AI Data Water

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  Valantic

  Valantic GmbH (stylised as valantic) is an IT service and consulting company headquartered in Munich, Germany. == History == Valantic GmbH was founded in 2012 under the name Dabero Service Group. Until it was renamed Valantic GmbH in 2017, the company merged with IT service providers and consulting firms. These included, among others, Realtime AG, a company for SAP systems. The companies involved in these mergers were also renamed in 2017 and have since used the Valantic brand name. Realtime AG, for example, became Valantic ERP Services AG. During the COVID-19 pandemic and the resulting economic pressures, demand increased for IT service providers, particularly those offering customised software, IT consulting, SAP services, customer experience, cybersecurity, IoT, and digital work environments. In the following years, Valantic expanded by integrating additional companies. In 2021, Valantic expanded into other European countries through the integration of the Dutch company ISM eCompany and the Portuguese consulting firm Abaco. In 2022, the consulting firm C-Clear/Atom Ideas from Belgium joined Valantic. In February 2019, DPE Deutsche Private Equity Management III GmbH (DPE) took over the majority shareholding in Valantic. The founder, Holger von Daniels, and the further management retained a 25% stake. By 2025, DPE had invested €500 million in Valantic. In the following years, Valantic expanded its international locations. In 2023, Valantic incorporated the Danish company Inspari into the group, thereby entering the Scandinavian market. Inspari is a company for Microsoft technologies such as Azure and Power Platform. In the same year, Valantic joined forces with the Aiopsgroup, an international provider of online shopping applications for private and business customers of large companies. The company is based in Bulgaria with additional locations across Eastern Europe and other places. Additionally, the SAP applications division was expanded through the merger with the Spanish company Saptools. As a result, the companies became one of the largest European end-to-end consulting and implementation house for SAP services. By the end of 2023, Valantic had locations in 18 countries. In November 2024, Valantic announced its merger with the Danish digital consultancy Venzo. Through the integration of the company, founded in 2007 and oriented towards Microsoft technologies and digital transformation projects in the areas of automation, artificial intelligence, security, infrastructure and change management, Valantic further expanded its presence in Denmark and the Nordic countries. In July 2025, Valantic announced its merger with Utiligence GmbH, a Mannheim-based consulting firm for SAP technologies. Utiligence works primarily for the energy industry and supports companies in the integration of SAP S/4HANA and the digitalisation of business processes. == Company structure == Valantic is a partnership-based organisation, with partners acting as decision-makers in matters relating to corporate strategy, employee development and acquisitions. Valantic pursues a holacratic approach, promoting an open and self-organised way of working instead of hierarchical structures. By merging with other companies, Valantic is expanding its range of services and tapping into international markets and market shares. The new companies use Valantic's core systems and support processes, but usually retain their original structure. In the 2024 financial year, the company generated revenue of €544 million and employed 3,874 on average. Valantic has over 40 locations internationally. == Services == Valantic GmbH is a consulting firm, software provider and implementation partner. The company offers services in the areas of digital strategy and analytics (business intelligence and data science), customer experience management, SAP services, smart industries (Industry 4.0, supply chain management, and production planning and control processes), and financial services automation. The automation of financial services is aimed at financial service providers and banks. Valantic has been offering services in the field of generative artificial intelligence (GenAI) since 2023. Part of these services involves enabling companies to use GenAI securely and in compliance with regulations in order to make internal work processes more efficient. Its customers include large corporations, several medium-sized companies and DAX-listed companies. == Research == Since 2018, Valantic has published an annual study on the development of the SAP landscape in German-speaking countries. The study examines topics such as the migration to SAP S/4HANA, cloud strategies, technological trends and the use of artificial intelligence in business processes. The 2025 survey of 201 SAP professionals from the DACH region showed, for example, an increase in ongoing and completed S/4HANA migration projects, as well as a further shift towards private-cloud systems. The use of artificial intelligence continued to grow, as did the use of the SAP Business Technology Platform and the Business Data Cloud. In 2025, Valantic, together with the Handelsblatt Research Institute, published the trend study Digital 2030 – The Rise of Applied AI. The study was based on a survey of around 700 executives from companies in Germany, Austria, and Switzerland on the economic effects of current digitalisation trends. According to the study, most respondents consider artificial intelligence, cybersecurity, and cloud computing to hold the greatest strategic importance for business success by 2030. Around 70% of the participating companies stated that they are already achieving measurable business benefits through the use of AI applications, for example in quality control, document management, logistics, or customer service.

