AI App On My Phone

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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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Key–value database

A key-value database, or key-value store, is a data storage paradigm designed for storing, retrieving, and managing associative arrays, a data structure more commonly known today as a dictionary. Dictionaries contain a collection of objects, or records, which in turn have many different fields within them. These records are stored and retrieved using a key that uniquely identifies the record, and is used to find the data within the database. Key-value databases differ from the better known relational databases (RDB). RDBs pre-define the data structure in the database as a series of tables containing fields with well-defined data types. Exposing the data types to the database program allows it to apply various optimizations. In contrast, key-value systems treat the value as opaque to the database itself, and typically support only simple operations such as storing, retrieving, updating, and deleting a value by its key. This offers considerable flexibility and makes such systems well suited to low-latency, high-throughput workloads dominated by direct key lookups, but less suitable for applications that require complex queries or explicit relationships among records. A lack of standardization, limited transaction support, and relatively simple query interfaces long restricted many key-value systems to specialized uses, but the rapid move to cloud computing after 2010 helped drive renewed interest in them as part of the broader NoSQL movement. Some graph databases, such as ArangoDB, are also key–value databases internally, adding the concept of relationships (pointers) between records as a first-class data type. == Types and examples == Key–value systems span a wide consistency spectrum, from eventually consistent designs to strongly consistent or serializable ones, and some allow the consistency level to be configured as part of the trade-off against latency and availability. Renewed interest in key–value and other NoSQL systems was driven in part by the demands of big data, distributed, and cloud applications. Their scalability and availability made them attractive for cloud data management, although limited transaction support, low-level query interfaces, and the lack of standardization remained obstacles to wider adoption. Some maintain data in memory (RAM), while others employ solid-state drives or rotating disks. Some key–value systems add additional structure to their keys. For example, Oracle NoSQL Database organizes records using composite keys with "major" and "minor" components, an arrangement that Oracle compares to a directory-path structure in a file system. More generally, however, key–value stores are defined by their use of unique keys associated with opaque values and by their emphasis on simple key-based operations. Unix included dbm (database manager), a minimal database library written by Ken Thompson for managing associative arrays with a single key and hash-based access. Later implementations and related libraries included sdbm, GNU dbm (gdbm), and Berkeley DB. A more recent example is RocksDB, a persistent key–value storage engine developed at Facebook and designed for large-scale applications. Other examples include in-memory systems such as Memcached and Redis, and persistent systems such as Berkeley DB, Riak, and Voldemort.
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Capture the flag (cybersecurity)

In computer security, Capture the Flag (CTF) is an exercise in which participants attempt to find text strings, called "flags", which are secretly hidden in purposefully vulnerable programs or websites. They can be used for both competitive or educational purposes. In two main variations of CTFs, participants either steal flags from other participants (attack/defense-style CTFs) or from organizers (jeopardy-style challenges). A mixed competition combines these two styles. Competitions can include hiding flags in hardware devices, they can be both online or in-person, and can be advanced or entry-level. The game is inspired by the traditional outdoor sport with the same name. CTFs are used as a tool for developing and refining cybersecurity skills, making them popular in both professional and academic settings. == Overview == Capture the Flag (CTF) is a cybersecurity competition that is used to test and develop computer security skills. It was first developed in 1996 at DEF CON, the largest cybersecurity conference in the United States which is hosted annually in Las Vegas, Nevada. The conference hosts a weekend of cybersecurity competitions, including their flagship CTF. Two popular CTF formats are jeopardy and attack-defense. Both formats test participant’s knowledge in cybersecurity, but differ in objective. In the Jeopardy format, participating teams must complete as many challenges of varying point values from a various categories such as cryptography, web exploitation, and reverse engineering. In the attack-defense format, competing teams must defend their vulnerable computer systems while attacking their opponent's systems. The exercise involves a diverse array of tasks, including exploitation and cracking passwords, but there is little evidence showing how these tasks translate into cybersecurity