AI Chatbot Online Characters

AI Chatbot Online Characters — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

Deep Learning Anti-Aliasing

Deep Learning Anti-Aliasing (DLAA) is a form of spatial anti-aliasing developed by Nvidia. DLAA depends on and requires Tensor Cores available in Nvidia RTX cards. DLAA is similar to Deep Learning Super Sampling (DLSS) in its anti-aliasing method, with one important differentiation being that the goal of DLSS is to increase performance at the cost of image quality, whereas the main priority of DLAA is improving image quality at the cost of performance (irrelevant of resolution upscaling or downscaling). DLAA is similar to temporal anti-aliasing (TAA) in that they are both spatial anti-aliasing solutions relying on past frame data. Compared to TAA, DLAA is substantially better when it comes to shimmering, flickering, and handling small meshes like wires. == Technical overview == DLAA collects game rendering data including raw low-resolution input, motion vectors, depth buffers, and exposure information. This information feeds into a convolutional neural network that processes the image to reduce aliasing while preserving fine detail. The neural network architecture employs an auto-encoder design trained on high-quality reference images. The training dataset includes diverse scenarios focusing on challenging cases like sub-pixel details, high-contrast edges, and transparent surfaces. The network then processes frames in real-time. Unlike traditional anti-aliasing solutions that rely on manually written heuristics, such as TAA, DLAA uses its neural network to preserve fine details while eliminating unwanted visual artifacts. == History == DLAA was initially called and marketed by Nvidia as DLSS 2x. The first game that added support for DLAA was The Elder Scrolls Online, which implemented the feature in 2021. By June 2022, DLAA was only available in six games. This number rose to 17 by February 2023. In June 2023, TechPowerUp reported that "DLAA is seeing sluggish adoption among game developers", and that Nvidia was working on adding DLAA to the quality presets of DLSS to boost adoption. By December 2023, DLAA was supported in 41 games. In early 2025, an update for the Nvidia App added a driver-based DLSS override feature that enables users to activate DLAA even in games that do not support it natively. == Differences between TAA and DLAA == TAA is used in many modern video games and game engines; however, all previous implementations have used some form of manually written heuristics to prevent temporal artifacts such as ghosting and flickering. One example of this is neighborhood clamping which forcefully prevents samples collected in previous frames from deviating too much compared to nearby pixels in newer frames. This helps to identify and fix many temporal artifacts, but deliberately removing fine details in this way is analogous to applying a blur filter, and thus the final image can appear blurry when using this method. DLAA uses an auto-encoder convolutional neural network trained to identify and fix temporal artifacts, instead of manually programmed heuristics as mentioned above. Because of this, DLAA can generally resolve detail better than other TAA and TAAU implementations, while also removing most temporal artifacts. == Differences between DLSS and DLAA == While DLSS handles upscaling with a focus on performance, DLAA handles anti-aliasing with a focus on visual quality. DLAA runs at the given screen resolution with no upscaling or downscaling functionality provided by DLAA. DLSS and DLAA share the same AI-driven anti-aliasing method. As such, DLAA functions like DLSS without the upscaling part. Both are made by Nvidia and require Tensor Cores. However, DLSS and DLAA cannot be enabled at the same time, only one can be selected depending on whether performance or image quality is prioritized. == Reception == TechPowerUp found that "[c]ompared to TAA and DLSS, DLAA is clearly producing the best image quality, especially at lower resolutions", arguing that, while "DLSS was already doing a better job than TAA at reconstructing small objects", "DLAA does an even better job". In a Cyberpunk 2077 performance test, IGN stated that "DLAA provided somewhat similar results [FPS wise] to the normal raster mode in most cases but got significant performance boost with the help of frame generation", a feature not available when using native resolution. Rock Paper Shotgun noted that, while DLAA is "not a completely perfect form of anti-aliasing, as the occasional jaggies are present", it "looks a lot sharper overall [than TAA], and especially in motion." According to PC World, "DLAA offers very good anti-aliasing without losing visual information — alternatives like TAA tend to struggle during motion-filled scenes, where DLAA doesn’t. Furthermore, DLAA’s loss of performance is lower than with conventional anti-aliasing methods."
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PNGOUT

PNGOUT is a freeware command line optimizer for PNG images written by Ken Silverman. The transformation is lossless, meaning that the resulting image is visually identical to the source image. According to its author, this program can often get higher compression than other optimizers by 5–10%. It is possible to compress some inflated PNGs to a size below 1% of the original file. PNGOUT was also available as a plug-in for the freeware image viewer IrfanView and can be enabled as an option when saving files. It allows editing of various PNGOUT settings via a dialog box. PNGOUT integration was removed in IrfanView version 4.58 in favour of OptiPNG. In 2006, a commercial version of PNGOUT with a graphical user interface, known as PNGOUTWin, was released by Ardfry Imaging, a small company Silverman co-founded in 2005. There is also a freeware GUI frontend to PNGOUT available, known as PNGGauntlet. == Main operation == The main function of PNGOUT is to reduce the size of image data contained in the IDAT chunk. This chunk is compressed using the deflate algorithm. Deflate algorithms can vary in speed and compression ratio, with higher compression ratios generally implying lower speed. Ken Silverman wrote a deflate compressor for PNGOUT that is slower than the ones used in most graphics software, but produces smaller files. PNGOUT also performs automatic bit depth, color, and palette reduction where appropriate.
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Tribute (website)

