AI For Business Reddit

AI For Business Reddit — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

  • MyChild App

    MyChild App

    MyChild App is an Android app that helps parents screen developmental disorders in their children between the age of 1 and 24 months. The app contains information for parents about the different stages of a child's development. == Background == Launched in 2015 on Google PlayStore, the app is a consumer product of the parent company, Time Ahead, Inc. Its office is based in Bhopal, Madhya Pradesh, India. As of August 2016, the app had been downloaded by 11,000+ users in 140+ countries and is a part of fbstart case study. == Funding == In 2015, MyChild App raised a seed round of $100k led by 500 Startups, followed by angel investors Samir Bangara, Anisha Mittal, Pallav Nadhani, Deobrat Singh, Lalit Mangal, Arihant Patni, Amit Gupta, Dr. Ritesh Malik, Saurab Paruthi, and Singapore Angel Network.

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  • MeituPic

    MeituPic

    Meitu Xiu Xiu ("Meitu") (Chinese: 美图秀秀) is an image editing software that is mostly used in Mainland China but is also popular in Hong Kong and Taiwan. It is only available on Google Play and App Store in certain countries. It provides tools for editing photos: filters, retouching, collage, scenes, frames, and photo decorations, as well as generative AI features such as text-to-images, AI removal and AI repainting etc. Meitu is one of the apps developed by Meitu, Inc.; it also produced BeautyCam, Wink and X-Design. == History == Meitu's PC version was created in 2008 by Wu Xinhong, the CEO of Meitu. In 2013, its mobile version became one of the first must-have mobile apps in China. Meitu, Inc. is a photo and video-centered app developer, which was founded in 2008 in Xiamen. Currently, the major revenue source of Meitu is premium subscription. Meitu, Inc. was initially funded by Cai Wensheng, a well-known angel investor. The company has an approximately 250 million monthly active users globally. == Function == === Edit === MeituPic provides a number of photo-editing tools. The major functions are auto enhance, edit, enhance, filters, frames, magic brush, mosaic, text, and blur. Auto enhance focuses on the nature of photos taken, while Edit includes functions of cropping, rotation, sharpening, and adjustment of ratio. For Enhance, users can apply slight adjustment on the photo by controlling the levels of brightness, contrast, colour temperature, saturation, highlight, shadow and smart light. Major types of filters are LOMO, beauty, style as well as art. Different frames can be chosen from poster, simple, and fantasy. Magic brush provides a great variety of brushes with different colours and patterns for users to decorate the photos. Mosaic brush enables users to cover certain parts of the photo. Texts can be added to the photo. Choices of different bubbles, font as well as style of words are available. Blurring effect is also available to make the photo less distinct and clear. === Beauty Retouch === There are seven major functions for retouching a photo: automatic retouch, smooth and whiten skin, remove blemish, make slimmer, remove dark circles and bags under the eyes, make taller, and enhance the eyes. Automatic retouch enhances portraits by lightening the skin tone, brightening the eyes, and simulating a face-lift by tapping on just one button. This helps to remove wrinkles and optimizes the skin tone. Acne, blemishes, and other skin imperfections can also be removed. The face-lift and weight-loss functions in the slimming option can be used to reshape the body. The option to make the subject taller can be used to change the perceived height of the subject and give the impression of slimmer, longer legs. The option to enhance the eyes can enlarge and brighten the eyes. === Collage === Collage has four types: template, freestyle, poster, PicStrip, which all maximize to insert nine photos. Template integrates photos in a vertical rectangle tightly. MeituPic has 15 frames or free download function for users. MeituPic also provides different templates according to number of photos inserted. Freestyle separates photos on a background freely. There are two parts of background: custom and more. For custom, users choose from album. For more, there are plain and picture with 18 choices. Poster makes a poster with photos. Users choose a poster among 8 choices or tap ‘more’ to download a new one. PicStrip combines photos vertically making an elongated file. Users choose a frame from 15 choices. Pinching thumb and forefinger together or apart zooms photos in/out. Putting two fingers and turning hand rotates photos. Pressing moves photos to ideal location. After designing, users tap ‘save/share’ on the upper right corner and the photo made is saved into album automatically. == Awards ==