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  Patent visualisation

  Patent visualisation is an application of information visualisation. The number of patents has been increasing, encouraging companies to consider intellectual property as a part of their strategy. Patent visualisation, like patent mapping, is used to quickly view a patent portfolio. Software dedicated to patent visualisation began to appear in 2000, for example Aureka from Aurigin (now owned by Thomson Reuters). Many patent and portfolio analytics platforms, such as Questel, Patent Forecast, PatSnap, Patentcloud, Relecura, and Patent iNSIGHT Pro, offer options to visualise specific data within patent documents by creating topic maps, priority maps, IP Landscape reports, etc. Software converts patents into infographics or maps, to allow the analyst to "get insight into the data" and draw conclusions. Also called patinformatics, it is the "science of analysing patent information to discover relationships and trends that would be difficult to see when working with patent documents on a one-and-one basis". Patents contain structured data (like publication numbers) and unstructured text (like title, abstract, claims and visual info). Structured data are processed by data-mining and unstructured data are processed with text-mining. == Data mining == The main step in processing structured information is data-mining, which emerged in the late 1980s. Data mining involves statistics, artificial intelligence, and machine learning. Patent data mining extracts information from the structured data of the patent document. These structured data are bibliographic fields such as location, date or status. === Structured fields === === Advantages === Data mining allows study of filing patterns of competitors and locates main patent filers within a specific area of technology. This approach can be helpful to monitor competitors' environments, moves and innovation trends and gives a macro view of a technology status. == Text-mining == === Principle === Text mining is used to search through unstructured text documents. This technique is widely used on the Internet, it has had success in bioinformatics and now in the intellectual property environment. Text mining is based on a statistical analysis of word recurrence in a corpus. An algorithm extracts words and expressions from title, summary and claims and gathers them by declension. "And" and "if" are labeled as non-information bearing words and are stored in the stopword list. Stoplists can be specialised in order to create an accurate analysis. Next, the algorithm ranks the words by weight, according to their frequency in the patent's corpus and the document frequency containing this word. The score for each word is calculated using a formula such as: W e i g h t = T e r m F r e q u e n c y D o c u m e n t F r e q u e n c y = F r e q u e n c y o f t h e w o r d o r e x p r e s s i o n i n t h e T e x t S e a N u m b e r o f d o c u m e n t s c o n t a i n i n g t h e e x p r e s s i o n o r w o r d {\displaystyle Weight={\frac {Term\ Frequency}{Document\ Frequency}}={\frac {Frequency\ of\ the\ word\ or\ expression\ in\ the\ Text\ Sea}{Number\ of\ documents\ containing\ the\ expression\ or\ word}}} A frequently used word in several documents has less weight than a word used frequently in a few patents. Words under a minimum weight are eliminated, leaving a list of pertinent words or descriptors. Each patent is associated to the descriptors found in the selected document. Further, in the process of clusterisation, these descriptors are used as subsets, in which the patent are regrouped or as tags to place the patents in predetermined categories, for example keywords from International Patent Classifications. Four text parts can be processed with text-mining : Title Abstract Claim Patent Full-Text Software offer different combinations but title, abstract and claim are generally the most used, providing a good balance between interferences and relevancy. === Advantages === Text-mining can be used to narrow a search or quickly evaluate a patent corpus. For instance, if a query produces irrelevant documents, a multi-level clustering hierarchy identifies them in order to delete them and refine the search. Text-mining can also be used to create internal taxonomies specific to a corpus for possible mapping. == Visualisations == Allying patent analysis and informatic tools offers an overview of the environment through value-added visualisations. As patents contain structured and unstructured information, visualisations fall in two categories. Structured data can be rendered with data mining in macrothematic maps and statistical analysis. Unstructured information can be shown in like clouds, cluster maps and 2D keyword maps. === Data mining visualisation === === Text mining visualisation === === Visualisation for both data-mining and text-mining === Mapping visualisations can be used for both text-mining and data-mining results. == Uses == What patent visualisation can highlight: Competitors Partners New innovations Technologic environment description Networks Field application: R&D strategy management Competitive intelligence Licensing Strategy

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  Text Database and Dictionary of Classic Mayan

  The project Text Database and Dictionary of Classic Mayan (abbr. TWKM) promotes research on the writing and language of pre-Hispanic Maya culture. It is housed in the Faculty of Arts at the University of Bonn and was established with funding from the North Rhine-Westphalian Academy of Sciences, Humanities and the Arts. The project has a projected run-time of fifteen years and is directed by Nikolai Grube from the Department of Anthropology of the Americas at the University of Bonn. The goal of the project is to conduct computer-based studies of all extant Maya hieroglyphic texts from an epigraphic and cultural-historical standpoint, and to produce and publish a database and a comprehensive dictionary of the Classic Mayan language. == Subject of the Project == The text database, as well as the dictionary that will be compiled by the conclusion of the project, will be assembled based on all known texts from the pre-Hispanic Maya culture. These texts were produced and used between approximately the third century B.C. through A.D. 1500, in a region that today includes parts of the countries of Mexico, Guatemala, Belize, and Honduras. The thousands of hieroglyphic inscriptions on monuments, ceramics, or daily objects that have survived into the present offer insight into the language's vocabulary and structure. The project's database and dictionary will digitally represent original spellings using the logo-syllabic Maya hieroglyphs, as well as their transcription and transliteration in the Roman alphabet. The data will be additionally annotated with various epigraphic analyses, translations, and further object-specific information. == Project Partners == TWKM will employ digital technologies in order to compile and make available the data and metadata, as well as to publish the project's research results. The project thereby methodologically positions itself in the field of the digital humanities. The project will be conducted in cooperation with the project partners (below), the research association for the eHumanities TextGrid, as well as the University and Regional Library of Bonn (ULB). The working environment that is currently under construction, in which the data and metadata will be compiled and annotated, will be realized in theTextGrid Laboratory, a software of the virtual research environment. A further component of this software, the TextGrid Repository, will make the data that are authorized for publication freely available online and ensure their long-term storage. The tools for data compilation and annotation attained from the modularly constructed and extended TextGrid lab thereby provide all the necessary materials for facilitating the research team's the typical epigraphic workflow. The workflow usually begins by documenting the texts and the objects on which they are preserved, and by compiling descriptive data. It then continues with the various levels of epigraphic and linguistic analysis, and concludes in the best case scenario with a translation of the analyzed inscription and a corresponding publication. In cooperation with the ULB, selected data will additionally be made available. The project's Virtual Inscription Archive will present online, in the Digital Collections of the ULB, hieroglyphic inscriptions selected from the published data in the repository, including an image of and brief information about the texts and the objects on which they are written, epigraphic analysis, and translation. == Project Goal == One of the project's goals is to produce a dictionary of Classic Mayan, in both digital and print form, towards the end of the project run-time. Additionally, a database with a corpus of inscriptions, including their translations and epigraphic analyses, will be made freely available online. The database furthermore will provide an ontology-like link of the contextual object data with the inscriptions and with each other, thereby allowing a cultural-historical arrangement of all contents within the periods of pre-Hispanic Maya culture. The contents of the database are additionally linked to citations of relevant literature. As a result, the database will also make freely available to both the scientific community and other interested parties a bibliography representing the research history and a base of knowledge concerning ancient Maya culture and script. In addition, the Classic Maya script, in its temporally defined stages of language development, will be gathered into and documented in a comprehensive language corpus with the aid of the information gathered by the project. In collaboration with all project participants, the corpus data can be used, together with the aid of various comparable analyses and also computational linguistic methods, such as inference-based methods, to confirm readings of some hieroglyphs that are currently only partially confirmed, and to eventually completely decipher the Classic Maya script.