Tribute is an American video-sharing website headquartered in Brooklyn. Created in 2014 by Andrew Horn and Rory Petty, the platform lets customers create video montages (called "tributes") for occasions including weddings, birthdays, anniversaries, get well soon, and memorials. Tribute.co allows users to record video messages, request submissions from friends and family, insert photos, add music, and send the resulting video tribute montage to a recipient. == Overview == Tribute's collaborative technology starts with inviting people to contribute via email, SMS or social media. Participants receive a prompt to record a short video via their phone, computer or tablet. The site's video editing software allows users to drag and drop the clips in their desired order without prior video editing experience. == History == When Andrew Horn turned twenty-seven, his girlfriend, Miki Agrawal surprised him with a video montage containing clips of his family and closest friends explaining why they loved him. This resulted in Andrew's idea to create Tribute–a "living eulogy" video-compilation service that he co-founded with software engineer Rory Petty. Founded in 2014, Tribute's activity accelerated in 2020 due to the COVID-19 pandemic, and it had sent over 5 million videos as of December 2021. While social distance restrictions were in effect, the site provided a way for people to connect while in-person celebrations were put on hold. For each video sold, Tribute makes one available to hospitals for free and has partnered with Cleveland Clinic Cancer Center in Ohio, Lurie Children's Hospital in Illinois and CarePoint Health in New Jersey.
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Diia

Diia (Ukrainian: Дія [ˈd⁽ʲ⁾ijɐ] , lit. 'Action'; also an acronym for Держава і Я, Derzhava i Ya, IPA: [derˈʒɑwɐ i ˈjɑ], lit. 'State and Me') is a mobile app, a web portal and a brand of e-governance in Ukraine. Launched in 2020, the Diia app allows Ukrainian citizens to use digital documents on their smartphones instead of physical ones for identification and sharing purposes. The Diia portal allows access to over 130 government services. Eventually, the government plans to make all kinds of state-person interactions available through Diia. Diia was built in partnership with the United States and is poised to be shared with other countries. On the sidelines of the 2023 World Economic Forum in Davos, USAID Administrator Samantha Power said the US hopes to replicate the success of Diia in other countries. == History == Diia was first presented on September 27, 2019, by the Ministry of Digital Transformation of Ukraine as a brand of the State in a Smartphone project. Vice Prime Minister and Minister of Digital Transformation Mykhailo Fedorov announced the creation of a mobile app and a web portal that would unite in a single place all the services provided by the state to citizens and businesses. On February 6, 2020, the mobile app Diia was officially launched. During the presentation, Ukrainian President Volodymyr Zelensky said that 9 million Ukrainians now have access to their driver's license and car registration documents on their phones, while Prime Minister Oleksiy Honcharuk called the implementation of the State in a Smartphone project a priority for the government. In April 2020, the Ukrainian government approved a resolution for experimental usage of digital ID-cards and passports which would be issued to all Ukrainians via the Diia. On October 5, 2020, during the Diia Summit, the government presented a first major update of the app and web portal branded "Diia 2.0". More types of documents were added to the app as well as the ability to share documents with others via a single tap on a push-message. The web portal in turn expanded the number of available services to 27, including the ability to register a private limited company in half an hour. President Zelensky who opened the summit, announced that in 2021 Ukraine will enter the "paper less" mode by prohibiting civil servants from demanding paper documents. By the end of 2020, the app had more than six million users, while the portal had 50 available services. In March 2021, the Ukrainian parliament adopted a bill equating digital identity documents with their physical analogues. Starting on August 23, Ukrainian citizens can use digital ID-cards and passports for all purposes while in Ukraine. According to Minister of Digital Transformation Mykhailo Fedorov, Ukraine will become the first country in the world where digital identity documents are considered legally equivalent to ordinary ones. In September 2024, Diia launched an online marriage registration service, which can be beneficial especially for military personnel who spend much time on the frontline separated from their partners. In October 2024, Diia's online marriage service appeared in Time's Inventions of the 2024 list. In the first month of its operations over 1.1 million Ukrainians tried to make proposals using the technology, and 435 couples got married. == Benefits and challenges == The first and most obvious benefit is the convenience of such a platform. Citizens can have many documents on their smartphones at once, without concern about losing or damaging them. Whenever needed, they can just open an app on their smartphones and show/check the document they need. The idea is that Diia will help cut the bureaucracy associated with public services, which in turn will help fight corruption and increase government savings. Fewer people are needed to be employed in the public sector and fewer human to human interactions are supposed to happen. With the start of the program, already 10% of government employees were reduced, which contributes to hundreds of millions of dollars in savings, but besides this, the initiative also improves the speed, efficiency, and transparency of government services. In addition, the digitalization of the government sector helps to develop the whole IT industry in the country, people become more digitally aware and educated, this affects other sectors as well, increasing the spread of digital infrastructure and expediting the speed of overall digitalization. The UN E-government Development Index, which assesses the capabilities of governments to integrate its functions electronically, such as the use of internet and mobile devices, ranked Ukraine 69th in 193 countries surveyed in 2020. Despite its low ranking in the e-government development index, Ukraine made a big jump on the e-participation index, which they ranked 43rd out of 193 countries from 0.66 in 2018 to 0.81 in 2020 (un.org, 2020), suggesting that the government and its citizens are adapting the IT-based government functions. The main goal of e-government according to Perez-Morote et.al. (2020) is to have accountability and transparency among the countries involved. But to do so, there are several challenges that a country should assess first prior to implementing e-government. In the research written by Heeks (2001), the author identified 2 main challenges that countries face in the development of e-government, first is the strategic challenge which involves the preparedness (e-readiness) of the entire government system for electronic transformation, and second challenge is the tactical challenge where the government must design (e-governance design) a system where it can be understood by every user, it's important that the information that needs to be communicated to the consumers is received clearly. For the first challenge (e-readiness), Ukraine had an internet penetration rate of 76% in 2020 and is expected to grow to 82%, it is important that consumers have the internet access for it to enable the consumers to utilize the service. Another factor is the readiness of its institutional infrastructure, which means that the government has its own organization which is solely focused on implementing the e-government project. In the case of Ukraine, the e-governance team is led by Oleksandr Ryzhenko, and the country's e-governance initiative is even further strengthened by ensuring that the data and legal infrastructure are already prepared. Ukraine has done this by modernizing their legislation that is more appropriate in the digital service, and the data exchange solution used by Ukraine is called Trembita. The human infrastructure is also being updated, as competent individuals must be the one doing the task, hence, EGOV4UKRAINE was launched, this aims to get IT developers for developing a system for administrative services. These efforts by the Ukrainian government did not go unnoticed, and they received an award from the e-Governance Academy as "partner of the year 2017". For the second challenge, which deals with the system design, the success of Ukraine can be seen on the latest data of UNDP, where it shows a high increase in the E-participation index. In 2018, Ukraine ranked 75th it ranked 46th in 2020 (un.org, 2020). Despite visible success, the implementation of the e-government was accompanied by problems. Data leakage became the main one. In May 2020, the data of 26 million driver's licenses appeared in the public domain on the Internet. The Ukrainian government said the Diia app was not linked to a data breach, but it is impossible to say for certain. Any storage of official documents in electronic format is associated with the risk of their leakage. In addition, the Diia application still has data protection issues, as the required protection system has not been implemented. This is also compounded by the country's weak data protection legal regime. In addition, since 2023, Ukrainians are able to register their cars with this app. Issued license plates are not using regional codes, but they are using special codes starting with DI or PD. == Diia City == In May 2020, the government presented Diia City headed by Oleksandr Borniakov, a large-scale project which would establish a virtual model of a free economic zone for representatives of the creative economy. It would provide for special digital residency with a particular taxation regime, intellectual property protection and simplified regulations. Diia City concurrently imposes certain constraints on contracts involving individual entrepreneurs (FOPs). It also offers the benefit of tax rebates. Diia City garners endorsement from the Ukrainian government, believing it will support the country's position in the IT market. As of July 30, 2023, the program had more than 600 residents, including companies like iGama, Avenga, SBRobotiks, and Intellectsoft.
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Friendica

