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EPUAP

ePUAP (Electronic Platform of Public Administration Services) is a Polish nationwide platform for communication of citizens with public administrations in a uniform and standardized way. Built as part of the ePUAP-WKP project (State Informatization Plan). Service providers are public administration units and public institutions (especially entities that perform tasks commissioned by the state). The platform provides service providers with technological infrastructure to provide services to citizens (recipients). Among the participants of ePUAP there are both central administration units and local governments, including municipal offices. Among the services offered by ePUAP is also Profil Zaufany (Trusted Profile), which enables electronic filing with legal effect without the need to use a qualified signature and SAML-based single sign-on mechanism, which enables the same ePUAP account to log on to websites of various service providers. The website www.epuap.gov.pl enables defining citizen and businesses service processes, creates channels of access to different systems of public administration and extends the package of public services provided electronically. Services available through the ePUAP platform may be accessed at the official website. Currently all administration services are available in Polish only. == Overview == It is described by the Polish government as "a coherent and systematic action program designed and developed to allow public institutions make their electronic services available to the public". The platform provides citizens, businesses and institutions with a number of services intended to ensure smooth and safe communication between: customer to administrations (C2A), business to administration (B2A), administration to administration (A2A). === Main goals === The main project objectives are to create a single, secure and electronic access channel to public services for citizens, businesses and public administration and also to reduce time and lower the costs of sharing information resources and functionalities of administration domain systems. Within the project, the following functionalities and services were delivered: Public services catalogue – a method of presenting and describing administration services, ePUAP platform – a web platform designed to provide public services on the Internet, Interoperability portal – a portal for experts working on recommendations for electronic documents and forms used within Polish administration systems to assure the uniformity of IT standards, Central Repository of Electronic Document Models – a database for valid document models and electronic forms. == History and background == The ePUAP project was carried out in the years 2005–2008. Currently, a continuation project ePUAP2 is being carried out with the following objectives: to increase the number of online services available to the public including the registry services, to widen the scale of usage of public electronic services, to integrate subsequent systems of public administration and business on ePUAP portal, to define new processes of customer and business services. === ePUAP2 === ePUAP2 is a public and administrative project that extends the set of functional services developed during the first edition of the project and is another step in the process of transforming Poland into a modern and citizen-friendly country. The implementation period for the project covers the years 2009–2013. Project financing The cost of the project “Construction of electronic Platform of Public Administration Services” – 32 million PLN was covered in 75% by the funds from the European Regional Development Fund (under the Sector Operational Programme "Supporting Competitiveness of Enterprises for the years 2004–2006"), while the remaining 25% of the cost was covered by a Polish national co-financing. Funds for the ePUAP2 project were gained from the 7th priority axis of the Innovative Economy Operational Programme and amounts to 140 million PLN (85% of eligible expenses were covered by the European Regional Development Fund, 15% were covered by a national co-financing). The trustee of ePUAP is the Polish Ministry of the Interior and Administration. == Legal regulations == According to the Polish law from 1 May 2008, public authorities are required to accept documents in electronic form (bringing applications and proposals and other activities in electronic form). ePUAP enables public institutions to meet this requirement by providing a service infrastructure to set up am electronic inbox. The ePUAP inbox meets legal requirements, in particular: issuing an official confirmation of receipt in accordance with the regulation of the Prime Minister of 29 September 2005 on the organizational and technical conditions for the delivery of electronic documents to public entities; cooperation with hardware security modules (HSM), meeting the technical requirements set out in the law; handling documents electronically in accordance with the minimum requirements set out in the Regulation of the Polish Council of Ministers of 11 October 2005 on minimum requirements for ICT systems. == Incidents == === Crashes === The ePUAP system very often happens smaller or larger failures. Because it is used to sign the application profiles trusted also in other electronic systems such as public administration. Electronic Services Platform created by ZUS, the system fault ePUAP it very difficult to settle official matters most electronically. === "Infoafera" === According to TVN and the release of TVP News from 10 April 2014, the creation of ePUAP is also associated with the so-called "Infoafera." On 10 April 2014, the Minister of Internal Affairs of Poland confirmed the information that the American technology company HP confessed to its participation in the Polish info-tour and corruption of Polish officials. By March 2014, the construction of ePUAP and its maintenance cost PLN 98.4 million. PLN 67.8 million has been used for this project. Challenged expenses only on the portal itself is approx. PLN 20 million.
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Supersampling

Supersampling or supersampling anti-aliasing (SSAA) is a spatial anti-aliasing method, i.e. a method used to remove aliasing (jagged and pixelated edges, colloquially known as "jaggies") from images rendered in computer games or other computer programs that generate imagery. Aliasing occurs because unlike real-world objects, which have continuous smooth curves and lines, a computer screen shows the viewer a large number of small squares. These pixels all have the same size, and each one has a single color. A line can only be shown as a collection of pixels, and therefore appears jagged unless it is perfectly horizontal or vertical. The aim of supersampling is to reduce this effect. Color samples are taken at several instances inside the pixel (not just at the center as normal)—hence the term "supersampling"—and an average color value is calculated. This can for example be achieved by rendering the image at a much higher resolution than the one being displayed, then shrinking it to the desired size, using the extra pixels for calculation, with the result being a downsampled image with smoother transitions from one line of pixels to another along the edges of objects, but each pixel could also be supersampled using other strategies (see the Supersampling patterns section). The number of samples determines the quality of the output. == Motivation == Aliasing is manifested in the case of 2D images as moiré pattern and pixelated edges, colloquially known as "jaggies". Common signal processing and image processing knowledge suggests that to achieve perfect elimination of aliasing, proper spatial sampling at the Nyquist rate (or higher) after applying a 2D Anti-aliasing filter is required. As this approach would require a forward and inverse fourier transformation, computationally less demanding approximations like supersampling were developed to avoid domain switches by staying in the spatial domain ("image domain"). == Method == === Computational cost and adaptive supersampling === Supersampling is computationally expensive because it requires much greater video card memory and memory bandwidth, since the amount of buffer used is several times larger. A way around this problem is to use a technique known as adaptive supersampling, where only pixels at the edges of objects are supersampled. Initially only a few samples are taken within each pixel. If these values are very similar, only these samples are used to determine the color. If not, more are used. The result of this method is that a higher number of samples are calculated only where necessary, thus improving performance. === Supersampling patterns === When taking samples within a pixel, the sample positions have to be determined in some way. Although the number of ways in which this can be done is infinite, there are a few ways which are commonly used. ==== Grid ==== The simplest algorithm. The pixel is split into several sub-pixels, and a sample is taken from the center of each. It is fast and easy to implement. Although, due to the regular nature of sampling, aliasing can still occur if a low number of sub-pixels is used. ==== Random ==== Also known as stochastic sampling, it avoids the regularity of grid supersampling. However, due to the irregularity of the pattern, samples end up being unnecessary in some areas of the pixel and lacking in others. ==== Poisson disk ==== The Poisson disk sampling algorithm places the samples randomly, but then checks that any two are not too close. The end result is an even but random distribution of samples. The naive "dart throwing" algorithm is extremely slow for large data sets, which once limited its applications for real-time rendering. However, many fast algorithms now exist to generate Poisson disk noise, even those with variable density. The Delone set provides a mathematical description of such sampling. ==== Jittered ==== A modification of the grid algorithm to approximate the Poisson disk. A pixel is split into several sub-pixels, but a sample is not taken from the center of each, but from a random point within the sub-pixel. Congregation can still occur, but to a lesser degree. ==== Rotated grid ==== A 2×2 grid layout is used but the sample pattern is rotated to avoid samples aligning on the horizontal or vertical axis, greatly improving antialiasing quality for the most commonly encountered cases. For an optimal pattern, the rotation angle is arctan (⁠1/2⁠) (about 26.6°) and the square is stretched by a factor of ⁠√5/2⁠, making it also a 4-queens solution.
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CodeCheck

