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  • Pocket (service)

    Pocket (service)

    Pocket, formerly known as Read It Later, was a social bookmarking service for storing, sharing and discovering web bookmarks, first released in 2007. Mozilla, the developer of Pocket, announced in May 2025 that it was discontinuing the service and would shut it down in July of that year. == History == Pocket was introduced in August 2007 as a Mozilla Firefox browser extension named Read It Later by Nathan (Nate) Weiner. Once his product was used by millions of people, he moved his office to Silicon Valley and four other people joined the Read It Later team. Weiner's intention was for the application to be like a TiVo directory for web content and to give users access to that content on any device. Read It Later obtained venture capital investments of US$2.5 million in 2011 and $5.0 million in 2012. The 2011 funding came from Foundation Capital, Baseline Ventures, Google Ventures, Founder Collective and unnamed angel investors. The company rejected an acquisition offer by Evernote after showing concerns that Evernote intended to shut down the Read It Later service and amalgamate its functionality into Evernote's main service. Initially, the Read It Later app was available in a free version and a paid version that included additional features. After the rebranding to Pocket, all paid features were made available in a free and advertisement-free app. In May 2014, a paid subscription service called Pocket Premium was introduced, adding server-side storage of articles and more powerful search tools. In June 2015, Pocket was included in Firefox, via a toolbar button and link to a user's Pocket list in the bookmark's menu. The integration was controversial, as users displayed concerns for the direct integration of a proprietary service into an open source application, and that it could not be completely disabled without editing advanced settings, unlike other third-party extensions. A Mozilla spokesperson stated that the feature was meant to leverage the service's popularity among Firefox users and clarified that all code related to the integration was open source. The spokesperson added that "[Mozilla had] gotten lots of positive feedback about the integration from users". On February 27, 2017, Pocket announced that it had been acquired by Mozilla Corporation, the commercial arm of Firefox's non-profit development group. Mozilla staff stated that Pocket would continue to operate as an independent subsidiary but that it would be leveraged as part of an ongoing "Context Graph" project. There were plans to open-source the server-side code of Pocket, though only parts of the project had been open-sourced as of 2024. On May 22, 2025, Mozilla announced that it would shut down Pocket on July 8, 2025. Exports of user data would be available until October 8, 2025, when accounts would be deleted. The email newsletter Pocket Hits was rebranded as Ten Tabs on June 12 as part of the closure, with it being changed to release only on weekdays. == Functions == The application allows the user to save an article or web page to remote servers for later reading. The article is sent to the user's Pocket list (synced to all of their devices) for offline reading. Pocket makes the article more readable by removing clutter and enabling the user to add tags and adjust text settings. == User base == The application had 17 million users and 1 billion saves, as of September 2015. Pocket was listed among Time magazine's 50 Best Android Applications for 2013. == Reception == Kent German of CNET said that "Read It Later is oh so incredibly useful for saving all the articles and news stories I find while commuting or waiting in line." Erez Zukerman of PC World said that supporting the developer is enough reason to buy what he deemed a "handy app". Bill Barol of Forbes said that although Read It Later works less well than Instapaper, "it makes my beloved Instapaper look and feel a little stodgy." In 2015, Pocket was awarded a Material Design Award for Adaptive Layout by Google for their Android application.