Friendica (formerly Friendika, originally Mistpark) is a free and open-source software distributed social network. It forms one part of the Fediverse, an interconnected and decentralized network of independently operated servers. == Features == Friendica users can connect with others via their own Friendica server, but may also fully integrate contacts from other platforms including Diaspora, Pump.io, GNU social, email, Discourse and more recently ActivityPub (including Mastodon, Pleroma and Pixelfed) and Bluesky into their 'newsfeed'. In addition to these two way connections, users can also use Friendica as a publishing platform to post content to WordPress, Tumblr, Insanejournal and Libertree. Posting to Google+ was also supported until that service was shut down. In addition, RSS feeds can be ingested. Because users are distributed across many servers, their "addresses" consist of a username, the "@" symbol, and the domain name of the Friendica instance in the same manner email addresses are formed. Twitter support was available but was deprecated due to API changes under Elon Musk's leadership rendering it unusable. Most of the functionality from major microblogging and social networking platforms are available in Friendica; for example, tagging users and groups via "@ mentions"; direct messages; hashtags; photo albums; "likes"; "dislikes"; comments; and re-shares of publicly visible posts. Published items can be edited and updated across the network. Comprehensive settings for privacy and the public visibility of posts allow users to regulate who can read which contributions, or see specific information about the user. Users can also create multiple profiles, allowing different groups of people (such as friends, or work mates) to see a different profile entirely when viewing the same page. User accounts can be downloaded or deleted, and can be imported to a different Friendica server if so required. Public forums can be created under different accounts, which can be switched between if the accounts are registered with the same email address. == Development == There is no corporation behind Friendica. The developers work on a voluntary basis and the project is run informally; the platform itself is used for the communication between the developers. There are different forums within Friendica, such as "Friendica Developers" and "Friendica Support". The source code of Friendica is hosted on GitHub. == Installation == The developers aim to make installation of the software as simple as possible for technical laymen. They argue that decentralization on small servers is a key condition for the freedom of users and their self-determination. The difficulty level is similar to an installation of WordPress. However, the installing on shared hosting is sometimes difficult because of missing PHP5 modules. Some volunteers also run public servers so that newcomers can also avoid the installation of their own software. == List of clients == Friendica implements multiple client-server API variants simultaneously. Along with endpoints needed to use enhanced Friendica features, it also implements the API used by GNU social, Twitter and since version 2021.06 also the one used by Mastodon. As a result, most GNU social and Mastodon clients can be used for Friendica. Examples of Friendica compatible clients include: Raccoon for Friendica, Friendiqa, Fedilab, AndStatus, Twidere and DiCa for Android, friendly for Sailfish OS, friclicli (CLI client), choqok and Friendiqa for Linux and Friendica Mobile for Windows 10. == Reception == Friendica was cited in January 2012 by Infoshop News as an "alternative to Google+ and Facebook" to be used on the Occupy Nigeria movement. In January 2012 Free Software Foundation Europe's blog cited Friendica as a reasonable alternative to centralized and controlled social networks such as Facebook or Google+. Biblical Notes writer J. Randal Matheny described Friendica in January 2012 as "One social networking option flying under the radar until recently deserves consideration as an already stable platform with a wide range of options, applications, plug-ins, and possibilities for opening up the Internet." In February 2012, the German computer magazine c't wrote: "Friendica demonstrates how decentralized social networks can become widely accepted." Another German publication, the professional magazine t3n listed Friendica as a Facebook rival in an online article in March 2012 about Facebook alternatives. It compared Friendica with similar social networks like Diaspora and identi.ca. MSN Tech & Gadgets contributor Emma Boyes wrote about Friendica in May 2012: "why you'll love it: you can use it to access all the other social networks and get recommendations of new friends and groups to join. Friendica is open source and decentralised. There's no corporation behind it and there are extensive privacy settings. You can choose from a variety of user interfaces and it boasts some cool features—for instance, being able to key in a list of your interests and use the 'profile match' feature to recommend other users who share them with you. A word of warning, though, the site is not as user-friendly as the others on this list, so it may be this one is one for the geeks." == Later reviews == Acquisition of Twitter by Elon Musk had revitalized public interest in Fediverse technologies in April 2022. Friendica received favorable reviews, with a PCMag article describing it as "mostly comparable to Facebook", drawing a parallel to Google+ and highlighting using it "for planning events, and its multiple profile feature means you can show a different face to your friends, coworkers, and family". The September 2022 issue of Linux Magazine contains a detailed comparison and walk-through of registering to and using basic functions of Diaspora, Friendica and Mastodon. They describe Friendica as "intuitive" and highlight the "huge choice of account settings" and that "Friendica does not require any specific hardware, so you can use an old computer system as a server." == Vulnerabilities == In September 2020, a hotfix was released to patch a security vulnerability that could leak sensitive information from the server environment since versions released in April 2019 (develop branch) and June 2019 (stable).
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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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Showbox.com