CodeCheck is a mobile app that provides consumers with information about the ingredients in cosmetic products, as well as the ingredients and nutritional values of food. Users can access this information by scanning the product’s barcode with a smartphone or by using a text-based search. The app is available for iOS and Android devices in Germany, Austria, Switzerland, the United Kingdom, the United States, and the Netherlands. == History == CodeCheck was founded in 2010 as an association, online database, and app by Roman Bleichenbacher, who was then a student in Zurich. A website of the same name had already been launched in 2002, where users could enter information about ingredients, nutritional values, and manufacturers of products. The first round of financing took place in July 2014 and raised over 1.1 million Swiss francs, which coincided with the founding of CodeCheck AG. Investors included Doodle founders Myke Näf and Paul E. Sevinç. The company subsequently expanded to Austria and Germany. In the same year, Boris Manhart became CEO. CodeCheck GmbH was established in Berlin in 2016. The app became available in the United States in 2017 and in the United Kingdom in November 2019. In 2020, it was also launched in the Netherlands. Following insolvency proceedings, the app has been owned by Producto Check GmbH since 2022. == Functions == The app can be used to scan the barcode of food and cosmetic products. It then displays information about ingredients, nutritional values, manufacturers and certification labels. For many years, users were able to enter and edit product information themselves and indicate advantages and disadvantages of individual products. Since 2020, the app has placed greater emphasis on machine text recognition. The collected data is combined with substance ratings using an algorithm. These ratings are based on scientific studies and expert assessments, including those from the Consumer Advice Centre in Hamburg, Greenpeace, the WWF and the German Association for the Environment and Nature Conservation (BUND e. V.), and cannot be modified by users or manufacturers. The app also provides information on the sugar and fat content of food products. In addition, it indicates whether a product contains hormone-active substances, microplastics, palm oil, animal-derived ingredients, lactose or gluten. Since 2020, the app has displayed a climate score for food products in cooperation with the Eaternity Institute. == Financing == CodeCheck is primarily financed through native advertising and banner ads. Since 2018, the company has also offered analysis services and survey tools directly to fast-moving consumer goods (FMCG) manufacturers. In addition, access to the API is available, enabling other companies to use the product database. With the introduction of a subscription model in 2019, the CodeCheck app can be used ad-free and in offline mode. Since 2021, CodeCheck has also offered its own “Green Label” certification for manufacturers. Products are certified if at least 90 percent of their ingredients are classified as harmless. == Awards == In May 2015, the app topped the download charts for the first time, reaching 2.3 million installations. By September 2019, the app had once again reached the top of the German app charts, surpassing five million downloads.
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Xara Designer Pro+

Xara Designer Pro+ is an image editing program incorporating photo editing and vector illustration tools created by British software company Xara. Xara Xtreme LX was an early open source version for Linux. The Windows version was previously sold under the names Xara Studio, Xara X and Xara Xtreme, and traces its origin in the late 1980s to a title called ArtWorks for the Acorn Archimedes line of computers using RISC OS. There is a pro version called Xara Designer Pro (formerly Xara Xtreme Pro). The current commercial version of Xara Photo & Graphic Designer runs only on Windows, although Xara documents can be edited in a web browser on any platform using the Xara Cloud service. Versions up to 4.x can be run on Linux using Wine. == History == ArtWorks, the predecessor of Xara Photo and Graphic Designer, was developed on Acorn Archimedes and Risc PC 32-bit RISC computers running RISC OS by Computer Concepts during the late 1980s. The first version, developed for Microsoft Windows was initially called Xara Studio. It was licensed to Corel Corporation before wide-scale public availability, and from 1995 to 2000 was released as CorelXARA. Corel ceded the licensing rights back to Xara in 2000. The first Xara X version released in 2000 by its original owner. The next version, Xara X¹, was released in 2004. Xara Xtreme was released in 2005. In November 2006, Xara Xtreme PRO (an enhanced version of Xara Xtreme) was released. Xara Xtreme 3.2 and Xtreme Pro 3.2 were released in May 2007. 3.2 Pro included Xara3D, and both versions had more robust typography. In April 2008, Xara Xtreme 4.0 was released. Xara Xtreme and Xara Xtreme Pro 5.1 were released in June 2009. Features included more text-area enhancements, content-aware scaling of bitmap images, improved file import and export, master-page (repeated) objects, an object gallery (replacing the layer gallery), website-creation tools, and multi-stage graduated transparency. In June 2010, Xara Photo & Graphic Designer 6 and Xara Designer Pro 6 were released. Xtreme was renamed Photo & Graphic Designer, and Xtreme Pro was renamed Designer Pro. In May 2011, Xara Photo & Graphic Designer 7 and Xara Designer Pro 7 were released. Features included "magic" photo erase, user interface improvements to docking galleries and snapping alignment, and (in Pro) new webpage and website-design features. In May 2012, Xara Photo & Graphic Designer 2013 and Xara Designer Pro X (v8) were released. Xara Photo & Graphic Designer 9 was released in May 2013. In July of that year, Xara Designer Pro X9 was released. Xara Photo & Graphic Designer 10 was released on 16 July 2014, and Xara Designer Pro X10 on 23 July. Xara Photo & Graphic Designer 11 was released on 29 June 2015, and Xara Designer Pro X11 was released the following month. In 2016, the delivery model was changed to an update service which can be renewed annually. Users are entitled to any updates released while the update service is active. The first update-service updates were in May 2016 for Xara Photo & Graphic Designer, and July 2016 for Xara Designer Pro X. == Features == Xara Photo & Graphic Designer is known for its usability and fast renderer. It provides a fully anti-aliased display, advanced gradient fill, and transparency tools. Among vector editors, Xara Photo & Graphic Designer is considered to be fairly easy to learn, with similarities to CorelDRAW and Inkscape in terms of interface. Alongside the vector illustration tools, Xara Photo & Graphic Designer also includes an integrated photo tool offering manual and automatic photo enhance, cropping, adjustment of brightness levels, red-eye fix, 'magic' erase, photo healing, color and background erase, panoramas and content aware resizing. Designer Pro includes a wider range of tools for other design tasks including the creation of web pages and websites, and text and page layout tools for DTP with the aim of providing a single solution for all graphic and web design tasks.
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Carrenza