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  • Blue check

    Blue check

    A blue check is used on social media platforms, notably X (formerly known as Twitter), to indicate the authenticity of an account. Since November 2022, Twitter users whose accounts are at least 90 days old and have a verified phone number receive verification upon subscribing to X Premium or Verified Organizations; this status persists as long as the subscription remains active. When introduced in June 2009, the system provided the site's readers with a means to distinguish genuine notable account holders, such as celebrities and organizations, from impostors or parodies. Until November 2022, a blue checkmark displayed against an account name indicated that Twitter had taken steps to ensure that the account was actually owned by the person or organization whom it claimed to represent. The checkmark does not imply endorsement from Twitter, and does not mean that tweets from a verified account are necessarily accurate or truthful in any way. People with verified accounts on Twitter are often colloquially referred to as "blue checks" on social media and by reporters. In November 2022, the verification program was modified heavily by new owner Elon Musk, extending verification to any account with a verified phone number and an active subscription to an eligible X Premium (formerly Twitter Blue) plan. These changes faced criticism from users and the media, who believed that the changes would ease impersonation, and allow accounts spreading misleading information to feign credibility. In a related change, Twitter introduced additional gold and gray checkmarks, used by Verified Organizations and government-affiliated accounts, respectively. Twitter claims that the changes to verification are required to "reduce fraudulent accounts and bots". Twitter users who had been verified through the previous system were known as "legacy verified" accounts; legacy verification was deprecated in April 2023, and stripped from accounts who do not meet the new payment requirements. Musk later implied that he had been personally paying for the X Premium subscriptions of several notable celebrities. == Until November 2022 == In June 2009, after being criticized by Kanye West and sued by Tony La Russa over unauthorized accounts run by impersonators, the company launched their "Verified Accounts" program. Twitter stated that an account with a "blue tick" verification badge indicates "we've been in contact with the person or entity the account is representing and verified that it is approved". After the beta period, the company stated in their FAQ that it "proactively verifies accounts on an ongoing basis to make it easier for users to find who they're looking for" and that they "do not accept requests for verification from the general public". Originally, Twitter took on the responsibility of reaching out to celebrities and other notable people to confirm their identities in order to establish a verified account. In July 2016, Twitter announced a public application process to grant verified status to an account "if it is determined to be of public interest" and that verification "does not imply an endorsement". In 2016, the company began accepting requests for verification, but it was discontinued the same year. Twitter explained that the volume of requests for verified accounts had exceeded its ability to cope; rather, Twitter determines on its own whom to approach about verified accounts, limiting verification to accounts which are "authentic, notable, and active". In November 2020, Twitter announced a relaunch of its verification system in 2021. According to the new policy, Twitter verifies six different types of accounts; for three of them (companies, brands, and influential individuals like activists), the existence of a Wikipedia page will be one criterion for showing that the account has "Off Twitter Notability". === Controversy === On June 21, 2014, actor William Shatner raised an issue with several Engadget editorial staff and their verification status on Twitter. Besides the site's social media editor, John Colucci, Shatner also targeted several junior members of the staff for being "nobodies", unlike some of his actor colleagues who did not bear such distinction. Shatner claimed Colucci and the team were bullying him when giving a text interview to Mashable. Over a month later, Shatner continued to discuss the issue on his Tumblr page, to which Engadget replied by defending its team and discussing the controversy surrounding the social media verification. Twitter's practice and process for verifying accounts came under scrutiny again in 2017 after the company verified the account of white supremacist and far-right political activist, Jason Kessler. Many who criticized Twitter's decision to verify Kessler's account saw this as a political act on the company's behalf. In response, Twitter put its verification process on hold. The company tweeted, "Verification was meant to authenticate identity & voice but it is interpreted as an endorsement or an indicator of importance. We recognize that we have created this confusion and need to resolve it. We have paused all general verifications while we work and will report back soon." As of November 2017, Twitter continued to deny verification of Julian Assange's account following his requests. In November 2019, Dalit activists of India alleged that higher-caste people get Twitter verification easily and trended hashtags #CancelAllBlueTicksInIndia and #CasteistTwitter. Critics have said that the company's verification process is not transparent and causes digital marginalisation of already marginalised communities. Twitter India rejected the allegations, calling them "impartial" and working on a "case-by-case" policy. == Since November 2022 == On April 20, 2023, Twitter (known as X since July 2023) began removing verification status for users of public interest, causing a controversy among Twitter users. The website's system was altered, allowing any individual to receive verification for a monthly fee, an act which saw significant criticism. Following the acquisition of Twitter by Elon Musk on October 28, 2022, Musk told Twitter employees to introduce paid verification by November 7 through Twitter Blue. The Verge reported that the updated Blue subscription would cost $19.99 per month, and users would lose their verification status if they did not join within 90 days. Following backlash, Musk tweeted, in response to author Stephen King, a lowered $8 price on November 1, 2022. Twitter confirmed the new price of $7.99 per month on November 5, 2022. The new verification system began rollout on November 9, 2022, a day after the 2022 United States elections. The decision to delay its rollout was to address concerns about users potentially spreading misinformation about voting results by posing as news outlets and lawmakers. At the same time, Twitter introduced a secondary gray "Official" label on some high-profile accounts, but removed them hours after launch. Less than 48 hours later, Twitter reinstated the gray "Official" label, after multiple users were suspended for deliberately impersonating reporters and high-profile athletes like LeBron James. A viral tweet from an account purporting to be the pharmaceutical company Eli Lilly and Company caused the company's stock to fall after announcing "insulin is free now". As a result, Twitter disabled new Blue subscriptions on November 11, 2022. === Announcement === In October 2022, Casey Newton of Platformer reported that executives at Twitter began discussing the possibility of users being forced to pay for Twitter Blue in order to keep their verification status. Musk publicly announced that verification was "being revamped right now" after Newton's article; according to The Verge, Twitter planned to increase the price of Twitter Blue from US$4.99 per month to US$19.99 per month. Users would have had 90 days to subscribe or face losing their verification status, and employees were told to implement paid verification by November 9 or risk getting fired. Upon the news that Twitter Blue would cost US$19.99 per month, author Stephen King expressed displeasure towards Twitter and stated that he would leave. Musk, replying to King's tweet, proposed that the service should cost US$7.99 instead. In a separate tweet, Musk wrote that Twitter Blue subscribers would receive priority in replies, mentions, and search, fewer advertisements, and longer audio and video. Although paid verification was expected to be launched by November 7, the reintroduction of Twitter Blue was delayed until after the 2022 United States elections on November 9, according to a memo obtained by The New York Times. The announcement of paid verification resulted in several accounts facetiously impersonating Musk, such as those of comedians Kathy Griffin and Sarah Silverman, being suspended. In response, Musk announced that impersonators using Twitter Blue "will be permanently suspended". An "official

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  • Virtual Print Fee

    Virtual Print Fee

    Virtual Print Fee (VPF) is a subsidy paid by a film distributor towards the purchase of digital cinema projection equipment for use by a film exhibitor in the presentation of first release motion pictures. The subsidy is paid in the form of a fee per booking of a movie, intended to match the savings that occurs by not shipping a film print. The model is designed to help redistribute the savings realized by studios when using digital distribution instead of film print distribution and is intended to vanish when the transition phase is over when the vast majority of cinemas screens are equipped. == History == The first public demonstration of digital projection for cinema took place at ShoWest in 1999, and it was readily apparent that the technology was further ahead than the business model. Early technology presentations attempted to claim that the technology would pay for itself through new revenues generated by new forms of content. But exhibitors knew their audience, and could see that digital projection was only a replacement technology, creating new financial liabilities, and not new revenue. It wasn’t until the rollout of digital 3-D years later in 2005 that digital projection demonstrated that it could be used to generate additional revenue. The economics were challenging. Film projectors and platters cost in the neighborhood of US$30,000, while early digital projectors cost up to US$150,000. Further, film projectors had a lifetime of 30 years with relatively small annual expenditures in maintenance and replacement parts. On the other hand, exhibitors felt they would be lucky to get 10 years of service from a digital projector, after which there would have to be a refresh in capital expenditure. Meanwhile, distributors would realize significant savings by eliminating the high cost of film prints with corresponding shipping costs, and instead distributing digital files either by satellite or hard drive. The Virtual Print Fee was designed to better balance savings and expenditures for both exhibitors and distributors. It is intended to primarily assist in the replacement of film projectors, and not assist in the purchase of new projection equipment for new construction. To give confidence to financial institutions that digital cinema technology was stable and worthy of investment, Digital Cinema Initiatives was created in 2002, resulting in the release of the first version of the DCI Digital Cinema System Specification in 2005. The DCI Specification continues to be the core specification for digital cinema, establishing the baseline technology and system requirements for which studios will release digital movies. The first set of VPF agreements executed with four major studios were announced by Christie/AIX in November 2005. Christie/AIX at that time was a subsidiary of Access Integrated Technology, now renamed Cinedigm Digital Cinema Corp. The agreements were for the rollout of digital cinema technology to 4000 screens. Since that time, numerous other Digital Cinema Deployment Agreements have been executed around the world, allowing exhibitors in nearly every territory to benefit from VPF subsidies in the conversion from film projection to digital projection.