knowledge held by security experts. Recent research has shown that the Capture the Flag tasks mainly covered technical knowledge but lacked social topics like social engineering and awareness on cybersecurity. == Educational applications == CTFs have been shown to be an effective way to improve cybersecurity education through gamification. There are many examples of CTFs designed to teach cybersecurity skills to a wide variety of audiences, including PicoCTF, organized by the Carnegie Mellon CyLab, which is oriented towards high school students, and Arizona State University supported pwn.college. Beyond educational CTF events and resources, CTFs has been shown to be a highly effective way to instill cybersecurity concepts in the classroom. CTFs have been included in undergraduate computer science classes such as Introduction to Information Security at the National University of Singapore. CTFs are also popular in military academies. They are often included as part of the curriculum for cybersecurity courses, with the NSA organized Cyber Exercise culminating in a CTF competition between the US service academies and military colleges. == Competitions == Many CTF organizers register their competition with the CTFtime platform. This allows the tracking of the position of teams over time and across competitions. These include "Plaid Parliament of Pwning", "More Smoked Leet Chicken", "Dragon Sector", "dcua", "Eat, Sleep, Pwn, Repeat", "perfect blue", "organizers" and "Blue Water". Overall the "Plaid Parliament of Pwning" and "Dragon Sector" have both placed first worldwide the most with three times each. === Community competitions === Every year there are dozens of CTFs organized in a variety of formats. Many CTFs are associated with cybersecurity conferences such as DEF CON, various editions of SANS Institute's NetWars, HITCON, and BSides. The DEF CON CTF, an attack-defence CTF, is notable for being one of the oldest CTF competitions to exist, and has been variously referred to as the "World Series", "Superbowl", and "Olympics", of hacking by media outlets. The NYU Tandon hosted Cybersecurity Awareness Worldwide (CSAW) CTF is one of the largest open-entry competitions for students learning cybersecurity from around the world. In 2021, it hosted over 1200 teams during the qualification round. In addition to conference organized CTFs, many CTF clubs and teams organize CTF competitions. Many CTF clubs and teams are associated with universities, such as the CMU associated Plaid Parliament of Pwning, which hosts PlaidCTF, and the ASU associated Shellphish. Some community CTFs are online and open to all participants. The SANS Institute Holiday Hack Challenge and TryHackMe Advent of Cyber. === Government-supported competitions === Governmentally supported CTF competitions include the DARPA Cyber Grand Challenge and ENISA European Cybersecurity Challenge. In 2023, the US Space Force-sponsored Hack-a-Sat CTF competition included, for the first time, a live orbital satellite for participants to exploit. === Corporate-supported competitions === Corporations and other organizations sometimes use CTFs as a training or evaluation exercise, with benefits similar to those in educational settings. In addition to internal CTF exercises, some corporations such as Google and Tencent host publicly accessible CTF competitions. == In popular culture == In Mr. Robot, a qualification round for the DEF CON CTF competition is depicted in the season 3 opener "eps3.0_power-saver-mode.h". The logo for DEF CON can be seen in the background. In The Undeclared War, a CTF is depicted in the opening scene of the series as a recruitment exercise used by GCHQ. Go Go Squid!, a Chinese television series, is based around training for and competing in highly stylized CTF competitions .
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RockMyRun

Rock My Run (stylized as RockMyRun; trademarked slogan: "The Best Running Music in the World™") is a mobile running/fitness app founded in 2011 that provides running and workout music in the form of DJ mixes. It is owned by Rock My World, Inc., a health and fitness technology company based in San Diego, California. The app allows users to listen to these professional DJ mixes on their smartphone while running or working out to enhance and motivate their performance. Rock My World, Inc. also developed the app Jolt.ai for the software Slack. == History == During the early stages of the company, Rock My World, Inc. raised more than $2 million in funding generated by the Irvine Company's The Vine SD and from institutional investors including Skullcandy, ZTE and Lighter Capital and were admitted to the Plug and Play Tech Center in Sunnyvale and to the tech incubator EvoNexus in San Diego. In an interview with co-founder and ex-Qualcomm staff Adam Riggs-Zeigen, he said that "from the beginning [their] big goal is to help people live healthier lives." == Features == The RockMyRun app contains thousands of mixes or "stations" produced by its professional DJs intended to increase enjoyment and performance during exercise. DJs who have provided mixes for the app include David Guetta, Zedd, Steve Aoki, Major Lazer and Afrojack. All of the music can be personalized based on the user's steps per minute, heart rate or ideal cadence allowing the user to "always hear the right music at the right time at the right tempo". All RockMyRun