Carrenza was a cloud-computing company based in London, United Kingdom. The company was acquired by Six Degrees Technology Group in 2016. == Operations == Carrenza was a UK-based IT company that provides Cloud computing technologies. It offered a range of public cloud, private cloud and hybrid cloud services, including Infrastructure as a Service (IaaS), Platform as a Service (PaaS), enterprise application integration and system integration. Carrenza partnered with several enterprise IT providers and was an accredited VMware Enterprise Service Partner and HP (Hewlett-Packard) Cloud Agile Partner. The company was based on Commercial Street, in the heart of the East London Tech City district, which is host to a large number of technology companies. == History == Carrenza was formed in 2001 as a consultancy by chief executive and founder Dan Sutherland. It began trading in 2004 and launched its first enterprise cloud computing platform in 2006, becoming one of the first companies in Europe to provide this type of hosting service. In 2009, it formed a partnership with Comic Relief and its affiliated campaigns Red Nose Day Sport Relief to provide IT infrastructure services to the charity, an arrangement that has won industry recognition. In 2013 it launched its first overseas services, with a mainland Europe cloud node based in Amsterdam. == Partnerships and customers == Carrenza had formed partnerships with a range of IT providers. It was one of the first companies in Europe to become a HP Cloud Agile partner., using HP blade servers and HP 3PAR SAN technology to power its cloud computing services. The company's products also use VMware vCloud IaaS tools and it is taking part in the VMware lighthouse initiative helping develop the next generation of VMware products and services. Other technology companies that Carrenza has worked closely with include Cisco, for enterprise security and loadblancing services, and Oracle. The company was the first to deploy Oracle Database 11g stretched RAC in production. It has also won two Oracle partner awards, including a Special Recognition award for its work with Comic Relief. The company has also been recognised by the UK IT Industry, receiving awards in 2009 for Community Project of the Year and in 2010 for best small business project for its Monopoly City Streets Work. Other companies that have partnered with Carrenza for their cloud-based IT services include Age UK, Haymarket Media Group, the World Wide Fund for Nature, Royal Bank of Scotland, eBay and Cineworld. == Accreditations == Carrenza's services are accredited for their compliance with several key international IT security and quality standards. These include: ISO27001:2005, Information Security Management System for all Carrenza services. UK Government G-Cloud, Carrenza has been awarded a place on the UK government's G-Cloud iii framework as an Infrastructure as a Service provider.
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Video browsing

Video browsing, also known as exploratory video search, is the interactive process of skimming through video content in order to satisfy some information need or to interactively check if the video content is relevant. While originally proposed to help users inspecting a single video through visual thumbnails, modern video browsing tools enable users to quickly find desired information in a video archive by iterative human–computer interaction through an exploratory search approach. Many of these tools presume a smart user that wants features to interactively inspect video content, as well as automatic content filtering features. For that purpose, several video interaction features are usually provided, such as sophisticated navigation in video or search by a content-based query. Video browsing tools often build on lower-level video content analysis, such as shot transition detection, keyframe extraction, semantic concept detection, and create a structured content overview of the video file or video archive. Furthermore, they usually provide sophisticated navigation features, such as advanced timelines, visual seeker bars or a list of selected thumbnails, as well as means for content querying. Examples of content queries are shot filtering through visual concepts (e.g., only shots showing cars), through some specific characteristics (e.g., color or motion filtering), through user-provided sketches (e.g., a visually drawn sketch), or through content-based similarity search. == History == Video browsing was originally proposed by Iranian engineer Farshid Arman, Taiwanese computer scientist Arding Hsu, and computer scientist Ming-Yee Chiu, while working at Siemens, and it was presented at the ACM International Conference in August 1993. They described a shot detection algorithm for compressed video that was originally encoded with discrete cosine transform (DCT) video coding standards such as JPEG, MPEG and H.26x. The basic idea was that, since the DCT coefficients are mathematically related to the spatial domain and represent the content of each frame, they can be used to detect the differences between video frames. In the algorithm, a subset of blocks in a frame and a subset of DCT coefficients for each block are used as motion vector representation for the frame. By operating on compressed DCT representations, the algorithm significantly reduces the computational requirements for decompression and enables effective video browsing. The algorithm represents separate shots of a video sequence by an r-frame, a thumbnail of the shot framed by a motion tracking region. A variation of this concept was later adopted for QBIC video content mosaics, where each r-frame is a salient still from the shot it represents. === Video Notebook === Modern video browsing solutions include Video Notebook, a Menlo Park startup founded in 2021 by Mike Lanza, which uses computer vision to extract slides and optical character recognition and speech recognition to facilitate video search. The software can be either used on the client side (using a browser extension), where the slides and text are extracted while the video is watched (e.g. on a video platform like YouTube or Udemy), or on the server side. Processed videos, which can be viewed in the Video Notebook web app, feature a video browsing user interface with extracted timestamped slides, a search bar for querying the video (or a collection of videos), and text chapters. Video Notebook customers include organisations like Ernst & Young. === Video Browser Showdown === The Video Browser Showdown (VBS) is an annual live evaluation competition for exploratory video search tools, where international researchers use video browsing tools to solve ad-hoc video search tasks on a moderately large data set as fast as possible. The main goal of the VBS, which started in 2012 at the International Conference on MultiMedia Modeling (MMM), is to advance the performance of video browsing tools. Since 2016, the VBS also collaborates with TRECVID. The aim of the VBS is to evaluate video browsing tools for efficiency at known-item search (KIS) tasks with a well-defined data set in direct comparison to other tools.
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Companion robot

