AI Assistant Examples

AI Assistant Examples — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

Indic computing

Indic Computing means "computing in Indic", i.e., Indian Scripts and Languages. It involves developing software in Indic Scripts/languages, Input methods, Localization of computer applications, web development, Database Management, Spell checkers, Speech to Text and Text to Speech applications and OCR in Indian languages. Unicode standard version 15.0 specifies codes for 9 Indic scripts in Chapter 12 titled "South and Central Asia-I, Official Scripts of India". The 9 scripts are Bengali, Devanagari, Gujarati, Gurmukhi, Kannada, Malayalam, Oriya, Tamil and Telugu. A lot of Indic Computing projects are going on. They involve some government sector companies, some volunteer groups and individual people. == Government sector == Indian Union Government made it mandatory for Mobile phone companies whose handsets manufactured, stored, sold and distributed in India to have support for displaying and typing text using fonts for all 22 languages. This move has seen rise in use of Indian languages by millions of users. === TDIL === The Department of Electronics and Information Technology, India initiated the TDIL (Technology Development for Indian Languages) with the objective of developing Information Processing Tools and Techniques to facilitate human-machine interaction without a language barrier; creating and accessing multilingual knowledge resources; and integrating them to develop innovative user products and services. In 2005, it started distributing language software tools developed by Government/Academic/Private companies in the form of CD for non commercial use. Some of the outcomes of TDIL program have been deployed on Indian Language Technology Proliferation & Deployment Centre. This Centre disseminates all the linguistic resources, tools & applications which have been developed under TDIL funding. This programme took to exponential expansion under the leadership of Dr. Swaran Lata who also created international foot-print of the programme. She has now retired. === C-DAC === C-DAC is an India based government software company which is involved in developing language related software. It is best known for developing InScript Keyboard, the standard keyboard for Indian languages. It has also developed lot of Indic language solutions including Word Processors, typing tools, text to speech software, OCR in Indian languages etc. ==== BharateeyaOO.org ==== The work developed out of CDAC, Bangalore (earlier known as NCST, Bangalore) became BharateeyaOO. OpenOffice 2.1 had support for over 10 Indian languages. ==== BOSS ==== BOSS linux was developed by the Centre for Development of Advanced Computing (CDAC) to promote use of open-source software in India. == NGO and Volunteer groups == === Indlinux === Indlinux organisation helped organise the individual volunteers working on different indic language versions of Linux and its applications. === Sarovar === Sarovar.org is India's first portal to host projects under Free/Open source licenses. It is located in Trivandrum, India and hosted at Asianet data center. Sarovar.org is customised, installed and maintained by Linuxense as part of their community services and sponsored by River Valley Technologies. Sarovar.org is built on Debian Etch and GForge and runs off METTLE. === Pinaak === Pinaak is a non-government charitable society devoted to Indic language computing. It works for software localization, developing language software, localizing open source software, enriching online encyclopedias etc. In addition to this Pinaak works for educating people about computing, ethical use of Internet and use of Indian languages on Internet. === Ankur Group === Ankur Group is working toward supporting Bengali language (Bengali) on Linux operating system including localized Bengali GUI, Live CD, English-to-Bengali translator, Bengali OCR and Bengali Dictionary etc. === BhashaIndia === === SMC === SMC is a free software group, working to bridge the language divide in Kerala in the technology front and is today the biggest language computing community in India. == Input methods == === Full size keyboards === With the advent of Unicode inputting Indic text on computer has become very easy. A number of methods exist for this purpose, but the main ones are:- ==== InScript ==== Inscript is the standard keyboard for Indian languages. Developed by C-DAC and standardized by Government of India. Nowadays it comes inbuilt in all major operating systems including Microsoft Windows (2000, XP, Vista, 7), Linux and Macintosh. ==== Phonetic transliteration ==== This is a typing method in which, for instance, the user types text in an Indian language using Roman characters and it is phonetically converted to equivalent text in Indian script in real time. This type of conversion is done by phonetic text editors, word processors and software plugins. Building up on the idea, one can use phonetic IME tools that allow Indic text to be input in any application. Some examples of phonetic transliterators are Xlit, Google Indic Transliteration, BarahaIME, Indic IME, Rupantar, SMC's Indic Keyboard and Microsoft Indic Language Input Tool. SMC's Indic Keyboard has support for as many as 23 languages whereas Google Indic Keyboard only supports 11 Indian languages. They can be broadly classified as: Fixed transliteration scheme based tools – They work using a fixed transliteration scheme to convert text. Some examples are Indic IME, Rupantar and BarahaIME. Intelligent/Learning based transliteration tools – They compare the word with a dictionary and then convert it to the equivalent words in the target language. Some of the popular ones are Google Indic Transliteration, Xlit, Microsoft Indic Language Input Tool and QuillPad. ==== Remington (typewriter) ==== This layout was developed when computers had not been invented or deployed with Indic languages, and typewriters were the only means to type text in Indic scripts. Since typewriters were mechanical and could not include a script processor engine, each character had to be placed on the keyboard separately, which resulted in a very complex and difficult to learn keyboard layout. With the advent of Unicode, the Remington layout was added to various typing tools for sake of backward compatibility, so that old typists did not have to learn a new keyboard layout. Nowadays this layout is only used by old typists who are used to this layout due to several years of usage. One tool to include Remington layout is Indic IME. A font that is based on the Remington keyboard layout is Kruti Dev. Another online tool that very closely supports the old Remington keyboard layout using Kruti Dev is the Remington Typing tool. === Braille === IBus Sharada Braille, which supports seven Indian languages was developed by SMC. === Mobile phones with Numeric keyboards === Mobile/Hand/cell phone basic models have 12 keys like the plain old telephone keypad. Each key is mapped to 3 or 4 English letters to facilitate data entry in English. For inputting Indian languages with this kind of keypad, there are two ways to do so. First is the Multi-tap Method and second uses visual help from the screen like Panini Keypad. The primary usage is SMS. 140 characters size used for English/Roman languages can be used to accommodate only about 70 language characters when Unicode Proprietary compression is used some times to increase the size of single message for Complex script languages like Hindi. A research study of the available methods and recommendations of proposed standard was released by Broadband Wireless Consortium of India (BWCI). ==== Transliteration/Phonetic methods ==== English is used to type in Indian languages. QuillPad IndiSMS ==== Native methods ==== In native methods, the letters of the language are displayed on the screen corresponding to the numeral keys based on the probabilities of those letters for that language. Additional letters can be accessed by using a special key. When a word is partially typed, options are presented from which the user can make a selection. === Smart phones with Qwerty keyboards === Most smart phones have about 35 keys catering primarily to the English language. Numerals and some symbols are accessed with a special key called Alt. Indic input methods are yet to evolve for these types of phones, as support of Unicode for rendering is not widely available. === For Smart Phones with Soft/Virtual keyboards === Inscript is being adopted for smart phone usage. For Android phones which can render Indic languages, Swalekh Multilingual Keypad Multiling Keyboard app are available. Gboard offers support for several Indian languages. == Localization == Localization means translating software, operating systems, websites etc. various applications in Indian language. Various volunteers groups are working in this direction. === Mandrake Tamil Version === A notable example is the Tamil version of Mandrake linux(defunct since 2011). Tamil speakers in Toronto (Canada) released Mandrake,
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Zé Delivery