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  • Open Mashup Alliance

    Open Mashup Alliance

    The Open Mashup Alliance (OMA) is a non-profit consortium that promotes the adoption of mashup solutions in the enterprise through the evolution of enterprise mashup standards like EMML. The initial members of the OMA include some large technology companies such as Adobe Systems, Hewlett-Packard, and Intel and some major technology users such as Bank of America and Capgemini. According to Dion Hinchcliffe, "Ultimately, the OMA creates a standardized approach to enterprise mashups that creates an open and vibrant market for competing runtimes, mashups, and an array of important aftermarket services such as development/testing tools, management and administration appliances, governance frameworks, education, professional services, and so on." == Specification development == The initial focus of the OMA is developing EMML, which is a declarative mashup domain-specific language (DSL) aimed at creating enterprise mashups. The EMML language provides a comprehensive set of high-level mashup-domain vocabulary to consume and mash a variety of web data sources. EMML provides a uniform syntax to invoke heterogeneous service styles: REST, WSDL, RSS/ATOM, RDBMS, and POJO. EMML also provides the ability to mix and match diverse data formats: XML, JSON, JDBC, JavaObjects, and primitive types. The OMA website provides the EMML specification, the EMML schema, a reference runtime implementation capable of running EMML scripts, sample EMML mashup scripts, and technical documentation. The OMA is developing EMML under a Creative Commons Attribution No Derivatives license. The eventual objective of the OMA is to submit the EMML specification and any other OMA specifications to a recognized industry standards body.

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  • Enterprise cognitive system

    Enterprise cognitive system

    Enterprise cognitive systems (ECS) are part of a broader shift in computing, from a programmatic to a probabilistic approach, called cognitive computing. An Enterprise Cognitive System makes a new class of complex decision support problems computable, where the business context is ambiguous, multi-faceted, and fast-evolving, and what to do in such a situation is usually assessed today by the business user. An ECS is designed to synthesize a business context and link it to the desired outcome. It recommends evidence-based actions to help the end-user achieve the desired outcome. It does so by finding past situations similar to the current situation, and extracting the repeated actions that best influence the desired outcome. While general-purpose cognitive systems can be used for different outputs, prescriptive, suggestive, instructive, or simply entertaining, an enterprise cognitive system is focused on action, not insight, to help in assessing what to do in a complex situation. == Key characteristics == ECS have to be: Adaptive: They must learn as information changes, and as goals and requirements evolve. They must resolve ambiguity and tolerate unpredictability. They must be engineered to feed on dynamic data in real time, or near real time. In the Enterprise, near-real time learning from data requires an agile information federation approach to ingest incremental data updates as they occur, and an unsupervised learning approach to ensure that new best practice is leveraged across the organization in a timely manner. Interactive: They must interact easily with users so that those users can define their needs comfortably. They may also interact with other processors, devices, and Cloud services, as well as with people. In the Enterprise, interactions are controlled via existing workflows and UIs. Therefore, embedding best practices directly into these existing interfaces, in the context of a specific step, is critical to ensure maximum end-user adoption. Iterative and stateful: They must aid in defining a problem by asking questions or finding additional source input if a problem statement is ambiguous or incomplete. They must “remember” previous interactions in a process and return information that is suitable for the specific application at that point in time. In the Enterprise, business context is often structured by a business process, and therefore sufficiently data-rich to make relevant recommendations without significant iterations from the end-user. A stateful memory of overall interactions across communication channels is critical for understanding of context, as a static profile will not capture intent and outcome potential the way behavior does. Contextual: They must understand, identify, and extract contextual elements such as meaning, syntax, time, location, appropriate domain, regulations, user's profile, process, task and goal. They may draw on multiple sources of information, including both structured and unstructured digital information, as well as sensory inputs (visual, gestural, auditory, or sensor-provided). In the Enterprise, Context is fragmented and must be aggregated across data types, sources, and locations. In most business environments, such data is captured in existing enterprise information systems, and the effort is linked to quickly source and unify such information. It is rare to have to directly process sensor, audio or visual data in real-time as direct input into the enterprise cognitive system. Instead, these data types are captured by Enterprise Applications and pre-processed into a binary or text format prior to consumption by the System. == Business applications powered by an ECS == Bottlenose – trends and brands monitoring Cybereason – security threat monitoring Dataminr – social media monitoring

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  • Ethiopian feminists facing digital gender-based violence

    Ethiopian feminists facing digital gender-based violence

    Against a background of traditional views of women, rising internet use, a young population and an unsafe offline life, women and girls in Ethiopia are facing increasing amounts of digital violence. Some women, feeling endangered, have left the country as a result. Researchers, activists and lawyers have called for online content to be taken down and specific digital legislation to be drafted and enforced. == Online violence and its offline effects == Sexual violence against women and girls in Ethiopia is common. In 2023, in the Women, Peace and Security Index by Georgetown University, Ethiopia came 146 out of 177 countries. Over several years online harassment of and violence against women and girls in Ethiopia has increased. It can range from sexist remarks about appearance and women’s role in society, to revenge porn, threats of beating, acid attacks, abduction, rape or death. The real-life effect on women and girls of these attacks can include mental health problems, damaged reputations and a withdrawal from public and economic life. When the online attacks migrate to the real world, for example when online attackers find out where the targeted women and girls live, this can result in physical attacks, street harassment, threats to children and can cause victims to move house or job or even flee the country in fear of femicide. In a country that criminalises homosexuality, it can also lead to physical attacks on LGBTQI+ people in particular and indeed on anybody labelled as homosexual. == Research studies == The Centre for Information Resilience (CIR) conducted interviews with Ethiopian women holding public roles or being active online. The centre published a report on this in 2024 entitled ‘Silenced, Shamed and Threatened’. They found that technology-facilitated gender-based violence (TFGBV) had become “normalised to the point of invisibility.” In 2024, CER also published an analysis of gendered hate speech on social media in Ethiopia called ‘Normalised and invisible.’ It is thought that traditional views of women, the young population, the rise in internet use and the war in Tigray, when sexual violence was used as a weapon of war by Ethiopian and Eritrean soldiers, have all helped to create an online environment in which even femicide is considered unremarkable. AFP Fact Check collaborated with Deutsche Welle Akademie, to investigate the cyber harassment of women in Ethiopia, analysing misogynistic posts published on TikTok and Facebook. They discovered disparaging remarks about women’s physical appearance, threats of acid attacks and other physical violence, and the public sharing of women’s phone numbers. == Individuals affected == Women in particular jeopardy of digital gender-based violence are feminists, activists, politicians and those with a public profile. Some women are known to have fled Ethiopia fearing for their lives after online and offline threats. Yordanos Bezabih, an Ethiopian women’s rights activist, started a campaign with the hashtag #JusticeforHeaven to fight against gender-based cyberspace violence. As a result, she herself become a target. She experienced years of online threats of acid attacks, gang-rape and death. In 2025, subscribers to an online community organised a search for her address. Deepfake nude images of her were shared, she was filmed in real life, her house and online accounts were broken into, her private photos and messages posted on social media. When the attackers finally circulated her address, suggesting that she be executed, she left Ethiopia on a human rights defender scholarship. In 2023, Lella Misikir helped to start a campaign, called ‘My Whistle, My Voice’, that suggested women carry whistles and use them if they were harassed in the street. A TikTok video of the campaign became popular. Shortly after, videos of Misikir were circulated suggesting that she was gay. Her online attackers next searched for her address. In November 2024, Misikir left the country. == Legal issues == Ethiopia has some laws on online harassment and defamation, for example the Computer Crimes Proclamation. However, technology-facilitated, gender-based violence (TFGBV), such as deepfakes, non-consensual image sharing, and coordinated harassment, is not explicitly recognized as crime. In practice too, women are often not believed when reporting such violence and are not taken seriously. Police advice is often that women affected should simply leave the online space. Social media platforms can remove content when it is brought to their attention but the offenders are not banned. Users can only block them.