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  • Geometric primitive

    Geometric primitive

    In vector computer graphics, CAD systems, and geographic information systems, a geometric primitive (or prim) is the simplest (i.e. 'atomic' or irreducible) geometric shape that the system can handle (draw, store). Sometimes the subroutines that draw the corresponding objects are called "geometric primitives" as well. The most "primitive" primitives are point and straight line segments, which were all that early vector graphics systems had. In constructive solid geometry, primitives are simple geometric shapes such as a cube, cylinder, sphere, cone, pyramid, torus. Modern 2D computer graphics systems may operate with primitives which are curves (segments of straight lines, circles and more complicated curves), as well as shapes (boxes, arbitrary polygons, circles). A common set of two-dimensional primitives includes lines, points, and polygons, although some people prefer to consider triangles primitives, because every polygon can be constructed from triangles (polygon triangulation). All other graphic elements are built up from these primitives. In three dimensions, triangles or polygons positioned in three-dimensional space can be used as primitives to model more complex 3D forms. In some cases, curves (such as Bézier curves, circles, etc.) may be considered primitives; in other cases, curves are complex forms created from many straight, primitive shapes. == Common primitives == The set of geometric primitives is based on the dimension of the region being represented: Point (0-dimensional), a single location with no height, width, or depth. Line or curve (1-dimensional), having length but no width, although a linear feature may curve through a higher-dimensional space. Planar surface or curved surface (2-dimensional), having length and width. Volumetric region or solid (3-dimensional), having length, width, and depth. In GIS, the terrain surface is often spoken of colloquially as "2 1/2 dimensional," because only the upper surface needs to be represented. Thus, elevation can be conceptualized as a scalar field property or function of two-dimensional space, affording it a number of data modeling efficiencies over true 3-dimensional objects. A shape of any of these dimensions greater than zero consists of an infinite number of distinct points. Because digital systems are finite, only a sample set of the points in a shape can be stored. Thus, vector data structures typically represent geometric primitives using a strategic sample, organized in structures that facilitate the software interpolating the remainder of the shape at the time of analysis or display, using the algorithms of Computational geometry. A Point is a single coordinate in a Cartesian coordinate system. Some data models allow for Multipoint features consisting of several disconnected points. A Polygonal chain or Polyline is an ordered list of points (termed vertices in this context). The software is expected to interpolate the intervening shape of the line between adjacent points in the list as a parametric curve, most commonly a straight line, but other types of curves are frequently available, including circular arcs, cubic splines, and Bézier curves. Some of these curves require additional points to be defined that are not on the line itself, but are used for parametric control. A Polygon is a polyline that closes at its endpoints, representing the boundary of a two-dimensional region. The software is expected to use this boundary to partition 2-dimensional space into an interior and exterior. Some data models allow for a single feature to consist of multiple polylines, which could collectively connect to form a single closed boundary, could represent a set of disjoint regions (e.g., the state of Hawaii), or could represent a region with holes (e.g., a lake with an island). A Parametric shape is a standardized two-dimensional or three-dimensional shape defined by a minimal set of parameters, such as an ellipse defined by two points at its foci, or three points at its center, vertex, and co-vertex. A Polyhedron or Polygon mesh is a set of polygon faces in three-dimensional space that are connected at their edges to completely enclose a volumetric region. In some applications, closure may not be required or may be implied, such as modeling terrain. The software is expected to use this surface to partition 3-dimensional space into an interior and exterior. A triangle mesh is a subtype of polyhedron in which all faces must be triangles, the only polygon that will always be planar, including the Triangulated irregular network (TIN) commonly used in GIS. A parametric mesh represents a three-dimensional surface by a connected set of parametric functions, similar to a spline or Bézier curve in two dimensions. The most common structure is the Non-uniform rational B-spline (NURBS), supported by most CAD and animation software. == Application in GIS == A wide variety of vector data structures and formats have been developed during the history of Geographic information systems, but they share a fundamental basis of storing a core set of geometric primitives to represent the location and extent of geographic phenomena. Locations of points are almost always measured within a standard Earth-based coordinate system, whether the spherical Geographic coordinate system (latitude/longitude), or a planar coordinate system, such as the Universal Transverse Mercator. They also share the need to store a set of attributes of each geographic feature alongside its shape; traditionally, this has been accomplished using the data models, data formats, and even software of relational databases. Early vector formats, such as POLYVRT, the ARC/INFO Coverage, and the Esri shapefile support a basic set of geometric primitives: points, polylines, and polygons, only in two dimensional space and the latter two with only straight line interpolation. TIN data structures for representing terrain surfaces as triangle meshes were also added. Since the mid 1990s, new formats have been developed that extend the range of available primitives, generally standardized by the Open Geospatial Consortium's Simple Features specification. Common geometric primitive extensions include: three-dimensional coordinates for points, lines, and polygons; a fourth "dimension" to represent a measured attribute or time; curved segments in lines and polygons; text annotation as a form of geometry; and polygon meshes for three-dimensional objects. Frequently, a representation of the shape of a real-world phenomenon may have a different (usually lower) dimension than the phenomenon being represented. For example, a city (a two-dimensional region) may be represented as a point, or a road (a three-dimensional volume of material) may be represented as a line. This dimensional generalization correlates with tendencies in spatial cognition. For example, asking the distance between two cities presumes a conceptual model of the cities as points, while giving directions involving travel "up," "down," or "along" a road imply a one-dimensional conceptual model. This is frequently done for purposes of data efficiency, visual simplicity, or cognitive efficiency, and is acceptable if the distinction between the representation and the represented is understood, but can cause confusion if information users assume that the digital shape is a perfect representation of reality (i.e., believing that roads really are lines). == In 3D modelling == In CAD software or 3D modelling, the interface may present the user with the ability to create primitives which may be further modified by edits. For example, in the practice of box modelling the user will start with a cuboid, then use extrusion and other operations to create the model. In this use the primitive is just a convenient starting point, rather than the fundamental unit of modelling. A 3D package may also include a list of extended primitives which are more complex shapes that come with the package. For example, a teapot is listed as a primitive in 3D Studio Max. == In graphics hardware == Various graphics accelerators exist with hardware acceleration for rendering specific primitives such as lines or triangles, frequently with texture mapping and shaders. Modern 3D accelerators typically accept sequences of triangles as triangle strips.