mixes are organized into stations to help users discover music that suits their needs. RockMyRun contains mixes of all genres and each station is categorized into their respective genres and displays tags to let users know the type of music contained in the mix. RockMyRun has two membership types; it is free as a standard member, but for uninterrupted listening and additional features, users can upgrade to a paid "Rockstar" membership. Since March 2023, couples can now be on the same RockMyRun playlists and "share" earbuds. This allows people to train together, easier. A group of DJs curate playlists for specific training needs and different energy levels. == Reception == RockMyRun has been featured on television programs such as The Today Show on two occasions and on The Rachael Ray Show, and in positive reviews by many publications and websites including The New York Times on four separate occasions, TIME, The Huffington Post, The Denver Post, Men's Fitness, Real Simple, The Vulcan Post, The L.A. Times, Glamour, Paste magazine, PCMag, Dubai Week, BetaNews, CNET, CNBC, Reuters, Insider, Tom's Guide and Yahoo! Tech. RockMyRun has also been mentioned/recommended in books/publications such as A Practical Guide to Teacher Wellbeing by Elizabeth Holmes and Applying Music in Exercise and Sport by Dr. Costas Karageorghis. Ultimate Ears placed RockMyRun at the top of their list at No. 1 on their "5 Favorite Workout Music Apps". In a positive review by David Strausser for AndroidGuys in 2015, he praised the app in a detailed review, saying "The mixes are incredible and the rates are reasonable. The app is quick, beautiful." In 2015, Jill Duffy of PC Magazine gave a review of the app, pointing out its key features, and stating that the app is great if you enjoy listening to different, or new music, that can match your tempo while running. Also in 2015, Digital Trends listed RockMyRun, as one of the best exercise music apps in the article "No need to make exercise playlists with these music apps". In 2018, Redbull.com recommended RockMyRun in preparation for the Wings for Life World Run in their article "10 essential hacks for running to work to get you in World Run shape". In 2019, The Fashion Spot included RockMyRun in their list of "The Best Workout Apps for People Who Hate to Work Out", saying: "RockMyRun matches music to the tempo of your running pace – the music literally follows your steps/heart rate. The app has thousands of mixes/music options along with tracking capabilities." Also in 2019, MakeUseOf.com included RockMyRun in their list of "The 7 Best Running and Workout Music Apps". In September 2022, VeryWellFit listed RockMyRun as the first of three "Other Playlist Options" in the article "How to Create a Running Playlist, According to Running Coaches". Tech Grapple recommended the app in "The best workout free music apps for iPhone and Android" saying that "RockMyRun is the best application that you can use during workout. It comes with amazing DJs to craft mixes that will keep you moving." == Partners == RockMyRun is partnered with the following brands/companies: C25K Del Taco JLab Audio iFit Active Network, LLC Night Nation Run (the world's first running music festival) Lady Foot Locker Mayweather Boxing + Fitness Mio Global Orangetheory Fitness Red Rock Apps Tapout Fitness
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Azure Data Lake

Azure Data Lake is a scalable data storage and analytics service. The service is hosted in Azure, Microsoft's public cloud. == History == Azure Data Lake service was released on November 16, 2016. It is based on COSMOS, which is used to store and process data for applications such as Azure, AdCenter, Bing, MSN, Skype and Windows Live. COSMOS features a SQL-like query engine called SCOPE upon which U-SQL was built. == Storage == Data Lake Storage is a cloud service to store structured, semi-structured or unstructured data produced from applications including social networks, relational data, sensors, videos, web apps, mobile or desktop devices. A single account can store trillions of files where a single file can be greater than a petabyte in size. == Analytics == Data Lake Analytics is a parallel on-demand job service. The parallel processing system is based on Microsoft Dryad. Dryad can represent arbitrary Directed Acyclic Graphs (DAGs) of computation. Data Lake Analytics provides a distributed infrastructure that can dynamically allocate resources so that customers pay for only the services they use. The system uses Apache YARN, the part of Apache Hadoop which governs resource management across clusters. Data Lake Store supports any application that uses the Hadoop Distributed File System (HDFS) interface. == U-SQL == U-SQL is a query language for Data Lake Analytics parallel data transformation and processing programs. It combines SQL and C#: it is and an evolution of the declarative SQL language with native extensibility through user code written in C#. U-SQL uses C# data types and the C# expression language. == Retirement == In 2021, Microsoft announced the 2024 retirement of the original Azure Data Lake Storage, now called "Gen1". The related Azure Data Lake Analytics / U-SQL technologies are also being retired. Azure Data Lake Storage Gen2, an extension of Azure Storage, will continue. The suggested replacement technologies are Azure Synapse Analytics and Apache Spark.