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  • Asynchronous module definition

    Asynchronous module definition

    Asynchronous module definition (AMD) is a specification for the programming language JavaScript. It defines an application programming interface (API) that defines code modules and their dependencies, and loads them asynchronously if desired. Implementations of AMD provide the following benefits: Website performance improvements. AMD implementations load smaller JavaScript files, and then only when they are needed. Fewer page errors. AMD implementations allow developers to define dependencies that must load before a module is executed, so the module does not try to use outside code that is not available yet.... In addition to loading multiple JavaScript files at runtime, AMD implementations allow developers to encapsulate code in smaller, more logically-organized files, in a way similar to other programming languages such as Java. For production and deployment, developers can concatenate and minify JavaScript modules based on an AMD API into one file, the same as traditional JavaScript. AMD provides some CommonJS interoperability. It allows for using a similar exports and require() interface in the code, although its own define() interface is more basal and preferred. The AMD specification is implemented by Dojo Toolkit, RequireJS, and other libraries.

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  • CloudLibrary

    CloudLibrary

    CloudLibrary (stylized as "cloudLibrary") is a cloud-based software system through which libraries lend electronic books; it is also the name of the app that users download to access the e-books. CloudLibrary was created in 2011 by 3M as part of its library systems unit as a competitor to OverDrive, Inc.; in 2015 3M sold the North American part of that unit to Bibliotheca Group GmbH, a company founded in 2011 that was funded by One Equity Partners Capital Advisors, a division of JP Morgan Chase. By 2019, Bibliotecha had tried, unsuccessfully, to negotiate with Amazon to add Kindle-ebook compatibility to cloudLibrary - something that, as of then, Amazon had only made available to Overdrive. In that year, cloudLibrary, along with hoopla offered by Midwest Tape, ODILO, and Baker & Taylor’s Axis 360, were the main competitors to the Overdrive and Libby apps offered by OverDrive, Inc. in the library e-book market. In April 2024, Bibliotheca sold cloudLibrary to the nonprofit cooperative OCLC. By that time, cloudLibrary was used by around 500 libraries in around 20 countries in around 50 languages, and was used to lend audiobooks, digital magazines, newspapers, and comics, and streaming media, along with e-books.

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  • Layers (digital image editing)

    Layers (digital image editing)

    Layers are used in digital image editing to separate different elements of an image. A layer can be compared to a transparency on which imaging effects or images are applied and placed over or under an image. Today they are an integral feature of image editors. In the early days of computing, memory was at a premium and the idea of using multi-layered images was considered infeasible in personal computer applications as the tradeoffs were image size and color depth. As the price of memory fell it became feasible to apply the concept of layering to raster images. The first software known to apply the concept of layers was LALF, which was released in 1989 for the NEC PC-9801. LALF's terminology for layers is "cells", after the concept of drawing animation frames over-top of a stencil. Layers were introduced in Western markets by Fauve Matisse (later Macromedia xRes), and then available in Adobe Photoshop 3.0, in 1994, which lead to widespread adoption. In vector image editors that support animation, layers are used to further enable manipulation along a common timeline for the animation; in SVG images, the equivalent to layers are "groups". == Layer types == There are different kinds of layers, and not all of them exist in all programs. They represent a part of a picture, either as pixels or as modification instructions. They are stacked on top of each other, and depending on the order, determine the appearance of the final picture. In graphics software, layers are the different levels at which one can place an object or image file. In the program, layers can be stacked, merged, or defined when creating a digital image. Layers can be partially obscured allowing portions of images within a layer to be hidden or shown in a translucent manner within another image. Layers can also be used to combine two or more images into a single digital image. For the purpose of editing, working with layers allows for applying changes to just one specific layer. == Layer (basic) == The standard layer available to most programs consists of a rectangular, semitransparent picture which may be superimposed over other layers. Some programs require that layers cover the same area as the final canvas, but others offer layers of multiple sizes. Each layer may bear individual settings, such as opacity, blending modes, dynamic filters, and potentially hundreds of other properties. == Layer mask == A layer mask is linked to a layer and hides part of the layer from the picture. What is painted black on the layer mask will not be visible in the final picture. What is grey will be more or less transparent depending on the shade of grey. As the layer mask can be both edited and moved around independently of both the background layer and the layer it applies to, it gives the user the ability to test a lot of different combinations of overlay. == Adjustment layer == An adjustment layer typically applies a common effect like brightness or saturation to other layers. However, as the effect is stored in a separate layer, it is easy to try it out and switch between different alternatives, without changing the original layer. In addition, an adjustment layer can easily be edited, just like a layer mask, so an effect can be applied to just part of the image.

