AI Code Ui

AI Code Ui — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

24SevenOffice

24SevenOffice is a Norwegian software company headquartered in Oslo, Norway, with offices in Stockholm, Sweden and London, United Kingdom. Founded in 1997, the company specializes in web-based (SaaS) ERP and CRM systems. == Company history == 24SevenOffice was founded in 1997 in Porsgrunn, Norway, as IKT Interactive AS and marketed as kontorplassen.no. The name "24SevenOffice" was introduced for the company's London branch when the company entered the British market in 2003. The company changed its name to 24SevenOffice in February 2005. Originally based in Skien, the company later moved to Oslo Innovation Center, then to Tjuvholmen in the waterfront Fjord City of Oslo, and now the headquarters are located in Inkognitogaten 33, Solli plass, Oslo. The idea for the company's product was developed in 1996, and 24SevenOffice was an early innovator in the Scandinavian market in web-based enterprise resource planning solutions (ERP). A British office was established at Surrey Business Park in May 2003, with the company launching its web-based (SaaS) utility computing system to the UK SME market in 2004. An office in Chennai, India, was established in 2005, and 24SevenOffice entered the Swedish market when they acquired the leading competitor and ERP-provider Start & Run in a cash deal. In August 2005, the company had an initial public offering that raised NOK 15 million, and the company entered The Norwegian Over the Counter Market list as of 5 October 2005 (the ticker was 24SO), reaching a market value of NOK 175 million, with 5000 customers in Norway. In 2006, the company signed a deal to sponsor rally driver Petter Solberg, at the time the largest private sponsorship in Norwegian sport. Instead of receiving NOK 5 million in cash, Solberg received a 2.9 per cent ownership in the company. The company entered the German-speaking market in April 2006 when an office in Frankfurt am Main was opened. In late August/early September, they established an office with ten sales agents plus a general manager in Stockholm for the Swedish market. 24SevenOffice initiated strategic cooperation with Active 24 in early 2006 to develop a common platform. During the summer, Active 24 was bought by 24SevenOffice's ERP/CRM competitor Mamut (company), and 24SevenOffice terminated the contract with Active 24 in October demanding NOK 200 million in compensation for lost revenue. After a breakdown of settlement negotiations in the Forliksråd in January 2007, 24SevenOffice filed a case against Active 24 for breach of agreement in the Oslo District Court in March. 24SevenOffice lost on all counts in the District Court in December 2007. In January 2008, 24SevenOffice appealed the case to the Borgarting Court of Appeal, reducing the cause of action from NOK 250 to 30 million. 24SevenOffice lost on all counts in the Court of Appeal in December 2008, and was ordered to cover the costs incurred by Active 24 in connection with the dispute totaling NOK 6.91 million. 24SevenOffice appealed the case to the Supreme Court of Norway, but the Supreme Court Appeals Committee in March 2008 unanimously rejected the appeal from 24SevenOffice over the Borgarting Appeal Court's unanimous judgment of December 2008. On a counterclaim from Active 24 and Mamut against 24SevenOffice, the Oslo District Court in May 2010 found, that 24SevenOffice should pay Active 24 NOK 12 million in compensation for wrongfully having terminated the agreement, and a further NOK 360.000 of the opponent's legal costs. 24SevenOffice disagreed with the court ruling, and appealed once again. The Borgarting Court of Appeal in November 2011, ruled to reduce the amount of damages to NOK 4.4 million plus NOK 900.000 in penal interest. With several scrip issues, 24SevenOffice raised 25 million NOK (about $4 million at the time) between October 2005 and July 2006. They entered into a strategic partnership with Bluegarden, who for 30 years had delivered digital services for payroll, human resource planning, recruitment and training, in March 2006, and they made a large-scale agreement in April 2006, with US telecommunications software company Webex, a competitor to Norwegian Tandberg videoconferencing equipment manufacturer. In September 2006, 24SevenOffice signed an agreement with Fokus Bank to provide their customers with extended functionality in Internet banking. 24SevenOffice had by 2007 reportedly 9000 customers, joined the OpenAjax Alliance, and entered into a strategic partnership with Dun & Bradstreet in May 2007, but despite getting listed on Oslo Axess on 22 June (ticker: TFSO), reaching a market capitalization of NOK 120 million, the company was still losing money. The company ended 2007 with a revenue of NOK 21.7 million. In 2008, 24SevenOffice bought 50% of the stocks in telecommunication company Oyatel, partnered with Nets Group to facilitate invoicing for businesses, and telecommunications company Telipol chose 24SevenOffice's second-generation Internet platform for its 8,000 users. They announced an increase in revenues in Q2 to 11.1 million, up from 4.7 million in the same period the year before. 24SevenOffice had a turnover of NOK 37 million in the first half of 2009, a doubling compared to the same period the previous year, and presented its first positive EBITDA in Q2. The Norwegian Association of Auditors signed an agreement with 24SevenOffice in 2011, whereby they only recommend 24SevenOffice as a system for their members to use. On 27 June 2013, the shareholders of 24SevenOffice took off from the stock exchange and privatized the company. In recent years, the company has invested heavily in finance and accounting – and got leading auditing companies such as PwC and KPMG on the customer list. == Products == 24SevenOffice is a web-based (SaaS) ERP system. It includes modules for CRM, accounting, invoicing, e-mail, file/document management and project management. == Awards == 24SevenOffice won the Seal of Excellence in Multimedia Award at the 2004 CeBIT, became Norwegian Gazelle Company of the year 2004, chosen by Dagens Næringsliv and Dun & Bradstreet, won Product of the Year in the Norwegian finance magazine Kapital, and the IKT Grenland Innovation Award in 2008.
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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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Browser sniffing