A companion robot is a robot created to create real or apparent companionship for human beings. Target markets for companion robots include the elderly and single children. Companions robots are expected to communicate with non-experts in a natural and intuitive way. They offer a variety of functions, such as monitoring the home remotely, communicating with people, or waking people up in the morning. Their aim is to perform a wide array of tasks including educational functions, home security, diary duties, entertainment and message delivery services, etc. The idea of companionship with robots has already existed on science fictions of 1970s, like R2-D2. Starting from the late 20th century, companion robots became a reality, mostly as robotic pets. Besides entertainment purposes, interactive robots were also introduced as a personal service robot for elderly care around 2000. == Characteristics == Companion robots try to interact with users. They gather information about users based on their interactions and yield feedback. This procedure varies slightly based on their specific roles. For example, social-companion robots make simple conversations, while pet-companion robots mimic being real pets. == Types == Companion robots can perform a variety of tasks and they are produced in a specialized manner according to their purpose or target audience in order to increase convenience and end user satisfaction. === Social companion robots === Social companion robots are designed to provide companionship and be a solution for unwanted solitude. They often mimic adult human, child or pet behaviours appealing to the user base. Robots which are specifically devised for simple conversations, conveying emotions and respond to user feelings fall under this category. === Assistive companion robots === Assistive companion robots are aimed at people who require constant care because of age, disability or rehabilitation purposes. Such robots can help disadvantaged users with their daily tasks, act as reminders (e.g., for regular medication) and facilitate mobility in everyday actions. Assistive companion robots reduce the intensity of labour that should be performed by caretakers, nurses and legal guardians. === Educational companion robots === Educational companion robots perform tutorship for students, regardless of their ages, and can teach desired subjects with activities tailored for the user such as interactive assignments and games. Rather than replacing teachers and instructors, educational companion robots are aides to them. === Therapeutic companion robots === Designed for individuals coping with stress (PTSD in severe cases), anxiety and loneliness; therapeutic companion robots support users' emotional and mental wellbeing. Such robots can be utilized in hospitals and care facilities as well as dwellings where the distressed user may need the most help. Therapeutic companion robots bear a vast resemblance to assistive companion robots to the extent of being a branch of them; the nuance between these two types of companion robots is that the former is for long-term/lifetime usage while the latter is mostly for the duration of the therapy received by the user. === Pet companion robots === Pet companion robots are for individuals who seek an alternative to live pets as live animals demand a considerable amount of care and may not be eligible for people with allergies. These robots aim to be perfect imitations of a pet while diminishing the chore aspect of having one. === Entertainment companion robots === Entertainment companion robots are designed solely for entertainment and can provide numerous ways of entertainment, ranging from dancing to playing games with the user. People who would appreciate an individual to have fun with are the main audience of such products. === Personal assistant robots === Personal assistant robots help people with daily tasks, management, scheduling, reminding etc. Their area of activity can be offices as well as homes and public spaces. === Sex robots === Sex robots are anthropomorphic robotic sex dolls that have human-like movement or behavior, and some degree of artificial intelligence. As of 2026, although elaborately instrumented sex dolls have been created by a number of inventors, no fully animated sex robots yet exist. Simple devices have been created which can speak, make facial expressions, or respond to touch. There is controversy as to whether developing them would be morally justifiable. In 2015, robot ethicist Kathleen Richardson called for a ban on the creation of anthropomorphic sex robots with concerns about normalizing relationships with machines and reinforcing female dehumanization. Questions about their ethics, effects, and possible legal regulations have been discussed since then. == Examples == There are several companion robot prototypes, and these include Paro, CompanionAble, and EmotiRob, among others. === Paro === Paro is a pet-type robot system developed by Japan's National Institute of Advanced Industrial Science and Technology (AIST). The robot, which looked like a small harp seal, was designed as a therapeutic tool for use in hospitals and nursing homes. The robot is programmed to cry for attention and respond to its name. Experiments showed that Paro facilitated elderly residents to communicate with each other, which led to psychological improvements. === CompanionAble === This robot is classified as an FP 7 EU project. It is built to "cooperate with Ambient Assistive Living environment". The autonomous device, which is also built to support the elderly, helps its owner interact with smart home environment as well as caregivers. The robot functions as a mobile friend, by which natural interaction is possible via speech and the touchscreen to detect and track people at home. === EmotiRob === EmotiRob is developed in a robotics project which is the continuity of the MAPH (Active Media For the Handicap) project in emotion synthesis. The aim of the project was to maintain emotional interaction with children. EmotiRob designed in a way that a child can hold it in a his/her arms and with which he/she could interact by talking to it, and then the robot would express itself through body postures or facial expressions. It has cognitive capabilities, which are further extended so that the robot can have a natural linguistic interaction with its owner through the DRAGON speech-recognition software developed by a company called NUANCE. Such interaction is expected to facilitate a child's cognitive development and develop new learning patterns. === LOVOT === Lovot is a Japanese company robot whose only purpose is "to make you happy". It features over 50 sensors that mimic the behavior of a human baby or small pet, a 360° camera with a microphone, the ability to distinguish humans from objects, neoteny eyes, and an internal warmth of 30° celsius. An interactive Lovot Café was opened in Japan October 3, 2020. === NICOBO === Nicobo was developed by Panasonic and was influenced by the loneliness of lockdowns created as a measure of the COVID-19 pandemic. It was designed to appear vulnerable, which creates empathy in its owners. Nicobo's name derives from the Japanese word for "smile". It wags its tail, engages in baby talk, and stays as a housemate. === Hyodol === Hyodol is an advanced care robot designed to support the elderly by reminding them to take their medications and monitoring their movements to keep their guardians informed. Additionally, this innovative robot can detect and respond to the emotional states of its elderly users, adding a layer of personalized care. Hyodol is designed with the appearance and speech style of a 7-year-old Korean grandchild, featuring a soft fabric exterior and user interaction methods such as striking the head or patting the back. It is equipped with various sensors and wireless communication technologies to collect and process data, supporting mobile apps and PC web monitoring systems for remote monitoring from anywhere. In South Korea, approximately 10,000 Hyodol robots are deployed to the homes of elderly individuals living alone, providing essential support and companionship. Local governments, including provincial and county offices, have embraced Hyodol as a solution to address social challenges stemming from the country's rapidly aging society.Furthermore, the robot is widely utilized in the treatment of dementia patients at a university hospital in Gangwon province. Hyodol was honored with the Mobile World Congress (MWC) Global Mobile Awards (GLOMO) in the "Best Mobile Innovation for Connected Health and Wellbeing" category on February 29, 2024. === Moxie === Moxie was a companion robot for autistic children developed by a company called Embodied. Although it had limited motion, it presented itself as a lifelike avatar. It was designed to help the children learn emotional cognition, using remotely hosted large language models to direct its respons
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Automated storage and retrieval system