Showbox is an online video streaming platform that enables users to stream and download many videos, commonly movies and TV shows, for free. == History == The company opened the platforms to users who registered from its beta in late 2015. The platform was officially launched in February 2016, enabling any visitor to sign up and create videos online. In April 2016, Showbox was featured on the Product Hunt website, coming to the top of the website's lists for that day and week with over 1400 upvotes from the Product Hunt community. Also in April 2016, Showbox partnered with YouTube's leading multi-channel networks, including Fullscreen, BroadbandTV, StyleHaul, AwesomenessTV, and BuzzMyVideos, to enable their communities of creators to access the platform. In June 2016, the company launched Showbox For Brands, a business-oriented video creation platform, enabling companies to create video content in-house and with their communities and influencers. In March 2017, the company launched Showbox Engage, a use case of its B2B product launched in 2016, enabling companies to launch user-generated content campaigns with their communities. In April 2017, Showbox and the United Nations announced a partnership around the 70th anniversary of the declaration of human rights, with an annual, ongoing global campaign in 135 languages, inviting people worldwide to create their part of the declaration in a video from anywhere around the world. In November 2017, Showbox partnered with the Ad:tech and Digital Marketing World Forum conferences (DMWF) in New York to provide their users and communities with a User Generated Content video solution. == Technology == Showbox's video creation technology includes an online green screen feature, proprietary computer vision algorithms, deep learning technology to support the automatic creation of videos in the cloud, and advanced video composition, including special effects. == Coverage and awards == In March 2015, Showbox was nominated as one of the 10 Israeli startups to take over our TV screens this year. In July 2016, Showbox won the Publicis90 award as part of Publicis' "global initiative to foster digital entrepreneurship". In March 2017, Showbox was chosen as one of The Culture Trip's 10 startups to watch for in 2017.
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Odor source localization