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  Trigger list

  Trigger list in its most general meaning refers to a list whose items are used to initiate ("trigger") certain actions. == United States: Private financial information == In the United States, when a person applies for a mortgage loan, the lender makes a credit inquiry about the potential borrower from the national credit bureaus, Equifax, Experian and TransUnion. Unless the borrower is opted out, the credit bureaus put the applicants onto a "trigger list" of "leads" about persons who are interested in new loans. These lists are sold to numerous lenders all over the United States, and soon after the application the applicant starts receiving offers from all parts of the country. The trigger lists contain a significant amount of personal financial information. Among the buyers of trigger lists are "lead generators" which resell filtered information to borrowers, e.g., of people who live in a certain area and have a certain credit score. While the Federal Trade Commission considers the market of "trigger lists" to be a legal business, many people and organizations (such as the National Association of Mortgage Brokers) consider this a serious breach of privacy and lobby for putting this practice under regulatory controls. As of now, American consumers may opt-out from "trigger lists" by calling 1-888-5-OPTOUT (1-888-567-8688). == Nuclear non-proliferation == The Zangger Committee and the Nuclear Suppliers Group maintain lists of items that may contribute to nuclear proliferation; The nuclear non-proliferation treaty forbids its members to export such items to non-treaty members. these items are said to trigger the countries' responsibilities under the NPT, hence the name.

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  Zamzar

  Zamzar is an online file converter and compressor, created by brothers Mike and Chris Whyley in England in 2006. It allows users to convert files online, without downloading a software tool, and supports over 1,200 different conversion types. Since its formation, the service has converted over 510 million files for users from 245 different countries. The service supports the conversion of documents, images, audio, video, e-Books, CAD files and compressed file formats. Users can type in a URL or upload one or more files (if they are all of the same format) from their computer; Zamzar will then convert the file(s) to another user-specified format, such as an Adobe PDF file to a Microsoft Word document. Once conversion is complete, users can immediately download the file from their web browser. Users can also choose to receive an email with a link to download the converted file. In February 2021 Zamzar expanded their tool and announced a new file compression service. The compressor is visually similar to the conversion tool with a drag and drop download feature. As with the converter, users have the option to subscribe for a paid plan if they wish to compress multiple or larger files than the free service permits == File conversion API == in 2015 Zamzar launched a file conversion API, allowing users to integrate file conversion capabilities into their own websites and applications. Sample code is provided to allow users to integrate file conversion capabilities in C#, Java, Node.js, PHP, Python and cURL. Zamzar also maintains a project on GitHub which allows users to perform file conversion from the command line on Linux, MacOS or Windows systems. == Email file conversion == It is also possible to send files for conversion by emailing them to Zamzar. Zamzar launched this capability in 2012, allowing users to email files to dedicated email addresses for the file to be automatically converted to a different format. A link is then emailed back to the end user to allow them to download their converted file. == User privilege levels == Zamzar is currently free to use, but there is a limit of two conversions per hour for files up to 100MB. Users can pay a monthly subscription in order to access preferential features, such as unlimited file conversions, online file management, shorter response and queuing times and other benefits. == Name == Its name comes from Franz Kafka's The Metamorphosis. Its main character is called Gregor Samsa and it is from his surname that Zamzar is derived. The founders of the service considered three other names – Konvertieren, Khamailen and Obrogo – before settling on Zamzar.

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  National Parking Platform

  The National Parking Platform is a digital platform in the United Kingdom providing interoperability between car park operators, parking apps, and other service providers. It enables all parking apps that support the system: RingGo, JustPark, PayByPhone, Apcoa Connect, AppyParking, and Caura to work at all participating car parks. It has been rolled out in 13 local authorities so far. It was first developed by the Department for Transport starting in 2019, and since May 2025 is controlled by the British Parking Association on a not-for-profit basis. == Participating local authorities == Buckinghamshire Cheshire West and Chester Coventry City East Hertfordshire East Suffolk Liverpool City Manchester City Oxfordshire County Peterborough City Stevenage Sutton Walsall Welwyn Hatfield

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  VSCO

  VSCO ( ), formerly known as VSCO Cam, is a photography mobile app available for iOS and Android devices. The app was created by Joel Flory and Greg Lutze. The VSCO app allows users to capture photos in the app and edit them, using preset filters and editing tools. == History == Visual Supply Company was founded by Joel Flory and Greg Lutze in California, in 2011. VSCO was launched in 2012. It raised $40 million from investors in May 2014. In 2017, VSCO launched a subscription model. As of 2018, Visual Supply Company has $90 million in funding from investors and over 2 million paying members. In 2019, VSCO acquired Rylo, a video editing startup founded by the original developer of Instagram’s Hyperlapse. Visual Supply Company has locations in Oakland, California, where it is headquartered, and Chicago, Illinois. In December 2020 VSCO acquired AI-powered video editing app Trash. In April 2018, VSCO reached over 30 million users. In September 2023, Eric Wittman was appointed as the new CEO and co-founder Joel Flory became executive chairman. == Usage == Users must register an account to use the app. Photos can be taken or imported from the camera roll, as well as short videos or animated GIFs (known in the app as DSCO; iOS only). The user can edit their photos through various preset filters, or through the "toolkit" feature which allows finer adjustments to fade, clarity, skin tone, tint, sharpness, saturation, contrast, temperature, exposure, and other properties. Users have the option of posting their photos to their profile, where they can also add captions and hashtags. Photos can also be exported back into the camera roll or shared with other social networking services. The users also have an option to edit their own videos from their camera roll with the VSCO yearly membership, but they are not able to post camera roll as VSCO Film X videos to their account on VSCO. JPEG and raw image files can be used. Research on image based social media platforms has found that engagement with posting, editing, and interacting with images can influence users' mood, self esteem, and body satisfaction. Studies also suggest that greater emotional investment in social media content is associated with increased negative psychological outcomes including stress and depressive symptoms. == In popular culture == VSCO's Oakland headquarters was a key filming location for Boots Riley's 2018 film Sorry to Bother You.