Zé Delivery is a startup developed by Brazilian drinks company AmBev which offers an app for delivering drinks. The app is available for Android and iOS. Created in 2016 by AmBev's ZX Ventures hub, the service has an international presence in Argentina, Paraguay, Bolivia, Panama and the Dominican Republic. It is also present in more than 300 Brazilian cities. Because it has an extensive category of alcoholic beverages, the service is only used by people over 18. It also offers soft drinks, juices, energy drinks and other non-alcoholic beverages.
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Supervisor Mode Access Prevention

Supervisor Mode Access Prevention (SMAP) is a feature of some CPU implementations such as the Intel Broadwell microarchitecture that allows supervisor mode programs to optionally set user-space memory mappings so that access to those mappings from supervisor mode will cause a trap. This makes it harder for malicious programs to "trick" the kernel into using instructions or data from a user-space program. == History == Supervisor Mode Access Prevention is designed to complement Supervisor Mode Execution Prevention (SMEP), which was introduced earlier. SMEP can be used to prevent supervisor mode from unintentionally executing user-space code. SMAP extends this protection to reads and writes. == Benefits == Without Supervisor Mode Access Prevention, supervisor code usually has full read and write access to user-space memory mappings (or has the ability to obtain full access). This has led to the development of several security exploits, including privilege escalation exploits, which operate by causing the kernel to access user-space memory when it did not intend to. Operating systems can block these exploits by using SMAP to force unintended user-space memory accesses to trigger page faults. Additionally, SMAP can expose flawed kernel code which does not follow the intended procedures for accessing user-space memory. However, the use of SMAP in an operating system may lead to a larger kernel size and slower user-space memory accesses from supervisor code, because SMAP must be temporarily disabled any time supervisor code intends to access user-space memory. == Technical details == Processors indicate support for Supervisor Mode Access Prevention through the Extended Features CPUID leaf. SMAP is enabled when memory paging is active and the SMAP bit in the CR4 control register is set. SMAP can be temporarily disabled for explicit memory accesses by setting the EFLAGS.AC (Alignment Check) flag. The stac (Set AC Flag) and clac (Clear AC Flag) instructions can be used to easily set or clear the flag. When the SMAP bit in CR4 is set, explicit memory reads and writes to user-mode pages performed by code running with a privilege level less than 3 will always result in a page fault if the EFLAGS.AC flag is not set. Implicit reads and writes (such as those made to descriptor tables) to user-mode pages will always trigger a page fault if SMAP is enabled, regardless of the value of EFLAGS.AC. == Operating system support == Linux kernel support for Supervisor Mode Access Prevention was implemented by H. Peter Anvin. It was merged into the mainline Linux 3.7 kernel (released December 2012) and it is enabled by default for processors which support the feature. FreeBSD has supported Supervisor Mode Execution Prevention since 2012 and Supervisor Mode Access Prevention since 2018. OpenBSD has supported Supervisor Mode Access Prevention and the related Supervisor Mode Execution Prevention since 2012, with OpenBSD 5.3 being the first release with support for the feature enabled. NetBSD support for Supervisor Mode Execution Prevention (SMEP) was implemented by Maxime Villard in December 2015. Support for Supervisor Mode Access Prevention (SMAP) was also implemented by Maxime Villard, in August 2017. NetBSD 8.0 was the first release with both features supported and enabled. Haiku support for Supervisor Mode Execution Prevention (SMEP) was implemented by Jérôme Duval in January 2018. macOS has support for SMAP at least since macOS 10.13 released 2017.
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Visualization (graphics)