Browser sniffing (also known as User agent sniffing and browser detection) is a set of techniques used in websites and web applications in order to determine the web browser a visitor is using, and to serve browser-appropriate content to the visitor. It is also used to detect mobile browsers and send them mobile-optimized websites. This practice is sometimes used to circumvent incompatibilities between browsers due to misinterpretation of HTML, Cascading Style Sheets (CSS), or the Document Object Model (DOM). While the World Wide Web Consortium maintains up-to-date central versions of some of the most important Web standards in the form of recommendations, in practice no software developer has designed a browser which adheres exactly to these standards; implementation of other standards and protocols, such as SVG and XMLHttpRequest, varies as well. As a result, different browsers display the same page differently, and so browser sniffing was developed to detect the web browser in order to help ensure consistent display of content. == Sniffer methods == === Client-side sniffing === Web pages can use programming languages such as JavaScript which are interpreted by the user agent, with results sent to the web server. For example: This code is run by the client computer, and the results are used by other code to make necessary adjustments on client-side. In this example, the client computer is asked to determine whether the browser can use a feature called ActiveX. Since this feature was proprietary to Microsoft, a positive result will indicate that the client may be running Microsoft's Internet Explorer. This is no longer a reliable indicator since Microsoft's open-source release of the ActiveX code, however, meaning that it can be used by any browser. === Standard Browser detection method === The web server communicates with the client using a communication protocol known as HTTP, or Hypertext Transfer Protocol, which specifies that the client send the server information about the browser being used to view the website in a User-Agent header. === Server-side sniffing === Extensive browser techniques enable persistent user tracking even if users try to stay anonymous. See device fingerprint for more details on browser fingerprinting. == Issues and standards == Many websites use browser sniffing to determine whether a visitor's browser is unable to use certain features (such as JavaScript, DHTML, ActiveX, or cascading style sheets), and display an error page if a certain browser is not used. However, it is virtually impossible to account for the tremendous variety of browsers available to users. Generally, a web designer using browser sniffing to determine what kind of page to present will test for the three or four most popular browsers, and provide content tailored to each of these. If a user is employing a user agent not tested for, there is no guarantee that a usable page will be served; thus, the user may be forced either to change browsers or to avoid the page. The World Wide Web Consortium, which sets standards for the construction of web pages, recommends that web sites be designed in accordance with its standards, and be arranged to "fail gracefully" when presented to a browser which cannot deal with a particular standard. Browser sniffing increases maintenance needed. Websites treating some browsers differently should provide an alternative version for other browsers. Use of user agent strings are error-prone because the developer must check for the appropriate part, such as "Gecko" instead of "Firefox". They must also ensure that future versions are supported. Furthermore, some browsers allow changing the user agent string, making the technique useless.
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Anti-social Media Bill (Nigeria)

