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  Teechart

  TeeChart is a charting library for programmers, developed and managed by Steema Software of Girona, Catalonia, Spain. It is available as commercial and non-commercial software. TeeChart has been included in most Delphi and C++Builder products since 1997, and TeeChart Standard currently is part of Embarcadero RAD Studio 13 Florence. TeeChart Pro version is a commercial product that offers shareware releases for all of its formats. The TeeChart Charting Library offers charts, maps and gauges in versions for Delphi VCL/FMX, ActiveX, C# for Microsoft Visual Studio .NET. Full source code has always been available for all versions except the ActiveX version. TeeChart's user interface is translated into 38 languages. == History == The first version of TeeChart was authored in 1995 by David Berneda, co-founder of Steema, using the Borland Delphi Visual Component Library programming environment and TeeChart was first released as a shareware version and made available via Compuserve in the same year. It was written in the first version of Delphi VCL, as a 16-bit Charting Library named TeeChart version 1. The next version of TeeChart was released as a 32-bit library (Delphi 2 supported 32-bit compilation) but was badged as TeeChart VCL v3 to coincide with Borland's naming convention for inclusion on the toolbox palette of Borland Delphi v3 in 1997 and with C++ Builder v3 in 1998. It has been on the Delphi/C++ Builder toolbox palette ever since. The current version is Embarcadero RAD Studio 13 Florence. TeeChart's first ActiveX version named "version 3" too, to match the VCL version's nomenclature, was released in 1998. The version was optimised to work with Microsoft's Visual Studio v97 and v6.0 developer suites that include Visual Basic and Microsoft Visual C++ programming languages. Support for new programming environments followed with TeeChart's first native C# version for Microsoft Visual Studio .NET released in 2002 and TeeChart.Lite for .NET, a free charting component, released for Visual Studio.NET in 2003 and supporting too, Mono (programming). Steema Software released the first native TeeChart Java (programming language) version in 2006 and TeeChart's first native PHP version was released in 2009 and published as open-source in June 2010. Mobile versions of TeeChart, for Android (operating system) devices and Windows Phone 7 devices were released during the first half of 2011. In 2012 TeeChart extended functionality to iPhone/iPad and BlackBerry OS devices and a new JavaScript version was released in the same year to support HTML5 Canvas. In 2013 Steema launched TeeChart for .NET Chart for Windows Store applications and included support for Microsoft's Windows Phone 8 mobile platform. TeeChart for Xamarin.Forms written with 100% C# code and cross-platform support for .NET desktops, Windows Phone, iOS and Android was released in 2014. Also since 2014 Webforms charts now offers HTML5 interactivity. Steema launched TeeChart for Avalonia (software framework) in 2022 and in 2023 .NET_MAUI support was added to the TeeChart for .NET. == Usage == TeeChart is a general purpose charting component designed for use in differing ambits, offering a wide range of aesthetics to chart data. Generally TeeCharts published in the field, in areas where large amounts of data must be interpreted regularly, remain by designer choice in their simplest form to maximize the "data-ink ratio". Sloan Digital Sky Survey, SDSS Web Services' use for charting "Scientific .. plotting of online data" at The Virtual Observatory Spectrum Services reflects that approach. The SDSS chart authors choose to represent data using TeeChart's standard 2D line display. Speed is also a factor when choosing how to most effectively plot data. Realtime data, at frequencies of up to tens or hundreds of data points or more per second, require the most processor economic approach to charting. Computer processing time dedicated to the plotting of data needs to be as lightweight as possible, freeing-up computer tasks "to achieve real-time data acquisition, display and analysis". A critical and stated aspect of many data visualisation applications is the ability to offer interactivity to the user; NASA's document, the Orbital Debris Engineering Model Model ORDEM 3.0 - User's Guide, 2014, states that "The user may manipulate the graphs to zoom, pan, and copy to the clipboard and export to various file types" and Computer and Computing Technologies in Agriculture II, Volume 1, Daoliang, Li; Chunjiang, Zhao (2009), also using TeeChart, states "the properties at any point in the chart can be viewed moving the mouse over it". Writing about control education, Juha Lindfors states "The desired charting functionality (such as zooming and scaling) is achieved..". Charting applications have become increasingly 'onlined', made available either to a wider public or to a territorially remote userbase via networked applications. The World Wide Web (the Web) has become "by far, the most popular Internet protocol" to disseminate online applications. Most major IDEs now offer environments for web application developede aimed at browser hosted applications. Charting components, TeeChart among them, have adapted to provide models that work within a browser environment, often using static images and scripted layering techniques such as Ajax (programming) to offer a level of interactivity, improve response times and hide apparent delay from the user. Options to enrich client, browser-side processing flexibility are exploited by TeeChart libraries via modules that offer 'micro-environments' within the browser, such as the long established ActiveX technology, Adobe Flash, Microsoft Silverlight or Java Applets. Serverside environments offer too, a means to interact with browser based script to dynamically respond to charting requests. Joomla and CodeIgniter are host environments for TeeChart PHP and an example of an Embarcadero IntraWeb VCL designed application using TeeChart, is documented here. == Programmer reference == The Code Project includes a demo that uses TeeChart.Lite, called 'Self-Organizing Feature Maps (Kohonen maps)' written by Bashir Magomedovl and SourceForge includes a Database Stress and Monitor that also uses TeeChart.Lite. Books and information sources that include substantial sections about working with the Delphi version of TeeChart include "Mastering Delphi 6" by Marco Cantù, "C++ Builder 5 developer's guide", a video Delphi Tutorial on charting JPEG compression and support forums and reference pages at TeeChart Support Forums. Non-English language document sources include, in Czech "Myslíme v jazyku Delphi 7: knihovna zkušeného programátora" by Marco Cantù, and Chinese, Delphi 6, Delphi, and Delphi 5.