An automated storage and retrieval system (ASRS or AS/RS) consists of a variety of computer-controlled systems for automatically placing and retrieving loads from defined storage locations. Automated storage and retrieval systems (AS/RS) are typically used in applications where: There is a very high volume of loads being moved into and out of storage Storage density is important because of space constraints No value is added in this process (no processing, only storage and transport) Accuracy is critical because of potential expensive damages to the load An AS/RS can be used with standard loads as well as nonstandard loads, meaning that each standard load can fit in a uniformly-sized volume; for example, the film canisters in the image of the Defense Visual Information Center are each stored as part of the contents of the uniformly sized metal boxes, which are shown in the image. Standard loads simplify the handling of a request of an item. In addition, audits of the accuracy of the inventory of contents can be restricted to the contents of an individual metal box, rather than undergoing a top-to-bottom search of the entire facility, for a single item. They can also be used in self storage places. == Overview == AS/RS systems are designed for automated storage and retrieval of parts and items in manufacturing, distribution, retail, wholesale and institutions. They first originated in the 1960s, initially focusing on heavy pallet loads but with the evolution of the technology the handled loads have become smaller. The systems operate under computerized control, maintaining an inventory of stored items. Retrieval of items is accomplished by specifying the item type and quantity to be retrieved. The computer determines where in the storage area the item can be retrieved from and schedules the retrieval. It directs the proper automated storage and retrieval machine (SRM) to the location where the item is stored and directs the machine to deposit the item at a location where it is to be picked up. A system of conveyors and or automated guided vehicles is sometimes part of the AS/RS system. These take loads into and out of the storage area and move them to the manufacturing floor or loading docks. To store items, the pallet or tray is placed at an input station for the system, the information for inventory is entered into a computer terminal and the AS/RS system moves the load to the storage area, determines a suitable location for the item, and stores the load. As items are stored into or retrieved from the racks, the computer updates its inventory accordingly. The benefits of an AS/RS system include reduced labor for transporting items into and out of inventory, reduced inventory levels, more accurate tracking of inventory, and space savings. Items are often stored more densely than in systems where items are stored and retrieved manually. Within the storage, items can be placed on trays or hang from bars, which are attached to chains/drives in order to move up and down. The equipment required for an AS/RS include a storage & retrieval machine (SRM) that is used for rapid storage and retrieval of material. SRMs are used to move loads vertically or horizontally, and can also move laterally to place objects in the correct storage location. The trend towards Just In Time production often requires sub-pallet level availability of production inputs, and AS/RS is a much faster way of organizing the storage of smaller items next to production lines. The Material Handling Institute of America (MHIA), the non-profit trade association for the material handling world, and its members have categorised AS/RS into two primary segments: Fixed Aisle and Carousels/Vertical Lift Modules (VLMs). Both sets of technologies provide automated storage and retrieval for parts and items, but use different technologies. Each technology has its unique set of benefits and disadvantages. Fixed Aisle systems are characteristically larger systems whereas carousels and Vertical Lift Modules are used individually or grouped, but in small to medium-sized applications. A fixed-aisle AS/R machine (stacker crane) is one of two main designs: single-masted or double masted. Most are supported on a track and ceiling guided at the top by guide rails or channels to ensure accurate vertical alignment, although some are suspended from the ceiling. The 'shuttles' that make up the system travel between fixed storage shelves to deposit or retrieve a requested load (ranging from a single book in a library system to a several ton pallet of goods in a warehouse system). The entire unit moves horizontally within an aisle, while the shuttles are able to elevate up to the necessary height to reach the load, and can extend and retract to store or retrieve loads that are several positions deep in the shelving. A semi-automated system can be achieved by utilizing only specialized shuttles within an existing rack system. Another AS/RS technology is known as shuttle technology. In this technology the horizontal movement is made by independent shuttles each operating on one level of the rack while a lift at a fixed position within the rack is responsible for the vertical movement. By using two separate machines for these two axes the shuttle technology is able to provide higher throughput rates than stacker cranes. Storage and Retrieval Machines pick up or drop off loads to the rest of the supporting transportation system at specific stations, where inbound and outbound loads are precisely positioned for proper handling. In addition, there are several types of Automated Storage & Retrieval Systems (AS/RS) devices called Unit-load AS/RS, Mini-load AS/RS, Mid-Load AS/RS, Vertical Lift Modules (VLMs), Horizontal Carousels and Vertical Carousels. These systems are used either as stand-alone units or in integrated workstations called pods or systems. These units are usually integrated with various types of pick to light systems and use either a microprocessor controller for basic usage or inventory management software. These systems are ideal for increasing space utilization up to 90%, productivity levels by 90%, accuracy to 99.9%+ levels and throughput up to 750 lines per hour/per operator or more depending on the configuration of the system. == Horizontal carousels == Robotic Inserter/Extractor devices can be used for horizontal carousels. The robotic device is positioned in the front or rear of up to three horizontal carousels tiered high. The robot grabs the tote required in the order and often replenishes at the same time to speed up throughput. The tote(s) are then delivered to a conveyor, which routes it to a work station for picking or replenishing. Up to eight transactions per minute per unit can be done. Totes or containers up to 36" x 36" x 36" can be used in a system. On a simplistic level, horizontal carousels are also often used as "rotating shelving". With simple "fetch" command, items are brought to the operator and otherwise wasted space is eliminated. AS/RS Applications: Most applications of AS/RS technology have been associated with warehousing and distribution operations. An AS/RS can also be used to store raw materials and work in process in manufacturing. Three application areas can be distinguished for AS/RS: (1) Unit load storage and handling, (2) Order picking, and (3) Work in process storage. Unit load storage and retrieval applications are represented by unit load AS/RS and deep-lane storage systems. These kinds of applications are commonly found in warehousing for finishing goods in a distribution center, rarely in manufacturing. Deep-lane systems are used in the food industry. As described above, order picking involves retrieving materials in less than full unit load quantities. Minilpass, man-on board, and items retrieval systems are used for this second application area. Work in process storage is a more recent application of automated storage technology. While it is desirable to minimize the amount of work in process, WIP is unavoidable and must be effectively managed. Automated storage systems, either automated storage/retrieval systems or carousel systems, represent an efficient way to store materials between processing steps, particularly in batch and job shop production. In high production, work in process is often carried between operations by conveyor system, which this serve both storage and transport functions. === Inventory Category-specific AS/RS === Each inventory category—raw materials, work-in-process, and finished goods—requires its own specialized Automated Storage and Retrieval System (AS/RS). Particularly for work-in-process (WIP) inventories, due to variations in manufacturing processes, the AS/RS systems are significantly different in design and function, tailored specifically to match unique handling, storage, and retrieval requirements === Installed applications === Installed applications of this technology can be wide-ranging. In some librarie
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Paint.NET