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  Spatiotemporal reservoir resampling

  Spatiotemporal reservoir resampling, commonly known as ReSTIR (from "Reservoir-based SpatioTemporal Importance Resampling"), is a collection of computer graphics techniques for reusing samples during rendering. It was developed primarily to allow more realistic lighting in real-time rendering, because relatively few rays can be traced per pixel while maintaining an acceptable frame rate. It can also be used to speed up off-line path tracing. The first ReSTIR paper, published in 2020, provided algorithms for direct lighting, allowing scenes containing thousands of lights to be rendered in real time on a high-end GPU. Researchers later proposed versions for rendering indirect lighting (and more recently, motion blur and depth of field) and built up a framework of mathematical concepts and notation conventions that help analyze such algorithms. A major focus of this work is removing or reducing the bias that could be introduced when samples from other pixels or frames are reused—or selectively allowing some bias in order to speed up rendering and reduce variance (visible as "noise" in the image). Versions for path tracing apply transformations called shift mappings to samples, typically reusing parts of paths closer to the light and modifying the portion closer to the camera. ReSTIR-related papers and talks have been presented every year at the SIGGRAPH conference since 2020. One of the first games to incorporate ReSTIR into its rendering was Cyberpunk 2077. == Overview and motivation == According to Chris Wyman, one of the co-authors of the original paper, although developers commonly thought that bias was acceptable for real-time rendering, end users (e.g. gamers) are well-aware of the artifacts caused by bias and many have a negative opinion of common sample-reuse techniques such as temporal anti-aliasing (TAA), which may cause "ghosting" when the camera moves, and denoising, which causes blurring and other artifacts. ReSTIR techniques can reduce or avoid these types of bias by reusing samples of the set of possible paths taken by light to reach the camera, instead of reusing rendered pixel color values (which are typically the average of multiple samples, discarding information such as the direction of the light). While other techniques reuse samples in a generic post-processing step, ReSTIR passes can test for shadowing, and reused samples are converted into pixel color values by rendering code that takes the characteristics of different materials into account (e.g. by implementing BRDFs). However the output of ReSTIR is noisy, and a denoising pass is typically still used. Stochastic ray tracing techniques such as path tracing need to average multiple samples (produced by tracing individual rays) in order to render a visually acceptable image. When using a simple unbiased renderer based on Monte Carlo integration, halving the deviation of the result (apparent as "noise" in the image) requires multiplying the number of samples by four, meaning that a rapidly increasingly number of samples is needed to improve quality, Standard ways to mitigate this problem include importance sampling (which requires finding improved sampling distributions for specific situations), and quasi-Monte Carlo integration (which usually still requires tracing a large number of rays). ReSTIR offers a solution that multiplies the effective number of samples while tracing a fixed number of additional rays per frame. Temporal reuse multiplies the effective sample count by the number of frames rendered. Spatial reuse multiplies the effective count by the number of neighboring pixels examined. These two types of reuse can be combined, allowing spatial reuse to be applied recursively, which appears to offer an exponentially increasing effective sample count, however this is quickly limited by the size of the neighborhood used for spatial reuse. Spatial reuse is also potentially less effective near shadow and object edges, especially for objects with fine geometric detail, and temporal reuse is limited by movement of the camera and scene elements. == Variations == Many variations of ReSTIR have been proposed that generalize or improve the original technique (which builds on an earlier method called RIS), specialize it for particular types of illumination or other visual effects, or allow incorporation into rendering algorithms other than standard path tracing. Some published versions are listed below. == Algorithms == === Basic algorithm === ReSTIR uses a combination of resampled importance sampling (RIS) and weighted reservoir sampling (WRS) which the authors call streaming RIS. RIS processes samples from an initial probability distribution (e.g. a probability distribution for which a cheap sampling method exists) and generates samples in a new probability distribution (e.g. a sampling distribution that is optimal for rendering but is impractical to draw samples from directly). WRS allows this to be done while storing only a small number of samples in memory, which is especially helpful on a GPU. Information about the samples is stored in a data structure called a reservoir. WRS also allows samples from multiple reservoirs to be combined ("merged") into a single reservoir; this is crucial for sample reuse. Each pixel has a reservoir, typically containing only a single sample when ReSTIR is used for real-time rendering (some implementations use a larger number, e.g. four samples). The reservoir is typically initialized to a sample drawn using a simple method and is then updated by RIS steps and by reservoir merging, so that the pixel value produced by shading using the sample(s) currently in the reservoir, times the weight for the sample, is always an unbiased estimate of the correct pixel value. If appropriate resampling steps are used, the variance of this estimate (or some function of it, typically the luminance of the RGB color value) decreases with each step. A possible sequence of steps performed for each frame, suitable for computing unbiased direct illumination (DI) is: Perform reservoir resampling by drawing multiple light samples and using streaming RIS to choose one, using probabilities based on a target function, e.g. the luminance of the sample's contribution to the pixel. A weight is also computed for the sample. Typically, a single visibility check is performed here, after choosing a sample, setting the weight to 0 if the light is shadowed. Resampling (combined with the visibility check) ensures that the expected value of the weight times the sample brightness is the correct (unbiased) value for the pixel. (temporal reuse) For each pixel, merge the sample(s) from the previous frame into the current reservoir. Multiple importance sampling (MIS) weights are used to avoid bias due to the fact that the samples in the previous frame's reservoirs may have a different target probability distribution if the objects, lights, or camera have moved. (spatial reuse) For each pixel, choose one or more neighboring pixels and merge their samples into the current pixel's reservoir. Multiple importance sampling (MIS) weights are used to avoid bias due to the fact that the samples in each pixel's reservoir have a different target probability distribution. Because computing unbiased MIS weights requires tracing additional rays (along with other work such as evaluating BRDFs), real-time rendering often uses only a single neighboring pixel. Use the sample in each pixel's reservoir, along with its weight, to determine the color of the pixel for the current frame. Alternatively, multiple samples examined during the preceding steps may be averaged and used to shade the pixel instead (decoupled shading and sampling). For direct lighting, the initial samples used in step 1 are typically drawn by importance sampling from the set of lights in a scene. The algorithm above (from the original ReSTIR paper) draws many lower-quality light samples (e.g. 32) using a fast method, without considering visibility, and chooses one using streaming RIS. Visibility is then tested for the final chosen sample. Considering visibility for each sample drawn would require tracing 32 rays, which would make it much more expensive. The intent is to reduce the number of rays traced, relying on the sample reuse in steps 2 and 3 to make up for the loss of quality caused by rejecting many of the rays due to shadowing. A large part of the initial efforts to optimize ReSTIR (to make it run in real-time on available hardware) went into reducing the cost of randomly sampling the lights. Glossy surfaces may require a larger number of samples, and combining light sampling with BRDF sampling (using MIS) may increase quality. Step 2 (temporal reuse) is sometimes skipped for off-line rendering, and the output of multiple repetitions of initial sampling and spatial reuse is averaged instead; this helps avoids artifacts due to correlations. Step 3 (spatial reuse) may be repeated multiple times in a single frame.