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  Web science

  Web science is an emerging interdisciplinary field concerned with the study of large-scale socio-technical systems, particularly the World Wide Web. It considers the relationship between people and technology, the ways that society and technology co-constitute one another and the impact of this co-constitution on broader society. Web Science combines research from disciplines as diverse as sociology, computer science, economics, and mathematics. The Web Science Institute, founded at the University of Southampton by director Wendy Hall and colleagues, describes Web Science as focusing "the analytical power of researchers from disciplines as diverse as mathematics, sociology, economics, psychology, law and computer science to understand and explain the Web. It is necessarily interdisciplinary – as much about social and organizational behaviour as about the underpinning technology." A central pillar of Web science development is Artificial Intelligence or "AI". The current artificial intelligence that in development at the moment is Human-Centered, with goals to further professional development courses as well as influencing public policy. Artificial intelligence developers are focused on the most impactful uses of this technology, while also hoping to expedite the growth and development of the human race. An early definition was given by American computer scientist Ben Shneiderman: "Web Science" is processing the information available on the web in similar terms to those applied to natural environment. == Areas of activity == === Emergent properties === Philip Tetlow, an IBM-based scientist influential in the emergence of web science as an independent discipline, argued for the concept of web life, which considers the Web not as a connected network of computers, as in common interpretations of the Internet, but rather as a sociotechnical machine capable of fusing together individuals and organisations into larger coordinated groups. It argues that unlike the technologies that have come before it, the Web is different in that its phenomenal growth and complexity are starting to outstrip our capability to control it directly, making it impossible for us to grasp its completeness in one go. Tetlow made use of Fritjof Capra's concept of the 'web of life' as a metaphor. == Research groups == There are numerous academic research groups engaged in Web Science research, many of which are members of WSTNet, the Web Science Trust Network of research labs. Health Web Science emerged as a sub-discipline of Web Science that studies the role of the Web's impact on human's health outcomes and how to further utilize the Web to improve health outcomes. These groups focus on the developmental possibilities, provided through Web Science, in areas such as health care and social welfare. Discussion of web science has been widely adopted as a method in which the internet can have a real world impact in the field of medicine, currently coined Medicine 2.0. The World Wide Web acts as a medium for the spread and circulation of knowledge, though these various research groups consider themselves responsible for maintaining verifiable and testable knowledge. Using their knowledge of the healthcare system as well as web science, researchers are focused on formatting and structuring their knowledge in a way that is easily accessible throughout the internet. The World Wide Web is quickly evolving meaning that the information we provide and its formatting must also. Recognizing the overlap between both aspects, the spread of knowledge and development of the internet, allows us to properly display our knowledge in a manner that evolves as quickly as the internet and everyday medical research. The accessibility of the internet and quick development of knowledge must be companied with efficient formatting to allocate successful dissemination of information, as described by these various researcher groups. == Related major conferences == Association for Computing Machinery (ACM), Hypertext Conference (HT) sponsored by SIGWEB ACM SIGCHI Conference on Human Factors in Computing Systems (CHI) International AAAI Conference on Weblogs and Social Media (ICWSM) The Web Conference (WWW) Association for Computing Machinery (ACM) Web Science Conference (WebSci)

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  Amino (app)

  Amino was a social media application originally developed by Narvii, Inc. It was originally created by Yin Wang and Ben Anderson in 2010, and then launched as an app in 2012. Amino was acquired by MediaLab AI Inc in January 2021, and the founders are no longer associated with the application. The platform ceased all operations in December 2025. == History == In 2010, Wang and Anderson came up with the idea for a convention-like community while attending an anime convention in Boston, Massachusetts. Later that year, they would release two apps revolving around K-pop and photography that allowed fans of those subjects to chat freely. That same year, Amino was officially released. === Shutdown === In early December 2025, the Amino platform abruptly stopped all operations. Users worldwide lost access to the mobile application and website, with server requests returning connection time-out errors. Parent company MediaLab AI has issued no official statement regarding the cause to date, or declared any possible cause behind it. === Final Message === According to Shawn, a member of Amino support, Amino has ceased operations as of December 19th. The message that was sent out from Shawn reads: "Hey there, Thanks for your message. Amino has ceased operations. As of December 19th, we no longer retain personal data relating to you. Accordingly, we are unable to provide a copy of your data. Kind regards, - Amino Support" This message was sent on January 4th, 2026. This was the final support message sent from the Amino Support mail. == Growth == Amino received 1.65 million dollars of seed funding in 2014, primarily from Union Ventures. Some additional seed investors include Google Ventures, SV Angel, Box Group, and other interested parties. By July 2014, Amino's apps were downloaded 500,000 times. Though only having 15 communities at that time, Amino eventually grew to have 41 communities in September 2015. Amino's apps had been downloaded 13 million times by July 2016. Fandoms had migrated from websites like Facebook and Reddit to Amino, partly because of the app's mobile-native experience. Before 2016, when a user wanted to join a new Amino, they had to download another app for the Amino they wanted to join, with each apps name beginning with "Amino for:". In 2016, Amino Apps launched a centralized portal that hosted every Amino community in one app, meaning users no longer had to download multiple apps. In July of the same year, ACM, an app that allowed users to create their own communities, was launched. This resulted in the number of communities on Amino skyrocketing to over 2.5 million as of June 2018. == Features == The main feature of Amino was communities dedicated to a certain topic that users could join. Users could also chat with other members of a community in three ways: text, voice, or screening room, which allowed users to watch videos together while voice chatting. Other features include polls, blog posts, image posts, wiki entries, stories, and quizzes. In some cases, posts that were very well-made and had been noticed by a community's administration would end up receiving a feature, making it appear on the front page along with other featured content. In 2018, a premium membership option called Amino+ was added. Amino+ comes with additional features such as exclusive stickers, the ability to make stickers, custom chat bubbles, high resolution images, and other perks. Membership can now only be purchased with money. Amino coins can be purchased or earned through enabling ads, watching ad videos, completing activities on the Offer Wall, and playing Lucky Draw when checking in, but are of little use due to the users not being able to buy Amino+ by amino coins anymore. Members can give and receive coins through props. In 2019, Amino introduced six original short-form animated series, labelled "Amino Originals," produced by independent artists from across the internet. ATJ's "Little Red," a re-imagining of Little Red Riding Hood, premiered on November 15, 2019. "Little Red" was joined by five other shows in late December. Sophie Feher's "The Reef," a comedy featuring an aspiring marine biologist meeting a merman, premiered on December 27 alongside "Princely," an LGBT fairy tale created by Matt Bruneau-Richardson of Tiny Siren Animation. "Spaced Out," an alien abduction comedy by Michael Jae, and YouTuber Alex Clark's "Wyndvania II" premiered on December 28. Mysie Pereira's fairy tale "Turned to Stone" and Marcin Pawlowski's "Stranded" premiered on December 29, 2019. == Administration == On each community, there are two types of staff members, these being ‘Leader’ and ‘Curator.’ Leaders are higher rank than curators. Curators are usually the ones who feature posts, or post important announcements for users to see. Curators are able to disable a post or public chat, delete comments or chat threads, manage featured content, manage posts in topic categories, and approve Wiki entries. Leaders have more power than curators. In addition to curator powers, leaders can submit a community to be listed, change the Amino's features, change navigation, alter the community appearance, change the Amino's privacy settings, manage the Amino's join requests, send invites, appoint or demote Curators, strike or ban members, manage flagged content, change users' custom titles, manage topics and wiki categories, and create broadcasts (notifications sent for posts). One leader will have the status of agent. An agent is the primary leader of a community; the person who created the community is automatically agent. An agent has the ability to delete their community as long as it is not too large or too active. An agent can appoint and remove both leaders and curators. Agent status can be transferred voluntarily to another leader, curator, or community member. If an agent is inactive, Team Amino may assist in transferring agent status. == Apps == === Amino Community Manager === Otherwise known as ACM, this application is what users use to create and manage their own community in Amino. This app allows moderators to customize a community's theme, icon, and categories. ACM also allows moderation to customize community descriptions, pick leaders, change language settings, create a tagline for the community, change the home page lay out, alter the side navigation menu, and more. Unlisted communities are able to change their community's title and Amino ID, but this is not an option once a community is listed. A leader can use ACM to submit a request for their community to be listed on the explore page, after which the community will be reviewed by Team Amino for approval. Communities can be deleted on ACM, but only by the agent of that community. == Guidelines == Amino has a set of guidelines that all communities must comply with. Amino does not allow harassment or hate, spam or self-promotion (including promotion of one's own Amino community), sexual/NSFW content, self harm, real graphic/gross content (fictional content is generally acceptable), unsafe/illegal content, or content that violates copyright. Communities are allowed to have additional rules so long as they do not violate Amino's rules. In addition to Amino's rules, users are required to be at least 13 years of age in the U.S. and 16 years of age in European Union countries. While sexual imagery is not allowed in any community and text based sexual content is not allowed in public areas, some private communities are allowed to discuss sexual themes. However, they are not exempt from Amino's rules on NSFW content. If guidelines are broken, a leader may disable content or impose a warning, strike, or ban, depending on the severity of the infringement. A warning is a message informing the user that they have violated a guideline and may face further punishment unless they change their behaviour. A strike will put the user in read-only mode for up to 24 hours; this mode prevents the user from posting, chatting, or interacting with posts in that community. A ban removes the user from the community. Team Amino can separately issue users with strikes or bans across the entire platform. == Controversies == In 2017, organizations in Argentina for the protection of minors reported inappropriate material on the app, ranging from pornography to material promoting suicide to underage users. In 2019, Abilene police in Texas released a statement that sexual predators were using Amino chat rooms to approach minors. In 2020, authorities from the Christian County in the state of Kentucky alerted parents about possible sexual predators on Amino. In 2025, the British Police identified Amino as one of several platforms used by a child exploitation network that had previously extorted minors in different countries in Europe and North America. Several families reported to the National Society for the Prevention of Cruelty to Children that pedophiles were using the app for the purpose of sexual role-playing with minors, c

