazi. == History == UpScrolled was launched in June 2025 by Recursive Methods Pty Ltd. It was founded by Issam Hijazi, a Palestinian-Australian app developer. UpScrolled is backed by the Tech for Palestine incubator. In January 2026, UpScrolled saw increased attention and number of downloads after the acquisition of TikTok by a group of pro-Donald Trump US investors, including Larry Ellison, which led to calls to boycott TikTok and migrate to other apps. TikTok was alleged to be suppressing pro-Palestinian content, as well as news surrounding the killing of Alex Pretti in Minneapolis on the platform. UpScrolled subsequently climbed to the top 10 of Apple's App Store list of free apps. The app saw a reported 2,850% increase in downloads between 22 and 24 January 2026. As of 27 January 2026, UpScrolled "had been downloaded about 400,000 times in the US and 700,000 globally since launching in June 2025". The app became the most downloaded app in the Apple App store on 29 January 2026, following allegations that TikTok was suppressing videos and content opposed to Immigration and Customs Enforcement (ICE) under its new ownership. By 2 February 2026, UpScrolled had reached 2.5 million users. According to the Google Play Store and the Apple App Store, it has become the most downloaded social media app in the United States and Canada, with rising interest in the United Kingdom, France, Germany and Italy. On 14 February, UpScrolled was suspended from the Google Play Store; the suspension was reverted by 15 February. == Founder == Hijazi was born in Jordan. His parents and grandparents are from Safad, a northern Israeli city near the Lebanese border. He worked for IBM and Oracle prior to starting UpScrolled. Hijazi told Rest of World that he launched UpScrolled in response to Israel's genocide in Gaza which followed the October 7 attacks. He said, "I couldn't take it anymore. I lost family members in Gaza, and I didn't want to be complicit. So I was like, I'm done with this, I want to feel useful. I found this gap in the market, with a lot of people asking why there is no alternative to the Big Tech platforms for their content, which was getting censored." Hijazi also alleges that social media accounts that were posting pro-Palestinian content were getting shadow banned on larger platforms, and alleges that even his account was not exempt from being targeted by censors. Hijazi has further elaborated on the importance of social media independence to further the Palestinian cause. In January 2026, Web Summit Qatar announced that Hijazi would be an opening night speaker. Following the announcement, there was a surge in ticket sales for the summit. Hijazi lives in Sydney with his wife and daughter. He lost 60 family members during the Gaza war. == Features == UpScrolled's algorithm allows users to discover posts based on likes, comments, and shares with time decay and some randomness, all chronologically, with "no manipulation" according to the app's website. UpScrolled has an interface resembling a mix of Instagram and Twitter, allowing users to post and view text posts, photos, and videos. It also lets users send private messages to each other. The app is currently available for iOS and Android devices, with plans to upscale. UpScrolled does not include Israel as an option in its location selection menu. Cities such as Tel Aviv are included under "Occupied Territories of Palestine", and Palestine can also be set as the location. UpScrolled says that it is against censorship and shadow banning, and describes itself as "belong[ing] to the people who use it — not to hidden algorithms or outside agendas". Hijazi said, "The other platforms claim to be free speech platforms. But when it comes to anything on Palestine, that's a different story." UpScrolled states that it "does