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  • Gnowit

    Gnowit

    Gnowit (pronounced "know it") is a Canadian software company that provides automated, near-real-time monitoring of legislative, regulatory, and political activity across Canada. Its platform aggregates and analyzes information from government publications, parliamentary debates, committee, and proceedings to provide searchable alerts and reports for organizations monitoring public policy and regulatory developments. The system uses natural-language processing and machine learning techniques to organize and filter large volumes of public information.; the company reports that new publication documents are captured and millions of items are added to its repository daily. == History, Founders and Leadership == Gnowit was co-founded in Ottawa in 2010 by Shahzad Khan and Mohammad Al-Azzouni; Khan serves as chief executive officer. Khan holds a PhD in Computer Science from the University of Cambridge, has more than two decades of experience in AI/ML and computational linguistics, and has authored or co-authored 37 peer-reviewed publications and five patents. Traditionally, companies performed this analysis manually; Gnowit has delivered efficiencies achieved through AI innovations. The company has participated in several Canadian startup and accelerator programs, including Carleton University's Lead To Win initiative, the University of Ottawa's Startup Garage, the Invest Ottawa incubator, and the League of Innovators' BOOST program. === Kubernetes validation (2019–2020) === As part of a Canada's Centre of Excellence in Next Generation Networks (CENGN) project, Gnowit validated a containerized version of its web-intelligence software on Kubernetes. Between 2019 and 2020, Gnowit participated in a project with Canada’s Centre of Excellence in Next Generation Networks (CENGN) to test and scale its platform using containerized infrastructure based on Kubernetes. The initiative focused on improving scalability and supporting the company’s transition from a monolithic software architecture to a cloud-native deployment model. == Products and services == Gnowit markets several modules for public-affairs, compliance, and market-intelligence teams. Legislative & Regulatory Monitoring (vAnalyst). vAnalyst is a monitoring platform that tracks legislative and regulatory activity across Canadian federal, provincial, and territorial jurisdictions. The system aggregates parliamentary debates, bills, committee proceedings, and regulatory publications and provides searchable alerts and reporting tools. The product monitors more than two million web sources to surface relevant items quickly. Parliamentary Live (vAnalyst). Monitors live video feeds from parliamentary sessions and committees with same-day transcripts, AI-generated summaries, witness summaries, and motion detection; municipal coverage is offered as an option. Gnowit can avail transcripts up to two weeks before official releases. These transcripts enable users to navigate and review lengthy parliamentary sittings and committee discussions through searchable text. Municipal Monitoring (vAnalyst). The platform also tracks council meetings, agendas, bylaws, and other municipal government publications from hundreds of Canadian municipalities. The platform aggregates these sources into a single searchable interface for reviewing local government decisions. Curation Edge (analyst service). Curation Edge is an add-on service in which expert analysts work and collaborate with clients to develop a tailored curation guide and deliver daily newsletters or briefs on legislation and media. These reports provide concise summaries, relevant links, and optional metadata, prioritizing key updates with additional context and analysis. The service is customizable, including branding and formatting for executive audiences, and is intended to reduce information overload, support decision-making, and streamline the synthesis and distribution of information. === Coverage and sources === Gnowit monitors sources span Canadian government materials across federal, provincial, and territorial jurisdictions Hansard transcripts (All Jurisdictions, including committees), order papers, committee transcripts, gazettes, bills, acts and regulations, consultations, regulatory-agency publications, and global news media as well as press releases and council-meeting materials from hundreds of municipalities. == Partnerships and support == Gnowit reports collaborations with Canadian academic and ecosystem partners, including: Algonquin College Carleton University McGill University University of Ottawa Université du Québec en Outaouais (UQO) Queen's University The company also participated in the accelerator program at Invest Ottawa and has received support from Canadian research and innovation programs, including: NRC Industrial Research Assistance Program (NRC-IRAP) Mitacs Ontario Centre of Innovation (OCI) (formerly OCE) Gnowit has also referenced membership in the Southern Ontario Smart Computing Innovation Platform (Government of Canada profile: FedDev Ontario – SOSCIP overview). == Technology == Gnowit develops technology intended to support timely decision-making by delivering updates from monitored web sources as they are published. The platform applies artificial intelligence (AI) and machine learning (ML) techniques to monitor, capture, clean, analyze, filter, and organize text, and to generate concise briefs. Its technical approach combines Boolean queries, shallow language processing techniques, and machine learning classifiers within a self-service interface. The company has described its longer-term development framework in relation to a belief–desire–intention (BDI) model of intelligent agents on the web. Gnowit and its founder are listed as inventors/assignees on patents concerning multi-document clustering, salient-content extraction, and sentiment analysis methods that are consistent with these features: US 9,600,470 – Method and system relating to re-labelling multi-document clusters (assignee: Whyz Technologies Ltd.). US 9,336,202 – Method and system relating to salient content extraction for information retrieval (assignee: Whyz Technologies Ltd.). CA 2,865,184 C – Method and system relating to re-labelling multi-document clusters. CA 2,865,186 C – Procédé et système concernant l'analyse de sentiment d'un contenu (sentiment analysis; French record). CA 2,865,187 C – Method and system relating to salient content extraction for information retrieval. == Research and community == In January 2025, Gnowit personnel contributed to regulatory NLP by co-authoring a peer-reviewed paper at the 1st Regulatory NLP Workshop (RegNLP 2025), co-located with COLING in Abu Dhabi. Titled Unifying Large Language Models and Knowledge Graphs for Efficient Regulatory Information Retrieval and Answer Generation, the work introduces PolicyInsight, a framework that joins a dynamic policy data model and knowledge graph with LLMs to monitor policy texts, detect changes, and support retrieval and answer generation; the author list includes Shahzad Khan (CEO, Gnowit Inc.). (ACL Anthology, aclweb.org). Similar information-retrieval technologies are widely used for competitive intelligence, policy monitoring, and media analysis. == White paper == Gnowit has published a practical guide, Automated Government Information Monitoring, which outlines how GR and regulatory teams can design a monitoring and briefing workflow and describes Gnowit's automation features and export options (PDF, email, dashboards, CSV/JSON/XML/API).