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  Temporal resolution

  Temporal resolution (TR) refers to the discrete resolution of a measurement with respect to time. It is defined as the amount of time needed to revisit and acquire data for the same location. When applied to remote sensing, this amount of time is influenced by the sensor platform's orbital characteristics and the features of the sensor itself. The temporal resolution is low when the revisiting delay is high and vice versa. Temporal resolution is typically expressed in days. == Physics == Often there is a trade-off between the temporal resolution of a measurement and its spatial resolution, due to Heisenberg's uncertainty principle. In some contexts, such as particle physics, this trade-off can be attributed to the finite speed of light and the fact that it takes a certain period of time for the photons carrying information to reach the observer. In this time, the system might have undergone changes itself. Thus, the longer the light has to travel, the lower the temporal resolution. == Technology == === Computing === In another context, there is often a tradeoff between temporal resolution and computer storage. A transducer may be able to record data every millisecond, but available storage may not allow this, and in the case of 4D PET imaging the resolution may be limited to several minutes. === Electronic displays === In some applications, temporal resolution may instead be equated to the sampling period, or its inverse, the refresh rate, or update frequency in Hertz, of a TV, for example. The temporal resolution is distinct from temporal uncertainty. This would be analogous to conflating image resolution with optical resolution. One is discrete, the other, continuous. The temporal resolution is a resolution somewhat the 'time' dual to the 'space' resolution of an image. In a similar way, the sample rate is equivalent to the pixel pitch on a display screen, whereas the optical resolution of a display screen is equivalent to temporal uncertainty. Note that both this form of image space and time resolutions are orthogonal to measurement resolution, even though space and time are also orthogonal to each other. Both an image or an oscilloscope capture can have a signal-to-noise ratio, since both also have measurement resolution. === Oscilloscopy === An oscilloscope is the temporal equivalent of a microscope, and it is limited by temporal uncertainty the same way a microscope is limited by optical resolution. A digital sampling oscilloscope has also a limitation analogous to image resolution, which is the sample rate. A non-digital non-sampling oscilloscope is still limited by temporal uncertainty. The temporal uncertainty can be related to the maximum frequency of continuous signal the oscilloscope could respond to, called the bandwidth and given in Hertz. But for oscilloscopes, this figure is not the temporal resolution. To reduce confusion, oscilloscope manufacturers use 'Sa/s' instead of 'Hz' to specify the temporal resolution. Two cases for oscilloscopes exist: either the probe settling time is much shorter than the real time sampling rate, or it is much larger. The case where the settling time is the same as the sampling time is usually undesirable in an oscilloscope. It is more typical to prefer a larger ratio either way, or if not, to be somewhat longer than two sample periods. In the case where it is much longer, the most typical case, it dominates the temporal resolution. The shape of the response during the settling time also has as strong effect on the temporal resolution. For this reason probe leads usually offer an arrangement to 'compensate' the leads to alter the trade off between minimal settling time, and minimal overshoot. If it is much shorter, the oscilloscope may be prone to aliasing from radio frequency interference, but this can be removed by repeatedly sampling a repetitive signal and averaging the results together. If the relationship between the 'trigger' time and the sample clock can be controlled with greater accuracy than the sampling time, then it is possible to make a measurement of a repetitive waveform with much higher temporal resolution than the sample period by upsampling each record before averaging. In this case the temporal uncertainty may be limited by clock jitter.

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  Association for Computational Linguistics

  The Association for Computational Linguistics (ACL) is a scientific and professional organization for people working on natural language processing. Its namesake conference is one of the primary high impact conferences for natural language processing research, along with EMNLP. The conference is held each summer in locations where significant computational linguistics research is carried out. It was founded in 1962, originally named the Association for Machine Translation and Computational Linguistics (AMTCL). It became the ACL in 1968. The ACL has a European (EACL), a North American (NAACL), and an Asian (AACL) chapter. == History == The ACL was founded in 1962 as the Association for Machine Translation and Computational Linguistics (AMTCL). The initial membership was about 100. In 1965, the AMTCL took over the journal Mechanical Translation and Computational Linguistics. This journal was succeeded by many other journals: the American Journal of Computational Linguistics (1974–1978, 1980–1983), and then Computational Linguistics (1984–present). Since 1988, the journal has been published for the ACL by MIT Press. The annual meeting was first held in 1963 in conjunction with the Association for Computing Machinery National Conference. The annual meeting was, for a long time, relatively informal and did not publish anything longer than abstracts. By 1968, the society took on its current name, the Association for Computational Linguistics (ACL). The publication of the annual meeting's Proceedings of the ACL began in 1979 and gradually matured into its modern form. Many of the meetings were held in conjunction with the Linguistic Society of America, and a few with the American Society for Information Science and the Cognitive Science Society. The United States government sponsored much research from 1989 to 1994, characterized by an increase in author retention rates and an increase in research in some key topics, such as speech recognition, in ACL. By the 21st century, it was able to maintain authors at a high rate who coalesced in a more stable arrangement around individual research topics. In 1991, the group published a prototype for a text generator based on the universal grammar theory of Noam Chomsky. The system, nicknamed Parrot, relied on a finite set of syntactic transformations and a hand-curated lexicon. Despite some initial success, including experimentation with morpheme syntactics, funding halted after the research team encountered intractable difficulties with inflection and abstract locutions. == Annual Meeting of the ACL == Every year, the ACL holds the Annual Meeting of the ACL. The location lies in Europe in years zero modulo three, North America in years one modulo three, and Asia–Australia in years two modulo three. In 2020, the Annual Meeting received for the first time more submissions from China than the United States. == Activities == The ACL organizes several of the top conferences and workshops in the field of computational linguistics and natural language processing. These include: Annual Meeting of the Association for Computational Linguistics (ACL), the flagship conference of the organization Empirical Methods in Natural Language Processing (EMNLP) International Joint Conference on Natural Language Processing (IJCNLP), held jointly one of the other conferences on a rotating basis Conference on Computational Natural Language Learning (CoNLL) Lexical and Computational Semantics and Semantic Evaluation (SemEval) Joint Conference on Lexical and Computational Semantics (SEM) Workshop on Statistical Machine Translation (WMT) Besides conferences, the ACL also sponsors the journals Computational Linguistics and Transactions of the Association for Computational Linguistics (TACL). Papers and other presentations at ACL and ACL-affiliated venues are archived online in the open-access ACL Anthology. == Special Interest Groups == ACL has a large number of Special Interest Groups (SIGs), focusing on specific areas of natural language processing. Some current SIGs within ACL are: == Presidents == Each year, the ACL elects a distinguished computational linguist who becomes vice-president of the organization in the next calendar year and president one year later. Recent ACL presidents are:

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  PureWow

  PureWow is an American digital media company that publishes women's lifestyle content. Acquired by Gary Vaynerchuk in 2017 as part of Gallery Media Group, PureWow tailors lifestyle topics for Millennials and Generation X, including fashion, beauty, home decor, recipes, entertainment, travel, technology, literature, wellness and money. == History == PureWow was founded by Ryan Harwood in September 2010, along with Bob Pittman's Pilot Group and the women of wowOwow Joni Evans, Mary Wells Lawrence, Whoopi Goldberg, Liz Smith, Candice Bergen, and Lesley Stahl, among others. In January 2013, PureWow hired former Real Simple editor Mary Kate McGrath as its first editor-in-chief. In August 2014, PureWow was listed as no. 352 on Inc. Magazine's 2014 list of the top 500 fastest-growing privately owned companies. In May 2015, PureWow raised $2.5 million. In 2017, serial entrepreneur Gary Vaynerchuk and Miami Dolphins' owner Stephen Ross' venture firm, RSE Ventures, acquired PureWow to form Gallery Media Group as a creative agency and media firm. PureWow's CEO, Ryan Harwood serves as the chief executive of Gallery Media Group. == Editions == PureWow publishes national content as well as local content for New York City, Los Angeles, Chicago, San Francisco, Dallas, and the Hamptons. The company publishes content across fashion, beauty, homecare topics, technology, entertainment, books, wellness and finances. PureWow articles are distributed via its website PureWow.com, email, and over social media channels.

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  WebCL

  WebCL (Web Computing Language) is a JavaScript binding to OpenCL for heterogeneous parallel computing within any compatible web browser without the use of plug-ins, first announced in March 2011. It is developed on similar grounds as OpenCL and is considered as a browser version of the latter. Primarily, WebCL allows web applications to actualize speed with multi-core CPUs and GPUs. With the growing popularity of applications that need parallel processing like image editing, augmented reality applications and sophisticated gaming, it has become more important to improve the computational speed. With these background reasons, a non-profit Khronos Group designed and developed WebCL, which is a Javascript binding to OpenCL with a portable kernel programming, enabling parallel computing on web browsers, across a wide range of devices. In short, WebCL consists of two parts, one being Kernel programming, which runs on the processors (devices) and the other being JavaScript, which binds the web application to OpenCL. The completed and ratified specification for WebCL 1.0 was released on March 19, 2014. == Implementation == Currently, no browsers natively support WebCL. However, non-native add-ons are used to implement WebCL. For example, Nokia developed a WebCL extension. Mozilla does not plan to implement WebCL in favor of WebGL Compute Shaders, which were in turn scrapped in favor of WebGPU. Mozilla (Firefox) - hg.mozilla.org/projects/webcl/ === WebCL working draft === Samsung (WebKit) - github.com/SRA-SiliconValley/webkit-webcl (unavailable) Nokia (Firefox) - github.com/toaarnio/webcl-firefox (down since Nov 2014, Last Version for FF 34) Intel (Crosswalk) - www.crosswalk-project.org === Example C code === The basic unit of a parallel program is kernel. A kernel is any parallelizable task used to perform a specific job. More often functions can be realized as kernels. A program can be composed of one or more kernels. In order to realize a kernel, it is essential that a task is parallelizable. Data dependencies and order of execution play a vital role in producing efficient parallelized algorithms. A simple example can be thought of the case of loop unrolling performed by C compilers, where a statement like:can be unrolled into:Above statements can be parallelized and can be made to run simultaneously. A kernel follows a similar approach where only the snapshot of the ith iteration is captured inside kernel. Rewriting the above code using a kernel:Running a WebCL application involves the following steps: Allow access to devices and provide context Hand over the kernel to a device Cause the device to execute the kernel Retrieve results from the device Use the data inside JavaScript Further details about the same can be found at == Exceptions List == WebCL, being a JavaScript based implementation, doesn't return an error code when errors occur. Instead, it throws an exception such as OUT_OF_RESOURCES, OUT_OF_HOST_MEMORY, or the WebCL-specific WEBCL_IMPLEMENTATION_FAILURE. The exception object describes the machine-readable name and human-readable message describing the error. The syntax is as follows: From the code above, it can be observed that the message field can be a NULL value. Other exceptions include: INVALID_OPERATION – if the blocking form of this function is called from a WebCLCallback INVALID_VALUE – if eventWaitList is empty INVALID_CONTEXT – if events specified in eventWaitList do not belong to the same context INVALID_DEVICE_TYPE – if deviceType is given, but is not one of the valid enumerated values DEVICE_NOT_FOUND – if there is no WebCLDevice available that matches the given deviceType More information on exceptions can be found in the specs document. There is another exception that is raised upon trying to call an object that is ‘released’. On using the release method, the object doesn't get deleted permanently but it frees the resources associated with that object. In order to avoid this exception, releaseAll method can be used, which not only frees the resources but also deletes all the associated objects created. == Security == WebCL, being an open-ended software developed for web applications, has lots of scope for vulnerabilities in the design and development fields too. This forced the developers working on WebCL to give security the utmost importance. Few concerns that were addressed are: Out-of-bounds Memory Access: This occurs by accessing the memory locations, outside the allocated space. An attacker can rewrite or erase all the important data stored in those memory locations. Whenever there arises such a case, an error must be generated at the compile time, and zero must be returned at run-time, not letting the program override the memory. A project WebCL Validator, was initiated by the Khronos Group (developers) on handling this vulnerability. Memory Initialization: This is done to prevent the applications to access the memory locations of previous applications. WebCL ensures that this doesn't happen by initializing all the buffers, variables used to zero before it runs the current application. OpenCL 1.2 has an extension ‘cl_khr_initialize_memory’, which enables this. Denial of Service: The most common attack on web applications cannot be eliminated by WebCL or the browser. OpenCL can be provided with watchdog timers and pre-emptive multitasking, which can be used by WebCL in order to detect and terminate the contexts that are taking too long or consume lot of resources. There is an extension of OpenCL 1.2 ‘cl_khr_terminate_context’ like for the previous one, which enables to terminate the process that might cause a denial of service attack. == Related browser bugs == Bug 664147 - [WebCL] add openCL in gecko, Mozilla Bug 115457: [Meta] WebCL support for WebKit, WebKit Bugzilla