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TeaOnHer

TeaOnHer is a male-oriented dating surveillance mobile app that allows men to anonymously rate and comment on women they are dating. It was set up in response to the existence of Tea, a female-oriented dating app that allowed women to rate and comment on men. In 2025, Cosmopolitian magazine described it as America's second most popular mobile app, with it being the second most popular app in the lifestyle section of Apple's App Store. The TeaOnHer app has fewer features than the rival Tea app, focusing instead on anonymous commenting. It is listed as having been developed by a company called Newville Media Corporation. TechCrunch reported in 2025 that TeaOnHer had leaked credentials of some of its users.
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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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Key frame

In animation and filmmaking, a key frame (or keyframe) is a drawing or shot that defines the starting and ending points of a smooth transition. These are called frames because their position in time is measured in frames on a strip of film or on a digital video editing timeline. A sequence of key frames defines which movement the viewer will see, whereas the position of the key frames on the film, video, or animation defines the timing of the movement. Because only two or three key frames over the span of a second do not create the illusion of movement, the remaining frames are filled with "inbetweens". == Use of key frames as a means to change parameters == In software packages that support animation, especially 3D graphics, there are many parameters that can be changed for any one object. One example of such an object is a light. In 3D graphics, lights function similarly to real-world lights. They cause illumination, cast shadows, and create specular highlights. Lights have many parameters, including light intensity, beam size, light color, and the texture cast by the light. Supposing that an animator wants the beam size to change smoothly from one value to another within a predefined period of time, that could be achieved by using key frames. At the start of the animation, a beam size value is set. Another value is set for the end of the animation. Thus, the software program automatically interpolates the two values, creating a smooth transition. == Video editing == In non-linear digital video editing, as well as in video compositing software, a key frame is a frame used to indicate the beginning or end of a change made to a parameter. For example, a key frame could be set to indicate the point at which audio will have faded up or down to a certain level. == Video compression == In video compression, a key frame, also known as an intra-frame, is a frame in which a complete image is stored in the data stream. In video compression, only changes that occur from one frame to the next are stored in the data stream, in order to greatly reduce the amount of information that must be stored. This technique capitalizes on the fact that most video sources (such as a typical movie) have only small changes in the image from one frame to the next. Whenever a drastic change to the image occurs, such as when switching from one camera shot to another or at a scene change, a key frame must be created. The entire image for the frame must be output when the visual difference between the two frames is so great that representing the new image incrementally from the previous frame would require more data than recreating the whole image. Because video compression only stores incremental changes between frames (except for key frames), it is not possible to fast-forward or rewind to any arbitrary spot in the video stream. That is because the data for a given frame only represents how that frame was different from the preceding one. For that reason, it is beneficial to include key frames at arbitrary intervals while encoding video. For example, a key frame may be output once for each 10 seconds of video, even though the video image does not change enough visually to warrant the automatic creation of the key frame. That would allow seeking within the video stream at a minimum of 10-second intervals. The downside is that the resulting video stream will be larger in disk size because many key frames are added when they are not necessary for the frame's visual representation. This drawback, however, does not produce significant compression loss when the bitrate is already set at a high value for better quality (as in the DVD MPEG-2 format).