Anti-social Media Bill was introduced by the Senate of the Federal Republic of Nigeria on 5 November 2019 to criminalise the use of the social media in peddling false or malicious information. The original title of the bill is Protection from Internet Falsehood and Manipulations Bill 2019. It was sponsored by Senator Mohammed Sani Musa from the largely conservative northern Nigeria. After the bill passed second reading on the floor of the Nigeria Senate and its details were made public, information emerged on the social media accusing the sponsor of the bill of plagiarising a similar law in Singapore which is at the bottom of global ranking in the freedom of speech and of the press. But the senator denied that he plagiarised Singaporean law. == Opposition to the bill == Angry reactions trailed the introduction of the bill, and a number of civil society organisations, human rights activists, and Nigerian citizens unanimously opposed the bill. International rights group, Amnesty International and Human Rights Watch condemned the proposed legislation saying it is aimed at gagging freedom of speech which is a universal right in a country of over two hundred million people. Opposition political parties are very critical of the bill and accused the government of attempting to strip bare, Nigerian citizens of their rights to free speech and destroying same social media on whose power and influence the ruling All Progressives Congress, APC came to power in 2015. Nigeria Information Minister, Lai Mohammed has been at the center of public criticism because he is suspected to be the brain behind the proposed act. Lai was a former spokesman of then opposition All Progressives Congress. A "Stop the Social Media Bill! You can no longer take our rights from us" online petition campaign to force the Nigeria parliament to drop the bill received over 90,000 signatures within 24 hours. In November 2019, after the bill passed second reading in the senate, Akon Eyakenyi, a senator from Akwa Ibom State publicly said he would resist the bill. === Support for the bill === Those who support the proposed act especially Senators have often argued that the law would help curtail hate speech. President Muhammad Buhari who is seen as a beneficiary of the influence and power of the social media and free speech has been mute about it. But the president's senior aides and family members have publicly spoken in support of the bill. In November 2019, the wife of the president, Aisha Buhari, told a gathering at the Nigeria's National Mosque in the capital, Abuja that if China with over one billion people could regulate the social media, Nigeria should do same. But Nigerians reacted saying Nigeria is not a one-party communist state like China. Days later, a daughter to the president, Zahra Indimi told a gathering of young people in Abuja that social media had become a potent weapon for bullying those they thought were doing better than them in terms of social class and called for a critical regulation. == Key provisions of the bill == === Title === Protection from Internet Falsehoods, Manipulations and Other Related Matters Bill 2019. === Explanatory memorandum === This Act is to prevent Falsehoods and Manipulations in Internet transmission and correspondences in Nigeria. To suppress falsehoods and manipulations and counter the effects of such communications and transmissions and to sanction offenders with a view to encouraging and enhancing transparency by Social Media Platforms using the internet correspondences. === Objectives === One objective of the bill is to prevent the transmission of false statements or declaration of facts in Nigeria. Another objective of the bill is to end the financing of online mediums that transmit false statements. Measures will be taken to detect and control inauthentic behaviour and misuse of online accounts (parody accounts). When paid content is posted towards a political end, there will be measures to ensure the poster discloses such information. There will be sanction for offenders. === Transmission of false statement === According to the bill, a person must not: Transmit a statement that is false or, Transmit a statement that might: i. Affect the security or any part of Nigeria. ii. Affect public health, public safety or public finance. iii. Affect Nigeria's relationship with other countries. iv. influence the outcome of an election to any office in a general election. v. Cause enmity or hatred towards a person or group of persons. Anyone guilty of the above is liable to a fine of N300,000 or three years' imprisonment or both (for individual); and a fine not exceeding ten million naira (for corporate organisations). Same punishment applies for fake online accounts that transmit statements listed above. === Parody accounts === The bill says a person shall not open an account to transmit false statement. Anyone found guilty will be fined N200,000 or three years' imprisonment or both (for an individual) or five million naira (for corporate organisations). If such accounts transmit a statement that will affect security or influence the outcome of an election, such a person will be fined N300,000 or three years' imprisonment or both. If a person receives payment or reward to help another to transmit false statements knowingly, he/she is liable to a fine of N150,000 or three years' imprisonment or both. If a person receives payment or reward to help another to transmit a statement affects security or influence the outcome of an election, the fine is N300,000 or three years' imprisonment or both (for individual) and ten million naira for organisations. === Declaration === According to the bill, a law enforcement department can issue a "declaration" to offenders. And this declaration will be issued even if the "false statement" has been corrected or pulled down. The offender will be required to publish a "correction notice" in a specified newspaper, online location or other printed publication of Nigeria. Failure to comply, a person is liable to N200,000 or 12 months' imprisonment or both (for individual) and five million naira for organisations. === Access blocking order === The bill says the law enforcement department will also issue an access blocking order to offenders. The law enforcement department may direct the NCC to order the internet access service provider to disable access by users in Nigeria to the online location and the NCC must give the internet access service provider an access blocking order. An internet access service provider that does not comply with any access blocking order is liable on conviction to a fine not exceeding ten million naira for each day during any part of which that order is not fully complied with, up to a total of five million naira.
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Robot learning

Robot learning is a research field at the intersection of machine learning and robotics. It studies techniques allowing a robot to acquire novel skills or adapt to its environment through learning algorithms. The embodiment of the robot, situated in a physical embedding, provides at the same time specific difficulties (e.g. high-dimensionality, real time constraints for collecting data and learning) and opportunities for guiding the learning process (e.g. sensorimotor synergies, motor primitives). Example of skills that are targeted by learning algorithms include sensorimotor skills such as locomotion, grasping, active object categorization, as well as interactive skills such as joint manipulation of an object with a human peer, and linguistic skills such as the grounded and situated meaning of human language. Learning can happen either through autonomous self-exploration or through guidance from a human teacher, like for example in robot learning by imitation. Robot learning can be closely related to adaptive control, reinforcement learning as well as developmental robotics which considers the problem of autonomous lifelong acquisition of repertoires of skills. While machine learning is frequently used by computer vision algorithms employed in the context of robotics, these applications are usually not referred to as "robot learning". == Imitation learning == Many research groups are developing techniques where robots learn by imitating. This includes various techniques for learning from demonstration (sometimes also referred to as "programming by demonstration") and observational learning. == Sharing learned skills and knowledge == In Tellex's "Million Object Challenge", the goal is robots that learn how to spot and handle simple items and upload their data to the cloud to allow other robots to analyze and use the information. RoboBrain is a knowledge engine for robots which can be freely accessed by any device wishing to carry out a task. The database gathers new information about tasks as robots perform them, by searching the Internet, interpreting natural language text, images, and videos, object recognition as well as interaction. The project is led by Ashutosh Saxena at Stanford University. RoboEarth is a project that has been described as a "World Wide Web for robots" − it is a network and database repository where robots can share information and learn from each other and a cloud for outsourcing heavy computation tasks. The project brings together researchers from five major universities in Germany, the Netherlands and Spain and is backed by the European Union. Google Research, DeepMind, and Google X have decided to allow their robots share their experiences. == Vision-language-action model == Research groups and companies are developing vision-language-action models, foundation models that allow robotic control through the combination of vision and language. Google DeepMind, Figure AI and Hugging Face are actively working on that.
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VibeOS