Odor source localization (OSL) is the problem of locating the origin of an airborne or waterborne chemical plume using one or more mobile sensors, typically robots equipped with chemical sensors. The task sits at the intersection of robotics, fluid dynamics and machine olfaction. Chemical plumes in turbulent flows are intermittent and patchy, and most chemical sensors respond slowly and have limited selectivity, so the instantaneous reading available to a moving sensor is a poor proxy for the underlying time-averaged concentration field. Robotic OSL has been studied since the late 1980s and has applications including the detection of gas leaks, search and rescue after industrial accidents, and environmental monitoring of industrial emissions. == History == Robotic odor search emerged in the late 1980s and 1990s, drawing on earlier work in chemical ecology that had described how moths and other insects locate distant pheromone sources. R. A. Russell at Monash University was among the first to build mobile robots that followed chemical trails on the floor and tracked airborne odor plumes. Distributed and multi-robot odor search were investigated by Hayes, Martinoli and Goodman at the California Institute of Technology and EPFL, who studied cooperative plume-tracing on simulated and physical robot swarms. In 2007 Vergassola, Villermaux and Shraiman introduced infotaxis, an information-theoretic search strategy in which a sensor moves so as to maximize the expected information gain about source location, rather than following a chemical concentration gradient; the paper appeared in Nature and prompted substantial follow-up work in the robotics community. From the mid-2010s, multi-rotor unmanned aerial vehicles carrying lightweight chemical sensors became a common experimental platform for OSL research. == Problem formulation == OSL is generally decomposed into three sub-problems: plume detection (deciding whether a chemical signal is present), plume traversal (moving so as to remain in contact with the plume), and source declaration (deciding when the source has been reached). The mathematical difficulty depends strongly on the assumed dispersion model. In laminar or low-Reynolds number flows a Gaussian advection–diffusion model gives a smooth concentration field with a well-defined gradient. In turbulent flows, which dominate most realistic environments, the plume is filamentary: the sensor receives short, randomly spaced bursts of chemical separated by periods of zero signal, and the time-averaged field is not a useful guide on the time scales at which a robot must act. Source-term estimation, surveyed by Hutchinson and colleagues, additionally aims to recover both the position and the release rate of the source from the observed concentrations, often using probabilistic filters. == Biological inspiration == Many OSL strategies are explicitly modeled on the behavior of male moths flying upwind toward a pheromone source. As reviewed by Cardé and Willis, moths combine an upwind surge whenever they detect a filament of pheromone with a wider crosswind cast when contact is lost, producing a characteristic zig-zag trajectory that has been transposed onto mobile robots by several groups. Other biological models draw on the search behavior of dogs and of marine animals such as blue crabs and lobsters, which integrate chemical and bilateral hydrodynamic cues over much shorter ranges. == Algorithms and strategies == === Reactive strategies === Reactive strategies select the next motion as a direct function of the current sensor reading. Chemotaxis steers along the locally estimated concentration gradient, which is effective in laminar plumes but degrades severely in turbulence. Anemotaxis exploits a measured wind direction by surging upwind when chemical contact is made. The bio-inspired cast-and-surge family combines anemotaxis with a deterministic crosswind cast on contact loss, and is the dominant reactive approach for turbulent environments. === Probabilistic and information-theoretic strategies === Probabilistic methods maintain a posterior distribution over possible source locations and choose actions that improve that distribution. The infotaxis strategy of Vergassola, Villermaux and Shraiman selects the move that maximizes the expected reduction in entropy of the source-location posterior, and is effective in regimes where the spatial gradient is unusable. Bayesian source-term estimation extends this idea by inferring both source position and release rate, typically using particle filters or sequential Monte Carlo. === Map-based strategies === Map-based methods build a spatial model of the time-averaged gas distribution from sensor readings collected along the robot's trajectory and search for local maxima in that model. Lilienthal and colleagues describe a family of kernel-based gas distribution mapping techniques in which point measurements are convolved with a Gaussian kernel to produce a spatially extrapolated estimate. Such methods are most useful when the source can be assumed quasi-stationary and the robot is able to revisit locations. === Multi-robot and swarm strategies === Multiple robots searching cooperatively can shorten search times. Cooperative formations spread the sensors across the crosswind axis, making detection of an intermittent plume more likely. Swarm-based approaches, reviewed by Wang and colleagues, deploy larger numbers of simpler agents and rely on collective behavior rather than centralized planning; reported advantages include improved coverage of the search area and the possibility of locating multiple sources in parallel. == Sensors and platforms == Most OSL systems use metal-oxide semiconductor (MOX) sensors, photoionization detectors or electrochemical cells, which trade off sensitivity, selectivity, response time and power consumption. Ishida and colleagues describe how these sensors interact with airflow around the robot body, an effect that motivates careful aerodynamic design and active sampling. Mobile platforms include wheeled ground robots for indoor and structured outdoor environments, multi-rotor unmanned aerial vehicles for open spaces and elevated sources, and autonomous underwater vehicles for chemical plumes in the marine environment. == Notable systems == Among the early demonstrations, R. A. Russell's series of differential-drive robots at Monash University localized volatile sources in still and ventilated rooms during the 1990s. The Smelling Nano Aerial Vehicle reported by Burgués and colleagues used a Crazyflie nano-quadcopter (approximately 27 grams in mass and 10 cm across) carrying a custom MOX gas sensing board, and built three-dimensional gas distribution maps of indoor releases from sweeping flights of less than three minutes. The GADEN simulator, released by Monroy and colleagues, couples three-dimensional dispersion computed from an OpenFOAM CFD solver with models of MOX and photo-ionization gas sensors, and is widely used to test mobile-robot olfaction algorithms in simulation. == Applications == Reported applications include the localization of natural-gas and methane leaks in urban infrastructure, search for chemical contamination after industrial accidents, search and rescue, and environmental monitoring of industrial emissions. Drug- and explosives-detection robots are an adjacent application area, although these typically rely on close-range sniffing rather than long-range plume tracking. == Open challenges == Open challenges identified in recent reviews include the limited speed, selectivity and stability of available chemical sensors; the scarcity of standardized, large-scale benchmarks comparable to those available in computer vision; reliable handling of multi-source environments, where standard single-source assumptions fail; and the integration of OSL with other autonomous-vehicle subsystems such as obstacle avoidance and navigation in three-dimensional turbulent flow.
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Open Syllabus Project