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E-on Vue

Vue is a software tool for world generation by Bentley Systems, with support for many visual effects, animations, and various other features. The tool has been used in several feature-length films. In 2024, Bentley Systems announced that Vue would be discontinued, and be freely available to those that still wish to use it. == Versions == == Features == This is a list of features as of the 2023 release of Vue: === Terrains === Heightfield terrains Procedural terrains Infinite terrains Planetary terrains Real-world terrains 3D terrain sculpting Terrain export === EcoSystem Instancing Technology === Material-based EcoSystems Global EcoSystems Dynamic EcoSystems 360° EcoSystem Population Paint EcoSystem instances EcoParticles Export EcoSystem populations === Vegetation === Built-in Plant editor Compatible with PlantFactory Vegetation assets === Atmosphere, Skies and Clouds === Standard atmospheric model Spectral atmospheric model Photometric atmospheric model Atmosphere presets Procedural Volumetric 3D cloud layers Standalone 3D Metaclouds Convert meshes to Clouds Cloud morphing Import OpenVDB Export standalone and cloud layer zones to OpenVDB Export skies as HDRI === Modeling === Primitive and Feature modeling 3D Text edition tool Metablobbing Hyperblobs Export baked hyperblobs Splines Built in Road Construction toolkit Random rock generator Export rocks === Texturing and UVs === Material presets PBR Substance support Node-based procedural materials Volumetric materials and Hypertextures Stacked UVs Unwrapped UVs Ptex === Interoperability, Integration And Export === Export single assets to generic 3D formats Full scene export Integration plugins Import and Export Camera data as FBX and Nuke.chan Python API ZBrush GoZ bridge === Animation === Animate objects, materials, atmospheres, clouds, waves... Automatic wind and breeze Localized wind effects per plant / per EcoSystem population Omni and directional ventilators for local modifications of plants Time spline editor Automatic keyframe creation Automatic synchronization of cameras and lights Animation export as AfterEffects Import motion tracking information === Lighting === Global illumination, Global Radiosity, Ambient occlusion Subsurface Scattering HDRI image based lighting Point light, Quadratic point light, Spotlight, Quadratic spotlight, Directional light Use IES distribution profiles on photometric lights Area lights, light panels, light portals Physically accurate caustics computation === Rendering === Render with Ray Tracer Render with Path Tracer Stereoscopic rendering 360/180 VR Panorama Render Option Spherical panoramic rendering Tone mapping options Multipass & G-Buffer Network rendering with HyperVue / RenderCows Network rendering with RenderNodes == Users == Blue Sky Studios Digital Domain DreamWorks Animation: Kung Fu Panda Industrial Light & Magic: Indiana Jones and the Kingdom of the Crystal Skull, Pirates of the Caribbean: Dead Man's Chest Sony Pictures Imageworks Warner Bros. Interactive Entertainment Weta Digital
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CloudPassage

CloudPassage is a company that provides an automation platform, delivered via software as a service, that improves security for private, public, and hybrid cloud computing environments. CloudPassage is headquartered in San Francisco. == History == CloudPassage was founded by Carson Sweet, Talli Somekh, and Vitaliy Geraymovych in 2010. The company used cloud computing and big data analytics to implement security monitoring and control in a platform called Halo. CloudPassage spent a year in stealth developing the Halo technology, coming out of stealth mode to a closed beta in January 2011. In June 2012, the company launched the commercial product that included configuration security monitoring, network microsegmentation, and two-factor authentication for privileged access management. By 2013, CloudPassage expanded Halo to support large enterprises with advanced security and compliance requirements with a product called Halo Enterprise. The first round of venture funding for the company raised $6.5 million. In April 2012, CloudPassage raised $14 million. The financing round was led by Tenaya Capital. In February 2014, CloudPassage announced that it had raised $25.5 million in funding led by Shasta Ventures. In total, the company has invested over $30 million in its technology and raised approximately $88 million in capital. == Product == The CloudPassage platform provides cloud workload security and compliance for systems hosted in public or private cloud infrastructure environments, including hybrid cloud and multi-cloud workload hosting models. The flagship product the company offers is called Halo. Halo secures virtual servers in public, private, and hybrid cloud infrastructures and provides file integrity monitoring (FIM) while also administering firewall automation, vulnerability monitoring, network access control, security event alerting, and assessment. The Halo platform also provides security applications such as privileged access management, software vulnerability scanning, multifactor authentication, and log-based IDS. In December 2013, CloudPassage set up six servers with Microsoft Windows and Linux operating systems and combinations of popular programs and invited hackers to attempt to hack into the servers. The top prize was $5,000 and the winning hacker was a novice that completed the task in four hours. CloudPassage programmed the servers to use basic default security settings to show how vulnerable cloud computing programs can be to security threats. == Awards and recognition == In May 2011, Gigaom named CloudPassage in its list of the Top 50 Cloud Innovators. That same month, eWeek recognized CloudPassage as one of 16 Hot Startup Companies Flying Under the Radar. SC Magazine named CloudPassage an Industry Innovator in the Virtualization and Cloud Security category in 2012. Also in 2012, The Wall Street Journal named CloudPassage a runner-up in the Information Security category of its Technology Innovation Awards. The CloudPassage large-scale security program, Halo, won Best Security Solution in 2014 at the SIIA Codie awards.