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  Text normalization

  Text normalization is the process of transforming text into a single canonical form that it might not have had before. Normalizing text before storing or processing it allows for separation of concerns, since input is guaranteed to be consistent before operations are performed on it. Text normalization requires being aware of what type of text is to be normalized and how it is to be processed afterwards; there is no all-purpose normalization procedure. == Applications == Text normalization is frequently used when converting text to speech. Numbers, dates, acronyms, and abbreviations are non-standard "words" that need to be pronounced differently depending on context. For example: "$200" would be pronounced as "two hundred dollars" in English, but as "lua selau tālā" in Samoan. "vi" could be pronounced as "vie," "vee," or "the sixth" depending on the surrounding words. Text can also be normalized for storing and searching in a database. For instance, if a search for "resume" is to match the word "résumé," then the text would be normalized by removing diacritical marks; and if "john" is to match "John", the text would be converted to a single case. To prepare text for searching, it might also be stemmed (e.g. converting "flew" and "flying" both into "fly"), canonicalized (e.g. consistently using American or British English spelling), or have stop words removed. == Techniques == For simple, context-independent normalization, such as removing non-alphanumeric characters or diacritical marks, regular expressions would suffice. For example, the sed script sed ‑e "s/\s+/ /g" inputfile would normalize runs of whitespace characters into a single space. More complex normalization requires correspondingly complicated algorithms, including domain knowledge of the language and vocabulary being normalized. Among other approaches, text normalization has been modeled as a problem of tokenizing and tagging streams of text and as a special case of machine translation. == Textual scholarship == In the field of textual scholarship and the editing of historic texts, the term "normalization" implies a degree of modernization and standardization – for example in the extension of scribal abbreviations and the transliteration of the archaic glyphs typically found in manuscript and early printed sources. A normalized edition is therefore distinguished from a diplomatic edition (or semi-diplomatic edition), in which some attempt is made to preserve these features. The aim is to strike an appropriate balance between, on the one hand, rigorous fidelity to the source text (including, for example, the preservation of enigmatic and ambiguous elements); and, on the other, producing a new text that will be comprehensible and accessible to the modern reader. The extent of normalization is therefore at the discretion of the editor, and will vary. Some editors, for example, choose to modernize archaic spellings and punctuation, but others do not. An edition of a text might be normalized based on internal criteria, where orthography is standardized according to the language of the original, or external criteria, where the norms of a different time period are applied. For an example of the latter, a published edition of a medieval Icelandic manuscript might be normalized to the conventions of modern Icelandic, or it might be normalized to Classical Old Icelandic. Standards of normalization vary based on language of the edition as well as the specific conventions of the publisher.

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  Commitment ordering

  Commitment ordering (CO) is a class of interoperable serializability techniques in concurrency control of databases, transaction processing, and related applications. It allows optimistic (non-blocking) implementations. With the proliferation of multi-core processors, CO has also been increasingly utilized in concurrent programming, transactional memory, and software transactional memory (STM) to achieve serializability optimistically. CO is also the name of the resulting transaction schedule (history) property, defined in 1988 with the name dynamic atomicity. In a CO compliant schedule, the chronological order of commitment events of transactions is compatible with the precedence order of the respective transactions. CO is a broad special case of conflict serializability and effective means (reliable, high-performance, distributed, and scalable) to achieve global serializability (modular serializability) across any collection of database systems that possibly use different concurrency control mechanisms (CO also makes each system serializability compliant, if not already). Each not-CO-compliant database system is augmented with a CO component (the commitment order coordinator—COCO) which orders the commitment events for CO compliance, with neither data-access nor any other transaction operation interference. As such, CO provides a low overhead, general solution for global serializability (and distributed serializability), instrumental for global concurrency control (and distributed concurrency control) of multi-database systems and other transactional objects, possibly highly distributed (e.g., within cloud computing, grid computing, and networks of smartphones). An atomic commitment protocol (ACP; of any type) is a fundamental part of the solution, utilized to break global cycles in the conflict (precedence, serializability) graph. CO is the most general property (a necessary condition) that guarantees global serializability, if the database systems involved do not share concurrency control information beyond atomic commitment protocol (unmodified) messages and have no knowledge of whether transactions are global or local (the database systems are autonomous). Thus CO (with its variants) is the only general technique that does not require the typically costly distribution of local concurrency control information (e.g., local precedence relations, locks, timestamps, or tickets). It generalizes the popular strong strict two-phase locking (SS2PL) property, which in conjunction with the two-phase commit protocol (2PC), is the de facto standard to achieve global serializability across (SS2PL based) database systems. As a result, CO compliant database systems (with any different concurrency control types) can transparently join such SS2PL based solutions for global serializability. In addition, locking based global deadlocks are resolved automatically in a CO based multi-database environment, a vital side-benefit (including the special case of a completely SS2PL based environment; a previously unnoticed fact for SS2PL). Furthermore, strict commitment ordering (SCO; Raz 1991c), the intersection of Strictness and CO, provides better performance (shorter average transaction completion time and resulting in better transaction throughput) than SS2PL whenever read-write conflicts are present (identical blocking behavior for write-read and write-write conflicts; comparable locking overhead). The advantage of SCO is especially during lock contention. Strictness allows both SS2PL and SCO to use the same effective database recovery mechanisms. Two major generalizing variants of CO exist, extended CO (ECO; Raz 1993a) and multi-version CO (MVCO; Raz 1993b). They also provide global serializability without local concurrency control information distribution, can be combined with any relevant concurrency control, and allow optimistic (non-blocking) implementations. Both use additional information for relaxing CO constraints and achieving better concurrency and performance. Vote ordering (VO or Generalized CO (GCO); Raz 2009) is a container schedule set (property) and technique for CO and all its variants. Local VO is necessary for guaranteeing global serializability if the atomic commitment protocol (ACP) participants do not share concurrency control information (have the generalized autonomy property). CO and its variants inter-operate transparently, guaranteeing global serializability and automatic global deadlock resolution together in a mixed, heterogeneous environment with different variants. == Overview == The Commitment ordering (CO; Raz 1990, 1992, 1994, 2009) schedule property has been referred to also as Dynamic atomicity (since 1988), commit ordering, commit order serializability, and strong recoverability (since 1991). The latter is a misleading name since CO is incomparable with recoverability, and the term "strong" implies a special case. This means that a substantial recoverability property does not necessarily have the CO property and vice versa. In 2009 CO has been characterized as a major concurrency control method, together with the previously known (since the 1980s) three major methods: Locking, Time-stamp ordering, and Serialization graph testing, and as an enabler for the interoperability of systems using different concurrency control mechanisms. In a federated database system or any other more loosely defined multidatabase system, which are typically distributed in a communication network, transactions span multiple and possibly Distributed databases. Enforcing global serializability in such system is problematic. Even if every local schedule of a single database is still serializable, the global schedule of a whole system is not necessarily serializable. The massive communication exchanges of conflict information needed between databases to reach conflict serializability would lead to unacceptable performance, primarily due to computer and communication latency. The problem of achieving global serializability effectively had been characterized as open until the public disclosure of CO in 1991 by its inventor Yoav Raz (Raz 1991a; see also Global serializability). Enforcing CO is an effective way to enforce conflict serializability globally in a distributed system since enforcing CO locally in each database (or other transactional objects) also enforces it globally. Each database may use any, possibly different, type of concurrency control mechanism. With a local mechanism that already provides conflict serializability, enforcing CO locally does not cause any other aborts, since enforcing CO locally does not affect the data access scheduling strategy of the mechanism (this scheduling determines the serializability related aborts; such a mechanism typically does not consider the commitment events or their order). The CO solution requires no communication overhead since it uses (unmodified) atomic commitment protocol messages only, already needed by each distributed transaction to reach atomicity. An atomic commitment protocol plays a central role in the distributed CO algorithm, which enforces CO globally by breaking global cycles (cycles that span two or more databases) in the global conflict graph. CO, its special cases, and its generalizations are interoperable and achieve global serializability while transparently being utilized together in a single heterogeneous distributed environment comprising objects with possibly different concurrency control mechanisms. As such, Commitment ordering, including its special cases, and together with its generalizations (see CO variants below), provides a general, high performance, fully distributed solution (no central processing component or central data structure are needed) for guaranteeing global serializability in heterogeneous environments of multidatabase systems and other multiple transactional objects (objects with states accessed and modified only by transactions; e.g., in the framework of transactional processes, and within Cloud computing and Grid computing). The CO solution scales up with network size and the number of databases without any negative impact on performance (assuming the statistics of a single distributed transaction, e.g., the average number of databases involved with a single transaction, are unchanged). With the proliferation of Multi-core processors, Optimistic CO (OCO) has also been increasingly utilized to achieve serializability in software transactional memory, and numerous STM articles and patents utilizing "commit order" have already been published (e.g., Zhang et al. 2006). == The commitment ordering solution for global serializability == === General characterization of CO === Commitment ordering (CO) is a special case of conflict serializability. CO can be enforced with non-blocking mechanisms (each transaction can complete its task without having its data-access blocked, which allows optimistic concurrency control; however, commitment could be blo