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  Enterprise bookmarking

  Enterprise bookmarking is a method for Web 2.0 users to tag, organize, store, and search bookmarks of both web pages on the Internet and data resources stored in a distributed database or fileserver. This is done collectively and collaboratively in a process by which users add tag (metadata) and knowledge tags. In early versions of the software, these tags are applied as non-hierarchical keywords, or terms assigned by a user to a web page, and are collected in tag clouds. Examples of this software are Connectbeam and Dogear. New versions of the software such as Jumper 2.0 and Knowledge Plaza expand tag metadata in the form of knowledge tags that provide additional information about the data and are applied to structured and semi-structured data and are collected in tag profiles. == History == Enterprise bookmarking is derived from Social bookmarking that got its modern start with the launch of the website del.icio.us in 2003. The first major announcement of an enterprise bookmarking platform was the IBM Dogear project, developed in Summer 2006. Version 1.0 of the Dogear software was announced at Lotusphere 2007, and shipped later that year on June 27 as part of IBM Lotus Connections. The second significant commercial release was Cogenz in September 2007. Since these early releases, Enterprise bookmarking platforms have diverged considerably. The most significant new release was the Jumper 2.0 platform, with expanded and customizable knowledge tagging fields. == Differences == === Versus social bookmarking === In a social bookmarking system, individuals create personal collections of bookmarks and share their bookmarks with others. These centrally stored collections of Internet resources can be accessed by other users to find useful resources. Often these lists are publicly accessible, so that other people with similar interests can view the links by category or by the tags themselves. Most social bookmarking sites allow users to search for bookmarks which are associated with given "tags", and rank the resources by the number of users which have bookmarked them. Enterprise bookmarking is a method of tagging and linking any information using an expanded set of tags to capture knowledge about data. It collects and indexes these tags in a web-infrastructure knowledge base server residing behind the firewall. Users can share knowledge tags with specified people or groups, shared only inside specific networks, typically within an organization. Enterprise bookmarking is a knowledge management discipline that embraces Enterprise 2.0 methodologies to capture specific knowledge and information that organizations consider proprietary and are not shared on the public Internet. === Tag management === Enterprise bookmarking tools also differ from social bookmarking tools in the way that they often face an existing taxonomy. Some of these tools have evolved to provide Tag management which is the combination of uphill abilities (e.g. faceted classification, predefined tags, etc.) and downhill gardening abilities (e.g. tag renaming, moving, merging) to better manage the bottom-up folksonomy generated from user tagging.

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  Semantic folding

  Semantic folding theory describes a procedure for encoding the semantics of natural language text in a semantically grounded binary representation. This approach provides a framework for modelling how language data is processed by the neocortex. == Theory == Semantic folding theory draws inspiration from Douglas R. Hofstadter's Analogy as the Core of Cognition which suggests that the brain makes sense of the world by identifying and applying analogies. The theory hypothesises that semantic data must therefore be introduced to the neocortex in such a form as to allow the application of a similarity measure and offers, as a solution, the sparse binary vector employing a two-dimensional topographic semantic space as a distributional reference frame. The theory builds on the computational theory of the human cortex known as hierarchical temporal memory (HTM), and positions itself as a complementary theory for the representation of language semantics. A particular strength claimed by this approach is that the resulting binary representation enables complex semantic operations to be performed simply and efficiently at the most basic computational level. == Two-dimensional semantic space == Analogous to the structure of the neocortex, Semantic Folding theory posits the implementation of a semantic space as a two-dimensional grid. This grid is populated by context-vectors in such a way as to place similar context-vectors closer to each other, for instance, by using competitive learning principles. This vector space model is presented in the theory as an equivalence to the well known word space model described in the information retrieval literature. Given a semantic space (implemented as described above) a word-vector can be obtained for any given word Y by employing the following algorithm: For each position X in the semantic map (where X represents cartesian coordinates) if the word Y is contained in the context-vector at position X then add 1 to the corresponding position in the word-vector for Y else add 0 to the corresponding position in the word-vector for Y The result of this process will be a word-vector containing all the contexts in which the word Y appears and will therefore be representative of the semantics of that word in the semantic space. It can be seen that the resulting word-vector is also in a sparse distributed representation (SDR) format [Schütze, 1993] & [Sahlgreen, 2006]. Some properties of word-SDRs that are of particular interest with respect to computational semantics are: high noise resistance: As a result of similar contexts being placed closer together in the underlying map, word-SDRs are highly tolerant of false or shifted "bits". boolean logic: It is possible to manipulate word-SDRs in a meaningful way using boolean (OR, AND, exclusive-OR) and/or arithmetical (SUBtract) functions . sub-sampling: Word-SDRs can be sub-sampled to a high degree without any appreciable loss of semantic information. topological two-dimensional representation: The SDR representation maintains the topological distribution of the underlying map therefore words with similar meanings will have similar word-vectors. This suggests that a variety of measures can be applied to the calculation of semantic similarity, from a simple overlap of vector elements, to a range of distance measures such as: Euclidean distance, Hamming distance, Jaccard distance, cosine similarity, Levenshtein distance, Sørensen-Dice index, etc. == Semantic spaces == Semantic spaces in the natural language domain aim to create representations of natural language that are capable of capturing meaning. The original motivation for semantic spaces stems from two core challenges of natural language: Vocabulary mismatch (the fact that the same meaning can be expressed in many ways) and ambiguity of natural language (the fact that the same term can have several meanings). The application of semantic spaces in natural language processing (NLP) aims at overcoming limitations of rule-based or model-based approaches operating on the keyword level. The main drawback with these approaches is their brittleness, and the large manual effort required to create either rule-based NLP systems or training corpora for model learning. Rule-based and machine learning-based models are fixed on the keyword level and break down if the vocabulary differs from that defined in the rules or from the training material used for the statistical models. Research in semantic spaces dates back more than 20 years. In 1996, two papers were published that raised a lot of attention around the general idea of creating semantic spaces: latent semantic analysis from Microsoft and Hyperspace Analogue to Language from the University of California. However, their adoption was limited by the large computational effort required to construct and use those semantic spaces. A breakthrough with regard to the accuracy of modelling associative relations between words (e.g. "spider-web", "lighter-cigarette", as opposed to synonymous relations such as "whale-dolphin", "astronaut-driver") was achieved by explicit semantic analysis (ESA) in 2007. ESA was a novel (non-machine learning) based approach that represented words in the form of vectors with 100,000 dimensions (where each dimension represents an Article in Wikipedia). However practical applications of the approach are limited due to the large number of required dimensions in the vectors. More recently, advances in neural networking techniques in combination with other new approaches (tensors) led to a host of new recent developments: Word2vec from Google and GloVe from Stanford University. Semantic folding represents a novel, biologically inspired approach to semantic spaces where each word is represented as a sparse binary vector with 16,000 dimensions (a semantic fingerprint) in a 2D semantic map (the semantic universe). Sparse binary representation are advantageous in terms of computational efficiency, and allow for the storage of very large numbers of possible patterns. == Visualization == The topological distribution over a two-dimensional grid (outlined above) lends itself to a bitmap type visualization of the semantics of any word or text, where each active semantic feature can be displayed as e.g. a pixel. As can be seen in the images shown here, this representation allows for a direct visual comparison of the semantics of two (or more) linguistic items. Image 1 clearly demonstrates that the two disparate terms "dog" and "car" have, as expected, very obviously different semantics. Image 2 shows that only one of the meaning contexts of "jaguar", that of "Jaguar" the car, overlaps with the meaning of Porsche (indicating partial similarity). Other meaning contexts of "jaguar" e.g. "jaguar" the animal clearly have different non-overlapping contexts. The visualization of semantic similarity using Semantic Folding bears a strong resemblance to the fMRI images produced in a research study conducted by A.G. Huth et al., where it is claimed that words are grouped in the brain by meaning. voxels, little volume segments of the brain, were found to follow a pattern were semantic information is represented along the boundary of the visual cortex with visual and linguistic categories represented on posterior and anterior side respectively.