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  • NHS COVID-19

    NHS COVID-19

    NHS COVID-19 was a voluntary contact tracing app for monitoring the spread of the COVID-19 pandemic in England and Wales, in use from 24 September 2020 until 27 April 2023. It was available for Android and iOS smartphones, and could be used by anyone aged 16 or over. Two versions of the app were created. The first was commissioned by NHSX and developed by the Pivotal division of American software company VMware. A pilot deployment began in May 2020, but on 18 June development of the app was abandoned in favour of a second design using the Apple/Google Exposure Notification system. Scotland and Northern Ireland had separate contact tracing apps. A 2023 study estimated that in its first year of use, the app's contact tracing function prevented an estimated 1 million cases, and 9,600 deaths. == Description == The app allowed users to: See the alert level of their local authority area (in Wales) or information about restrictions (in England); to enable this, the user must enter the first half of their postcode "Check in" at places displaying an NHS QR code poster (no longer required by legislation after 26 January 2022, removed from the app the next month) Be notified when they have been in close contact with someone who has tested positive for the virus Be notified when local health protection teams determine that people with the virus had attended a business or other venue around the same time as the user Check their symptoms, and book a coronavirus test if necessary If asked to self-isolate, receive information and a daily "countdown". At first, "close contact" was defined as being within 2 metres for 15 minutes, or within 4 metres for a longer time. These time durations were reduced from 29 October 2020, to as little as three minutes when the other person is at their most infectious, i.e. soon after they begin showing symptoms. === Implementation === The Android app was coded in Kotlin, and the iOS app in Swift. The backend used Java and is deployed to Amazon Web Services using Terraform. The code of the app and back-end is open-source and available on GitHub. == Context == The app was part of the UK's test and trace programme which was chaired by Dido Harding; from 12 May 2020 Tom Riordan, chief executive of Leeds City Council, led the tracing effort. == First phase and cancellation == === Description === In March 2020, NHSX commissioned a contact tracing app to monitor the spread in the United Kingdom of the coronavirus disease 2019 (COVID-19) in the 2020 pandemic, developed by the Pivotal division of American software company VMware. The app used a centralised approach, in contrast to the Google / Apple contact tracing project. NHSX consulted ethicists and GCHQ's National Cyber Security Centre (NCSC) about the privacy aspects. The app recorded the make and model of the phone and asked the user for their postcode area. It generated a unique installation identification number and also a daily identification number. It then used Bluetooth Low Energy (BLE) to record the daily identification number of other users nearby. If a user was unwell, they could tell the app about symptoms which are characteristic of COVID-19, such as a fever and cough. These details were then passed to a central NHS server. This would assess the information and notify other users that have been in contact, giving them appropriate advice such as physical distancing. The NHS would also arrange for a swab test of the unwell user and the outcome would determine further notifications to contacts: if the test confirmed infection with COVID-19, the contacts would be asked to isolate. By June 2020, £11.8 million had been spent on the app; in 2020–21, £35 million was spent on the app. === Deployment === The first public trial of the app began on the Isle of Wight on 5 May 2020 and by 11 May it had been downloaded 55,000 times. When the first national contact tracing schemes were launched – Test, Trace, Protect in Wales on 13 May, then on 28 May NHS Test and Trace in England, and Test and Protect in Scotland – the app was not ready to be included. Replying to a question at the government's daily briefing on 8 June, Hancock was unable to give a date for rollout of the app in England, saying it would be brought in "when it's right to do so". On 17 June, Lord Bethell, junior minister for Innovation at the Department of Health and Social Care, said "we're seeking to get something going before the winter ... it isn't a priority for us at the moment". On 18 June, Health Secretary Matt Hancock announced development would switch to the Apple/Google system after admitting that Apple's restrictions on usage of Bluetooth prevented the app from working effectively. At the same press briefing Dido Harding, leader of the UK's test and trace programme, said "What we've done in really rigorously testing both our own Covid-19 app and the Google-Apple version is demonstrate that none of them are working sufficiently well enough to be actually reliable to determine whether any of us should self-isolate for two weeks [and] that's true across the world". === Concerns === The first, ultimately rejected, version of the app was subject to privacy concerns, the government backtracking on initial statements that the data collected from the app would not be shared outside the NHS. Matthew Gould, CEO of NHSX, the government department responsible for the app, said the data would be accessible to other organisations, but did not disclose which. Data collected would not necessarily be anonymised and would be held in a centralised repository. Over 150 of the UK's security and privacy experts warned the app's data could be used by 'a bad actor (state, private sector, or hacker)' to spy on citizens. Fears were discussed by the House of Commons' Human Rights Select Committee about plans for the app to record user location data. Parliament's Joint Committee on Human Rights said this version of the app should not be released without proper privacy protections. The second version of the app, released nationwide, addressed these concerns by employing a decentralised framework, the Apple/Google Exposure Notification system. Under this system, users remain pseudonymous: a person diagnosed with COVID-19 does not know which people are informed about an encounter, and contacted persons do not receive any information about the person diagnosed with COVID-19. The functionality of the app was also questioned in late April and early May 2020, as the software's use of Bluetooth required the app to be constantly running, meaning users could not use other apps or lock their device if the app was to function properly. The developers of the app were said to have found a way of working around this restriction. === Related contracts === Faculty – a company linked to Cambridge Analytica – provided research and modelling to NHSX in support of the response to the pandemic. Palantir, also linked to Cambridge Analytica, provided their data management platform. These contracts began in February and March respectively. == Second phase == As outlined on cancellation of the first app on 18 June 2020, the Department of Health and Social Care published on 30 July a brief description of the "next phase" app. Users would be able to scan a QR code at venues they visit, and later be notified if they had visited a place which was the source of a number of infections; the app would also assist with identifying symptoms and ordering a test. By using the Exposure Notification system from Apple and Google, personal data would be decentralised. Zuhlke Engineering Ltd, the UK branch of Swiss-based Zühlke Group, used 70 staff to complete the development of the app in 12 weeks. Zuhlke Engineering was awarded "Development Team of the Year" title at UK IT Industry awards in November 2021 for development of NHS COVID-19 application. === Timeline === Testing of the app by NHS volunteer responders, and selected residents of the Isle of Wight and the London Borough of Newham, began around 13 August. The app was made available to the public (aged 16 or over) in England and Wales on 24 September. An updated app released on 29 October, in part from collaboration with the Alan Turing Institute, improved the accuracy of measurements of the distance between the user's phone and other phones. At the same time, the duration threshold for determining exposure was reduced; this was expected to lead to an increase in the number of users told to self-isolate. An update to the app in April 2021, timed to coincide with easing of restrictions on hospitality businesses, was blocked by Apple and Google. It was intended that users who tested positive would be asked to share their history of visited venues, to assist in warning others, but this would have contravened assurances by Apple and Google that location data from devices would not be shared. === Statistics and effectiveness === The app was downloaded six million times on the first day it was generally availa