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  Film recorder

  A film recorder is a graphical output device for transferring images to photographic film from a digital source. In a typical film recorder, an image is passed from a host computer to a mechanism to expose film through a variety of methods, historically by direct photography of a high-resolution cathode-ray tube (CRT) display. The exposed film can then be developed using conventional developing techniques, and displayed with a slide or motion picture projector. The use of film recorders predates the current use of digital projectors, which eliminate the time and cost involved in the intermediate step of transferring computer images to film stock, instead directly displaying the image signal from a computer. Motion picture film scanners are the opposite of film recorders, copying content from film stock to a computer system. Film recorders can be thought of as modern versions of kinescopes. == Design == === Operation === All film recorders typically work in the same manner. The image is fed from a host computer as a raster stream over a digital interface. A film recorder exposes film through various mechanisms; flying spot (early recorders); photographing a high resolution video monitor; electron beam recorder (Sony HDVS); a CRT scanning dot (Celco); focused beam of light from a light valve technology (LVT) recorder; a scanning laser beam (Arrilaser); or recently, full-frame LCD array chips. For color image recording on a CRT film recorder, the red, green, and blue channels are sequentially displayed on a single gray scale CRT, and exposed to the same piece of film as a multiple exposure through a filter of the appropriate color. This approach yields better resolution and color quality than possible with a tri-phosphor color CRT. The three filters are usually mounted on a motor-driven wheel. The filter wheel, as well as the camera's shutter, aperture, and film motion mechanism are usually controlled by the recorder's electronics and/or the driving software. CRT film recorders are further divided into analog and digital types. The analog film recorder uses the native video signal from the computer, while the digital type uses a separate display board in the computer to produce a digital signal for a display in the recorder. Digital CRT recorders provide a higher resolution at a higher cost compared to analog recorders due to the additional specialized hardware. Typical resolutions for digital recorders were quoted as 2K and 4K, referring to 2048×1366 and 4096×2732 pixels, respectively, while analog recorders provided a resolution of 640×428 pixels in comparison. Higher-quality LVT film recorders use a focused beam of light to write the image directly onto a film loaded spinning drum, one pixel at a time. In one example, the light valve was a liquid-crystal shutter, the light beam was steered with a lens, and text was printed using a pre-cut optical mask. The LVT will record pixel beyond grain. Some machines can burn 120-res or 120 lines per millimeter. The LVT is basically a reverse drum scanner. The exposed film is developed and printed by regular photographic chemical processing. === Formats === Film recorders are available for a variety of film types and formats. The 35 mm negative film and transparencies are popular because they can be processed by any photo shop. Single-image 4×5 film and 8×10 are often used for high-quality, large format printing. Some models have detachable film holders to handle multiple formats with the same camera or with Polaroid backs to provide on-site review of output before exposing film. == Uses == Film recorders are used in digital printing to generate master negatives for offset and other bulk printing processes. For preview, archiving, and small-volume reproduction, film recorders have been rendered obsolete by modern printers that produce photographic-quality hardcopies directly on plain paper. They are also used to produce the master copies of movies that use computer animation or other special effects based on digital image processing. However, most cinemas nowadays use Digital Cinema Packages on hard drives instead of film stock. === Computer graphics === Film recorders were among the earliest computer graphics output devices; for example, the IBM 740 CRT Recorder was announced in 1954. Film recorders were also commonly used to produce slides for slide projectors; but this need is now largely met by video projectors that project images directly from a computer to a screen. The terms "slide" and "slide deck" are still commonly used in presentation programs. === Current uses === Currently, film recorders are primarily used in the motion picture film-out process for the ever increasing amount of digital intermediate work being done. Although significant advances in large venue video projection alleviates the need to output to film, there remains a deadlock between the motion picture studios and theater owners over who should pay for the cost of these very costly projection systems. This, combined with the increase in international and independent film production, will keep the demand for film recording steady for at least a decade. == Key manufacturers == Traditional film recorder manufacturers have all but vanished from the scene or have evolved their product lines to cater to the motion picture industry. Dicomed was one such early provider of digital color film recorders. Polaroid, Management Graphics, Inc, MacDonald-Detwiler, Information International, Inc., and Agfa were other producers of film recorders. Arri is the only current major manufacturer of film recorders. Kodak Lightning I film recorder. One of the first laser recorders. Needed an engineering staff to set up. Kodak Lightning II film recorder used both gas and diode laser to record on to film. The last LVT machines produced by Kodak / Durst-Dice stopped production in 2002. There are no LVT film recorders currently being produced. LVT Saturn 1010 uses a LED exposure (RGB) to 8"x10" film at 1000-3000ppi. LUX Laser Cinema Recorder from Autologic/Information International in Thousand Oaks, California. Sales end in March 2000. Used on the 1997 film “Titanic”. Arri produces the Arrilaser line of laser-based motion picture film recorders. MGI produced the Solitaire line of CRT-based motion picture film recorders. Matrix, originally ImaPRO, a branch of Agfa Division, produced the QCR line of CRT-based motion picture film recorders. CCG, formerly Agfa film recorders, has been a steady manufacturer of film recorders based in Germany. In 2004 CCG introduced Definity, a motion picture film recorder utilizing LCD technology. In 2010 CCG introduced the first full LED LCD film recorder as a new step in film recording. Cinevator was made by Cinevation AS, in Drammen, Norway. The Cinevator was a real-time digital film recorder. It could record IN, IP and prints with and without sound Oxberry produced the Model 3100 film recorder camera system, with interchangeable pin-registered movements (shuttles) for 35 mm (full frame/Silent, 1.33:1) and 16 mm (regular 16, "2R"), and others have adapted the Oxberry movements for CinemaScope, 1.85:1, 1.75:1, 1.66:1, as well as Academy/Sound (1.37:1) in 35 mm and Super-16 in 16 mm ("1R"). For instance, the "Solitaire" and numerous others employed the Oxberry 3100 camera system. == History == Before video tape recorders or VTRs were invented, TV shows were either broadcast live or recorded to film for later showing, using the kinescope process. In 1967, CBS Laboratories introduced the Electronic Video Recording format, which used video and telecined-to-video film sources, which were then recorded with an electron-beam recorder at CBS' EVR mastering plant at the time to 35mm film stock in a rank of 4 strips on the film, which was then slit down to 4 8.75 mm (0.344 in) film copies, for playback in an EVR player. All types of CRT recorders were (and still are) used for film recording. Some early examples used for computer-output recording were the 1954 IBM 740 CRT Recorder, and the 1962 Stromberg-Carlson SC-4020, the latter using a Charactron CRT for text and vector graphic output to either 16 mm motion picture film, 16 mm microfilm, or hard-copy paper output. Later 1970 and 80s-era recording to B&W (and color, with 3 separate exposures for red, green, and blue)) 16 mm film was done with an EBR (Electron Beam Recorder), the most prominent examples made by 3M), for both video and COM (Computer Output Microfilm) applications. Image Transform in Universal City, California used specially modified 3M EBR film recorders that could perform color film-out recording on 16 mm by exposing three 16 mm frames in a row (one red, one green and one blue). The film was then printed to color 16 mm or 35 mm film. The video fed to the recorder could either be NTSC, PAL or SECAM. Later, Image Transform used specially modified VTRs to record 24 frame for their "Image Vision" system. The modified 1 inch type B videotape VTRs would record