Visualization (or visualisation in Commonwealth English; see spelling differences), also known as graphics visualization, is any technique for creating images, diagrams, or animations to communicate a message. Visualization through visual imagery has been an effective way to communicate both abstract and concrete ideas since the dawn of humanity. Examples from history include cave paintings, Egyptian hieroglyphs, Greek geometry, and Leonardo da Vinci's revolutionary methods of technical drawing for engineering purposes that actively involve scientific requirements. Visualization today has ever-expanding applications in science, education, engineering (e.g., product visualization), interactive multimedia, medicine, etc. Typical of a visualization application is the field of computer graphics. The invention of computer graphics (and 3D computer graphics) may be the most important development in visualization since the invention of central perspective in the Renaissance period. The development of animation also helped advance visualization. == Overview == The use of visualization to present information is not a new phenomenon. It has been used in maps, scientific drawings, and data plots for over a thousand years. Examples from cartography include Ptolemy's Geographia (2nd century AD), a map of China (1137 AD), and Minard's map (1861) of Napoleon's invasion of Russia a century and a half ago. Most of the concepts learned in devising these images carry over in a straightforward manner to computer visualization. Edward Tufte has written three critically acclaimed books that explain many of these principles. Computer graphics has from its beginning been used to study scientific problems. However, in its early days the lack of graphics power often limited its usefulness. The recent emphasis on visualization started in 1987 with the publication of Visualization in Scientific Computing, a special issue of Computer Graphics. Since then, there have been several conferences and workshops, co-sponsored by the IEEE Computer Society and ACM SIGGRAPH, devoted to the general topic, and special areas in the field, for example volume visualization. Most people are familiar with the digital animations produced to present meteorological data during weather reports on television, though few can distinguish between those models of reality and the satellite photos that are also shown on such programs. TV also offers scientific visualizations when it shows computer drawn and animated reconstructions of road or airplane accidents. Some of the most popular examples of scientific visualizations are computer-generated images that show real spacecraft in action, out in the void far beyond Earth, or on other planets. Dynamic forms of visualization, such as educational animation or timelines, have the potential to enhance learning about systems that change over time. Apart from the distinction between interactive visualizations and animation, the most useful categorization is probably between abstract and model-based scientific visualizations. The abstract visualizations show completely conceptual constructs in 2D or 3D. These generated shapes are completely arbitrary. The model-based visualizations either place overlays of data on real or digitally constructed images of reality or make a digital construction of a real object directly from the scientific data. Scientific visualization is usually done with specialized software, though there are a few exceptions, noted below. Some of these specialized programs have been released as open source software, having very often its origins in universities, within an academic environment where sharing software tools and giving access to the source code is common. There are also many proprietary software packages of scientific visualization tools. Models and frameworks for building visualizations include the data flow models popularized by systems such as AVS, IRIS Explorer, and VTK toolkit, and data state models in spreadsheet systems such as the Spreadsheet for Visualization and Spreadsheet for Images. == Applications == === Scientific visualization === As a subject in computer science, scientific visualization is the use of interactive, sensory representations, typically visual, of abstract data to reinforce cognition, hypothesis building, and reasoning. Scientific visualization is the transformation, selection, or representation of data from simulations or experiments, with an implicit or explicit geometric structure, to allow the exploration, analysis, and understanding of the data. Scientific visualization focuses and emphasizes the representation of higher order data using primarily graphics and animation techniques. It is a very important part of visualization and maybe the first one, as the visualization of experiments and phenomena is as old as science itself. Traditional areas of scientific visualization are flow visualization, medical visualization, astrophysical visualization, and chemical visualization. There are several different techniques to visualize scientific data, with isosurface reconstruction and direct volume rendering being the more common. === Data and information visualization === Data visualization is a related subcategory of visualization dealing with statistical graphics and geospatial data (as in thematic cartography) that is abstracted in schematic form. Information visualization concentrates on the use of computer-supported tools to explore large amount of abstract data. The term "information visualization" was originally coined by the User Interface Research Group at Xerox PARC and included Jock Mackinlay. Practical application of information visualization in computer programs involves selecting, transforming, and representing abstract data in a form that facilitates human interaction for exploration and understanding. Important aspects of information visualization are dynamics of visual representation and the interactivity. Strong techniques enable the user to modify the visualization in real-time, thus affording unparalleled perception of patterns and structural relations in the abstract data in question. === Educational visualization === Educational visualization is using a simulation to create an image of something so it can be taught about. This is very useful when teaching about a topic that is difficult to otherwise see, for example, atomic structure, because atoms are far too small to be studied easily without expensive and difficult to use scientific equipment. === Knowledge visualization === The use of visual representations to transfer knowledge between at least two persons aims to improve the transfer of knowledge by using computer and non-computer-based visualization methods complementarily. Thus properly designed visualization is an important part of not only data analysis but knowledge transfer process, too. Knowledge transfer may be significantly improved using hybrid designs as it enhances information density but may decrease clarity as well. For example, visualization of a 3D scalar field may be implemented using iso-surfaces for field distribution and textures for the gradient of the field. Examples of such visual formats are sketches, diagrams, images, objects, interactive visualizations, information visualization applications, and imaginary visualizations as in stories. While information visualization concentrates on the use of computer-supported tools to derive new insights, knowledge visualization focuses on transferring insights and creating new knowledge in groups. Beyond the mere transfer of facts, knowledge visualization aims to further transfer insights, experiences, attitudes, values, expectations, perspectives, opinions, and estimates in different fields by using various complementary visualizations. See also: picture dictionary, visual dictionary === Product visualization === Product visualization involves visualization software technology for the viewing and manipulation of 3D models, technical drawing and other related documentation of manufactured components and large assemblies of products. It is a key part of product lifecycle management. Product visualization software typically provides high levels of photorealism so that a product can be viewed before it is actually manufactured. This supports functions ranging from design and styling to sales and marketing. Technical visualization is an important aspect of product development. Originally technical drawings were made by hand, but with the rise of advanced computer graphics the drawing board has been replaced by computer-aided design (CAD). CAD-drawings and models have several advantages over hand-made drawings such as the possibility of 3-D modeling, rapid prototyping, and simulation. 3D product visualization promises more interactive experiences for online shoppers, but also challenges retailers to overcome hurdles in the production of 3D content, as large-scale 3D content production can be extremel
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Inverse depth parametrization