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  • QF-Test

    QF-Test

    QF-Test from Quality First Software is a cross-platform software tool for automated testing of programs via the graphical user interface (GUI) test automation). The program is specialized on (Java/Swing, Standard Widget Toolkit (SWT), Eclipse plug-ins and rich client platform (RCP) applications, ULC and JavaFX) cross-web browser test automation of static and dynamic web applications (HTML and web frameworks like Angular, Ext JS, Fluent UI React, Google Web Toolkit (GWT), jQuery UI, jQueryEasyUI Remote Application Platform (RAP), Qooxdoo, RichFaces, Vaadin, React, Smart GWT, Vue.js, ICEfaces and ZK). Version 4.1 added support for macOS and the Apple Safari and Microsoft Edge browsers via the Selenium WebDriver. Representational State Transfer (RESTful) web service testing. From version 5.0, Windows applications can also be tested (classic Win32 applications, .NET framework applications (often developed in C#) based on Windows Presentation Foundation (WPF) or Windows Forms, Windows apps and Universal Windows Platform (UWP) applications using Extensible Application Markup Language (XAML) controls) and modern C++ applications (such as Qt applications). Version 5.3 added support for the Chrome DevTools protocol, which allows browsers to be controlled using CDP drivers. Since then, mobile testing for iOS and Android, accessibility testing of web applications and SmartID, a new approach for more flexible and robust component recognition, have been introduced. Powerful enhancements such as WebAPI testing and AI-assisted validation complement the test automation tool. == Overview == QF-Test (the successor of qftestJUI, available since 2001) enables regression and load testing and runs on Windows, Unix and macOS. It is mainly used commercially by testers, developers or business analysts (modelling, low code approaches) with or without programming knowledge as part of software Quality Assurance. Since December 2008, a webtest add-on is available which allows test automation of browser-based GUIs (such as Internet Explorer, Mozilla Firefox, Google Chrome, Apple Safari, and Microsoft Edge) along with extant Java GUI test functions, which was extended to include JavaFX in July 2014. From 2018, QF-Test version 4.2 can test PDF documents, from 2020 native desktop applications (QF-Test version 5) and in 2022, mobile application testing will be added. The basis for efficient use in test automation is stable component recognition (IDs, logical screen elements, labels, CustomWebResolver, SmartID, ...) with low maintenance effort. == Features == General – QF-Test's capture/replay function enables recording of tests for beginners, while modular programming (modularizing) allows creating large test suites in a concise arrangement. For the advanced user who requires even more control over his application, the tool offers access to internal program structures through the standard scripting languages Jython, the Java implementation of the popular Python language, JavaScript, and Groovy. The tool also offers a batch processing mode, allowing to run tests unattended and then generate XML, HTML and JUnit reports. Thus the tool can be integrated into existing build/test frameworks like Jenkins, Ant or Maven. Another mode is the so-called Daemon mode for distributed test execution. A specific integration with many test management tools exists. There is a test debugger (enabling arbitrary stepping and editing variables at runtime) and a fully automated dependency management that takes care of pre- and postconditions and helps isolating test cases. Data-driven testing with no need for scripting is possible. Web testing: cross-browser on Internet Explorer, Chrome, Firefox, Edge (including Chromium-based), Opera and Safari for static and dynamic websites (HTML5, Ajax, DOM). A headless browser can also be used for testing. QF-Test fully supports frameworks like Angular, React and Vue.js, but also many specific UI toolkits like Smart (GWT), GXT/ExtGWT, ExtJS, ICEfaces, jQuery UI, Kendo UI, PrimeFaces, Qooxdoo, RAP, RichFaces, Vaadin and ZK. Easy integration with Selenium makes it easy to balance development and functional testing. Electron applications can also be tested. Other (e.g., SAP UI5, Siebel Open UI, Salesforce) and future web toolkits can be integrated with little effort. Short-term and individual customisations (CustomWebResolver) are possible via an optimised interface JavaFX, Java Swing, SWT, Eclipse plug-ins and RCP applications and ULC. Support for testing when migrating from JavaSwing or JavaFX to web applications (e.g. via Webswing). Hybrid applications based on multiple technologies are also supported, e.g. applications that integrate HTML content into Java applications using JxBrowser. Windows-based applications (Win32, .NET, Windows Forms, WPF, Windows apps, Qt). Android applications can be tested on real devices and with the Android Studio emulator. iOS applications can also be tested on real devices and with the Xcode Simulator. Testing of PDF documents (document comparisons, checking content, texts, images/graphic objects, layouts, "invisible" or partially hidden objects). QF-Test 9 introduces web accessibility testing to automatically check compliance with WCAG and other standards. QF-Test 10 introduces powerful enhancements for WebAPI testing and AI-assisted validation.

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  • RockMyRun

    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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  • Kernel Assisted Superuser

    Kernel Assisted Superuser

    Kernel Assisted Superuser (short: KernelSU) is an alternative method for obtaining root privileges on Android devices. KernelSU implementations are developed as free and open-source software under the terms of the GPLv3 license. == Technical differences == KernelSU differs from other methods in that root access is implemented directly in the kernel. Compared to other root methods that run in userspace, such as Magisk, this has the advantage that commands with su can be executed like normal commands, but still have root privileges. This is not prevented by SELinux or detected by the PlayIntegrity API check, so applications that use it will continue to function. Unlike Magisk, /system/bin/su is a virtual file implemented by hooking system calls with kprobes, and overlayfs is used for systemless modifications to the system partition instead of magic mount. == History == The planning of KernelSU was started in 2018 by developer Jason Donenfeld, also known as XDA user zx2c4. The lack of a root manager app and the difficulty of creating boot images meant that KernelSU was not suitable for productive use, and for a long time this method remained theoretical and could only be used by developers. In 2021, Google launched Generic Kernel Images (GKI for short), which facilitates the creation of a set of device-independent rooted boot images. In response, the developer known on XDA as weishu, who had also worked on projects such as VirtualXposed, adapted KernelSU for GKI-compatible kernels. The adaptation, which was released in January 2023, ensures that any device booting with Linux kernel version 5.10 or higher should be compatible. In addition, the developer also offers a special manager app that, in addition to managing root privileges, also offers overlay-based modding similar to Magisk modules. As of November 2025, 310 developers have contributed to the development of the KernelSU implementation. == Distribution == KernelSU can be installed on all devices that use GKI, as well as on individually supported devices without GKI. Some custom ROMs already have it integrated by default, including ROMs such as CrDroid, Bliss OS, and Evolution X.