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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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Shader lamps

Shader lamps is a computer graphic technique used to change the appearance of physical objects. The still or moving objects are illuminated, using one or more video projectors, by static or animated texture or video stream. The method was invented at University of North Carolina at Chapel Hill by Ramesh Raskar, Greg Welch, Kok-lim Low and Deepak Bandyopadhyay in 1999 [1] as a follow on to Spatial Augmented Reality [2] also invented at University of North Carolina at Chapel Hill in 1998 by Ramesh Raskar, Greg Welch and Henry Fuchs. A 3D graphic rendering software is typically used to compute the deformation caused by the non perpendicular, non-planar or even complex projection surface. Complex objects (or aggregation of multiple simple objects) create self shadows that must be compensated by using several projectors. The objects are typically replaced by neutral color ones, the projection giving all its visual properties, thus the name shader lamps. The technique can be used to create a sense of invisibility, by rendering transparency. The object is illuminated not by a replacement of its own visual properties, but by the corresponding visual surface placed behind the object as seen from an arbitrary viewing point.
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Text normalization

Text normalization is the process of transforming text into a single canonical form that it might not have had before. Normalizing text before storing or processing it allows for separation of concerns, since input is guaranteed to be consistent before operations are performed on it. Text normalization requires being aware of what type of text is to be normalized and how it is to be processed afterwards; there is no all-purpose normalization procedure. == Applications == Text normalization is frequently used when converting text to speech. Numbers, dates, acronyms, and abbreviations are non-standard "words" that need to be pronounced differently depending on context. For example: "$200" would be pronounced as "two hundred dollars" in English, but as "lua selau tālā" in Samoan. "vi" could be pronounced as "vie," "vee," or "the sixth" depending on the surrounding words. Text can also be normalized for storing and searching in a database. For instance, if a search for "resume" is to match the word "résumé," then the text would be normalized by removing diacritical marks; and if "john" is to match "John", the text would be converted to a single case. To prepare text for searching, it might also be stemmed (e.g. converting "flew" and "flying" both into "fly"), canonicalized (e.g. consistently using American or British English spelling), or have stop words removed. == Techniques == For simple, context-independent normalization, such as removing non-alphanumeric characters or diacritical marks, regular expressions would suffice. For example, the sed script sed ‑e "s/\s+/ /g" inputfile would normalize runs of whitespace characters into a single space. More complex normalization requires correspondingly complicated algorithms, including domain knowledge of the language and vocabulary being normalized. Among other approaches, text normalization has been modeled as a problem of tokenizing and tagging streams of text and as a special case of machine translation. == Textual scholarship == In the field of textual scholarship and the editing of historic texts, the term "normalization" implies a degree of modernization and standardization – for example in the extension of scribal abbreviations and the transliteration of the archaic glyphs typically found in manuscript and early printed sources. A normalized edition is therefore distinguished from a diplomatic edition (or semi-diplomatic edition), in which some attempt is made to preserve these features. The aim is to strike an appropriate balance between, on the one hand, rigorous fidelity to the source text (including, for example, the preservation of enigmatic and ambiguous elements); and, on the other, producing a new text that will be comprehensible and accessible to the modern reader. The extent of normalization is therefore at the discretion of the editor, and will vary. Some editors, for example, choose to modernize archaic spellings and punctuation, but others do not. An edition of a text might be normalized based on internal criteria, where orthography is standardized according to the language of the original, or external criteria, where the norms of a different time period are applied. For an example of the latter, a published edition of a medieval Icelandic manuscript might be normalized to the conventions of modern Icelandic, or it might be normalized to Classical Old Icelandic. Standards of normalization vary based on language of the edition as well as the specific conventions of the publisher.