In computer vision, the inverse depth parametrization is a parametrization used in methods for 3D reconstruction from multiple images such as simultaneous localization and mapping (SLAM). Given a point p {\displaystyle \mathbf {p} } in 3D space observed by a monocular pinhole camera from multiple views, the inverse depth parametrization of the point's position is a 6D vector that encodes the optical centre of the camera c 0 {\displaystyle \mathbf {c} _{0}} when in first observed the point, and the position of the point along the ray passing through p {\displaystyle \mathbf {p} } and c 0 {\displaystyle \mathbf {c} _{0}} . Inverse depth parametrization generally improves numerical stability and allows to represent points with zero parallax. Moreover, the error associated to the observation of the point's position can be modelled with a Gaussian distribution when expressed in inverse depth. This is an important property required to apply methods, such as Kalman filters, that assume normality of the measurement error distribution. The major drawback is the larger memory consumption, since the dimensionality of the point's representation is doubled. == Definition == Given 3D point p = ( x , y , z ) {\displaystyle \mathbf {p} =(x,y,z)} with world coordinates in a reference frame ( e 1 , e 2 , e 3 ) {\displaystyle (e_{1},e_{2},e_{3})} , observed from different views, the inverse depth parametrization y {\displaystyle \mathbf {y} } of p {\displaystyle \mathbf {p} } is given by: y = ( x 0 , y 0 , z 0 , θ , ϕ , ρ ) {\displaystyle \mathbf {y} =(x_{0},y_{0},z_{0},\theta ,\phi ,\rho )} where the first five components encode the camera pose in the first observation of the point, being c 0 = ( x 0 , y 0 , z 0 ) {\displaystyle \mathbf {c_{0}} =(x_{0},y_{0},z_{0})} the optical centre, ϕ {\displaystyle \phi } the azimuth, θ {\displaystyle \theta } the elevation angle, and ρ = 1 ‖ p − c 0 ‖ {\displaystyle \rho ={\frac {1}{\left\Vert \mathbf {p} -\mathbf {c} _{0}\right\Vert }}} the inverse depth of p {\displaystyle p} at the first observation.
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Visual hull

A visual hull is a geometric entity created by shape-from-silhouette 3D reconstruction technique introduced by A. Laurentini. This technique assumes the foreground object in an image can be separated from the background. Under this assumption, the original image can be thresholded into a foreground/background binary image, which we call a silhouette image. The foreground mask, known as a silhouette, is the 2D projection of the corresponding 3D foreground object. Along with the camera viewing parameters, the silhouette defines a back-projected generalized cone that contains the actual object; this cone is called a silhouette cone. The intersection of the two silhouette cones defines a visual hull. which is a bounding geometry of the actual 3D object. When the reconstructed geometry is only used for rendering from a different viewpoint, the implicit reconstruction together with rendering can be done using graphics hardware. == In two dimensions == A technique used in some modern touchscreen devices employs cameras placed in the corners situated opposite infrared LEDs. The one-dimensional projection (shadow) of objects on the surface may be used to reconstruct the convex hull of the object. Visual hull generation method has also been used within experimental tele-meeting systems that aim to allow a user in a remote location to interact with virtual objects. The method uses multiple cameras to capture the real-world movements and interactions of the "sender", employing hardware-accelerated volumetric visual hull representation to create 3D volume from 2D multi-view images. Its ultimate aim is to allow 3D collaboration between the two users in the virtual realm, with the visual hull technique reducing the computational power required to allow this type of interaction and enabling the use of consumer goods such as the Wii Remote as a tool for interaction.
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Mobile Passport Control