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  • Quality of experience

    Quality of experience

    Quality of experience (QoE) is a measure of the delight or annoyance of a customer's experiences with a service (e.g., web browsing, phone call, TV broadcast). QoE focuses on the entire service experience; it is a holistic concept, similar to the field of user experience, but with its roots in telecommunication. QoE is an emerging multidisciplinary field based on social psychology, cognitive science, economics, and engineering science, focused on understanding overall human quality requirements. == Definition and concepts == In 2013, within the context of the COST Action QUALINET, QoE has been defined as:The degree of delight or annoyance of the user of an application or service. It results from the fulfillment of his or her expectations with respect to the utility and / or enjoyment of the application or service in the light of the user’s personality and current state.This definition has been adopted in 2016 by the International Telecommunication Union in Recommendation ITU-T P.10/G.100. Before, various definitions of QoE had existed in the domain, with the above-mentioned definition now finding wide acceptance in the community. QoE has historically emerged from Quality of Service (QoS), which attempts to objectively measure service parameters (such as packet loss rates or average throughput). QoS measurement is most of the time not related to a customer, but to the media or network itself. QoE however is a purely subjective measure from the user's perspective of the overall quality of the service provided, by capturing people's aesthetic and hedonic needs. QoE looks at a vendor's or purveyor's offering from the standpoint of the customer or end user, and asks, "What mix of goods, services, and support, do you think will provide you with the perception that the total product is providing you with the experience you desired and/or expected?" It then asks, "Is this what the vendor/purveyor has actually provided?" If not, "What changes need to be made to enhance your total experience?" In short, QoE provides an assessment of human expectations, feelings, perceptions, cognition and satisfaction with respect to a particular product, service or application. QoE is a blueprint of all human subjective and objective quality needs and experiences arising from the interaction of a person with technology and with business entities in a particular context. Although QoE is perceived as subjective, it is an important measure that counts for customers of a service. Being able to measure it in a controlled manner helps operators understand what may be wrong with their services and how to improve them. == QoE factors == QoE aims at taking into consideration every factor that contributes to a user's perceived quality of a system or service. This includes system, human and contextual factors. The following so-called "influence factors" have been identified and classified by Reiter et al.: Human Influence Factors Low-level processing (visual and auditory acuity, gender, age, mood, …) Higher-level processing (cognitive processes, socio-cultural and economic background, expectations, needs and goals, other personality traits…) System Influence Factors Content-related Media-related (encoding, resolution, sample rate, …) Network-related (bandwidth, delay, jitter, …) Device-related (screen resolution, display size, …) Context Influence Factors Physical context (location and space) Temporal context (time of day, frequency of use, …) Social context (inter-personal relations during experience) Economic context Task context (multitasking, interruptions, task type) Technical and information context (relationship between systems) Studies in the field of QoE have typically focused on system factors, primarily due to its origin in the QoS and network engineering domains. Through the use of dedicated test laboratories, the context is often sought to be kept constant. == QoE versus User Experience == QoE is strongly related to but different from the field of User Experience (UX), which also focuses on users' experiences with services. Historically, QoE has emerged from telecommunication research, while UX has its roots in Human–Computer Interaction. Both fields can be considered multi-disciplinary. In contrast to UX, the goal of improving QoE for users was more strongly motivated by economic needs. Wechsung and De Moor identify the following key differences between the fields: == QoE measurement == As a measure of the end-to-end performance at the service level from the user's perspective, QoE is an important metric for the design of systems and engineering processes. This is particularly relevant for video services because – due to their high traffic demands –, bad network performance may highly affect the user's experience. So, when designing systems, the expected output, i.e. the expected QoE, is often taken into account – also as a system output metric and optimization goal. To measure this level of QoE, human ratings can be used. The mean opinion score (MOS) is a widely used measure for assessing the quality of media signals. It is a limited form of QoE measurement, relating to a specific media type, in a controlled environment and without explicitly taking into account user expectations. The MOS as an indicator of experienced quality has been used for audio and speech communication, as well as for the assessment of quality of Internet video, television and other multimedia signals, and web browsing. Due to inherent limitations in measuring QoE in a single scalar value, the usefulness of the MOS is often debated. Subjective quality evaluation requires a lot of human resources, establishing it as a time-consuming process. Objective evaluation methods can provide quality results faster, but require dedicated computing resources. Since such instrumental video quality algorithms are often developed based on a limited set of subjective data, their QoE prediction accuracy may be low when compared to human ratings. QoE metrics are often measured at the end devices and can conceptually be seen as the remaining quality after the distortion introduced during the preparation of the content and the delivery through the network, until it reaches the decoder at the end device. There are several elements in the media preparation and delivery chain, and some of them may introduce distortion. This causes degradation of the content, and several elements in this chain can be considered as "QoE-relevant" for the offered services. The causes of degradation are applicable for any multimedia service, that is, not exclusive to video or speech. Typical degradations occur at the encoding system (compression degradation), transport network, access network (e.g., packet loss or packet delay), home network (e.g. WiFi performance) and end device (e.g. decoding performance). == QoE management == Several QoE-centric network management and bandwidth management solutions have been proposed, which aim to improve the QoE delivered to the end-users. When managing a network, QoE fairness may be taken into account in order to keep the users sufficiently satisfied (i.e., high QoE) in a fair manner. From a QoE perspective, network resources and multimedia services should be managed in order to guarantee specific QoE levels instead of classical QoS parameters, which are unable to reflect the actual delivered QoE. A pure QoE-centric management is challenged by the nature of the Internet itself, as the Internet protocols and architecture were not originally designed to support today's complex and high demanding multimedia services. As an example for an implementation of QoE management, network nodes can become QoE-aware by estimating the status of the multimedia service as perceived by the end-users. This information can then be used to improve the delivery of the multimedia service over the network and proactively improve the users' QoE. This can be achieved, for example, via traffic shaping. QoE management gives the service provider and network operator the capability to minimize storage and network resources by allocating only the resources that are sufficient to maintain a specific level of user satisfaction. As it may involve limiting resources for some users or services in order to increase the overall network performance and QoE, the practice of QoE management requires that net neutrality regulations are considered.