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Couchbase Server

Couchbase Server, originally known as Membase, is a source-available, distributed (shared-nothing architecture) multi-model NoSQL document-oriented database software package optimized for interactive applications. These applications may serve many concurrent users by creating, storing, retrieving, aggregating, manipulating and presenting data. In support of these kinds of application needs, Couchbase Server is designed to provide easy-to-scale key-value, or JSON document access, with low latency and high sustainability throughput. It is designed to be clustered from a single machine to very large-scale deployments spanning many machines. Couchbase Server provided client protocol compatibility with memcached, but added disk persistence, data replication, live cluster reconfiguration, rebalancing and multitenancy with data partitioning. == Product history == Membase was developed by several leaders of the memcached project, who had founded a company, NorthScale, to develop a key-value store with the simplicity, speed, and scalability of memcached, but also the storage, persistence and querying capabilities of a database. The original membase source code was contributed by NorthScale, and project co-sponsors Zynga and Naver Corporation (then known as NHN) to a new project on membase.org in June 2010. On February 8, 2011, the Membase project founders and Membase, Inc. announced a merger with CouchOne (a company with many of the principal players behind CouchDB) with an associated project merger. The merged company was called Couchbase, Inc. In January 2012, Couchbase released Couchbase Server 1.8. In September of 2012, Orbitz said it had changed some of its systems to use Couchbase. In December of 2012, Couchbase Server 2.0 (announced in July 2011) was released and included a new JSON document store, indexing and querying, incremental MapReduce and replication across data centers. == Architecture == Every Couchbase node consists of a data service, index service, query service, and cluster manager component. Starting with the 4.0 release, the three services can be distributed to run on separate nodes of the cluster if needed. In the parlance of Eric Brewer's CAP theorem, Couchbase is normally a CP type system meaning it provides consistency and partition tolerance, or it can be set up as an AP system with multiple clusters. === Cluster manager === The cluster manager supervises the configuration and behavior of all the servers in a Couchbase cluster. It configures and supervises inter-node behavior like managing replication streams and re-balancing operations. It also provides metric aggregation and consensus functions for the cluster, and a RESTful cluster management interface. The cluster manager uses the Erlang programming language and the Open Telecom Platform. ==== Replication and fail-over ==== Data replication within the nodes of a cluster can be controlled with several parameters. In December of 2012, support was added for replication between different data centers. === Data manager === The data manager stores and retrieves documents in response to data operations from applications. It asynchronously writes data to disk after acknowledging to the client. In version 1.7 and later, applications can optionally ensure data is written to more than one server or to disk before acknowledging a write to the client. Parameters define item ages that affect when data is persisted, and how max memory and migration from main-memory to disk is handled. It supports working sets greater than a memory quota per "node" or "bucket". External systems can subscribe to filtered data streams, supporting, for example, full text search indexing, data analytics or archiving. ==== Data format ==== A document is the most basic unit of data manipulation in Couchbase Server. Documents are stored in JSON document format with no predefined schemas. Non-JSON documents can also be stored in Couchbase Server (binary, serialized values, XML, etc.) ==== Object-managed cache ==== Couchbase Server includes a built-in multi-threaded object-managed cache that implements memcached compatible APIs such as get, set, delete, append, prepend etc. ==== Storage engine ==== Couchbase Server has a tail-append storage design that is immune to data corruption, OOM killers or sudden loss of power. Data is written to the data file in an append-only manner, which enables Couchbase to do mostly sequential writes for update, and provide an optimized access patterns for disk I/O. === Performance === A performance benchmark done by Altoros in 2012, compared Couchbase Server with other technologies. Cisco Systems published a benchmark that measured the latency and throughput of Couchbase Server with a mixed workload in 2012. == Licensing and support == Couchbase Server is a packaged version of Couchbase's open source software technology and is available in a community edition without recent bug fixes with an Apache 2.0 license and an edition for commercial use. Couchbase Server builds are available for Ubuntu, Debian, Red Hat, SUSE, Oracle Linux, Microsoft Windows and macOS operating systems. Couchbase has supported software developers' kits for the programming languages .NET, PHP, Ruby, Python, C, Node.js, Java, Go, and Scala. == SQL++ == A query language called SQL++ (formerly called N1QL), is used for manipulating the JSON data in Couchbase, just like SQL manipulates data in RDBMS. It has