not tolerate hate speech, propaganda, or bad-faith behaviour, but it also refuses to silence voices quietly or without explanation". == User base and content == Al Jazeera reported that posts expressing pro-Palestinian sentiment or depicting the continued suffering in the Gaza Strip were "flooding" the app. Political and global issues such as the Gaza war are prominent. Content includes updates from the Gaza Freedom Flotilla, posts by doctors working in Gaza, video essays about Palantir’s influence within the military and calls for boycotts of Israel. It has been used by Gazans to crowdfund and record daily life. Celebrity users of UpScrolled include American labour activist Chris Smalls and actor Jacob Berger, both of whom were on the July 2025 Gaza Freedom Flotilla. Political figures have also joined UpScrolled, such as South African politician and Economic Freedom Fighters leader Julius Malema, and Islamic Revolutionary Guard Corps commander Esmail Qaani. One user said that most early users were attracted to the platform for the opportunity to criticize Zionism. The Jewish Telegraphic Agency (JTA) reported that UpScrolled was observed to be "flooded" with antisemitic and anti-Israel content, including Holocaust denial and accusations that Israel carried out the 9/11 attacks. In a statement, UpScrolled said, "Our content moderation hasn't been able to keep up with the massive rise of users this week. We're working with digital rights experts to grow our Trust & Safety team and are beefing up our content moderation to prevent this. We apologise to all impacted users, thank you for being part of Upscrolled." The Times reported in February 2026 that UpScrolled was hosting content that could potentially breach UK law, including antisemitic content and posts promoting Hamas, Hezbollah, Islamic State and Al-Qaeda, as well as footage of the 2019 Christchurch mosque shootings and content praising the perpetrators of the 2019 Halle synagogue shooting and 2018 Pittsburgh synagogue shooting. Antisemitic influencers Lucas Gage, Jake Shields, Stew Peters and Anastasia Maria Loupis have accounts on UpScrolled. UpScrolled’s policies prohibit threats, glorification of harm or support for terrorist or violent groups. Hijazi said harmful content was being uploaded to UpScrolled and the company had expanded its content moderation team and upgraded its technology infrastructure to deal with the issue. In May 2026, Moment magazine said that users had identified some antisemitic content, pornography and extremist videos on the platform. The magazine said there were gaps in content moderation due to the small size of the developer team. == Reception == In January 2026, the Council on American–Islamic Relations (CAIR) praised UpScrolled for "pledging to protect the free flow of ideas on its platform, including both support for and opposition to the Israeli government's human rights abuses." Guy Christensen, a pro-Palestinian social media celebrity, has encouraged his audience to download UpScrolled. Christensen characterized UpScrolled as having "no censorship, no ownership by billionaires who put their interests and biases onto you to control you". He compared the platform to others like TikTok, saying that Israel is behind censorship that wouldn't happen on UpScrolled. Jaigris Hodson, an associate professor of Interdisciplinary Studies at Royal Roads University in Canada, has argued that "Network effects mean that unless UpScrolled continues its explosive growth, people are unlikely to continue to choose it over the more established TikTok. At best, we might see a Twitter/X effect, which is where TikTok will host more pro-U.S. government content creators and those people who want to follow them, and UpScrolled will host more critical content creators and their followers."