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  Digital content

  Digital content is any content that exists in the form of digital data. Digital content is stored on digital media or analog storage in specific formats. Forms of digital content include information that is digitally broadcast, streamed, or contained in computer files. Viewed narrowly, digital content includes popular media types, while a broader approach considers any type of digital information (e. g. digitally updated weather forecasts, GPS maps, and so on) as digital content. Digital content has increased as more households have accessed the Internet. Expanded access has made it easier for people to receive their news and watch TV online, challenging the popularity of traditional platforms. Increased access to the Internet has also led to the mass publication of digital content through individuals in the form of eBooks, blog posts, and even Facebook posts. == History == At the beginning of the Digital Revolution, computers facilitated the discovery, retrieval, and creation of new information in every field of human knowledge. As information became increasingly more accessible, the Digital Revolution also facilitated the creation of digital content. Despite an evolution to digital technology, which occurred somewhere between the late 1970s, distribution of digital content did not begin until the late 1990s with the rise in popularity of the Internet. In the past, digital content was primarily distributed through computers and the Internet. Methods of distribution are rapidly changing as the Digital Revolution brings new channels, such as mobile apps and eBooks. These new technologies will create challenges for content creators, as they determine the best channel to bring content to their consumers. Despite the benefits, new technologies have created new intellectual property issues. Users can easily share, modify, and redistribute content outside of the creator's control. While new technologies have made digital content available to large audiences, managing copyright and limiting content movement will continue to be an issue that digital content creators face in the future. == Types of digital content == Examples include: Video – Types of video content include home videos, music videos, TV shows, and movies. Many of these can be viewed on websites such as YouTube, Hulu, Paramount+, Disney+, HBO Max, and so on, in which people and companies alike can post content. However, many movies and television shows are not available for free legally, but rather can be purchased from sites such as iTunes and Amazon. Audio – Music is the most common form of audio. Spotify has emerged as a popular way for people to listen to music either over the Internet or from their computer desktop. Digital content in the form of music is also available through Pandora and last.fm, both of which allow listeners to listen to music online for no charge. Images – Photo and image sharing is another example of digital content. Popular sites used for this type of digital content includes Imgur, where people share self-created pictures, Flickr, where people share their photo albums, and DeviantArt, where people share their artwork. Popular apps that are used for images include Instagram and Snapchat. Visual Stories - Stories are a new type of digital content that got introduced by Snapchat. Since then, stories as a format has been introduced in a couple of other platforms such as Facebook and Linkedin. In 2018, Google introduced their AMP Stories, which provides content publishers with a mobile-focused format for delivering news and information as visually rich, tap-through stories. Text - Type of digital content which is available in text or written format. Blog websites which store data in form of textual format. === Paid digital content === In order to have access to more premium digital goods, consumers usually have to pay an upfront charge for digital content, or a subscription based fee. Video – Many licensed videos, such as movies and television shows, require money in order to be viewed or downloaded. Popular services used by many include streaming giant Netflix and Amazon's streaming service, as well as recent notice put forth by the online video platform YouTube. Audio – While songs can be streamed for free, generally in order to download most licensed music, consumers need to purchase songs from web stores, such as the popular iTunes. However, Spotify Premium is emerging as a new model for purchasing digital content on the web: consumers pay a monthly fee to unlimited streaming and downloading from Spotify's music library. According to a report done by IHS Inc. in 2013, the global consumer spending on digital content grew to over $57 billion in 2013, which was up almost 30% from $44 billion in 2012. In past years, the US has always been a leader in consumer expenditure on digital content, but as of 2013, many countries have emerged with great consumer expenditure. South Korea's overall digital spend per capita is now greater than the US. ==== Consolidation ==== According to research firm Ampere Analysis, in 2024, a small group of six media conglomerates; Disney, Comcast, Google, Warner Bros. Discovery, Netflix, and Paramount Global—are poised to dominate the global content market. These companies are projected to account for 51% of all global spending on content, a significant increase from 47% in 2020. Disney, in particular, is a major player, with an estimated $35.8 billion investment in television and film content, representing 14% of global spending. This significant increase, fueled by Disney's full ownership of Hulu, highlights the company's strategic focus on streaming services. A substantial portion of the projected $126 billion global content spending is allocated to streaming platforms. === Non-purchasable digital content === Not all digital content is purchasable, and is simply anything published digitally. This would include: News – in recent years newspapers have attempted to expand their readership by creating access to their newspapers digitally. As of 2012, 39% of readers learned about news from online formats, making news a prevalent form of digital content. Advertisements – as media consumers increasingly use digital formats to watch TV, check the weather, and search for content, advertisements have shifted to digital forms to keep up with their viewership. Advertisements are now being made digitally and placed on sites ranging from Facebook to YouTube. Question and Answer sites – these sites are a type of Internet forum where people can post questions they want answered, or provide responses to previous inquiries. With millions of questions posted each day, anyone has the ability to create content on these sites, so the information provided may not be 100% reliable or accurate. Popular sites include Yahoo! Answers, WikiAnswers and Quora. Web mapping – sites such as MapQuest and Google Maps provide users with map content. These sites give people the ability to quickly look up the location of a landmark and create routes to a destination. Online maps are a form of free content provided by companies such as Google and AOL, serving as much more efficient alternatives to the traditional Thomas Guide. == Business implications == === Digital companies === Digital content businesses can include news, information, and entertainment distributed over the Internet and consumed digitally by both consumers and businesses. Based on revenue, the leading digital businesses are ranked Google, China Mobile, Bloomberg, Reed Elsevier, and Apple. The 50 companies with the highest revenue are split between those offering free and paid digital content, but these top 50 companies combined generate revenue of $150 billion. === Educational opportunities === Programs such as CUNY's Macaulay Honors College in their New Media Lab, run by industry professional Robert Small, is set up to train and introduce students to the various disciplines within the digital content industry. The goal is to offer information and access to professional work opportunities. They also explore within an incubator how to create businesses and start ups within the world of digital content. There are many educational events in support of choosing digital content as a career. === Government support === The Irish government adopted a "Strategy for the Digital Content Industry in Ireland" in 2002.