Mobile Passport Control (MPC) is a mobile app that enables eligible travelers entering the United States to submit their passport information and customs declaration form to Customs and Border Protection via smartphone or tablet and go through the inspections process using an expedited lane. It is available to "U.S. citizens, U.S. lawful permanent residents, Canadian B1/B2 citizen visitors and returning Visa Waiver Program travelers with approved ESTA". The app is available on iOS and Android devices and is operational at 34 US airports, 14 international airports offering preclearance facilities, and 4 seaports. The use of Mobile Passport Control operations have increased threefold from 2016 to 2017. == History == Mobile Passport Control operations were launched in Atlanta at the Hartsfield-Jackson International Airport in 2016 and is now available at 34 U.S. airports, 14 international airports that offer preclearance and 4 U.S. cruise ports. The Mobile Passport app is authorized by CBP and sponsored by the Airports Council International-North America, Boeing, and the Port of Everglades. Airside Mobile, Inc. secured a Series A funding of $6 million in the fall of 2017. == How it works == During the customs process at the Federal Inspection Service (FIS) area of a U.S. airport, travelers arriving from international locations typically wait in long lines before presenting passports and paperwork and verbally answering questions made by CBP officials. Eligible travelers who have downloaded the Mobile Passport app can expedite this process by submitting information regarding their passport and trip details, and a newly-taken selfie, via their mobile device to CBP officials, then access an expedited line. Mobile Passport Control users will be required to show their physical passport(s) and briefly talk to a CBP officer. == Locations == === US airports === Atlanta (ATL) Baltimore (BWI) Boston (BOS) Charlotte (CLT) Chicago (ORD) Dallas/Ft Worth (DFW) Denver (DEN) Detroit (DTW) as of 7/2024 Ft. Lauderdale (FLL) Honolulu (HNL) Houston (HOU and IAH) Kansas City (MCI) Las Vegas (LAS) Los Angeles (LAX) Miami (MIA) Minneapolis (MSP) New York (JFK) Newark (EWR) Oakland (OAK) Orlando (MCO) Palm Beach (PBI) Philadelphia (PHL) Phoenix (PHX) Pittsburgh (PIT) Portland (PDX) Sacramento (SMF) San Diego (SAN) San Francisco (SFO) San Jose (SJC) San Juan (SJU) Seattle (SEA) Tampa (TPA) Washington Dulles (IAD) === International Preclearance locations === Abu Dhabi (AUH) Aruba (AUA) Bermuda (BDA) Calgary (YYC) Dublin (DUB) Edmonton (YEG) Halifax (YHZ) Montreal (YUL) Nassau (NAS) Ottawa (YOW) Shannon (SNN) Toronto (YYZ) Vancouver (YVR) Winnipeg (YWG) Sepinggan (BPN) === Seaports === Fort Lauderdale (PEV) Miami (MSE) San Juan (PUE) West Palm Beach (WPB)
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Digital Image Processing with Sound

DIPS (Digital Image Processing with Sound) is a set of plug-in objects that handle real-time digital image processing in Max/MSP programming environment. Combining with the built-in objects of the environment, DIPS enables to program the interaction between audio and visual events with ease, and supports the realization of interactive multimedia art as well as interactive computer music. == Summary of Features == A plug-in software for Max/MSP (Max 5 and 6) More than 300 Max external objects and abstractions More than 90 OpenGL objects included More than 110 visual effect objects (Dfx library, Core Image Filters) A utility library for the easy of programming (prefix Dlib) A comprehensive set of sample patches, and a detailed tutorial Handling images & movie files (QuickTime, OpenGL) Render and move 3D models (OpenGL) Video signal input (QuickTime, video texture) Video input analysis: motion detect, face tracking (OpenCV, OpenGL) Importing 3D models (.obj file) Importing Quartz Composer files OpenGL Shading Language (GLSL) programming interface Easy integration of visual events using DIPSWindowMixer (OpenGL) == Description == DIPS is a free plug-in software (a set of external objects) for Max/MSP. It supports the designing of the interaction between sound and visual events in Max using Apple’s Core Image, OpenGL and OpenCV technologies, and consequently, provides a powerful and user-friendly programming environment for the creation of interactive multimedia art. DIPS can be used to detect a performer’s motions and to track positions of subtle details, such as the face, mouth, and eyes. It can also be used to measure the distance between objects and a Kinect sensor system, and offers powerful tools for realtime image processing of incoming video stream and stored movie files. In addition, it can be used to create complex images in a virtual three-dimensional space. The DIPS consists of a library of more than 300 Max external objects and abstractions, a comprehensive set of sample patches, and a detailed tutorial. Some of its strong points, in comparison with other similar plug-ins and software, are its ease of programming, power, and efficiency. The sample patches and tutorial contained in the installation package allows composers and artists who are interested in the creation of interactive art to realize sophisticated realtime video effects on a live video signal at their first practice. And because of its ease of programming, it is likely that one will soon acquire skills needed to create state-of-the-art interactive performance works, multimedia installations, interactive multimedia artworks, and Max VJ applications using DIPS. == History == Initially developed by Shu Matsuda in 1997, DIPS was a plug-in software for Max/FTS running on SGI Octane and O2 computers. Since 2000, it has been developed by the DIPS Development Group supervised by Takayuki Rai. Current active group members are Shu Matsuda, Yota Morimoto, Takuto Fukuda, and Keitaro Takahashi. Previously, Chikashi Miyama, Daichi Ando and Takayuki Hamano also contributed to its development. 2013 DIPS5 for Max (Mac OS X) 2009 DIPS4 for Max/MSP (Mac OS X) 2006 DIPS3 for Max/MSP (Mac OS X) 2003 DIPS2 for jMax4 (Mac OS X) 2002 DIPS for jMax2 (Mac OS X & Linux) 2000 DIPS for jMax (Linux)
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LMArena