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  MultiValue database

  A MultiValue database is a type of NoSQL and multidimensional database. It is typically considered synonymous with PICK, a database originally developed as the Pick operating system. MultiValue databases include commercial products from Rocket Software, Revelation, InterSystems, Northgate Information Solutions, ONgroup, and other companies. These databases differ from a relational database in that they have features that support and encourage the use of attributes which can take a list of values, rather than all attributes being single-valued. They are often categorized with MUMPS within the category of post-relational databases, although the data model actually pre-dates the relational model. Unlike SQL-DBMS tools, most MultiValue databases can be accessed both with or without SQL. == History == Don Nelson designed the MultiValue data model in the early to mid-1960s. Dick Pick, a developer at TRW, worked on the first implementation of this model for the US Army in 1965. Pick considered the software to be in the public domain because it was written for the military, this was but the first dispute regarding MultiValue databases that was addressed by the courts. Ken Simms wrote DataBASIC, sometimes known as S-BASIC, in the mid-1970s. It was based on Dartmouth BASIC, but had enhanced features for data management. Simms played a lot of Star Trek (a text-based early computer game originally written in Dartmouth BASIC) while developing the language, to ensure that DataBASIC functioned to his satisfaction. Three of the implementations of MultiValue - PICK version R77, Microdata Reality 3.x, and Prime Information 1.0 - were very similar. In spite of attempts to standardize, particularly by International Spectrum and the Spectrum Manufacturers Association, who designed a logo for all to use, there are no standards across MultiValue implementations. Subsequently, these flavors diverged, although with some cross-over. These streams of MultiValue database development could be classified as one stemming from PICK R83, one from Microdata Reality, and one from Prime Information. Because of the differences, some implementations have provisions for supporting several flavors of the languages. An attempt to document the similarities and differences can be found at the Post-Relational Database Reference (PRDB). One reasonable hypothesis for this data model lasting 50 years, with new database implementations of the model even in the 21st century is that it provides inexpensive database solutions. == Data model example == In a MultiValue database system: a database or schema is called an "account" a table or collection is called a "file" a column or field is called a field or an "attribute", which is composed of "multi-value attributes" and "sub-value attributes" to store multiple values in the same attribute. a row or document is called a "record" or "item" Data is stored using two separate files: a "file" to store raw data and a "dictionary" to store the format for displaying the raw data. For example, assume there's a file (table) called "PERSON". In this file, there is an attribute called "eMailAddress". The eMailAddress field can store a variable number of email address values in a single record. The list [[email protected], [email protected], [email protected]] can be stored and accessed via a single query when accessing the associated record. Achieving the same (one-to-many) relationship within a traditional relational database system would include creating an additional table to store the variable number of email addresses associated with a single "PERSON" record. However, modern relational database systems support this multi-value data model too. For example, in PostgreSQL, a column can be an array of any base type. == MultiValue Basic Language == Multivalue Basic (now commonly styled as mvBasic) is a family of programming languages more or less common (and portable) to all the multivalue databases derived from the original Pick Operating System. The variations between implementations are known as flavours. The language originates from Dartmouth Basic and the earliest implementation of PickBASIC (now D3 FlashBasic). Over time various customisations and extensions have been added to take advantage of capabilities added to the different flavours while staying mainly in sync. mvBasic statements and functions are designed to access and take advantage of the multivalue database model and providing the usual capabilities of most modern languages. For example, cryptography and communications. mvBasic is typeless and lends itself to structured programming techniques. Example code is available but limited. Whilst there are commercial applications and tools available, the multivalue database community has not embraced the open source library/package model to the degree seen with other languages. The typical mvBasic compiler compiles program source to a P-code executable object and runs in an interpreter, with D3 FlashBasic and jBASE being notable exceptions. == MultiValue Query Language == Known as ENGLISH, ACCESS, AQL, UniQuery, Retrieve, CMQL, and by many other names over the years, corresponding to the different MultiValue implementations, the MultiValue query language differs from SQL in several respects. Each query is issued against a single dictionary within the schema, which could be understood as a virtual file or a portal to the database through which to view the data. LIST PEOPLE LAST_NAME FIRST_NAME EMAIL_ADDRESSES WITH LAST_NAME LIKE "Van..." The above statement would list all e-mail addresses for each person whose last name starts with "Van". A single entry would be output for each person, with multiple lines showing the multiple e-mail addresses (without repeating other data about the person).