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  Hardware-based encryption

  Hardware-based encryption is the use of computer hardware to assist software, or sometimes replace software, in the process of data encryption. Typically, this is implemented as part of the processor's instruction set. For example, the AES encryption algorithm (a modern cipher) can be implemented using the AES instruction set on the ubiquitous x86 architecture. Such instructions also exist on the ARM architecture. However, more unusual systems exist where the cryptography module is separate from the central processor, instead being implemented as a coprocessor, in particular a secure cryptoprocessor or cryptographic accelerator, of which an example is the IBM 4758, or its successor, the IBM 4764. Hardware implementations can be faster and less prone to exploitation than traditional software implementations, and furthermore can be protected against tampering. == History == Prior to the use of computer hardware, cryptography could be performed through various mechanical or electro-mechanical means. An early example is the Scytale used by the Spartans. The Enigma machine was an electro-mechanical system cipher machine notably used by the Germans in World War II. After World War II, purely electronic systems were developed. In 1987 the ABYSS (A Basic Yorktown Security System) project was initiated. The aim of this project was to protect against software piracy. However, the application of computers to cryptography in general dates back to the 1940s and Bletchley Park, where the Colossus computer was used to break the encryption used by German High Command during World War II. The use of computers to encrypt, however, came later. In particular, until the development of the integrated circuit, of which the first was produced in 1960, computers were impractical for encryption, since, in comparison to the portable form factor of the Enigma machine, computers of the era took the space of an entire building. It was only with the development of the microcomputer that computer encryption became feasible, outside of niche applications. The development of the World Wide Web lead to the need for consumers to have access to encryption, as online shopping became prevalent. The key concerns for consumers were security and speed. This led to the eventual inclusion of the key algorithms into processors as a way of both increasing speed and security. == Implementations == === In the instruction set === ==== x86 ==== The X86 architecture, as a CISC (Complex Instruction Set Computer) Architecture, typically implements complex algorithms in hardware. Cryptographic algorithms are no exception. The x86 architecture implements significant components of the AES (Advanced Encryption Standard) algorithm, which can be used by the NSA for Top Secret information. The architecture also includes support for the SHA Hashing Algorithms through the Intel SHA extensions. Whereas AES is a cipher, which is useful for encrypting documents, hashing is used for verification, such as of passwords (see PBKDF2). ==== ARM ==== ARM processors can optionally support Security Extensions. Although ARM is a RISC (Reduced Instruction Set Computer) architecture, there are several optional extensions specified by ARM Holdings. === As a coprocessor === IBM 4758 – The predecessor to the IBM 4764. This includes its own specialised processor, memory and a Random Number Generator. IBM 4764 and IBM 4765, identical except for the connection used. The former uses PCI-X, while the latter uses PCI-e. Both are peripheral devices that plug into the motherboard. === Proliferation === Advanced Micro Devices (AMD) processors are also x86 devices, and have supported the AES instructions since the 2011 Bulldozer processor iteration. Due to the existence of encryption instructions on modern processors provided by both Intel and AMD, the instructions are present on most modern computers. They also exist on many tablets and smartphones due to their implementation in ARM processors. == Advantages == Implementing cryptography in hardware means that part of the processor is dedicated to the task. This can lead to a large increase in speed. In particular, modern processor architectures that support pipelining can often perform other instructions concurrently with the execution of the encryption instruction. Furthermore, hardware can have methods of protecting data from software. Consequently, even if the operating system is compromised, the data may still be secure (see Software Guard Extensions). == Disadvantages == If, however, the hardware implementation is compromised, major issues arise. Malicious software can retrieve the data from the (supposedly) secure hardware – a large class of method used is the timing attack. This is far more problematic to solve than a software bug, even within the operating system. Microsoft regularly deals with security issues through Windows Update. Similarly, regular security updates are released for Mac OS X and Linux, as well as mobile operating systems like iOS, Android, and Windows Phone. However, hardware is a different issue. Sometimes, the issue will be fixable through updates to the processor's microcode (a low level type of software). However, other issues may only be resolvable through replacing the hardware, or a workaround in the operating system which mitigates the performance benefit of the hardware implementation, such as in the Spectre exploit.

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  Giditraffic

  GidiTraffic (or GIDITRAFFIC) is an online social service started on 23 September 2011. Based primarily on social media, the service employs crowdsourcing as its primary means of providing real-time traffic updates to subscribers on its platform. The service, delivered free of charge, affords its users access to various types of information. Though its broadest category of users is road users and motorists, GIDITRAFFIC lends itself as a platform for answering inquiries from anyone who requires information on any subject of interest. GIDITRAFFIC's core competence is in vehicular traffic reports, however, the service also handles all other forms of traffic (going by the fact that the word traffic also means "the mutual exchange of information"). == Operation == Users of the service log on to its Twitter feed to get up-to-date traffic information or to post a general inquiry, which GIDITRAFFIC then publishes to all subscribers. Through crowdsourced replies, a requester receives numerous responses from other subscribers who have seen the question and can provide a relevant answer. In addition, updates are provided by subscribers to the platform via their mobile devices, thereby making the service effective in delivering traffic updates as they occur, and providing timely answers to other user inquiries. This informs GIDITRAFFIC's motto of "Lending each other an eye", alluding to the collaboration and cooperation between the platform's users in making the service indispensable to its users. == Reception == On Twitter, which is its primary platform, the service caters to over 1,800,000 subscribers, with the number increasing daily. The popularity of the platform stems from the fact that it not only keeps its subscribers abreast of the traffic situation in Lagos, the commercial capital city of Nigeria (well known for its many traffic jams), but users in other parts of the world. For a regular user of the platform, knowing where to avoid getting to a set destination in good time is well worth the two or three minutes it takes to access and scroll through the GIDITRAFFIC feed for updates. Another interesting aspect of this platform is the identity of the person behind it. The sustained anonymity of this individual has sparked many discussions centering on his or her possible identity. Online, GIDITRAFFIC continuously publishes traffic updates and user questions, while keeping up witty interactions with the platform's followers round the clock – adding to the mystery and persona of the GIDITRAFFIC owner. == Awards and recognition == In early 2012, GIDITRAFFIC received a nomination for a Shorty Award in the Life-Saving Hero category. Although this did not translate into a win, it brought recognition and wider exposure for the service from international news outlets such as the BBC, Washington Post. and New York Times. Back home in Nigeria, also in 2012, GIDITRAFFIC was honored with a Future Award for Best Use of New Media in recognition of the huge impact the service has had in terms of helping Lagos residents better manage time spent in traffic. == Mobile Applications == In 2012, GIDITRAFFIC partnered with telecommunications company Nokia to produce a downloadable mobile traffic application (the GIDITRAFFIC application, available for Nokia Asha phones on Nokia's online store). There are plans to extend the application to a wider range of mobile phone platforms. On 4 September 2013, the GIDITRAFFIC application for Nokia Lumia phones using Windows Phone 8 was launched on the Windows App Store.