The Open Syllabus Project (OSP) is an online open-source platform that catalogs and analyzes millions of college syllabi. Founded by researchers from the American Assembly at Columbia University, the OSP has amassed the most extensive collection of searchable syllabi. Since its beta launch in 2016, the OSP has collected over 7 million course syllabi from over 80 countries, primarily by scraping publicly accessible university websites. The project is directed by Joe Karaganis. == History == The OSP was formed by a group of data scientists, sociologists, and digital-humanities researchers at the American Assembly, a public-policy institute based at Columbia University. The OSP was partly funded by the Sloan Foundation and the Arcadia Fund. Joe Karaganis, former vice-president of the American Assembly, serves as the project director of the OSP. The project builds on prior attempts to archive syllabi, such as H-Net, MIT OpenCourseWare, and historian Dan Cohen's defunct Syllabus Finder website (Cohen now sits on the OSP's advisory board). The OSP became a non-profit and independent of the American Assembly in November 2019. In January 2016, the OSP launched a beta version of their "Syllabus Explorer," which they had collected data for since 2013. The Syllabus Explorer allows users to browse and search texts from over one million college course syllabi. The OSP launched a more comprehensive version 2.0 of the Syllabus Explorer in July 2019. The newer version includes an interactive visualization that displays texts as dots on a knowledge map. As of 2022, the OSP has collected over 7 million course syllabi. The Syllabus Explorer represents the "largest collection of searchable syllabi ever amassed." == Methodology == The OSP has collected syllabi data from over 80 countries dating to 2000. The syllabi stem from over 4,000 worldwide institutions. Most of the OSP's data originates from the United States. Canada, Australia, and the U.K also have large datasets. The OSP primarily collects syllabi by scraping publicly accessible university websites. The OSP also allows syllabi submissions from faculty, students, and administrators. The OSP developers use machine learning and natural language processing to extract metadata from such syllabi. Since only metadata is collected, no individual syllabus or personal identifying information is found in the OSP database. The OSP classifies the syllabi into 62 subject fields – corresponding to the U.S. Department of Education's Classification of Instructional Programs (CIP). Additionally, the OSP assigns each text a "teaching score" from 0–100. This score represents the text's percentile rank among citations in the total citation count and is a numerical indicator of the relative frequency of which a particular work is taught. The OSP also has data on which texts are most likely to be assigned together. The developers behind the OSP admit that the database is incomplete and likely contains "a fair number of errors." Karaganis estimates that 80–100 million syllabi exist in the United States alone. The OSP is unable to access syllabi behind private course-management software like Blackboard. == Notable findings == === Anthropology === Using data from the OSP, anthropologist Laurence Ralph uncovered that black anthropologists are "woefully under-represented in (if not erased from) most anthropology syllabi." Black authors wrote less than 1 percent of the top 1,000 assigned works. === Economics === The database indicates Greg Mankiw is the most frequently cited author for college economics courses. === English literature === The OSP found that Mary Shelley's Frankenstein was the most widely taught novel in college courses. Additionally, the majority of novels published after 1945 taught in English classes were historical fiction. === Female writers === The most read female writer on college campuses is Kate L. Turabian for her A Manual for Writers of Research Papers, Theses, and Dissertations . Turabian is followed by Diana Hacker, Toni Morrison, Jane Austen, and Virginia Woolf. === Film === The most assigned film according to the OSP is the 1929 Soviet documentary film, Man with a Movie Camera. English filmmaker Alfred Hitchcock is the most assigned director in college courses. === History === Historians George Brown Tindall and David Emory Shi's America: A Narrative History is the number one assigned textbook for history, followed by Anne Moody's memoir, Coming of Age in Mississippi. === Philosophy === The most assigned texts in the field of philosophy include Aristotle's Nicomachean Ethics, John Stuart Mill's Utilitarianism, and Plato's Republic. Plato's Republic was also the second most assigned text in universities in the English-speaking world (only behind Strunk and White's Elements of Style). === Physics === David Halliday's et al. Fundamentals of Physics is the number one ranked physics textbook in the OSP's database. === Political science === Data from the OSP indicates that the dominant political science texts are written almost exclusively by white men and scholars based in the West. In the top 200 most-frequently assigned works, 15 are authored by at least one woman. === Public administration === American president Woodrow Wilson's article "The Study of Administration" was the most frequently assigned text in public affairs and administration syllabi. == Reception == According to William Germano et al., the OSP is a "fascinating resource but is also prone to misrepresenting or at least distracting us from the most important business of a syllabus: communicating with students." Historian William Caferro remarks that the OSP is a "tacit experience of sharing, but a useful one." English professor Bart Beaty writes that, "Despite the many reservations about the completeness of its data, the OSP provides a rare opportunity for scholars to move beyond the anecdotal in discussions of canon-formation in teaching." Media theorist Elizabeth Losh opines that "big data approaches", like the OSP, may "raise troubling questions for instructors about informed consent, pedagogical privacy, and quantified metrics."
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ViEWER

ViEWER, the Virtual Environment Workbench for Education and Research, is a proprietary, freeware computer program for Microsoft Windows written by researchers at the University of Idaho for the study of visual perception and complex immersive three-dimensional environments. It was created using C++ and OpenGL, and has been used by Dr. Brian Dyre, Dr. Steffen Werner, Dr. Ernesto Bustamante, Dr. Ben Barton, and their undergraduate and graduate researchers in visual perception, signal detection, and child-safety experiments.
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Ave!Comics

Ave!Comics Production is a privately owned French company editing comics on smartphones, tablets and computers. It was founded in 2008 and it is a subsidiary of Aquafadas, a software development company in digital publishing owned by Kobo Inc. AveComics is a comic book store for digital comic books that can be used on computers, tablets, and smartphones.(iOS, Android) Readers can buy and read comic books, manga and graphic novels in French, English and Spanish. AveComics uses a technology created by Aquafadas for comics transformation, distribution and reading, based around its AVE format. The AveComics application was also a finalist in the BlackBerry Innovation Awards 2009, in the "Entertainment" category. == Company history == Aquafadas, a company working on creative software for Flash, HTML5, photo, and video editing, created the application MyComics to allow the reading of comics on mobile in 2006. This application was made available in 2008, to enable the reading and storing of comics on iPhone and iPod Touch. A reading system adapted to low resolution screens was also available. In October of the same year, the company launched a comics library on both devices, in partnership with the Angoulême International Comics Festival, Fnac and SNCF. This library included the official selection of the festival, and was downloaded over 150 000 times. In December 2008 "The Adventures of Lucky Luke n°3", at Lucky Comics was published on both devices. The comic made a 50 000 € turnover. In April 2009, "Les Blondes" 10th volume was the top-selling comic for 10 months on the AppStore. After, in August 2009, the AveComics application was launched on iPhone, iPod Touch and BlackBerry. The company's website was launched in September when more than 100 titles were available on smartphones and computers. == Catalogue == AveComics works with over 80 international publishers including Glénat, Marsu Productions, Delcourt, Casterman, Soleil, Ubisoft, Les Humanoïdes Associés and Mad Fabrik. Comics such as "Assassin's Creed", "Talisman", "Titeuf", and "Seoul District" are sold by the company. == Award == Grand Prix Software Venture Capital - Senate 2008.
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DataScene