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  • ISO/IEC 11801

    ISO/IEC 11801

    International standard ISO/IEC 11801 Information technology — Generic cabling for customer premises specifies general-purpose telecommunication cabling systems (structured cabling) that are suitable for a wide range of applications (analog and ISDN telephony, various data communication standards, building control systems, factory automation). It is published by ISO/IEC JTC 1/SC 25/WG 3 of the International Organization for Standardization (ISO) and the International Electrotechnical Commission (IEC). It covers both balanced copper cabling and optical fibre cabling. The standard was designed for use within commercial premises that may consist of either a single building or of multiple buildings on a campus. It was optimized for premises that span up to 3 km, up to 1 km2 office space, with between 50 and 50,000 persons, but can also be applied for installations outside this range. A major revision was released in November 2017, unifying requirements for commercial, home and industrial networks. == Classes and categories == The standard defines several link/channel classes and cabling categories of twisted-pair copper interconnects, which differ in the maximum frequency for which a certain channel performance is required: Class A: Up to 100 kHz using Category 1 cable and connectors Class B: Up to 1 MHz using Category 2 cable and connectors Class C: Up to 16 MHz using Category 3 cable and connectors Class D: Up to 100 MHz using Category 5e cable and connectors Class E: Up to 250 MHz using Category 6 cable and connectors Class EA: Up to 500 MHz using category 6A cable and connectors (Amendments 1 and 2 to ISO/IEC 11801, 2nd Ed.) Class F: Up to 600 MHz using Category 7 cable and connectors Class FA: Up to 1 GHz (1000 MHz) using Category 7A cable and connectors (Amendments 1 and 2 to ISO/IEC 11801, 2nd Ed.) Class BCT-B: Up to 1 GHz (1000 MHz) using with coaxial cabling for BCT applications. (ISO/IEC 11801-1, Edition 1.0 2017-11) Class I: Up to 2 GHz (2000 MHz) using Category 8.1 cable and connectors (ISO/IEC 11801-1, Edition 1.0 2017-11) Class II: Up to 2 GHz (2000 MHz) using Category 8.2 cable and connectors (ISO/IEC 11801-1, Edition 1.0 2017-11) The standard link impedance is 100 Ω. (The older 1995 version of the standard also permitted 120 Ω and 150 Ω in Classes A−C, but this was removed from the 2002 edition.) The standard defines several classes of optical fiber interconnect: OM1: Multimode, 62.5 μm core; minimum modal bandwidth of 200 MHz·km at 850 nm OM2: Multimode, 50 μm core; minimum modal bandwidth of 500 MHz·km at 850 nm OM3: Multimode, 50 μm core; minimum modal bandwidth of 2000 MHz·km at 850 nm OM4: Multimode, 50 μm core; minimum modal bandwidth of 4700 MHz·km at 850 nm OM5: Multimode, 50 μm core; minimum modal bandwidth of 4700 MHz·km at 850 nm and 2470 MHz·km at 953 nm OS1: Single-mode, maximum attenuation 1 dB/km at 1310 and 1550 nm OS1a: Single-mode, maximum attenuation 1 dB/km at 1310, 1383, and 1550 nm OS2: Single-mode, maximum attenuation 0.4 dB/km at 1310, 1383, and 1550 nm Grandfathered === OM5 === OM5 fiber is designed for wideband applications using SWDM multiplexing of 4–16 carriers (40G=4λ×10G, 100G=4λ×25G, 400G=4×4λ×25G) in the 850–953 nm range. === Category 7 === Class F channel and Category 7 cable are backward compatible with Class D/Category 5e and Class E/Category 6. Class F features even stricter specifications for crosstalk and system noise than Class E. To achieve this, shielding was added for individual wire pairs and the cable as a whole. Unshielded cables rely on the quality of the twists to protect from EMI. This involves a tight twist and carefully controlled design. Cables with individual shielding per pair such as Category 7 rely mostly on the shield and therefore have pairs with longer twists. The Category 7 cable standard was ratified in 2002, and primarily introduced to support 10 gigabit Ethernet over 100 m of copper cabling. Like the earlier standards, it contains four twisted copper wire pairs rated for transmission frequencies of up to 600 MHz. However, in 2006, Category 6A was ratified for Ethernet to allow 10 Gbit/s while still using the conventional 8P8C connector. Care is required to avoid signal degradation by mixing cable and connectors not designed for that use, however similar. Most manufacturers of active equipment and network cards have chosen to support the 8P8C for their 10 gigabit Ethernet products on copper and not GG45, ARJ45, or TERA connectors as Class F would have originally called for. Therefore, the Category 6 specification was revised to Category 6A to permit this use; products therefore require a Class EA channel (ie, Cat 6A). As of 2019, some equipment has been introduced which has connectors supporting the Class F (Category 7) channel. Note, however, that Category 7 is not recognized by the TIA/EIA. === Category 7A === Class FA (Class F Augmented) channels and Category 7A cables, introduced by ISO 11801 Edition 2 Amendment 2 (2010), are defined at frequencies up to 1000 MHz. The intent of the Class FA was to possibly support the future 40 gigabit Ethernet: 40GBASE-T. Simulation results have shown that 40 gigabit Ethernet may be possible at 50 meters and 100 gigabit Ethernet at 15 meters. In 2007, researchers at Pennsylvania State University predicted that either 32 nm or 22 nm circuits would allow for 100 gigabit Ethernet at 100 meters. However, in 2016, the IEEE 802.3bq working group ratified the amendment 3 which defines 25GBASE-T and 40GBASE-T on Category 8 cabling specified to 2000 MHz. The Class FA therefore does not support 40G Ethernet. As of 2025, there is no equipment that has connectors supporting the Class FA (Category 7A) channel. Category 7A is not recognized in TIA/EIA. === Category 8 === Category 8 was ratified by the TR43 working group under ANSI/TIA 568-C.2-1. It is defined up to 2000 MHz and only for distances up to 30 m or 36 m, depending on the patch cords used. ISO/IEC JTC 1/SC 25/WG 3 developed the equivalent standard ISO/IEC 11801-1:2017/COR 1:2018, with two options: Class I channel (Category 8.1 cable): minimum cable design U/FTP or F/UTP, fully backward compatible and interoperable with Class EA (Category 6A) using 8P8C connectors; Class II channel (Category 8.2 cable): F/FTP or S/FTP minimum, interoperable with Class FA (Category 7A) using TERA or GG45. == Abbreviations for twisted pairs == Annex E, Acronyms for balanced cables, provides a system to specify the exact construction for both unshielded and shielded balanced twisted pair cables. It uses three letters—U for unshielded, S for braided shielding, and F for foil shielding—to form a two-part abbreviation in the form of xx/xTP, where the first part specifies the type of overall cable shielding, and the second part specifies shielding for individual cable elements. Common cable types include U/UTP (unshielded cable); U/FTP (individual pair shielding without the overall screen); F/UTP, S/UTP, or SF/UTP (overall screen without individual shielding); and F/FTP, S/FTP, or SF/FTP (overall screen with individual foil shielding). == 2017 edition == In November 2017, a new edition was released by ISO/IEC JTC 1/SC 25 "Interconnection of information technology equipment" subcommittee. It is a major revision of the standard which has unified several prior standards for commercial, home, and industrial networks, as well as data centers, and defines requirements for generic cabling and distributed building networks. The new series of standards replaces the former 11801 standard and includes six parts: == Versions == ISO/IEC 11801:1995 (Ed. 1) ISO/IEC 11801:2000 (Ed. 1.1) – Edition 1, Amendment 1 ISO/IEC 11801:2002 (Ed. 2) ISO/IEC 11801:2008 (Ed. 2.1) – Edition 2, Amendment 1 ISO/IEC 11801:2010 (Ed. 2.2) – Edition 2, Amendment 2 ISO/IEC 11801-1:2017, -1:2017/Cor 1:2018, -2:2017, -3:2017, -3:2017/Amd 1:2021, -3:2017/Cor 1:2018, -4:2017, -4:2017/Cor 1:2018, -5:2017, -5:2017/Cor 1:2018, -6:2017, -6:2017/Cor 1:2018 (As of September 2023, this set is current.)

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  • Sensory, Inc.

    Sensory, Inc.