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  Ciscogate

  Ciscogate, also known as the Black Hat Bug, is the name given to a legal incident that occurred at the Black Hat Briefings security conference in Las Vegas, Nevada, on July 27, 2005. On the morning of the first day of the conference, July 26, 2005, some attendees noticed that 30 pages of text had been physically ripped out of the extensive conference presentation booklet the night before at the request of Cisco Systems and the CD-ROM with presentation slides was not included. It was determined the pages covered a talk to be given by Michael Lynn, a security researcher with Atlanta-based IBM Internet Security Systems (ISS). Instead of the pages with the details, attendees found a photographed copy of a notice from Black Hat saying "Due to some last minute changes beyond Black Hat's control, and at the request of the presenter, the included materials aren't up to the standards Black Hat tries to meet. Black Hat will be the first to apologize. We hope the vendors involved will follow suit." According to Lynn's lawyer, his employer had approved of the talk leading up to the conference but changed their minds two days before the scheduled talk, forbidding him from presenting. Lynn's original presentation was to cover a vulnerability in Cisco routers. The presentation was one of four scheduled to follow Jeff Moss' keynote address on the first day of the conference, titled "Cisco IOS Security Architecture". After being told by his employer that he could not present on the topic, Lynn chose an alternate topic. Cisco and ISS had offered to give new joint presentation but this was turned down by Black Hat because the original speaking slot was given to Lynn, not Cisco. Lynn's presentation began by covering security issues in services that allow users to make Voice over IP telephone calls. Shortly after beginning the presentation Lynn changed back to his original topic and began disclosing some technical details of the vulnerability he found in Cisco routers stating that he would rather resign from his job at ISS than keep the details private. == Lawsuit == Shortly after Lynn concluded his talk he met Jennifer Granick, who would soon become his lawyer. During their initial meeting Lynn told Granick that he expected to be sued. Later in the evening Lynn had heard that Cisco and ISS had filed a lawsuit and requested a temporary restraining order against Black Hat but not himself. A public relations representative from Black Hat told Granick that the lawsuit was against both Black Hat and Lynn and that the companies had scheduled an Ex parte hearing in San Francisco the next morning to request the restraining order. That night, Andrew Valentine, an attorney for ISS and Cisco called Lynn who directed them to Granick. During the conversation Valentine explained the claims and accusations against Lynn, which included three things: 1) ISS claimed copyright over the presentation that Lynn gave, 2) Cisco claimed copyright over the decompiled machine code obtained from the router which was included in the presentation, and 3) Cisco claimed the presentation contained trade secrets. These complaints were outlined in a civil complaint at the U.S. Northern District of California and filed against both Lynn and Black Hat. According to Granick, she and Valentine were able agree to an injunction to settle the case without court proceedings. This deal was almost called off due to an inadvertent mistake by Black Hat in which they had restored Lynn's presentation on their web server. Black Hat, Granick, and the plaintiff's lawyers were able to resolve this problem and the deal stood. One condition of the settlement required Lynn to provide an image of all computer data he used in his research to be provided to a third party for forensic analysis before erasing his research and any Cisco data from his systems. The settlement also stipulated that Lynn was prohibited from talking about the vulnerability in the future. == FBI Investigation == Shortly after lawyers for Lynn and ISS / Cisco filed settlement papers, FBI agents from the Las Vegas office arrived at the conference to begin asking questions. According to Granick, they were there at the request of the Atlanta FBI office and Lynn was not of interest. Granick asserted the Fifth and Sixth amendment rights on behalf of her client, Lynn. Granick asserted his rights for the Atlanta office and asked if an arrest warrant had been issued for Lynn. Over the next 24 hours Granick was not able to ascertain the status of a warrant but ultimately determined no warrant was issued. When the FBI was asked about the case by a journalist, spokesman Paul Bresson declined to discuss the case saying "Our policy is to not make any comment on anything that is ongoing. That's not to confirm that something is, because I really don't know". Granick would only confirm to journalists that the "investigation has to do with the presentation". == Response == === Attendees === Attendees of Black Hat Briefings, as well as many that also attended DEF CON, were not happy with vendors threatening legal action over vulnerability disclosure. The term "Ciscogate" was coined quickly by an unknown person, but some attendees were quick to create shirts to commemorate the incident. === Cisco === Mojgan Khalili, a senior manager for corporate PR at Cisco, issued a statement to the press saying "It is important to note that the information Mr. Lynn presented was not a disclosure of a new vulnerability or a flaw with Cisco IOS software. Mr. Lynn's research explores possible ways to expand exploitations of existing security vulnerabilities impacting routers." === ISS === Kim Duffy, managing director of ISS Australia, was asked about ISS's response to the incident. Duffy responded that it was "business as usual" as the company handled the incident "strictly by the book". He gave a brief statement to ZDNet UK saying "ISS has published rules for disclosure and that is what we stick to. We didn't care to publish [the disclosure] because we were not ready. We had not completed the research to our satisfaction so it was not ready to be disclosed". ISS spokesperson Roger Fortier confirmed that Lynn was no longer employed with the company and that ISS was still working with Cisco on the matter. He gave a statement to the Washington Post saying "ISS and Cisco have been working on this in the background and didn't feel at this time that the material was ready for publication. The decision was made on Monday to pull the presentation because we wanted to make sure the research was fully baked."