Paint.NET (sometimes stylized as paint.net) is a freeware general-purpose raster graphics editor program for Microsoft Windows, developed with the .NET platform. Paint.NET was originally created by Rick Brewster as a Washington State University student project, and has evolved from a simple replacement for the Microsoft Paint program into a program for editing mainly graphics, with support for plugins. == History == Paint.NET originated as a computer science senior design project by Rick Brewster during spring 2004 at Washington State University. Version 1.0 consisted of 36,000 lines of code and was written in four months. In contrast, version 3.35 has approximately 162,000 lines of code. The Paint.NET project continued over the summer and into the autumn 2004 semester for both the version 1.1 and 2.0 releases. Development continued with one programmer who worked on previous versions of Paint.NET while he was a student at WSU. As of May 2006 the program had been downloaded at least 2 million times, at a rate of about 180,000 per month. Initially, Paint.NET was released under a modified version of the MIT License, with the exclusion of the installer, text, and graphics. However, citing issues with the open source code being plagiarized by others that had rebranded the software as their own and bundled user content without their permission, the availability of the source code was restricted, in December 2007 Brewster announced his intent to restrict access to components of the program (including its installer, resources, and user interface). In November 2009, the software was made proprietary, restricting the sale or creation of derivative works of the software. Starting with version 4.0.18, Paint.NET is published in two editions: A classic edition remains freeware, similar to all other versions since 3.5. Another edition, however, is published to Microsoft Store under a trialware license and is available to purchase for US$14.99. According to the developer, this was done to enable the users to contribute to the development with more convenience, even though the old avenue of donation was not closed. In May 2026, Brewster revealed that he obtained the paint.net domain after attempting to do so for 22 years. Historically, the editor was hosted on getpaint.net, and according to Brewster, the previous owners of paint.net would not sell the domain and asked for "lots and lots of money". In December of the previous year, paint.net began hosting content that impersonated Paint.NET, therefore becoming a clear case of trademark infringement and domain squatting. Brewster stated that he was able to obtain the domain afterwards with the help of a lawyer. == Overview == Paint.NET is primarily programmed in the C# programming language. Its native image format, .PDN, is a compressed representation of the application's internal object format, which preserves layering and other information. == Plugins == Paint.NET supports plugins, which add image adjustments, effects, and support for additional file types. They can be programmed using any .NET Framework programming language, though they are most commonly written in C#. These are created by volunteer coders on the program's discussion board, the Paint.NET Forum. Though most are simply published via the discussion board, some have been included with a later release of the program. For instance, a DirectDraw Surface file type plugin, (originally by Dean Ashton) and an Ink Sketch and Soften Portrait effect (originally by David Issel) were added to Paint.NET in version 3.10. Hundreds of plugins have been produced; such as Shape3D, which renders a 2D drawing into a 3D shape. Some plugins expand on the functionality that comes with Paint.NET, such as Curves+ and Sharpen+, which extend the included tools Curves and Sharpen, respectively. Examples of file type plugins include an Animated Cursor and Icon plugin and an Adobe Photoshop file format plugin. Several of these plugins are based on existing open source software, such as a raw image format plugin that uses dcraw and a PNG optimization plugin that uses OptiPNG. == Forks == === paint-mono === Paint.NET was created exclusively for Windows and has no native support for other operating systems. Due to its former open-source licensing, the development of alternative versions was possible. In May 2007, Miguel de Icaza officially started a porting project called paint-mono. This project had partially ported Paint.NET 3.0 to Mono, an open-source implementation of the Common Language Infrastructure on which the .NET Framework is based. This allowed Paint.NET to be run on Mono-supported platforms, such as Linux. This port is no longer maintained and has not been updated since March 2009. Newer Mono runtime 6 versions are able to run original Paint.NET releases up to 3.5.11 with only minor issues. === Pinta === In 2010, developer Jonathan Pobst started a project called Pinta, describing it as a clone of Paint.NET for Mono and Gtk#. Pinta reused the adjustments and effects code from Paint.NET but otherwise is original code.
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Digital art

Digital art, or the digital arts, is artistic work that uses digital technology as part of the creative or presentational process. It can also refer to computational art that uses and engages with digital media. Since the 1960s, various names have been used to describe digital art, including computer art, electronic art, multimedia art, and new media art. Digital art includes pieces stored on physical media, such as with digital painting, as well as digital galleries on websites. Digital art also extends to the field of visual computing. == History == In the early 1960s, John Whitney developed the first computer-generated art using mathematical operations. In 1963, Ivan Sutherland invented the first user interactive computer-graphics interface known as Sketchpad. Between 1974 and 1977, Salvador Dalí created two big canvases of Gala Contemplating the Mediterranean Sea which at a distance of 20 meters is transformed into the portrait of Abraham Lincoln (Homage to Rothko) and prints of Lincoln in Dalivision based on a portrait of Abraham Lincoln processed on a computer by Leon Harmon published in "The Recognition of Faces". The technique is similar to what later became known as photographic mosaics. Andy Warhol created digital art using an Amiga where the computer was publicly introduced at the Lincoln Center in July 1985. An image of Debbie Harry was captured in monochrome from a video camera and digitized into a graphics program called ProPaint. Warhol manipulated the image by adding color using flood fills. == Art made for digital media == Artwork that is highly computational, presented through digital media, and explicitly engages with digital technologies are categorized as "art made for digital media". This differs from art using digital tools, which incorporate digital technology in the creation process but may exist outside the digital world. Digital art historian Christiane Paul writes that it "is highly problematic to classify all art that makes use of digital technologies somewhere in its production and dissemination process as digital art since it makes it almost impossible to arrive at any unifying statement about the art form". == Art that uses digital tools == Digital art can be purely computer-generated (such as fractals and algorithmic art) or taken from other sources, such as a scanned photograph or an image drawn using vector graphics software using a mouse or graphics tablet. Artworks are considered digital paintings when created similarly to non-digital paintings but using software on a computer platform and digitally outputting the resulting image as painted on canvas. Despite differing viewpoints on digital technology's impact on the arts, a consensus exists within the digital art community about its significant contribution to expanding the creative domain, i.e., that it has greatly broadened the creative opportunities available to professional and non-professional artists alike. == Art theorists and art historians == Notable art theorists and historians in this field include: Oliver Grau, Jon Ippolito, Christiane Paul, Frank Popper, Jasia Reichardt, Mario Costa, Christine Buci-Glucksmann, Dominique Moulon, Roy Ascott, Catherine Perret, Margot Lovejoy, Edmond Couchot, Tina Rivers Ryan, Fred Forest and Edward A. Shanken. === Digital painting === Digital painting is either a physical painting made with the use of digital electronics and spray paint robotics within the digital art fine art context or pictorial art imagery made with pixels on a computer screen that mimics artworks from the traditional histories of painting and illustration. === Artificial intelligence art === Artists have used artificial intelligence to create artwork since at least the 1960s. Since their design in 2014, some artists have created artwork using a generative adversarial network (GAN), which is a machine learning framework that allows two "algorithms" to compete with each other and iterate. It can be used to generate pictures that have visual effects similar to traditional fine art. The essential idea of image generators is that people can use text descriptions to let AI convert their text into visual picture content. Anyone can turn their language into a painting through a picture generator. == Digital art education == Digital art education has become more common with the advancement of digital hardware and software. From hardware such as graphics tablets, styluses, tablets, 3D scanners, virtual reality headsets, and digital cameras; to software such as digital art software, 3D modeling software, 3D rendering, digital sculpting, 2D graphics software, digital painting, 3D terrain generation, 2D animation software, 3D animation software, raster graphics editors, vector graphics editors, mathematical art software, and video editing software. == Scholarship and archives == In addition to the creation of original art, research methods that utilize AI have been generated to quantitatively analyze digital art collections. This has been made possible due to the large-scale digitization of artwork in the past few decades. Although the main goal of digitization was to allow for accessibility and exploration of these collections, the use of AI in analyzing them has brought about new research perspectives. Two computational methods, close reading and distant viewing, are the typical approaches used to analyze digitized art. Close reading focuses on specific visual aspects of one piece. Some tasks performed by machines in close reading methods include computational artist authentication and analysis of brushstrokes or texture properties. In contrast, through distant viewing methods, the similarity across an entire collection for a specific feature can be statistically visualized. Common tasks relating to this method include automatic classification, object detection, multimodal tasks, knowledge discovery in art history, and computational aesthetics. Whereas distant viewing includes the analysis of large collections, close reading involves one piece of artwork. Whilst 2D and 3D digital art is beneficial as it allows the preservation of history that would otherwise have been destroyed by events like natural disasters and war, there is the issue of who should own these 3D scans – i.e., who should own the digital copyrights. === Computer demos === Computer demos are based on computer programs, usually non-interactive. It produces audiovisual presentations. They are a novel form of art, which emerged as a consequence of the home computer revolution in the early 1980s. In the classification of digital art, they can be best described as real-time procedurally generated animated audio-visuals. This form of art does not concentrate only on the aesthetics of the final presentation, but also on the complexities and skills involved in creating the presentation. As such, it can be fully enjoyed only by persons with a relatively high knowledge level of relevant computer technologies. An example is that, as said by Hua Jin and Jie Yang, Using computer-aided design software to present the class content in art design teaching," is not to advocate computer-aided design instead of hand-drawn performance, but to make it serve the profession earlier through a more reasonable course arrangement." On the other hand, many of the created pieces of art are primarily aesthetic or amusing, and those can be enjoyed by the general public. === Digital installation art === Digital installation art constitutes a broad field of artistic practices and a variety of forms. Some resemble video installations, especially large-scale works involving projections and live video capture. By using projection techniques that enhance an audience's impression of sensory envelopment, many digital installations attempt to create immersive environments. While others go even further and attempt to facilitate a complete immersion in virtual realms. This type of installation is generally site-specific, scalable, and without fixed dimensionality, meaning it can be reconfigured to accommodate different presentation spaces. Scott Snibbe's "Boundary Functions" is an example of augmented reality digital installation art, which responds to people who enter the installation by drawing lines between people, indicating their personal space.Noah Wardrip-Fruin's "Screen"(2003) utilizes a Cave Automatic Virtual Environment (CAVE) to create an interactive, text-based digital experience that engages the viewer in a multi-sensory interaction. === Internet art and net.art === Internet art is digital art that uses the specific characteristics of the Internet and is exhibited on the Internet. The term "internet art" is included by "net art" for which artists assume that network will be refreshed through history. So the term "post-internet art" is used to exclude artworks outside of the internet media. A representative example is Protocols for Achievements, which is a digital photo frame that confronts the aestheti
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Autonomous things