    Sensory, Inc. is an American company which develops software AI technologies for speech, sound and vision. It is based in Santa Clara, California. Sensory’s technologies have shipped in over three billion products from hundreds of leading consumer electronics manufacturers including AT&T, Hasbro, Huawei, Google, Amazon, Samsung, LG, Mattel, Motorola, Plantronics, GoPro, Sony, Tencent, Garmin, LG, Microsoft, Lenovo, and more. Sensory has over 60 issued patents covering speech recognition in consumer electronics, biometric authentication, sensor/speech combinations, wake word technology, and more. == History == Sensory, Inc. was founded in 1994, originally as Sensory Circuits, by Forrest Mozer, Mike Mozer and Todd Mozer. The three had also co-founded ESS Technology years earlier. In 1999 Sensory acquired Fluent Speech Technologies, which was formed and started by a group of professors out of the Oregon Graduate Institute (formerly OGI, now OHSU). Fluent Speech Technologies developed high performance embedded speech engines, the technology from this acquisition is now the core technology used throughout Sensory's chip and software line. === Company timeline === 1994 – Founded 1995 – Introduces the RSC 164 - first commercially successful speech recognition IC 1998 – Introduces first speaker verification IC 2000 – Acquires Oregon based Fluent-Speech Technologies 2002 – Acquires Texas Instruments line of speech output ICs (the SC series) 2007 – Introduces first Voice User Interface for Bluetooth silicon (CSR BC-5) - BlueGenie 2008 - Sensory and BlueAnt partner on the V1 - Revolutionary new Bluetooth headset with a voice user interface. First wearable to use a voice user interface for control and best-reviewed speech recognition product in history 2009 – Introduced world's smallest text to speech system (TTS) and Truly HandsfreeTM Triggers/ wake words. 2010 – Introduced the NLP-5x – First Natural Language Voice Processor and TrulyHandsfree wake words in SDKs for Android, iOS, Linux, and Windows. NLP5x used the first generation of TrulyHandsfree wake words with low power and enhanced accuracy. 2011 – Sensory partners with Google and Microsoft to enable TrulyHandsfree as a front end to Goog411 and Bing411 2012 – Partnered with Tensilica to offer ultra-low power TrulyHandsfree wake words; introduced Speaker Verification and Speaker Identification for mobile phones and other consumer electronics. 2012 - TrulyHandsfree released into Samsung's Galaxy S2 for "Hey Galaxy" wake word 2013 – TrulyHandsfree wake words migrated to many new platforms and began shipping as MotoVoice in the Google-owned MotoX. Sensory's TrulyHandsfree in mobile takes off with the Galaxy S3 and S4 and Galaxy Note and is licensed into wearables like Google Glass. 2014 – Announced new initiative in Vision; added LG and Motorola as customers; received the 2014 Global Mobile Award for Best Mobile Technology Breakthrough at the GSMA Mobile World Congress in Barcelona, Spain (judges commented, "A big advance for the wearables market, this offers many benefits for consumers, increasing uptake and usage of many mobile apps, driving revenue for operators and content providers.") 2015-2018 - Licensed Google, Amazon, MSFT, Baidu, Huawei, ZTE, and many others with TrulyHandsfree wake words. Sensory develops first wake words for OK Google, Hey Siri, and Hey Cortana. 2019 - Sensory launched two new solutions: SoundID, sound identification, and TrulyNatural, embedded large vocabulary speech recognition. Sensory also acquired Vocalize.ai, an independent testing lab. 2020 - Sensory introduced VoiceHub, which allows the automated generation of wake words. 2021 - Sensory expands VoiceHub with speech recognition and NLU capabilities. The company initiated a new cloud platform, SensoryCloud.ai. 2022-Sensory rolls out SensoryCloud.ai with speech to text, text to speech, face & voice biometrics 2024- Sensory Automotive & TrulyNatural Speech-to-text On-Device launched == Technology and products == Sensory originally developed both hardware (Integrated Circuit - IC or "chip") and software platforms but migrated to software only around 2005 and added cloud and hybrid computing capabilities in 2021. Sensory's RSC-164 IC (Integrated Circuit or "chip") was used on NASA's Mars Polar Lander in the Mars Microphone on the Lander. Speech Synthesis SC-6x chips – acquired some speech synthesis technology from Texas Instruments. Sensory’s embedded AI solutions include the following: TrulyHandsfree (THF) - wake word detection and phrase spotting. TrulyNatural (TNL) - large vocabulary continuous speech recognition with NLU. TrulySecure (TS) - face and voice biometrics. TrulySecureSpeakerVerification (TSSV) - speaker and sound identification. VoiceHub - Online portal for creating custom wake words and speech recognition models with NLU. Sensory Automotive- Sensory Automotive is a full voice and vision suite of AI technologies that operate efficiently in the car without connecting to a network. The cloud initiative, SensoryCloud.ai, is targeting Speech To Text (STT), Text To Speech (TTS), Wake Word verification, face and voice recognition, and sound identification.

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  • Personal media

    Personal media

    Personal media are media of communication which are used by an individual rather than by a corporation or institution. They are generally contrasted with mass media which are produced by teams of people and broadcast to a general population. In other words, personal media allow individuals, as opposed to corporate entities, to contribute knowledge and opinion to the public. The term dates from the 1980s. New technologies such as social media and self-publishing are creating a variety of modes for modern media. Marika Lüders suggests a two-dimensional model for classifying such media with one dimension being the degree of interaction between the senders and receivers; and the other dimension being the level of institutionalisation and professionalism. Katherine Nashleanas links the concept of personal media to the notion of 'control' by an individual as opposed to a centralised authority. She argues that although personal media including the fax have been available to the general public since the 1960s, more recent technologies such as the smartphone confer greater control over content production and distribution to their users.

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  • Single address space operating system

    Single address space operating system

    In computer science, a single address space operating system (or SASOS) is an operating system that provides only one globally shared address space for all processes. In a single address space operating system, numerically identical (virtual memory) logical addresses in different processes all refer to exactly the same byte of data. In a traditional OS with private per-process address space, memory protection is based on address space boundaries ("address space isolation"). Single address-space operating systems make translation and protection orthogonal, which in no way weakens protection. The core advantage is that pointers (i.e. memory references) have global validity, meaning their meaning is independent of the process using it. This allows sharing pointer-connected data structures across processes, and making them persistent, i.e. storing them on backup store. Some processor architectures have direct support for protection independent of translation. On such architectures, a SASOS may be able to perform context switches faster than a traditional OS. Such architectures include Itanium, and Version 5 of the Arm architecture, as well as capability architectures such as CHERI. A SASOS should not be confused with a flat memory model, which provides no address translation and generally no memory protection. In contrast, a SASOS makes protection orthogonal to translation: it may be possible to name a data item (i.e. know its virtual address) while not being able to access it. SASOS projects using hardware-based protection include the following: Angel IBM i (formerly called OS/400) Iguana at NICTA, Australia Mungi at NICTA, Australia Nemesis Opal Scout Sombrero Related are OSes that provide protection through language-level type safety: Br1X Genera JX a research Java OS Phantom OS Singularity Theseus OS Torsion

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