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  • Anthem medical data breach

    Anthem medical data breach

    The Anthem medical data breach was a medical data breach of information held by Elevance Health, known at that time as Anthem Inc. On February 4, 2015, Anthem, Inc. disclosed that criminal hackers had broken into its servers and had potentially stolen over 37.5 million records that contain personally identifiable information from its servers. On February 24, 2015 Anthem raised the number to 78.8 million people whose personal information had been affected. According to Anthem, Inc., the data breach extended into multiple brands Anthem, Inc. uses to market its healthcare plans, including, Anthem Blue Cross, Anthem Blue Cross and Blue Shield, Blue Cross and Blue Shield of Georgia, Empire Blue Cross and Blue Shield, Amerigroup, Caremore, and UniCare. Healthlink says that it was also a victim. Anthem says users' medical information and financial data were not compromised. Anthem has offered free credit monitoring in the wake of the breach. Michael Daniel, chief adviser on cybersecurity for President Barack Obama, said he would be changing his own password. According to The New York Times, about 80 million company records were hacked, and there is a fear that the stolen data will be used for identity theft. The compromised information contained names, birthdays, medical IDs, social security numbers, street addresses, e-mail addresses and employment information, including income data. == Theft of the data == The data was stolen over a period of weeks the month before the data breach was discovered. Because no medical information was compromised, Anthem was not required by law to encrypt the data. However, Anthem faced several civil class-action lawsuits, which were settled in 2017 at a cost of $115 million. Anthem did not admit any wrongdoing in the settlement. Data from the attack is expected to be sold on the black market. == Impact == Persons whose data was stolen could have resulting problems about identity theft for the rest of their lives. Anthem had a US$100 million insurance policy for cyber problems from American International Group. One report suggested that all of this money could be consumed by the process of notifying customers of the breach. == Responses == Anthem hired Mandiant, a cybersecurity firm, to review their security systems and advised people whose data was stolen to monitor their accounts and remain vigilant. The theft of the data raised fears generally about the theft of medical information. A writer from Harvard Law School suggested that this data breach might spark reform of security practices and government data safety regulation. An investigation conducted by several state insurance commissioners blames the breach on an attacker whose identity was withheld, and claims that the breach was likely ordered by a foreign government whose name was withheld. It also concluded that Anthem had taken reasonable measures to protect its data before the breach and that its remediation plan was effective at shutting down the breach once it was discovered. It also marks the starting date of the breach as February 18, 2014. The lead investigator was the Indiana Department of Insurance (DOI) -- Anthem's principal regulator, because Anthem is headquartered in Indiana. The Indiana DOI hired independent auditors to conduct a security assessment at Anthem, which concluded, "While deficiencies within Anthem’s cybersecurity posture were noted by the Examination Team, these deficiencies were not, in our experience, uncommon to companies comparable to Anthem in size and scope. While the pre-breach deficiencies impacted Anthem’s ability to reduce the likelihood of and quickly detect the Data Breach, the controls implemented subsequent to the Data Breach should improve Anthem’s ability to detect future breaches and enable Anthem to respond more effectively to a future attack than was the case in this instance." Federal regulators also conducted an investigation of the Anthem data breach, resulting in a $16 million settlement between Anthem and the Department of Health and Human Services (HHS) -- by far the largest HHS data breach settlement. An HHS Director overseeing the investigation said, "The largest health data breach in U.S. history fully merits the largest HIPAA settlement in history. Unfortunately, Anthem failed to implement appropriate measures for detecting hackers who had gained access to their system to harvest passwords and steal people's private information." The HHS settlement also required Anthem to perform a risk assessment and correct any identified deficiencies in its cybersecurity, with HHS oversight of Anthem's progress. Approximately 100 private class action lawsuits were filed against Anthem over the data breach and consolidated in California federal court, in front of Judge Koh, a respected authority in data breach litigation. After contested briefing over who should lead the litigation efforts, Judge Koh appoints Eve Cervantez of Altshuler Berzon and Andy Friedman of Cohen Milstein as co-lead counsel, and appointed Eric Gibbs of Gibbs Law Group and Michael Sobel of Lieff Cabraser to head a Plaintiffs' Steering Committee. In 2017, Anthem agreed to settle the litigation for $115 million, the largest ever data breach settlement at the time. The attorneys requested $38 million in fees for their work on the case, but Judge Koh slashed the fee request, finding that only $31 million in fees were merited.