VibeOS is an operating system built from scratch entirely by generative artificial intelligence, using code produced through prompts to Claude (vibe coding). It is capable of running on QEMU and was successfully tested on a Raspberry Pi Zero. It has been released under the MIT license. == Features == === Core === Custom kernel with cooperative multitasking (preemptive backup) FAT32 filesystem with long filename support Memory allocator, process scheduler, interrupt handling GIC-400 (QEMU) and BCM2836/BCM2835 (Pi) interrupt controllers Configurable boot (splash screen, boot target) === GUI === Desktop environment with draggable windows Menu bar, dock, window minimize/maximize/close Mouse and keyboard input Modern macOS-inspired aesthetic === Networking === Full TCP/IP stack (Ethernet, ARP, IP, ICMP, UDP, TCP) DNS resolver HTTP client TLS 1.2 with HTTPS support === Apps === Web browser with HTML/CSS rendering Terminal emulator with readline-style shell Text editor (vim clone) with syntax highlighting File manager with drag-and-drop Music player (MP3/WAV) Calculator, system monitor VibeCode IDE Doom port === Development === TCC (Tiny C Compiler) - compile C programs directly on VibeOS MicroPython interpreter with full kernel API bindings 60+ userspace programs (coreutils, games, GUI apps) === Hardware === Runs on Raspberry Pi Zero 2W USB keyboard and mouse via DWC2 driver SD card via EMMC driver 1920×1080 framebuffer == Further projects == There are other independent projects under the VibeOS name, including an independent development by Ben, also developed using vibe coding, aimed at creating a Unix-like operating system for educational purposes. Another project is Vib-OS, an operating system also built using vibe coding, capable of booting on a Raspberry Pi. It offers a desktop environment with a customizable wallpaper, a file manager, and a web browser currently in an early stage of development, a functional Doom port, among other features that are not very polished given the state of development.
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Blend4Web

Blend4Web is a free and open source framework for creating and displaying interactive 3D computer graphics in web browsers. == Overview == The Blend4Web framework leverages Blender to edit 3D scenes. Content rendering relies on WebGL, Web Audio, WebVR, and other web standards, without the use of plug-ins. It is dual-licensed. The framework is distributed under the free and open source GPLv3 and, a non-free license - with the source code being hosted on GitHub. A 3D scene can be prepared in Blender and then exported as a pair of JSON and binary files to load in a web application. It can also be exported as a single, self-contained HTML file, in which exported data, the web player GUI, and the engine itself are packed. The HTML option is considered to be the simplest way. The resulting file, which has a minimum size of 1 MB, can be embedded in a web page using a standard iframe HTML element. Blend4Web-powered web applications can be deployed on social networking websites such as Facebook. The Blend4Web toolchain consists of JavaScript libraries, the Blender add-on, and a set of tools for tweaking 3D scene parameters, debugging, and optimization. Developed by Moscow-based company Triumph in 2010, Blend4Web was publicly released on March 28, 2014. At the end of 2017, the project founders Yuri and Alex Kovelenov quit Triumph to start the development of a new WebGL framework Verge3D. In October 2019, an "Absolutely new Blend4Web" was announced, planned to make developing 3D apps easier and to add a new marketplace where people can offer their 3D models. == Features == The framework has a number of components typically found in game engines, including a positional audio system, physics engine (a fork of Bullet ported to JavaScript), animation system, and an abstraction layer for game logic programming. Up to 8 different types of animations can be assigned to a single object, including skeletal and per-vertex animation. The speed and the direction of animation (forward/backward play), as well as particle system parameters (size, initial velocity, and count), can be changed through the API. Among other supported features are: scene data dynamic loading and unloading, subsurface scattering simulation, and image-based lighting. Some out-of-box options exist for rendering extended outdoor environments, including foliage-wind interaction, water, atmosphere, and sunlight simulation. One example demonstrating these effects is "The Farm" tech demo, which also features multiple animated NPCs and the ability to walk, interact with objects and drive a vehicle in first-person mode. Being based on the cross-browser WebGL API, Blend4Web runs in the majority of web browsers, including mobile ones. There are some caveats for browsers with experimental WebGL support, such as Internet Explorer. There are also applications developed to run on Tizen-powered devices such as the Samsung Gear S2 smartwatch. Other features include: draw call batching, hidden surface determination, threaded physics simulation and ocean simulation. In version 14.09, Blend4Web introduced the possibility of adding interactivity to 3D scenes using a visual programming tool. The tool is reminiscent of the BGE's logic editor as it uses logic blocks that are placed inside Blender. It plays back animation tracks authored by an artist when the user interacts with predefined 3D objects. Since version 15.03, Blend4Web has supported attaching HTML elements (such as information windows) to 3D objects ("annotations") and copying objects in run time ("instancing"). The following post-processing effects are supported: glow, bloom, depth of field, crepuscular rays, motion blur, and screen space ambient occlusion. == Virtual reality and augmented reality == Virtual reality devices have been supported since the end of 2015. Specifically, Oculus Rift head-mounted display works over experimental WebVR API. The software also now includes preliminary support for gamepads, based on the Gamepad API. In 2017, the option to author augmented reality content was added. The system is based on the open-source tracking library ARToolKit and uses the WebRTC protocols. Starting from version 17.08, finger tracking is supported through the Leap Motion device. == Blender integration == The Blender add-on is written in Python and C and can be compiled for the Linux x86/x64, OS X x64, and MS Windows x86/x64 platforms. A Blend4Web-specific profile can be activated in the add-on settings. When switching to this profile, the Blender interface changes so that it only reveals settings relevant to Blend4Web. Blend4Web supports a set of Blender-specific features such as the node material editor (a tool for visual shader programming) and the particle system. There is basic support for Blender's non-linear animation (NLA) editor for creating simple scenarios. Blend4Web is based on Blender's real-time GLSL rendering engine, which users are recommended to use in order to enable WYSIWYG editing. == Notable uses == NASA developed an interactive web application called Experience Curiosity to celebrate the 3rd anniversary of the Curiosity rover landing on Mars. This Blend4Web-based app makes it possible to operate the rover, control its cameras and the robotic arm, and reproduce some of the prominent events of the Mars Science Laboratory mission. The application got presented at the beginning of the WebGL section at SIGGRAPH 2015. Experience Curiosity was ported to Verge3D for Blender in 2018 with several performance improvements and bug fixes. A General Motors authorized dealer in the United Arab Emirates has placed a functional Chevrolet Camaro 3D configurator on its website. Greenpeace created interactive 3D infographics to back Greenpeace's Detox campaign in Russia. Tallink featured an interactive 3D presentation of its MS Megastar vessel to allow visitors to browse details of the ship.
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Blocking of Twitter in Nigeria