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  Deblurring

  Deblurring is the process of removing blurring artifacts from images. Deblurring recovers a sharp image S from a blurred image B, where S is convolved with K (the blur kernel) to generate B. Mathematically, this can be represented as B = S ∗ K {\displaystyle B=SK} (where represents convolution). While this process is sometimes known as unblurring, deblurring is the correct technical word. The blur K is typically modeled as point spread function and is convolved with a hypothetical sharp image S to get B, where both the S (which is to be recovered) and the point spread function K are unknown. This is an example of an inverse problem. In almost all cases, there is insufficient information in the blurred image to uniquely determine a plausible original image, making it an ill-posed problem. In addition the blurred image contains additional noise which complicates the task of determining the original image. This is generally solved by the use of a regularization term to attempt to eliminate implausible solutions. This problem is analogous to echo removal in the signal processing domain. Nevertheless, when coherent beam is used for imaging, the point spread function can be modeled mathematically. By proper deconvolution of the point spread function K and the blurred image B, the blurred image B can be deblurred (unblur) and the sharp image S can be recovered.

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  Esdat

  ESdat is a data management, analysis and reporting software for environmental and groundwater data, developed by EarthScience Information Systems (EScIS). It is used to manage many types of environmental data including laboratory chemistry (analytical results, QA data, lab sample planning, and electronic Chain of Custody), field chemistry (water, gas, and soil), hydrogeological data (groundwater, borehole and well construction, lithological, geotechnical and stratigraphic, and LNAPL), meteorological data (rain, wind, and temperature), emission data (dust deposition, HiVol, air quality, and noise) and logger data. Data can be compared against environmental standards or site-specific trigger levels to generate exceedence tables, time series graphs, maps, statistics, and other outputs. ESdat integrates with Power BI and ArcGIS and data can also be exported in a range of other database formats, including USEPA Regions 2,4 & 5, and NYS DEC. ESdat is used by environmental consultants, government, mining and industry for validation, interrogation, and reporting of data derived from complex environmental programs, such as contaminated sites, groundwater investigations, and regulatory compliance for landfills or mining operations.

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  Sports Card Investor

  Sports Card Investor is an American sports collectibles media platform and mobile application founded by Geoff Wilson. The platform provides market data, analysis, and editorial content focused on sports trading cards and related collectibles. It operates a website, mobile app, and digital media channels covering developments in the sports card industry. The company posted its first YouTube video in July 2019, shortly before a period of rapid growth in sports card collecting in the early 2020s, which was marked by increased trading volumes and mainstream media attention. == History == Sports Card Investor was founded by Geoff Wilson, an entrepreneur and collector who began publishing sports card–related content online before launching the platform's dedicated app and subscription tools. In February 2020, the company launched Market Movers, the first website and app to chart sports card prices and track card collections. The platform expanded its media presence through partnerships and distribution agreements. In 2023, Yahoo Sports announced a new collectibles coverage initiative that included additional content from Sports Card Investor. In February 2024, the Sports Card Investor studio relocated to CardsHQ in Atlanta, Georgia, and visitors to the facility can watch Sports Card Investor videos being filmed. == Platform and content == The Sports Card Investor app provides users with pricing data, portfolio-tracking tools, and market-trend analysis for trading cards. The company also produces video and editorial content discussing market developments, grading trends, and major card releases. Coverage in industry publications has referenced Sports Card Investor in discussions about shifts in sports card licensing rights and hobby market reactions. == Industry context == The growth of Sports Card Investor coincided with a broader resurgence in trading card markets, including record sales and expanded retail presence. Mainstream outlets have cited the company and its founder in reporting on collectibles investing trends, grading practices, and market volatility. The Sports Card Investor app has attracted over 37,000 reviews on the Apple App Store, reflecting its strong user engagement within the sports card community.

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