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  • POP-11

    POP-11

    POP-11 is a reflective, incrementally compiled programming language with many of the features of an interpreted language. It is the core language of the Poplog programming environment developed originally by the University of Sussex, and recently in the School of Computer Science at the University of Birmingham, which hosts the main Poplog website. POP-11 is an evolution of the language POP-2, developed in Edinburgh University, and features an open stack model (like Forth, among others). It is mainly procedural, but supports declarative language constructs, including a pattern matcher, and is mostly used for research and teaching in artificial intelligence, although it has features sufficient for many other classes of problems. It is often used to introduce symbolic programming techniques to programmers of more conventional languages like Pascal, who find POP syntax more familiar than that of Lisp. One of POP-11's features is that it supports first-class functions. POP-11 is the core language of the Poplog system. The availability of the compiler and compiler subroutines at run-time (a requirement for incremental compiling) gives it the ability to support a far wider range of extensions (including run-time extensions, such as adding new data-types) than would be possible using only a macro facility. This made it possible for (optional) incremental compilers to be added for Prolog, Common Lisp and Standard ML, which could be added as required to support either mixed language development or development in the second language without using any POP-11 constructs. This made it possible for Poplog to be used by teachers, researchers, and developers who were interested in only one of the languages. The most successful product developed in POP-11 was the Clementine data mining system, developed by ISL. After SPSS bought ISL, they renamed Clementine to SPSS Modeler and decided to port it to C++ and Java, and eventually succeeded with great effort, and perhaps some loss of the flexibility provided by the use of an AI language. POP-11 was for a time available only as part of an expensive commercial package (Poplog), but since about 1999 it has been freely available as part of the open-source software version of Poplog, including various added packages and teaching libraries. An online version of ELIZA using POP-11 is available at Birmingham. At the University of Sussex, David Young used POP-11 in combination with C and Fortran to develop a suite of teaching and interactive development tools for image processing and vision, and has made them available in the Popvision extension to Poplog. == Simple code examples == Here is an example of a simple POP-11 program: define Double(Source) -> Result; Source2 -> Result; enddefine; Double(123) => That prints out: 246 This one includes some list processing: define RemoveElementsMatching(Element, Source) -> Result; lvars Index; [[% for Index in Source do unless Index = Element or Index matches Element then Index; endunless; endfor; %]] -> Result; enddefine; RemoveElementsMatching("the", [[the cat sat on the mat]]) => ;;; outputs [[cat sat on mat]] RemoveElementsMatching("the", [[the cat] [sat on] the mat]) => ;;; outputs [[the cat] [sat on] mat] RemoveElementsMatching([[= cat]], [[the cat]] is a [[big cat]]) => ;;; outputs [[is a]] Examples using the POP-11 pattern matcher, which makes it relatively easy for students to learn to develop sophisticated list-processing programs without having to treat patterns as tree structures accessed by 'head' and 'tail' functions (CAR and CDR in Lisp), can be found in the online introductory tutorial. The matcher is at the heart of the SimAgent (sim_agent) toolkit. Some of the powerful features of the toolkit, such as linking pattern variables to inline code variables, would have been very difficult to implement without the incremental compiler facilities.

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  • Kernel Assisted Superuser

    Kernel Assisted Superuser

    Kernel Assisted Superuser (short: KernelSU) is an alternative method for obtaining root privileges on Android devices. KernelSU implementations are developed as free and open-source software under the terms of the GPLv3 license. == Technical differences == KernelSU differs from other methods in that root access is implemented directly in the kernel. Compared to other root methods that run in userspace, such as Magisk, this has the advantage that commands with su can be executed like normal commands, but still have root privileges. This is not prevented by SELinux or detected by the PlayIntegrity API check, so applications that use it will continue to function. Unlike Magisk, /system/bin/su is a virtual file implemented by hooking system calls with kprobes, and overlayfs is used for systemless modifications to the system partition instead of magic mount. == History == The planning of KernelSU was started in 2018 by developer Jason Donenfeld, also known as XDA user zx2c4. The lack of a root manager app and the difficulty of creating boot images meant that KernelSU was not suitable for productive use, and for a long time this method remained theoretical and could only be used by developers. In 2021, Google launched Generic Kernel Images (GKI for short), which facilitates the creation of a set of device-independent rooted boot images. In response, the developer known on XDA as weishu, who had also worked on projects such as VirtualXposed, adapted KernelSU for GKI-compatible kernels. The adaptation, which was released in January 2023, ensures that any device booting with Linux kernel version 5.10 or higher should be compatible. In addition, the developer also offers a special manager app that, in addition to managing root privileges, also offers overlay-based modding similar to Magisk modules. As of November 2025, 310 developers have contributed to the development of the KernelSU implementation. == Distribution == KernelSU can be installed on all devices that use GKI, as well as on individually supported devices without GKI. Some custom ROMs already have it integrated by default, including ROMs such as CrDroid, Bliss OS, and Evolution X.

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  • Tea (app)

    Tea (app)

    Tea, officially Tea Dating Advice, is a dating surveillance mobile phone application that allows women to post personal data about men they are interested in or are currently dating. Founded by Sean Cook, the app rose to prominence in July 2025 after it was the subject of three major data leaks in July and August 2025. It was removed from Apple's App Store in October 2025, but remains available on the Google Play Store. == History == The app enables its users to upload, view, and comment on photos of men, check men's public records, and perform image searches. It also provides the ability to rate and review men, as well as a group chat function. The app uses artificial intelligence to verify that the user is a woman through facial analysis and other personal information to preserve the app as a women-only space. Users are required to submit their photo and an ID to access the app. The company that created the app was founded by businessman and tech capitalist Sean Cook, who stated in July 2025 that he was inspired to create the app because of his mother's experiences from online dating. According to the company, users remain anonymous, and the requirement to upload an ID was removed in 2023. An August 2025 investigation by 404 Media suggested that much of the information given by Cook on the historical background of the company was inaccurate. In July 2025, private messages, other personally identifying information, and approximately 72,000 images were leaked via 4chan. A further 1.1 million private messages were subsequently leaked using a separate security vulnerability; these included intimate conversations about controversial topics such as adultery and other forms of infidelity to their partners, discussions of abortion, phone numbers, meeting locations, and other confidential communications. The app's publishers subsequently revoked the ability to private message users in the app. Shortly after, the app was hidden from search on Android and an interactive, unverified map was also created of those in the files. By 7 August 2025, ten class action lawsuits had been filed. A further leak was reported later that month. Proponents have praised the app as an aid for women's safety by helping them check men for adultery, catfishing, criminal convictions and other "red flag" behaviors. Critics have described the app as a doxing tool and a violation of privacy, an opportunity for defamation against innocent individuals, and a witch hunt. Cook has stated that the company's legal team receives about three legal threats per day. Another mobile app, called TeaOnHer, was created in response of the app’s popularity. It was described as the male version of the Tea app. The app also reported a data breach in August 2025. In October 2025, Apple removed the app from their app store, telling journalists that the removal was due to a failure to meet company terms regarding content moderation and user privacy. Apple also mentioned an excessive amount of complaints, including allegations that the personal information of minors was being shared. The app remains on the Google Play Store.