Twitter was blocked in Nigeria from 5 June 2021 to 13 January 2022. The government imposed a ban on the social network after it deleted tweets made by, and temporarily suspended, the Nigerian president Muhammadu Buhari, warning the southeastern people of Nigeria, predominantly Igbo people, of a potential repeat of the 1967 Nigerian Civil War due to the ongoing insurgency in Southeastern Nigeria. The Nigerian government claimed that the deletion of the president's tweets factored into their decision, but it was ultimately based on "a litany of problems with the social media platform in Nigeria, where misinformation and fake news spread through it have had real world violent consequences", citing the persistent use of the platform for activities that are capable of undermining Nigeria's corporate existence. In January 2022, Nigeria lifted its blocking of Twitter after the platform agreed to establish a legal entity within the country sometime in the first quarter of 2022. == Background == On 1 June 2021, Nigerian President Muhammadu Buhari posted a tweet threatening a crackdown on regional separatists "in the language they understand". The next day, Twitter deleted the tweet, claiming it was in violation of Twitter rules, but gave no further details. Nigeria's Information Minister Lai Mohammed said that Twitter's actions were part of an unfair double standard, as Twitter had not banned incitement tweets from other groups. During the Nigerian Civil War a majority of deaths resulted from the blockade of Biafra which caused the deaths of millions of civilians from starvation, a fact that was not alluded to in the tweet. The Nigerian government has long held concerns over the use of Twitter in the country. The ongoing local End SARS protest began on Twitter and got amplified in 2020 when it had 48 million tweets in ten days. Buhari's government floated the idea of social media regulation on different occasions prior to banning Twitter. Attempts to pass an anti-social media bill in the past have failed majorly due to massive outcry on Twitter. Days before the ban, the country's minister of information called Twitter's activities in Nigeria suspicious, citing its influence on the End SARS protests. == Aftermath == Three days after Twitter was suspended, it was reported that the move had cost the country over 6 billion naira and would also contribute to the worsening unemployment in the country. ExpressVPN reported an over 200 percent increase in web traffic and searches for VPN spiked across the country. In response, Nigeria's Minister of Justice and Attorney General of the Federation Abubakar Malami at first openly threatened to prosecute citizens who bypass the ban using a VPN but then denied saying so after a screenshot of a Twitter deactivation notification he shared on Facebook showed a VPN logo. Nigeria's cultural minister Lai Mohammed stated the ban would be lifted once Twitter submitted to locally licensing, registration and conditions. "It will be licensed by the broadcasting commission, and must agree not to allow its platform to be used by those who are promoting activities that are inimical to the corporate existence of Nigeria." In late June 2021, Twitter announced it would enter talks with the Nigerian government over the platform's suspension. The talks began in July 2021. On 15 September 2021, Mohammed said the Nigerian government will lift the ban on Twitter in a "few days." The Minister said Twitter gave a progress report of their talks with them, adding that it has been productive and quite respectful. On 1 October 2021, President Muhammadu Buhari in his Independence Day broadcast said Twitter must meet the Nigerian government's five conditions before the suspension of the social media platform will be lifted. The conditions are: Respect for national security and cohesion; registration, physical presence and representation in Nigeria; fair taxation; dispute resolution; local content. == Reactions == The ban was condemned by Amnesty International, the British, Canadian and Swedish diplomatic missions to Nigeria, as well as the United States and the European Union in a joint statement. Two domestic organizations, the Socio-Economic Rights and Accountability Project (SERAP) and the Nigerian Bar Association, indicated intent to challenge the ban in court. Twitter itself called the ban "deeply concerning". Former U.S. President Donald Trump, who was permanently suspended from Twitter following the United States Capitol attack in January, praised the ban, stating "Congratulations to the country of Nigeria, who just banned Twitter because they banned their President", and also called on other countries to ban Twitter and Facebook due to "not allowing free and open speech." == Lifting of the ban == On 12 January 2022, the Nigerian Government lifted the ban after Twitter agreed to pay an "applicable tax" and establish "a legal entity in Nigeria during the first quarter of 2022".
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Vismon