SELECT, INSERT, UPDATE, DELETE, MERGE statements to operate on JSON data. It was initially announced in March 2015 as "SQL for documents". The SQL++ data model is non-first normal form (N1NF) with support for nested attributes and domain-oriented normalization. The SQL++ data model is also a proper superset and generalization of the relational model. === Example === Like query SELECT FROM `bucket` WHERE email LIKE "%@example.org"; Array query SELECT FROM `bucket` WHERE ANY x IN friends SATISFIES x.name = "Pavan" END; == Couchbase Mobile == Couchbase Mobile / Couchbase Lite is a mobile database providing data replication. Couchbase Lite (originally TouchDB) provides native libraries for offline-first NoSQL databases with built-in peer-to-peer or client-server replication mechanisms. Sync Gateway manages secure access and synchronization of data between Couchbase Lite and Couchbase Server. Couchbase Lite added support for Vector Search in version 3.2, allowing cloud to edge support for vector search in mobile applications. == Uses == Couchbase began as an evolution of Memcached, a high-speed data cache, and can be used as a drop-in replacement for Memcached, providing high availability for memcached application without code changes. Couchbase is used to support applications where a flexible data model, easy scalability, and consistent high performance are required, such as tracking real-time user activity or providing a store of user preferences or online applications. Couchbase Mobile, which stores data locally on devices (usually mobile devices) is used to create “offline-first” applications that can operate when a device is not connected to a network and synchronize with Couchbase Server once a network connection is re-established. The Catalyst Lab at Northwestern University uses Couchbase Mobile to support the Evo application, a healthy lifestyle research program where data is used to help participants improve dietary quality, physical activity, stress, or sleep. Amadeus uses Couchbase with Apache Kafka to support their “open, simple, and agile” strategy to consume and integrate data on loyalty programs for airline and other travel partners. High scalability is needed when disruptive travel events create a need to recognize and compensate high value customers. Starting in 2012, it played a role in LinkedIn's caching systems, including backend caching for recruiter and jobs products, counters for security defense mechanisms, for internal applications. == Alternatives == For caching, Couchbase competes with Memcached and Redis. For document databases, Couchbase competes with other document-oriented database systems. It is commonly compared with MongoDB, Amazon DynamoDB, Oracle RDBMS, DataStax, Google Bigtable, MariaDB, IBM Cloudant, Redis Enterprise, SingleStore, and MarkLogic.
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Starlight Information Visualization System

Starlight is a software product originally developed at Pacific Northwest National Laboratory and now by Future Point Systems. It is an advanced visual analysis environment. In addition to using information visualization to show the importance of individual pieces of data by showing how they relate to one another, it also contains a small suite of tools useful for collaboration and data sharing, as well as data conversion, processing, augmentation and loading. The software, originally developed for the intelligence community, allows users to load data from XML files, databases, RSS feeds, web services, HTML files, Microsoft Word, PowerPoint, Excel, CSV, Adobe PDF, TXT files, etc. and analyze it with a variety of visualizations and tools. The system integrates structured, unstructured, geospatial, and multimedia data, offering comparisons of information at multiple levels of abstraction, simultaneously and in near real-time. In addition Starlight allows users to build their own named entity-extractors using a combination of algorithms, targeted normalization lists and regular expressions in the Starlight Data Engineer (SDE). As an example, Starlight might be used to look for correlations in a database containing records about chemical spills. An analyst could begin by grouping records according to the cause of the spill to reveal general trends. Sorting the data a second time, they could apply different colors based on related details such as the company responsible, age of equipment or geographic location. Maps and photographs could be integrated into the display, making it even easier to recognize connections among multiple variables. Starlight has been deployed to both the Iraq and Afghanistan wars and used on a number of large-scale projects. PNNL began developing Starlight in the mid-1990s, with funding from the Land Information Warfare Agency, a part of the Army Intelligence and Security Command and continued developed at the laboratory with funding from the NSA and the CIA. Starlight integrates visual representations of reports, radio transcripts, radar signals, maps and other information. The software system was recently honored with an R&D 100 Award for technical innovation. In 2006 Future Point Systems, a Silicon Valley startup, acquired rights to jointly develop and distribute the Starlight product in cooperation with the Pacific Northwest National Laboratory. The software is now also used outside of the military/intelligence communities in a number of commercial environments.
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