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  • Document-oriented database

    Document-oriented database

    A document-oriented database, or document store, is a computer program and data storage system designed for storing, retrieving, and managing document-oriented information, also known as semi-structured data. Document-oriented databases are one of the main categories of NoSQL databases, and the popularity of the term "document-oriented database" has grown alongside the adoption of NoSQL itself. XML databases are a subclass of document-oriented databases optimized for XML documents. Graph databases are similar, but add another layer, the relationship, which allows them to link documents for rapid traversal. Document-oriented databases are conceptually an extension of the key–value store, another type of NoSQL database. In key-value stores, data is treated as opaque by the database, whereas document-oriented systems exploit the internal structure of documents to extract metadata and optimize storage and queries. Although in practice the distinction can be minimal due to modern tooling, document stores are designed to provide a richer programming experience with modern programming techniques. Document databases differ significantly from traditional relational databases (RDBs). Relational databases store data in predefined tables, often requiring an object to be split across multiple tables. In contrast, document databases store all information for a given object in a single document, with each document potentially having a unique structure. This design eliminates the need for object-relational mapping when loading data into the database. == Documents == The central concept of a document-oriented database is the notion of a document. Although implementations vary in their specific definitions, document-oriented databases generally treat documents as self-contained units that encapsulate and encode data in a standardized format. Common encoding formats include XML, YAML, JSON, as well as binary representations such as BSON. Documents in a document store are equivalent to the programming concept of an object. They are not required to adhere to a fixed schema, and documents within the same collection may contain different fields or structures. Fields may be optional, and documents of the same logical type may differ in composition. For example, the following illustrates a document encoded in JSON: A second document might be encoded in XML as: The two example documents share some structural elements but also contain unique fields. The structure, text, and other data within each document are collectively referred to as the document's content and can be accessed or modified using retrieval or editing operations. Unlike relational databases, in which each record contains the same fields and unused fields are left empty, document-oriented databases do not require uniform fields across documents. This design allows new information to be added to some documents without affecting the structure of others. Document databases often support the storage of additional metadata alongside the document content. Such metadata may relate to organizational features, security, indexing, or other implementation-specific features. === CRUD operations === The core operations supported by a document-oriented database for manipulating documents are similar to those in other databases. Although terminology is not perfectly standardized, these operations are generally recognized as Create, Read, Update, and Delete (CRUD). Creation (C): Adds a new document to the database. Retrieval (R): Retrieves documents or fields based on queries. Update (U): Modifies the contents of existing documents. Deletion (D): Removes documents from the database. === Keys === Documents in a document-oriented database are addressed via a unique identifier. This identifier, often a string, URI, or path, can be used to retrieve the document from the database. Most document stores maintain an index on the key to optimize retrieval, and in some implementations the key is required when creating or inserting a new document. === Retrieval === In addition to key-based access, document-oriented databases typically provide an API or query language that enables retrieval based on document content or associated metadata. For example, a query may return all documents with a specific field matching a given value. The available query features, indexing options, and performance characteristics vary across implementations. Document stores differ from key-value stores in that they exploit the internal structure and metadata of stored documents. In many key-value stores, values are treated as opaque or "black-box" data, meaning the database system does not interpret their internal structure. By contrast, document-oriented databases can classify and interpret document content. This enables queries that distinguish between types of data––for example, retrieving all phone numbers containing "555" without also matching a postal code such as "55555." === Editing === Document databases typically provide mechanisms for updating or editing the content or metadata of a document. Updates may involve replacing the entire document or modifying individual elements or fields within the document. === Organization === Document database implementations support a variety of methods for organizing documents, including: Collections: Groups of documents. Depending on the implementation, a document may be required to belong to a single collection or may be allowed in multiple collections. Tags and non-visible metadata: Additional data stored outside the main document content. Directory hierarchies: Documents organized in a tree-like structure, often based on path or URI. These organizational structures may differ between logical and physical representations (e.g. on disk or in memory). == Relationship to other databases == === Relationship to key-value stores === A document-oriented database can be viewed as a specialized form of key-value store, which is itself a category of NoSQL database. In a basic key-value store, the stored value is typically treated as opaque by the database system. By contrast, a document-oriented database provides APIs or a query and update language that allows queries and modifications based on the internal structure of the document. For users who do not require advanced query, retrieval, or update capabilities, the distinction between document-oriented databases and key-value stores may be minimal. === Relationship to search engines === Some search engine and information retrieval systems, such as Apache Solr and Elasticsearch, provide document storage and support core document operations. As a result, they may meet certain functional definitions of a document-oriented database, although their primary design goals differ. === Relationship to relational databases === In a relational database, data is organized into predefined types represented as tables. Each table contains rows (records) with a fixed set of columns (fields), so all records in a table share the same structure. Administrators typically define indexes on selected fields to improve query performance. A central principle of relational database design is database normalization, in which data that might otherwise be repeated is stored in separate tables and linked using keys. When records in different tables are related, a foreign key is used to associate them. For example, an address book application may store a contact's name, image, phone numbers, mailing addresses, and email addresses. In a normalized relational design, separate tables might be created for contacts, phone numbers, and email addresses. The phone number table would include a foreign key referencing the associated contact. To reconstruct a complete contact record, the database retrieves related information from each table using the foreign keys and combines it into a single record. In contrast, a document-oriented database stores all data related to an object within a single document, and stored in the database as a single entry. In the address book example,the contact's name, image, and contact information may be stored together in one document. The document is retrieved using a unique key, and all related information is returned together, without needing to look up multiple tables. A key difference between the document-oriented and relational models is that the data formats are not predefined in the document case. In most cases, any sort of document can be stored in a database, and documents can change in type and form over time. For example, a new field such as COUNTRY_FLAG can be added to new documents as they are inserted without affecting existing documents. To aid retrieval, document-oriented systems generally allow the administrator to provide hints to the database for locating certain types of information. These hints work in a similar fashion to indexes in relational databases. Many systems also allow additional metadata outside the content of the document itself