Vismon was the Bell Labs system which displayed authors' faces on one of their internal e-mail systems. The name was a pun on the sysmon program used at Bell to show the load on computer systems. It can also be interpreted as "visual monitor". The system inspired Rich Burridge to develop the similar but more widespread faces system, which spread with Unix distributions in the 1980s. This in turn inspired Steve Kinzler to develop the Picons, or personal icons, which have the goal of offering symbols and other images, as well as faces, to represent individuals and institutions in email messages. Other systems such as the faces available on the LAN email functions of the NeXTSTEP platform also seem to have been influenced by the original Vismon capabilities. The faces program in Plan 9 is the direct descendant of this system. Vismon was the work of Rob Pike and Dave Presotto. It was based on some early experiments by Luca Cardelli. Many other scientists and engineers of the Computing Science Research Center of the Murray Hill facility were also involved. All had been spurred by the introduction in 1983 of the new Blit graphics terminal developed by Pike and Bart Locanthi and marketed by Teletype Corporation of Skokie, Illinois as the DMD 5620. Pike was eager, along with his colleagues, to exploit the new graphic capabilities. Pike and company went around their Center, convincing everybody, from directors and administrative assistants to engineers and scientists, to pose as they got out a 4×5 view camera with a Polaroid back and took black-and-white photos (Polaroid type 52) of their faces. Their efforts yielded nearly 100 faces, which they digitised with a scanner from graphics colleagues. They wrote several programs to transform the faces, store them and serve them on several machines at the lab. As time went by, they added faces from outside their Center and outside Bell Labs. This database also led to the pico image editor (originally named zunk) which was used for image transformations, many of them with colleagues as the preferred target. The first programs built around vismon were used to announce incoming mail in a dedicated window, using the 48 by 48 pixel faces. Later on the faces were also used to decorate line printer banners.
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Boba liberal

Boba liberal is a term mostly used within the Asian diaspora communities in the West, especially in the United States. It describes someone of East or Southeast Asian descent living in the West who has a shallow, surface-level liberal outlook. It is also occasionally used to describe conservatives who weaponize their East or Southeast Asian identity. The neologism emerged among the Asian American leftist community on Twitter who accused "boba liberals" of only holding their liberal beliefs to appear more white-adjacent by engaging in progressive social movements or viewpoints, while at the same time disregarding and trivializing issues concerning Asians. Mary Chao, writing for The North Jersey Record, said that "Asians call peers boba liberals when they aspire to liberal whiteness." An article in The Yale Herald described it as a term "used to describe the ethnocentric politics of Asian Americans, usually of East Asian descent, who exclusively advocate for issues that benefit themselves, without acknowledging problematic dimensions of their own history and working to support other people of color." The feminist magazine Fem said that "the faces of boba liberalism are Asian Americans that are part of the middle and upper economic class. As a result, boba liberals disregard the negative effects of capitalism because they profit from it. For instance, boba liberals tend to focus on advocating for Asian representation in white spaces, or discussing whether or not wearing chopsticks in one's hair is culture appropriation. These topics are popular within boba liberal circles, all while dialogue regarding inequality, globalization, and racial injustice are purposely neglected." UnHerd notes that conservative Asian Americans have used the term not to critique capitalism, but to "aim at a small but influential group of progressive Asian-American activists who are supposedly selling out other Asians, especially working-class Asians, in order to win brownie points from elite, generally white liberals." MRAsians have similarly used the term to attack Asian American feminists who supported the Black Lives Matter movement. The Asian identity of boba liberals has often been accused of being shallow and superficial. Boba liberals are accused of using surface-level stereotypical Asian traits such as liking boba tea to bolster their Asian credentials. Plan A Magazine, an Asian diaspora magazine, described the film Crazy Rich Asians and the sitcom Fresh Off the Boat as "boba liberal media", calling them the result of "a specific kind of atomized identity politics". Other media outlets have connected the Crazy Rich Asians film to boba liberalism. == Controversy == The term "boba liberal" was coined in 2019 by Vietnamese American Twitter user Redmond (@diaspora_is_red) to analyze a form of Asian American liberalism through a Marxist lens. Redmond has criticized the misappropriation of their neologism by stripping away the Marxist framework by failing to discuss "socialism, communism, the capitalist system, imperialism, and the diaspora bourgeoisie" and conflating "boba liberalism" with the flawed concept of "East Asian privilege". In 2024, Redmond criticized misuse of the term by conservatives and liberals, and said "The term boba liberalism can go away for all I care. It's corny and stale". === United States === One commentator described boba liberals as supporting policies that primarily benefit upper-income Asian-Americans, and not necessarily the Asian-American community as a whole. Therefore, while the word "liberal" is used in the term, it is not mutually exclusive to one specific ideology, as it may also extend to conservative-aligned Asians in some areas, as they would often take advantage of the "model minority" label by defending such measures.
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Content reference identifier