DataScene is a scientific graphing, animation, data analysis, and real-time data monitoring software package. It was developed with the Common Language Infrastructure technology and the GDI+ graphics library. With the two Common Language Runtime engines - the .Net and Mono frameworks - DataScene runs on all major operating systems. With DataScene, the user can plot 39 types 2D & 3D graphs (e.g., Area graph, Bar graph, Boxplot graph, Pie graph, Line graph, Histogram graph, Surface graph, Polar graph, Water Fall graph, etc.), manipulate, print, and export graphs to various formats (e.g., Bitmap, WMF/EMF, JPEG, PNG, GIF, TIFF, PostScript, and PDF), analyze data with different mathematical methods (fitting curves, calculating statics, FFT, etc.), create chart animations for presentations (e.g. with PowerPoint), classes, and web pages, and monitor and chart real-time data. == History == DataScene was first released (version 1.0) in March 2009 for the Windows platform and the .Net 2.0 framework. Since version 2.0, DataScene has been ported to the Mono framework 2.6 and all Linux and Unix/X11 operating systems. Cyberwit offers free licensing for the Express edition of DataScene.
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SlideRocket

SlideRocket was an online presentation platform that let users create, manage, share and measure presentations. SlideRocket was provided via a SaaS model. The company was acquired by VMware in April 2011, who sold it to ClearSlide, a similar SaaS application, in March 2013. It is no longer offering independent signups, as the platform is being integrated into ClearSlide. == History == SlideRocket was founded in Jan 2006, and launched as a private beta in March 2008 at the Under The Radar Spring event. A public beta was announced in September 2008 followed shortly by public release on October 28, 2008. SlideRocket is most commonly credited with inventing the PResuMÉ or Presentation Résumé in early 2009. On April 26, 2011, SlideRocket was acquired by VMware. On March 5, 2013, VMware sold SlideRocket to ClearSlide. SlideRocket is based in San Francisco.
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Pandemonium architecture