Arena (formerly LMArena and Chatbot Arena) is a public, web-based platform that evaluates large language models (LLMs). Users enter prompts for two anonymous models to respond to and vote on the model that gave the better response, after which the models' identities are revealed. Users can also choose models to test themselves via the "Direct" selection. Companies which have supplied the company with their large language models include OpenAI, Google DeepMind, and Anthropic. The website has been used for preview releases of upcoming models. Chinese company DeepSeek tested its prototype models in the Arena months before its R1 model gained attention in Western media. Other notable pre-release models include OpenAI's GPT-5 under the codename "summit" and Google DeepMind's Gemini 2.5 Flash Image (an image-generation and editing model) under the codename "Nano Banana". Research has identified specific limitations in Arena's methodology. == History == Chatbot Arena was released on April 24, 2023. In June 2024, Chatbot Arena added image support. In September 2024, Chatbot Arena moved to its own dedicated domain name, lmarena.ai (or LMArena). In April 2025, Meta released Llama 4. Llama 4 Maverick beat GPT-4o and Gemini 2.0 Flash on LMArena, but the version of Maverick on LMArena unfairly differed from the publicly available version. LMArena updated their policies in response. In April 2025, LMArena incorporated as an independent company. That May, LMArena raised $100 million in a seed funding round, valuing the company at $600 million. Participants in the seed funding round included Andreessen Horowitz, UC Investments, Lightspeed Venture Partners, Felicis Ventures, and Kleiner Perkins. On January 6, 2026, LMArena announced the closing of a $150 million Series A funding round, bringing the company’s post-money valuation to approximately $1.7 billion. The round was led by Felicis and UC Investments (University of California), with participation from Andreessen Horowitz, The House Fund, LDVP, Kleiner Perkins, Lightspeed Venture Partners, and Laude Ventures. In January 2026, LMArena added video support. On January 28, 2026, LMArena rebranded to "Arena".
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Collision detection