A content reference identifier or CRID is a concept from the standardization work done by the TV-Anytime forum. It is or closely matches the concept of the Uniform Resource Locator, or URL, as used on the World-Wide Web: A unit of content, in a broadcast stream, can be referred to by its globally unique CRID in the same way that a webpage can be referred to by its globally unique URL on the web. The concept of CRID permits referencing contents unambiguously, regardless of their location, i.e., without knowing specific broadcast information (time, date and channel) or how to obtain them through a network, for instance, by means of a streaming service or by downloading a file from an Internet server. The receiver must be capable of resolving these unambiguous references, i.e. of translating them into specific data that will allow it to obtain the location of that content in order to acquire it. This makes it possible for recording processes to take place without knowing that information, and even without knowing beforehand the duration of the content to be recorded: a complete series by a simple click, a program that has not been scheduled yet, a set of programs grouped by a specific criterion... This framework allows for the separation between the reference to a given content (the CRID) and the necessary information to acquire it, which is called a “locator”. Each CRID may lead to one or more locators which will represent different copies of the same content. They may be identical copies broadcast in different channels or dates, or cost different prices. They may also be distinct copies with different technical parameters such as format or quality. It may also be the case that the resolution process of a CRID provides another CRID as a result (for example, its reference in a different network, where it has an alternative identifier assigned by a different operator) or a set of CRIDs (for instance, if the original CRID represents a TV series, in which case the resolution process would result in the list of CRIDs representing each episode). From the above it can be concluded that provided that a given content can belong to many groups (each possibly defined by distinctive qualities), it is possible that many CRIDs carry the same content. That is, several CRIDs may be resolved into the same locator. A CRID is not exactly a universal, unique and exclusive identifier for a given content. It is closely related to the authority that creates it, to the resolution service provider, and to the content provider in such a way that the same content may have different CRIDs depending on the field in which they are used (for example, a different one for each television operator that has the rights to broadcast the content). == Format == A CRID is specified much like URLs. In fact, a CRID is a so-called URI. Typically, the content creator, the broadcaster or a third party will use their DNS-names in a combination with a product-specific name to create globally unique CRIDs. That is, the syntax of a CRID is: crid://authority/data The authority field represents the entity that created the CRID and its format is that of a DNS name. The data field represents a string of characters that will unambiguously identify the content within the authority scope (it is a string of characters assigned by the authority itself). As an example, let's assume that BBC wanted to make a CRID for (all the programs of) the Olympics in China. It may have looked something like this crid://bbc.co.uk/olympics/2008/ This would be a group CRID, that is, a CRID representing a group of contents. Then, to refer to a specific event – such as the women's shot-put final – they could have used the following inside their metadata. crid://bbc.co.uk/olympics/2008/final/shotput/women Currently, four types of CRIDs are playing a major role in some unidirectional television networks: programme CRID, series CRID, group CRID, and recommendation CRID. One of the most important applications of CRIDs is the so-called series link recording function (SL) of modern digital video recorders (DVR, PVR). In turn, a locator is a string of characters that contains all the necessary information for a receiver to find and acquire a given content, whether it is received through a transport stream, located in local storage, downloaded as a file from an Internet server, or through a streaming service. For example, a DVB locator will include all the necessary parameters to identify a specific content within a transport stream: network, transport stream, service, table and/or event identifiers. The locators' format, as established in TV-Anytime, is quite generic and simple, and corresponds to: [transport-mechanism]:[specific-data] The first part of the locator's format (the transport mechanism) must be a string of characters that is unique for each mechanism (transport stream, local file, HTTP Internet access...). The second part must be unambiguous only within the scope of a given transport mechanism and will be standardized by the organism in charge of the regulation of the mechanism itself. For instance, a DVB locator to identify a content within the transport stream of networks that follow this standard would be: dvb://112.4a2.5ec;2d22~20121212T220000Z—PT01H30M which would indicate a content (identified by the string “2d22”) that airs on a channel available on a DVB network identified by the address “112.4a2.5ec” (network “112”, transport stream “4a2” and service “5ec”), on 12 December 2012 at 10 p.m. and with a duration of 90 minutes. == The location resolution process == The location resolution process is the procedure by which, starting from the CRID of a given content, one or several locators of that content are obtained. Resolving a CRID can be a direct process, which leads immediately to one or many locators, or it may also happen that in the first place one or many intermediate CRIDs are returned, which must undergo the same procedure to finally obtain one or several locators. This procedure involves some information elements, among which we find two structures named resolving authority record (RAR) and ContentReferencingTable, respectively. Consulting them repeatedly will take the receiver from a CRID to one or many locators that will allow it to acquire the content. The RAR table The RAR table is one or many data structures that provide the receiver, for each authority that submits CRIDs, information on the corresponding resolution service provider. Among other things, it informs about which mechanism is used to provide information to resolve the CRIDs from each authority. That is, one or many RAR records must exist for each authority that indicate the receiver where it has to go to resolve the CRIDs of that particular authority. For example, in the record of the figure (expressed by means of a XML structure, according to the XML Schema defined in the TV-Anytime) there is an authority called “tve.es”, whose resolution service provider is the entity “rtve.es”, available on the URL "http://tva.rtve.es/locres/tve", which means there is resolution information in that URL. These RAR records will have reached the receiver in an indefinite form, unimportant for the TV-Anytime specification, which will depend on the specific transport mechanism of the network to which the receiver is connected. Each family of standards that regulates distribution networks (DVB, ATSC, ISDB, IPTV...) will have previously defined such procedure, which will be used by devices certified according to those standards. The ContentReferencingTable table The second structure involved in the location resolution process is a proper resolution table which, given a content's CRID, returns one or several locators that enable the receiver to access an instance of that content, or one or many CRIDs that allow it to move forward in the resolution process. The figure shows an example of this second structure, an XML document according to the specifications of the XML Schema defined in TV-Anytime. In it, several sections are included ( elements) that structure the information that describes each resolution case. The first one declares how a CRID (crid://tv.com/Friends/all), which corresponds to a group content that encompasses several episodes (two) of the “Friends” series is resolved. The result of the resolution process provides two new CRIDs each of them corresponding to one of the two episodes. The second element resolves the CRID of the first episode of the first season. The result of the resolution process is two DVB locators. The “acquire” attribute with “any” value indicates that any of them are good (the second one is a repetition broadcast a week later). The third element gives information about the second episode. It indicates that it cannot be resolved yet (“status” attribute with the “cannot yet resolve” value), indicating a date on which the request for resolution information must be repeated. The pro
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General-Purpose Serial Interface