Pandemonium architecture is a theory in cognitive science that describes how visual images are processed by the brain. It has applications in artificial intelligence and pattern recognition. The theory was introduced by the artificial intelligence pioneer Oliver Selfridge in his 1959 paper "Pandemonium - A Paradigm for Learning". It describes the process of object recognition as the exchange of signals within a hierarchical system of detection and association, the elements of which Selfridge metaphorically termed "demons". This model is now recognized as the basis of visual perception in cognitive science. Pandemonium architecture arose in response to the inability of template matching theories to offer a biologically plausible explanation of the image constancy phenomenon. Contemporary researchers praise this architecture for its elegancy and creativity; that the idea of having multiple independent systems (e.g., feature detectors) working in parallel to address the image constancy phenomena of pattern recognition is powerful yet simple. The basic idea of the pandemonium architecture is that a pattern is first perceived in its parts before the "whole". Pandemonium architecture was one of the first computational models in pattern recognition. Although not perfect, the pandemonium architecture influenced the development of modern connectionist, artificial intelligence, and word recognition models. == History == Most research in perception has been focused on the visual system, investigating the mechanisms of how we see and understand objects. A critical function of our visual system is its ability to recognize patterns, but the mechanism by which this is achieved is unclear. The earliest theory that attempted to explain how we recognize patterns is the template matching model. According to this model, we compare all external stimuli against an internal mental representation. If there is "sufficient" overlap between the perceived stimulus and the internal representation, we will "recognize" the stimulus. Although some machines follow a template matching model (e.g., bank machines verifying signatures and accounting numbers), the theory is critically flawed in explaining the phenomena of image constancy: we can easily recognize a stimulus regardless of the changes in its form of presentation (e.g., T and T are both easily recognized as the letter T). It is highly unlikely that we have a stored template for all of the variations of every single pattern. As a result of the biological plausibility criticism of the template matching model, feature detection models began to rise. In a feature detection model, the image is first perceived in its basic individual elements before it is recognized as a whole object. For example, when we are presented with the letter A, we would first see a short horizontal line and two slanted long diagonal lines. Then we would combine the features to complete the perception of A. Each unique pattern consists of different combination of features, which means those that are formed with the same features will generate the same recognition. That is, regardless of how we rotate the letter A, is still perceived as the letter A. It is easy for this sort of architecture to account for the image constancy phenomena because you only need to "match" at the basic featural level, which is presumed to be limited and finite, thus biologically plausible. The best known feature detection model is called the pandemonium architecture. == Pandemonium architecture == The pandemonium architecture was originally developed by Oliver Selfridge in the late 1950s. The architecture is composed of different groups of "demons" working independently to process the visual stimulus. Each group of demons is assigned to a specific stage in recognition, and within each group, the demons work in parallel. There are four major groups of demons in the original architecture. The concept of feature demons, that there are specific neurons dedicated to perform specialized processing is supported by research in neuroscience. Hubel and Wiesel found there were specific cells in a cat's brain that responded to specific lengths and orientations of a line. Similar findings were discovered in frogs, octopuses and a variety of other animals. Octopuses were discovered to be only sensitive to verticality of lines, whereas frogs demonstrated a wider range of sensitivity. These animal experiments demonstrate that feature detectors seem to be a very primitive development. That is, it did not result from the higher cognitive development of humans. Not surprisingly, there is also evidence that the human brain possesses these elementary feature detectors as well. Moreover, this architecture is capable of learning, similar to a back-propagation styled neural network. The weight between the cognitive and feature demons can be adjusted in proportion to the difference between the correct pattern and the activation from the cognitive demons. To continue with our previous example, when we first learned the letter R, we know is composed of a curved, long straight, and a short angled line. Thus when we perceive those features, we perceive R. However, the letter P consists of very similar features, so during the beginning stages of learning, it is likely for this architecture to mistakenly identify R as P. But through constant exposure of confirming R's features to be identified as R, the weights of R's features to P are adjusted so the P response becomes inhibited (e.g., learning to inhibit the P response when a short angled line is detected). In principle, a pandemonium architecture can recognize any pattern. As mentioned earlier, this architecture makes error predictions based on the amount of overlapping features. Such as, the most likely error for R should be P. Thus, in order to show this architecture represents the human pattern recognition system we must put these predictions into test. Researchers have constructed scenarios where various letters are presented in situations that make them difficult to identify; then types of errors were observed, which was used to generate confusion matrices: where all of the errors for each letter are recorded. Generally, the results from these experiments matched the error predictions from the pandemonium architecture. Also as a result of these experiments, some researchers have proposed models that attempted to list all of the basic features in the Roman alphabet. == Criticism == A major criticism of the pandemonium architecture is that it adopts a completely bottom-up processing: recognition is entirely driven by the physical characteristics of the targeted stimulus. This means that it is unable to account for any top-down processing effects, such as context effects (e.g., pareidolia), where contextual cues can facilitate (e.g., word superiority effect: it is relatively easier to identify a letter when it is part of a word than in isolation) processing. However, this is not a fatal criticism to the overall architecture, because is relatively easy to add a group of contextual demons to work along with the cognitive demons to account for these context effects. Although the pandemonium architecture is built on the fact that it can account for the image constancy phenomena, some researchers have argued otherwise; and pointed out that the pandemonium architecture might share the same flaws from the template matching models. For example, the letter H is composed of 2 long vertical lines and a short horizontal line; but if we rotate the H 90 degrees in either direction, it is now composed of 2 long horizontal lines and a short vertical line. In order to recognize the rotated H as H, we would need a rotated H cognitive demon. Thus we might end up with a system that requires a large number of cognitive demons in order to produce accurate recognition, which would lead to the same biological plausibility criticism of the template matching models. However, it is rather difficult to judge the validity of this criticism because the pandemonium architecture does not specify how and what features are extracted from incoming sensory information, it simply outlines the possible stages of pattern recognition. But of course that raises its own questions, to which it is almost impossible to criticize such a model if it does not include specific parameters. Also, the theory appears to be rather incomplete without defining how and what features are extracted, which proves to be especially problematic with complex patterns (e.g., extracting the weight and features of a dog). Some researchers have also pointed out that the evidence supporting the pandemonium architecture has been very narrow in its methodology. Majority of the research that supports this architecture has often referred to its ability to recognize simple schematic drawings that are selected from a small finite set (e.g., letters in the Roman alphabet). Evidence from these types of exper
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CamScanner

CamScanner is a Chinese mobile app first released in 2010 that allows iOS and Android devices to be used as image scanners. It allows users to 'scan' documents (by taking a photo with the device's camera) and share the photo as either a JPEG or PDF. This app is available free of charge on the Google Play Store and the Apple App Store. The app is based on freemium model, with ad-supported free version and a premium version with additional functions. == History == On August 27, 2019, Russian cyber security company Kaspersky Lab discovered that recent versions of the Android app distributed an advertising library containing a Trojan Dropper, which was also included in some apps preinstalled on several Chinese mobiles. The advertising library decrypts a Zip archive which subsequently downloads additional files from servers controlled by hackers, allowing the hackers to control the device, including by showing intrusive advertising or charging paid subscriptions. Google took the app down after Kaspersky reported its findings. An updated version of the app with the advertising library removed was made available on the Google Play Store as of September 5, 2019. Kaspersky later acknowledged "We appreciate the willingness to cooperate that we've seen from CamScanner representatives, as well as the responsible attitude to user safety they demonstrated while eliminating the threat…The malicious modules were removed from the app immediately upon Kaspersky's warning, and Google Play has restored the app." In June 2020, as tensions along the Line of Actual Control between China and India continued, the Government of India decided to ban 118 Chinese apps, including TikTok and CamScanner citing data and privacy issues. On January 5, 2021, US President Donald Trump signed Executive Order 13971 banning Alipay, Tencent's QQ, QQ Wallet, WeChat Pay, CamScanner, Shareit, VMate and WPS Office to conduct US transactions. The Trump administration explained this act by saying that this move helps prevent personal information such as text, phone calls and photos collected from rivals. However, the Biden administration did not meet the February 2021 deadline for implementing the executive order, allowing these apps to operate in the US and revoked the previous executive order Executive Order 14034 of June 9, 2021.
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