Collision detection is the computational problem of detecting an intersection of two or more objects in virtual space. More precisely, it deals with the questions of if, when, and where two or more objects intersect. Collision detection is a classic problem of computational geometry with applications in computer graphics, physical simulation, video games, robotics (including autonomous driving), and computational physics. Collision detection algorithms can be divided into operating on 2D or 3D spatial objects. == Overview == Collision detection is closely linked to calculating the distance between objects, as objects collide when the distance between them is less than or equal to zero. Negative distances indicate that one object has penetrated another. Performing collision detection requires more context than just the distance between the objects. Accurately identifying the points of contact on both objects' surfaces is also essential for computing a physically accurate collision response. The complexity of this task increases with the level of detail in the objects' representations: the more intricate the model, the greater the computational cost. Collision detection frequently involves dynamic objects, adding a temporal dimension to distance calculations. Instead of simply measuring distance between static objects, collision detection algorithms often aim to determine whether the objects' motion will bring them to a point in time when their distance is zero—an operation that adds significant computational overhead. In collision detection involving multiple objects, a naive approach would require detecting collisions for all pairwise combinations of objects. As the number of objects increases, the number of required comparisons grows rapidly: for n {\displaystyle n} objects, n ( n − 1 ) / 2 {n(n-1)}/{2} intersection tests are needed with a naive approach. This quadratic growth makes such an approach computationally expensive as n {\displaystyle n} increases. Due to the complexity mentioned above, collision detection is a computationally intensive process. Nevertheless, it is essential for interactive applications like video games, robotics, and real-time physics engines. To manage these computational demands, extensive efforts have gone into optimizing collision detection algorithms. A commonly used approach towards accelerating the required computations is to divide the process into two phases: the broad phase and the narrow phase. The broad phase aims to answer the question of whether objects might collide, using a conservative but efficient approach to rule out pairs that clearly do not intersect, thus avoiding unnecessary calculations. Objects that cannot be definitively separated in the broad phase are passed to the narrow phase. Here, more precise algorithms determine whether these objects actually intersect. If they do, the narrow phase often calculates the exact time and location of the intersection. == Broad phase == This phase aims at quickly finding objects or parts of objects for which it can be quickly determined that no further collision test is needed. A useful property of such approach is that it is output sensitive. In the context of collision detection this means that the time complexity of the collision detection is proportional to the number of objects that are close to each other. An early example of that is the I-COLLIDE where the number of required narrow phase collision tests was O ( n + m ) {\displaystyle O(n+m)} where n {\displaystyle n} is the number of objects and m {\displaystyle m} is the number of objects at close proximity. This is a significant improvement over the quadratic complexity of the naive approach. === Spatial partitioning === Several approaches can be grouped under the spatial partitioning umbrella, which includes octrees (for 3D), quadtrees (for 2D), binary space partitioning (or BSP trees) and other, similar approaches. If one splits space into a number of simple cells, and if two objects can be shown not to be in the same cell, then they need not be checked for intersection. Dynamic scenes and deformable objects require updating the partitioning which can add overhead. === Bounding volume hierarchy === Bounding Volume Hierarchy (BVH) is a tree structure over a set of bounding volumes. Collision is determined by doing a tree traversal starting from the root. If the bounding volume of the root doesn't intersect with the object of interest, the traversal can be stopped. If, however there is an intersection, the traversal proceeds and checks the branches for each there is an intersection. Branches for which there is no intersection with the bounding volume can be culled from further intersection test. Therefore, multiple objects can be determined to not intersect at once. BVH can be used with deformable objects such as cloth or soft-bodies but the volume hierarchy has to be adjusted as the shape deforms. For deformable objects we need to be concerned about self-collisions or self intersections. BVH can be used for that end as well. Collision between two objects is computed by computing intersection between the bounding volumes of the root of the tree as there are collision we dive into the sub-trees that intersect. Exact collisions between the actual objects, or its parts (often triangles of a triangle mesh) need to be computed only between intersecting leaves. The same approach works for pair wise collision and self-collisions. === Exploiting temporal coherence === During the broad-phase, when the objects in the world move or deform, the data-structures used to cull collisions have to be updated. In cases where the changes between two frames or time-steps are small and the objects can be approximated well with axis-aligned bounding boxes, the sweep and prune algorithm can be a suitable approach. Several key observation make the implementation efficient: Two bounding-boxes intersect if, and only if, there is overlap along all three axes; overlap can be determined, for each axis separately, by sorting the intervals for all the boxes; and lastly, between two frames updates are typically small (making sorting algorithms optimized for almost-sorted lists suitable for this application). The algorithm keeps track of currently intersecting boxes, and as objects move, re-sorting the intervals helps keep track of the status. === Pairwise pruning === Once a pair of physical bodies has been selected for further investigation, collisions need to be checked more carefully. However, in many applications, individual objects (if they are not too deformable) are described by a set of smaller primitives, mainly triangles. So there are two sets of triangles, S = S 1 , S 2 , … , S n {\displaystyle S={S_{1},S_{2},\dots ,S_{n}}} and T = T 1 , T 2 , … , T n {\displaystyle T={T_{1},T_{2},\dots ,T_{n}}} (for simplicity, each set has the same number of triangles.) The obvious thing to do is to check all triangles S j {\displaystyle S_{j}} against all triangles T k {\displaystyle T_{k}} for collisions, but this involves n 2 {\displaystyle n^{2}} comparisons, which is highly inefficient. If possible, it is desirable to use a pruning algorithm to reduce the number of pairs of triangles that need to be checked. The most widely used family of algorithms is known as the hierarchical bounding volumes method. As a preprocessing step, for each object (e.g., S {\displaystyle S} and T {\displaystyle T} ) calculates a hierarchy of bounding volumes. Then, at each time step, when collisions need to be checked between S {\displaystyle S} and T {\displaystyle T} , the hierarchical bounding volumes are used to reduce the number of pairs of triangles under consideration. For simplicity, provide an example using bounding spheres, although it has been noted that spheres are undesirable in many cases. If E {\displaystyle E} is a set of triangles, a bounding sphere is pre-calculated. B ( E ) {\displaystyle B(E)} . There are many ways of choosing B ( E ) {\displaystyle B(E)} , B ( E ) {\displaystyle B(E)} is a sphere that completely contains E {\displaystyle E} and is as small as possible. Ahead of time, B ( S ) {\displaystyle B(S)} and B ( T ) {\displaystyle B(T)} can be computed. Clearly, if these two spheres do not intersect (and that is very easy to test), then neither do S {\displaystyle S} and T {\displaystyle T} . This is not much better than an n-body pruning algorithm, however. If E = E 1 , E 2 , … , E m {\displaystyle E={E_{1},E_{2},\dots ,E_{m}}} is a set of triangles, then split it into two halves L ( E ) := E 1 , E 2 , … , E m / 2 {\displaystyle L(E):={E_{1},E_{2},\dots ,E_{m/2}}} and R ( E ) := E m / 2 + 1 , … , E m − 1 , E m {\displaystyle R(E):={E_{m/2+1},\dots ,E_{m-1},E_{m}}} . Apply this to S {\displaystyle S} and T {\displaystyle T} , and calculate (ahead of time) the bounding spheres B ( L ( S ) ) , B ( R ( S ) ) {\displaystyle B(L(S)),B(R(S))} and B ( L ( T ) ) , B ( R ( T ) ) {\displaystyle B(L(T)),B(R(T))} . T
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Event condition action