General-Purpose Serial Interface, also known as GPSI, 7-wire interface, or 7WS, is a 7 wire communications interface. It is used as an interface between Ethernet MAC and PHY blocks. Data is received and transmitted using separate data paths (TXD, RXD) and separate data clocks (TXCLK, RXCLK). Other signals consist of transmit enable (TXEN), receive carrier sense (CRS), and collision (COL).
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Google Clips

Google Clips is a discontinued miniature clip-on camera device developed by Google. == History == It was announced on October 4, 2017 and went on sale on January 27, 2018. Google Clips automatically captured video clips (without audio) at moments its machine learning algorithms determined to be interesting or relevant. An indicator flashed when the camera was looking for scenes to capture. Google Clips' artificial intelligence (AI) could learn the faces of people to take photographs with certain people, and could automatically set lighting and framing. It had 16 GB of storage built-in storage and could record clips for up to 3 hours. This camera was originally priced at US$249 in the United States. It was withdrawn from sale on October 15, 2019, but supported until the end of December 2021. == Reception == The Independent wrote that Google Clips is "an impressive little device, but one that also has the potential to feel very creepy." According to The Verge's generally negative review, "it didn't capture anything special" over two weeks of testing.
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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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Hardware security

Hardware security is a discipline originated from the cryptographic engineering and involves hardware design, access control, secure multi-party computation, secure key storage, ensuring code authenticity, measures to ensure that the supply chain that built the product is secure among other things. A hardware security module (HSM) is a physical computing device that safeguards and manages digital keys for strong authentication and provides cryptoprocessing. These modules traditionally come in the form of a plug-in card or an external device that attaches directly to a computer or network server. Some providers in this discipline consider that the key difference between hardware security and software security is that hardware security is implemented using "non-Turing-machine" logic (raw combinatorial logic or simple state machines). One approach, referred to as "hardsec", uses FPGAs to implement non-Turing-machine security controls as a way of combining the security of hardware with the flexibility of software. Hardware backdoors are backdoors in hardware. Conceptionally related, a hardware Trojan (HT) is a malicious modification of electronic system, particularly in the context of integrated circuit. A physical unclonable function (PUF) is a physical entity that is embodied in a physical structure and is easy to evaluate but hard to predict. Further, an individual PUF device must be easy to make but practically impossible to duplicate, even given the exact manufacturing process that produced it. In this respect it is the hardware analog of a one-way function. The name "physical unclonable function" might be a little misleading as some PUFs are clonable, and most PUFs are noisy and therefore do not achieve the requirements for a function. Today, PUFs are usually implemented in integrated circuits and are typically used in applications with high security requirements. Many attacks on sensitive data and resources reported by organizations occur from within the organization itself.
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