Autonomous things, abbreviated AuT, or the Internet of autonomous things, abbreviated as IoAT, is an emerging term for the technological developments that are expected to bring computers into the physical environment as autonomous entities without human direction, freely moving and interacting with humans and other objects. Self-navigating drones are the first AuT technology in (limited) deployment. It is expected that the first mass-deployment of AuT technologies will be the autonomous car, generally expected to be available around 2020. Other currently expected AuT technologies include home robotics (e.g., machines that provide care for the elderly, infirm or young), and military robots (air, land or sea autonomous machines with information-collection or target-attack capabilities). AuT technologies share many common traits, which justify the common notation. They are all based on recent breakthroughs in the domains of (deep) machine learning and artificial intelligence. They all require extensive and prompt regulatory developments to specify the requirements from them and to license and manage their deployment (see the further reading below). And they all require unprecedented levels of safety (e.g., automobile safety) and security, to overcome concerns about the potential negative impact of the new technology. As an example, the autonomous car both addresses the main existing safety issues and creates new issues. It is expected to be much safer than existing vehicles, by eliminating the single most dangerous element – the driver. The US's National Highway Traffic Safety Administration estimates 94 percent of US accidents were the result of human error and poor decision-making, including speeding and impaired driving, and the Center for Internet and Society at Stanford Law School claims that "Some ninety percent of motor vehicle crashes are caused at least in part by human error". So while safety standards like the ISO 26262 specify the required safety, there is still a burden on the industry to demonstrate acceptable safety. While car accidents claim every year 35,000 lives in the US, and 1.25 million worldwide, some believe that even "a car that's 10 times as safe, which means 3,500 people die on the roads each year [in the US alone]" would not be accepted by the public. The acceptable level may be closer to the current figures on aviation accidents and incidents, with under a thousand worldwide deaths in most years – three orders of magnitude lower than cars. This underscores the unprecedented nature of the safety requirements that will need to be met for cars, with similar levels of safety expected for other Autonomous Things.
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Dispo

Dispo (formerly David's Disposable) is an American photo sharing and social networking app owned by Dispo, Inc. and co-founded by CEO Daniel Liss, YouTuber David Dobrik, and Natalie Mariduena. When the app initially launched on iOS in December 2019, it briefly charted as the most downloaded free app on the App Store, ahead of both Disney+ and Instagram. The app was rebranded and relaunched as Dispo, expanding from a simple camera app to a full social network in March 2021. It is based on the disposable camera. == History == On December 21, 2019, the app was first launched on the App Store under the name "David's Disposable." In its first week of release, it was downloaded more than a million times, reaching number one among free apps in the App Store. In June 2020, the team decided to rename the app to Dispo, purchasing the Dispo.fun domain on June 21, 2020. The company announced the change in September 2020. The early Dispo team consisted of Dobrik's longtime friend and business associate Natalie Mariduena as its treasurer, entrepreneur and venture capitalist Daniel Liss as chief executive officer, Regynald Augustin as first engineer, and Briana Hokanson as lead designer. In October 2020, the company raised a $4M seed round with backing from Alexis Ohanian's venture fund Seven Seven Six alongside other investors including Unshackled Ventures, Shrug Capital, and Weekend Fund. In February 2021, Axios reported that the app had generated US$20 million in its series A round, led by Spark Capital. At this time, the app was valued at US$200 million. A New York Times profile asked, "Are Disposables the Future of Photosharing?" In March 2021, the app was officially relaunched with new social network features and its invite-only feature was dropped. On March 21, 2021, it was announced that Spark Capital would sever all ties with Dispo in light of several disparaging allegations against David Dobrik and The Vlog Squad. The same day, it was announced that Dobrik would leave the company and step down from the company's board of directors. On March 22, 2021, Seven Seven Six and Unshackled Ventures announced they would be standing by the company and its remaining employees but donating profits to charity. In June, 2021, CEO Daniel Liss announced Dispo's official Series A. Investors and advisors in the new Dispo include Ohanian's Seven Seven Six, Unshackled, Endeavor, photographers Annie Leibovitz and Raven B. Varona, NBA stars Kevin Durant and Andre Iguodala (through their 35 Ventures and F9 Strategies venture firms, respectively). Other participants include Cara Delevingne, Sofia Vergara, Shade Room CEO Angelica Nwandu, Latin World Entertainment CEO Luis Balaguer, and Amplify Africa co-founders Damilare Kujembola and Timi Adeyeba. == Overview == Dispo has been compared to other image sharing and social networking services, most notably Instagram and VSCO, although users cannot immediately see the photos they have taken using the app. When a user attempts to take a photo, the interface mimics the developing process of a disposable camera. Users can take as many photos on the app as they want; they do not appear on the app however, until 9 am the next day. Once the set of photos appear on the app, users can choose to save them or share them with other users in a "roll". == Reception == Screen Rant has called the app "like Clubhouse [referring to the app] but for photos," comparing the early invite-only features of the apps. As it greatly restricts the user's editing options and sets out to offer a more authentic social networking experience, the app has been widely dubbed the "anti-Instagram". Between March 2021 and June 2021, the app reached the top ten in the App Store's photo/video rankings on 5 continents including in the US, Japan, Spain, Germany, Brazil, and Australia. It has been a notable success in Japan, where it opened its first international office in July 2021. In July 2021, NBA number one draft pick Cade Cunningham announced he had selected Dispo as his exclusive social media partner for the NBA draft.
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Random (software)