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  WaveNet

  WaveNet is a deep neural network for generating raw audio. It was created by researchers at London-based AI firm DeepMind. The technique, outlined in a paper in September 2016, is able to generate relatively realistic-sounding human-like voices by directly modelling waveforms using a neural network method trained with recordings of real speech. Tests with US English and Mandarin reportedly showed that the system outperforms Google's best existing text-to-speech (TTS) systems, although as of 2016 its text-to-speech synthesis still was less convincing than actual human speech. WaveNet's ability to generate raw waveforms means that it can model any kind of audio, including music. == History == Generating speech from text is an increasingly common task thanks to the popularity of software such as Apple's Siri, Microsoft's Cortana, Amazon Alexa and the Google Assistant. Most such systems use a variation of a technique that involves concatenated sound fragments together to form recognisable sounds and words. The most common of these is called concatenative TTS. It consists of large library of speech fragments, recorded from a single speaker that are then concatenated to produce complete words and sounds. The result sounds unnatural, with an odd cadence and tone. The reliance on a recorded library also makes it difficult to modify or change the voice. Another technique, known as parametric TTS, uses mathematical models to recreate sounds that are then assembled into words and sentences. The information required to generate the sounds is stored in the parameters of the model. The characteristics of the output speech are controlled via the inputs to the model, while the speech is typically created using a voice synthesiser known as a vocoder. This can also result in unnatural sounding audio. == Design and ongoing research == === Background === WaveNet is a type of feedforward neural network known as a deep convolutional neural network (CNN). In WaveNet, the CNN takes a raw signal as an input and synthesises an output one sample at a time. It does so by sampling from a softmax (i.e. categorical) distribution of a signal value that is encoded using μ-law companding transformation and quantized to 256 possible values. === Initial concept and results === According to the original September 2016 DeepMind research paper WaveNet: A Generative Model for Raw Audio, the network was fed real waveforms of speech in English and Mandarin. As these pass through the network, it learns a set of rules to describe how the audio waveform evolves over time. The trained network can then be used to create new speech-like waveforms at 16,000 samples per second. These waveforms include realistic breaths and lip smacks – but do not conform to any language. WaveNet is able to accurately model different voices, with the accent and tone of the input correlating with the output. For example, if it is trained with German, it produces German speech. The capability also means that if the WaveNet is fed other inputs – such as music – its output will be musical. At the time of its release, DeepMind showed that WaveNet could produce waveforms that sound like classical music. === Content (voice) swapping === According to the June 2018 paper Disentangled Sequential Autoencoder, DeepMind has successfully used WaveNet for audio and voice "content swapping": the network can swap the voice on an audio recording for another, pre-existing voice while maintaining the text and other features from the original recording. "We also experiment on audio sequence data. Our disentangled representation allows us to convert speaker identities into each other while conditioning on the content of the speech." (p. 5) "For audio, this allows us to convert a male speaker into a female speaker and vice versa [...]." (p. 1) According to the paper, a two-digit minimum amount of hours (c. 50 hours) of pre-existing speech recordings of both source and target voice are required to be fed into WaveNet for the program to learn their individual features before it is able to perform the conversion from one voice to another at a satisfying quality. The authors stress that "[a]n advantage of the model is that it separates dynamical from static features [...]." (p. 8), i. e. WaveNet is capable of distinguishing between the spoken text and modes of delivery (modulation, speed, pitch, mood, etc.) to maintain during the conversion from one voice to another on the one hand, and the basic features of both source and target voices that it is required to swap on the other. The January 2019 follow-up paper Unsupervised speech representation learning using WaveNet autoencoders details a method to successfully enhance the proper automatic recognition and discrimination between dynamical and static features for "content swapping", notably including swapping voices on existing audio recordings, in order to make it more reliable. Another follow-up paper, Sample Efficient Adaptive Text-to-Speech, dated September 2018 (latest revision January 2019), states that DeepMind has successfully reduced the minimum amount of real-life recordings required to sample an existing voice via WaveNet to "merely a few minutes of audio data" while maintaining high-quality results. Its ability to clone voices has raised ethical concerns about WaveNet's ability to mimic the voices of living and dead persons. According to a 2016 BBC article, companies working on similar voice-cloning technologies (such as Adobe Voco) intend to insert watermarking inaudible to humans to prevent counterfeiting, while maintaining that voice cloning satisfying, for instance, the needs of entertainment-industry purposes would be of a far lower complexity and use different methods than required to fool forensic evidencing methods and electronic ID devices, so that natural voices and voices cloned for entertainment-industry purposes could still be easily told apart by technological analysis. == Applications == At the time of its release, DeepMind said that WaveNet required too much computational processing power to be used in real world applications. As of October 2017, Google announced a 1,000-fold performance improvement along with better voice quality. WaveNet was then used to generate Google Assistant voices for US English and Japanese across all Google platforms. In November 2017, DeepMind researchers released a research paper detailing a proposed method of "generating high-fidelity speech samples at more than 20 times faster than real-time", called "Probability Density Distillation". At the annual I/O developer conference in May 2018, it was announced that new Google Assistant voices were available and made possible by WaveNet; WaveNet greatly reduced the number of audio recordings that were required to create a voice model by modeling the raw audio of the voice actor samples.

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  W3C Device Description Working Group

  The W3C Device Description Working Group (DDWG), operating as part of the World Wide Web Consortium (W3C) Mobile Web Initiative (MWI), was chartered to "foster the provision and access to device descriptions that can be used in support of Web-enabled applications that provide an appropriate user experience on mobile devices." Mobile devices exhibit the greatest diversity of capabilities, and therefore present the greatest challenge to content adaptation technologies. The group published several documents, including a list of requirements for an interface to a Device Description Repository (DDR) and a standard interface meeting those requirements. The group was rechartered in 2006 to work in public towards the development of the Application Programming Interface (API) for a DDR. Early in 2007, the group launched a wiki and a blog to add to the public mailing list. The group subsequently published a formal vocabulary of core device properties, and an API called the DDR Simple API, which became a W3C Recommendation in December 2008. The group closed at the end of 2008, but with the intention of maintaining the Web pages, blog and wiki through W3C volunteer effort. == Publications == The DDWG published several W3C Working Group Notes and one W3C Recommendation. A W3C WG Note that articulates what the W3C and other organizations are doing or have already done with regard to device information. This document suggests an environment in which these technologies work together to meet the goals of content adaptation. The completed document was published on 31 October 2007. A W3C WG Note describing the ecosystem surrounding creation, maintenance and use of device descriptions. The completed document was published on 31 October 2007. A W3C WG Note describing a set of requirements for a reference repository of device descriptions. The completed document was published on 17 December 2007. A W3C WG Note describing a process to manage contributions to an initial core vocabulary, identification of key device properties, a formal initial core vocabulary and the identification of a maintainer for the core vocabulary. The details were contained in the Working Group Note describing the DDWG Core Vocabulary published on 14 April 2008. A W3C WG Note defining useful grouping and structure patterns in device descriptions. The Device Description Structures document was published as a Working Draft on 5 December 2008. The intention is that this document will be future input to other W3C groups. A W3C Recommendation defining a language-neutral programming interface to a Device Description Repository. The DDR Simple API was published on 5 December 2008. There is the possibility of future publications on the DDWG wiki describing implementations of the API in various languages, including Java, IDL, WSDL, C# etc. Much of the DDWG's material was developed in public via the DDWG Wiki and through their public mailing lists.

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  Visopsys

  Visopsys (Visual Operating System), is an operating system, written by Andy McLaughlin. Development of the operating system began in 1997. The operating system is licensed under the GNU GPL, with the headers and libraries under the less restrictive LGPL license. It runs on the 32-bit IA-32 architecture. It features a multitasking kernel, supports asynchronous I/O and the FAT line of file systems. It requires a Pentium processor. == History == The development of Visopsys began in 1997, being written by Andy McLaughlin. The first public release of the Operating System was on 2 March 2001, with version 0.1. In this release, Visopsys was a 32 bit operating system, supporting preemptive multitasking and virtual memory. == System overview == Visopsys uses a monolithic kernel, written in the C programming language, with elements of assembly language for certain interactions with the hardware. The operating system supports a graphical user interface, with a small C library.

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