Event condition action (ECA) is a short-cut for referring to the structure of active rules in event-driven architecture and active database systems. Such a rule traditionally consisted of three parts: The event part specifies the signal that triggers the invocation of the rule The condition part is a logical test that, if satisfied or evaluates to true, causes the action to be carried out The action part consists of updates or invocations on the local data This structure was used by the early research in active databases which started to use the term ECA. Current state of the art ECA rule engines use many variations on rule structure. Also other features not considered by the early research is introduced, such as strategies for event selection into the event part. In a memory-based rule engine, the condition could be some tests on local data and actions could be updates to object attributes. In a database system, the condition could simply be a query to the database, with the result set (if not null) being passed to the action part for changes to the database. In either case, actions could also be calls to external programs or remote procedures. Note that for database usage, updates to the database are regarded as internal events. As a consequence, the execution of the action part of an active rule can match the event part of the same or another active rule, thus triggering it. The equivalent in a memory-based rule engine would be to invoke an external method that caused an external event to trigger another ECA rule. ECA rules can also be used in rule engines that use variants of the Rete algorithm for rule processing. == ECA rule engines == Rulecore Concurrent Rules Apart Database Detect Invocation Rules ConceptBase ECArules
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Tokken

Tokken is a payment system and mobile app most known for being a legal and secure option for businesses transactions within the cannabis industry, because of its compliance with bank requirements. The startup company was created by Lamine Zarrad, a former regulator at the Office of the Comptroller of the Currency. == Operability == In order for a person to start using the app, they need to provide evidence, in the form of bioidentification data and mobile carrier records, that they can legally purchase weed. After they have been verified, customers can pay directly through the app at any dispensary that is using Tokken. Tokken turns credit card transactions into a digital token, which can be exchanged back for money that can later be deposited into a bank account. All transactions are logged publicly through a blockchain leger, making the process both anonymous and verified. === Banking services === Tokken has a "pay taxes" function which enables dispensaries to pay their taxes directly to the department.
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Spleak

Spleak was an IM platform where users could publish and rate content. It existed in the form of six bots covering as many subject areas: CelebSpleak, SportSpleak, VoteSpleak, TVSpleak, GameSpleak, and StyleSpleak. == Overview == Users can add a "multi-Spleak" (which contains all of the different Spleak bots in one) or add the separate bots to their IM buddy lists on MSN and AIM. Users are also allowed access to Spleak online by using a CelebSpleak, SportSpleak, or VoteSpleak widget, or through the CelebSpleak and SportSpleak applications with Facebook. Spleak was an alternate reality game and is moving to its own company, Spleak Media Network. "Celebrate Spleak" was introduced throughout 2007, launched in 2008, and was forced to retire in 2009. == Key people == Spleak was co-founded by Morten Lund and Nicolaj Reffstrup. The company's chief executive officer is Morrie Eisenburg; Josh Scott is Vice President in Product and Tyler Wells is Vice President in Engineering.
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Security information management

Security information management (SIM) is an information security industry term for the collection of data such as log files into a central repository for trend analysis. == Overview == SIM products generally are software agents running on the computer systems that are monitored. The recorded log information is then sent to a centralized server that acts as a "security console". The console typically displays reports, charts, and graphs of that information, often in real time. Some software agents can incorporate local filters to reduce and manipulate the data that they send to the server, although typically from a forensic point of view you would collect all audit and accounting logs to ensure you can recreate a security incident. The security console is monitored by an administrator who reviews the consolidated information and takes action in response to any alerts issued. The data that is sent to the server to be correlated and analyzed are normalized by the software agents into a common form, usually XML. Those data are then aggregated in order to reduce their overall size. == Terminology == The terminology can easily be mistaken as a reference to the whole aspect of protecting one's infrastructure from any computer security breach. Due to historic reasons of terminology evolution; SIM refers to just the part of information security which consists of discovery of 'bad behavior' or policy violations by using data collection techniques. The term commonly used to represent an entire security infrastructure that protects an environment is commonly called information security management (InfoSec). Security information management is also referred to as log management and is different from SEM (security event management), but makes up a portion of a SIEM (security information and event management) solution. == Regulatory compliance == Security information management systems support compliance with regulatory frameworks that require centralized collection and analysis of security data. The Health Insurance Portability and Accountability Act (HIPAA) Security Rule requires covered entities to implement audit controls that record and examine activity in information systems containing electronic protected health information (45 CFR 164.312(b))."45 CFR § 164.312 - Technical safeguards". Legal Information Institute. Retrieved April 1, 2026. SIM platforms aggregate these audit records to support the required regular review of information system activity records (45 CFR 164.308(a)(1)(ii)(D)). The December 2024 HIPAA Security Rule NPRM proposed requiring regulated entities to deploy automated systems capable of monitoring and recording access to ePHI, including the ability to detect unauthorized access attempts in near real-time."HIPAA Security Rule To Strengthen the Cybersecurity of Electronic Protected Health Information". Federal Register. January 6, 2025. Retrieved April 1, 2026. The Payment Card Industry Data Security Standard (PCI DSS) similarly requires centralized log management and daily review of security events (Requirements 10.4 and 10.6)."PCI DSS v4.0" (PDF). PCI Security Standards Council. March 2022. Retrieved April 1, 2026. NIST Special Publication 800-53 addresses security information management through the AU (Audit and Accountability) control family, which specifies requirements for audit event generation, content, storage, and analysis."NIST SP 800-53 Rev. 5: Security and Privacy Controls". National Institute of Standards and Technology. September 2020. Retrieved April 1, 2026.
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Catalog server

A catalog server provides a single point of access that allows users to centrally search for information across a distributed network. In other words, it indexes databases, files and information across large network and allows keywords, Boolean and other searches. If you need to provide a comprehensive searching service for your intranet, extranet or even the Internet, a catalog server is a standard solution.
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