Random was an iOS mobile app that used algorithms and human-curation to create an adaptive interface to the Internet. The app served a remix of relevance and serendipity that allowed people to find diverse topics and interesting content that they might not have encountered otherwise. Random did not require a login or sign-up - the use of the app was anonymous. The app was powered by an artificial intelligence that learned from direct and indirect user interactions inside the app. While learning and adapting to a person, Random created a unique anonymous choice profile that was then used for recommending topics and content. The app didn't recommend the same content twice. == User interface == Random's user interface was made of ever-changing topic blocks that contained keywords and images. By choosing any of the blocks, the user would see related web content. By closing the web content, the user could access new related topics. The user interface allowed people to get more information about a specific topic area or then just leap freely from topic to topic. The content recommended by Random could be any type of web content, varying from news articles to long-form stories and from photographs to videos. Every user of the Random was curating content for other users by using the app. == History == Random was launched in March 2014. The startup was backed by Skype co-founder Janus Friis. The Random app received a strong reception from the likes of The New York Times, TechCrunch, New Scientist, Vice, and other leading publications. The app went on to gain traction with an active and loyal user community of several hundreds of thousands. This was not enough to support the free app model the team strongly believed in, and the service was terminated in December 2015. == Reception == Various reviews in media have emphasized that Random enables people to break their filter bubble and find diverse content they might not find elsewhere. Alan Henry of Lifehacker wrote: "Random... breaks you out by intentionally guiding you to new topics and interesting articles at sites you may not otherwise read." Vice Motherboard's Claire Evans says that: "Random never turns into a filter bubble, because it perpetually injects the irrational into my experience… in a cocktail of relevancy and serendipity." The app has been said to have a unique, minimalistic user experience. Kit Eaton of The New York Times commented that Random "let's you browse the news in a different way to all the other news sites you've probably ever used." Mashable reviewed Random by concluding that the "app may be one of the most simple content-discovery apps on the market."
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List of computer graphics journals

List of computer graphics journals includes notable peer-reviewed scientific and academic journals that focus on computer graphics, visualization, and related areas such as rendering, animation, image processing, and geometric modeling. == Journals == ACM Transactions on Graphics Computers & Graphics IEEE Computer Graphics and Applications IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems Graphical Models Journal of Computer Graphics Techniques Presence: Teleoperators and Virtual Environments Virtual Reality Simulation & Gaming
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Shape factor (image analysis and microscopy)

Shape factors are dimensionless quantities used in image analysis and microscopy that numerically describe the shape of a particle, independent of its size. Shape factors are calculated from measured dimensions, such as diameter, chord lengths, area, perimeter, centroid, moments, etc. The dimensions of the particles are usually measured from two-dimensional cross-sections or projections, as in a microscope field, but shape factors also apply to three-dimensional objects. The particles could be the grains in a metallurgical or ceramic microstructure, or the microorganisms in a culture, for example. The dimensionless quantities often represent the degree of deviation from an ideal shape, such as a circle, sphere or equilateral polyhedron. Shape factors are often normalized, that is, the value ranges from zero to one. A shape factor equal to one usually represents an ideal case or maximum symmetry, such as a circle, sphere, square or cube. == Aspect ratio == The most common shape factor is the aspect ratio, a function of the largest diameter and the smallest diameter orthogonal to it: A R = d min d max {\displaystyle A_{R}={\frac {d_{\min }}{d_{\max }}}} The normalized aspect ratio varies from approaching zero for a very elongated particle, such as a grain in a cold-worked metal, to near unity for an equiaxed grain. The reciprocal of the right side of the above equation is also used, such that the AR varies from one to approaching infinity. == Circularity == Another very common shape factor is the circularity (or isoperimetric quotient), a function of the perimeter P and the area A: f circ = 4 π A P 2 {\displaystyle f_{\text{circ}}={\frac {4\pi A}{P^{2}}}} The circularity of a circle is 1, and much less than one for a starfish footprint. The reciprocal of the circularity equation is also used, such that fcirc varies from one for a circle to infinity. == Elongation shape factor == The less-common elongation shape factor is defined as the square root of the ratio of the two second moments in of the particle around its principal axes. f elong = i 2 i 1 {\displaystyle f_{\text{elong}}={\sqrt {\frac {i_{2}}{i_{1}}}}} == Compactness shape factor == The compactness shape factor is a function of the polar second moment in of a particle and a circle of equal area A. f comp = A 2 2 π i 1 2 + i 2 2 {\displaystyle f_{\text{comp}}={\frac {A^{2}}{2\pi {\sqrt {{i_{1}}^{2}+{i_{2}}^{2}}}}}} The fcomp of a circle is one, and much less than one for the cross-section of an I-beam. == Waviness shape factor == The waviness shape factor of the perimeter is a function of the convex portion Pcvx of the perimeter to the total. f wav = P cvx P {\displaystyle f_{\text{wav}}={\frac {P_{\text{cvx}}}{P}}} Some properties of metals and ceramics, such as fracture toughness, have been linked to grain shapes. == An application of shape factors == Greenland, the largest island in the world, has an area of 2,166,086 km2; a coastline (perimeter) of 39,330 km; a north–south length of 2670 km; and an east–west length of 1290 km. The aspect ratio of Greenland is A R = 1290 2670 = 0.483 {\displaystyle A_{R}={\frac {1290}{2670}}=0.483} The circularity of Greenland is f circ = 4 π ( 2166086 ) 39330 2 = 0.0176. {\displaystyle f_{\text{circ}}={\frac {4\pi (2166086)}{39330^{2}}}=0.0176.} The aspect ratio is agreeable with an eyeball-estimate on a globe. Such an estimate on a typical flat map, using the Mercator projection, would be less accurate due to the distorted scale at high latitudes. The circularity is deceptively low, due to the fjords that give Greenland a very jagged coastline (see the coastline paradox). A low value of circularity does not necessarily indicate a lack of symmetry, and shape factors are not limited to microscopic objects.
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