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  Zynn

  Zynn was a Chinese video-sharing social networking service owned by Kuaishou, a Beijing-based internet technology company established in 2011 by Su Hua and Cheng Yixiao. It was used to create and share short videos, and it pays its users for using the app and referring others. Zynn was launched on May 7, 2020. It became the most-downloaded app in the App Store in the same month. It has also been criticized for being a "pyramid scheme", and it has faced accusations of plagiarism and stealing content. Aside from Zynn in North America, Kuaishou is available under the name Kwai in Russia, South Korea, Japan, Thailand, Vietnam, Philippines, Malaysia, Indonesia, Brazil, America, India, and the Middle East. Kwai used to be available in Australia and the United States on the App Store, but was removed at an unknown date. Zynn was permanently shut down on the 20th of August, 2021. == History == In 2011, entrepreneur Su Hua co-founded Kuaishou with business partner Cheng Yixiao. Originally a GIF-making app, Kuaishou soon moved to short video content. Su Hua also serves as the current Kuaishou CEO. In December 2019, Chinese internet conglomerate Tencent invested $2 billion in Kuaishou, reportedly to compete with rival ByteDance. In December 2019, Kuaishou acquired an app developer called Owlii, which is the developer of Zynn. Zynn was developed to be a North American Market edition of Kuaishou. On May 7, 2020, the app was launched and it was downloaded over 2 million times in that month. On May 12, 2020, Kuaishou filed a lawsuit seeking compensation for "unfair competition", and accused Douyin, the sister app of TikTok, of "interfering" with search results on app stores. Zynn shut down on the 20th of August, 2021. == Features == Zynn allows its users to create, edit and share short videos of themselves. Its interface has been described as a "complete clone" of TikTok, its main competitor. The Zynn app was unique in the way that they paid users for using the platform. Each user earned $1 for signing up, and they could earn money for referring users to the platform. Watching videos resulted in earning "points", which could be redeemed for gift cards or be cashed out via PayPal.[1] == Criticisms and controversies == Multiple TikTok users had reported seeing their entire accounts plagiarized, with one account pretending to be Addison Rae. Despite being launched in May, many videos were posted in February. Zynn has employed "intermittent variable rewards" in its point system, which has been criticized as being the "same reinforcement strategy used to addict people to slot machines". Cash payouts for using the app have resulted in criticism and accusations of anti-competitive behavior. The app was taken down from the Google Play store on June 10. Zynn blamed it on an "isolated incident". Six days later, it was taken down from the App Store as well. US Senator Josh Hawley has criticized the platform, calling it "predatory" and "anti-competitive" in a letter to the Federal Trade Commission asking for an investigation into Zynn. He said "[Zynn] smacks of a textbook predatory-pricing scheme, one calculated to attain immediate market dominance for Zynn by driving competitors out of the market."

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  Geofence warrant

  A geofence warrant or a reverse location warrant is a search warrant issued by a court to allow law enforcement to search a database to find all active mobile devices within a particular geo-fence area. Courts have granted law enforcement geo-fence warrants to obtain information from databases such as Google's Sensorvault, which collects users' historical geolocation data. Geo-fence warrants are a part of a category of warrants known as reverse search warrants. == History == Geofence warrants were first used in 2016. Google reported that it had received 982 such warrants in 2018, 8,396 in 2019, and 11,554 in 2020. A 2021 transparency report showed that 25% of data requests from law enforcement to Google were geo-fence data requests. Google is the most common recipient of geo-fence warrants and the main provider of such data, although companies including Apple, Snapchat, Lyft, and Uber have also received such warrants. == Legality == === United States === Some lawyers and privacy experts believe reverse search warrants are unconstitutional under the Fourth Amendment to the United States Constitution, which protects people from unreasonable searches and seizures, and requires any search warrants be specific to what and to whom they apply. The Fourth Amendment specifies that warrants may only be issued "upon probable cause, supported by Oath or affirmation, and particularly describing the place to be searched, and the persons or things to be seized." Some lawyers, legal scholars, and privacy experts have likened reverse search warrants to general warrants, which were made illegal by the Fourth Amendment. Groups including the Electronic Frontier Foundation have opposed geo-fence warrants in amicus briefs filed in motions to quash such orders to disclose geo-fence data. In 2024, a panel of the United States Fourth Circuit Court of Appeals considered data acquired from Google’s Sensorvault not to be a search, but non-private business records when users opt-in to Google’s location history. However, upon a rehearing en banc, the Court vacated that decision. In April 2025, the full Court affirmed the judgment solely on the 'good faith' exception, leaving the underlying constitutional question of whether geofence warrants constitute a search unsettled in the Circuit. However, the United States Fifth Circuit Court of Appeals found that geofence warrants are "categorically prohibited by the Fourth Amendment." The split in Circuits prompted the United States Supreme Court to agree to hear Chatrie v. United States in January 2026.

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