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  • Load file

    Load file

    A load file in the litigation community is commonly referred to as the file used to import data (coded, captured or extracted data from ESI processing) into a database; or the file used to link images. These load files carry commands, commanding the software to carry out certain functions with the data found in them. Load files are usually ASCII text files that have delimited fields of information. Such load files may have data about documents to be imported into a document management software such as Concordance or Summation. Or they may have the path or directory where images may reside so that the software can link such images to their corresponding records. Some database programs take one load file for importing images and another for importing data while others take only one load file for both pieces of information. OCR or Search-able Text which is considered "data" is also imported into most database programs via the same load files. Though some people prefer to load the OCR into their databases by running a separate command to search and find the desired text. Commonly used databases and their corresponding file extensions are: Summation (DII , CSV), Concordance (OPT, DAT), Sanction (SDT), IPRO (LFP), Ringtail (MDB) and DB/TextWorks (TXT).

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  • Physical information security

    Physical information security

    Physical information security is the intersection or common ground between physical security and information security. It primarily concerns the protection of tangible information-related assets such as computer systems and storage media against physical, real-world threats such as unauthorized physical access, theft, fire and flood. It typically involves physical controls such as protective barriers and locks, uninterruptible power supplies, and shredders. Information security controls in the physical domain complement those in the logical domain (such as encryption), and procedural or administrative controls (such as information security awareness and compliance with policies and laws). == Background == Asset are inherently valuable and yet vulnerable to a wide variety of threats, both malicious (e.g. theft, arson) and accidental/natural (e.g. lost property, bush fire). If threats materialize and exploit those vulnerabilities causing incidents, there are likely to be adverse impacts on the organizations or individuals who legitimately own and utilize the assets, varying from trivial to devastating in effect. Security controls are intended to reduce the probability or frequency of occurrence and/or the severity of the impacts arising from incidents, thus protecting the value of the assets. Physical security involves the use of controls such as smoke detectors, fire alarms and extinguishers, along with related laws, regulations, policies and procedures concerning their use. Barriers such as fences, walls and doors are obvious physical security controls, designed to deter or prevent unauthorized physical access to a controlled area, such as a home or office. The moats and battlements of Mediaeval castles are classic examples of physical access controls, as are bank vaults and safes. Information security controls protect the value of information assets, particularly the information itself (i.e. the intangible information content, data, intellectual property, knowledge etc.) but also computer and telecommunications equipment, storage media (including papers and digital media), cables and other tangible information-related assets (such as computer power supplies). The corporate mantra "Our people are our greatest assets" is literally true in the sense that so-called knowledge workers qualify as extremely valuable, perhaps irreplaceable information assets. Health and safety measures and even medical practice could therefore also be classed as physical information security controls since they protect humans against injuries, diseases and death. This perspective exemplifies the ubiquity and value of information. Modern human society is heavily reliant on information, and information has importance and value at a deeper, more fundamental level. In principle, the subcellular biochemical mechanisms that maintain the accuracy of DNA replication could even be classed as vital information security controls, given that genes are 'the information of life'. Malicious actors who may benefit from physical access to information assets include computer crackers, corporate spies, and fraudsters. The value of information assets is self-evident in the case of, say, stolen laptops or servers that can be sold-on for cash, but the information content is often far more valuable, for example encryption keys or passwords (used to gain access to further systems and information), trade secrets and other intellectual property (inherently valuable or valuable because of the commercial advantages they confer), and credit card numbers (used to commit identity fraud and further theft). Furthermore, the loss, theft or damage of computer systems, plus power interruptions, mechanical/electronic failures and other physical incidents prevent them being used, typically causing disruption and consequential costs or losses. Unauthorized disclosure of confidential information, and even the coercive threat of such disclosure, can be damaging as we saw in the Sony Pictures Entertainment hack at the end of 2014 and in numerous privacy breach incidents. Even in the absence of evidence that disclosed personal information has actually been exploited, the very fact that it is no longer secured and under the control of its rightful owners is itself a potentially harmful privacy impact. Substantial fines, adverse publicity/reputational damage and other noncompliance penalties and impacts that flow from serious privacy breaches are best avoided, regardless of cause! == Examples of physical attacks to obtain information == There are several ways to obtain information through physical attacks or exploitations. A few examples are described below. === Dumpster diving === Dumpster diving is the practice of searching through trash in the hope of obtaining something valuable such as information carelessly discarded on paper, computer disks or other hardware. === Overt access === Sometimes attackers will simply go into a building and take the information they need. Frequently when using this strategy, an attacker will masquerade as someone who belongs in the situation. They may pose as a copy room employee, remove a document from someone's desk, copy the document, replace the original, and leave with the copied document. Individuals pretending to building maintenance may gain access to otherwise restricted spaces. They might walk right out of the building with a trash bag containing sensitive documents, carrying portable devices or storage media that were left out on desks, or perhaps just having memorized a password on a sticky note stuck to someone's computer screen or called out to a colleague across an open office. == Examples of Physical Information Security Controls == Shredding paper documents prior to their disposal can prevent unintended information leakage. Digital data can be encrypted or securely wiped. Offices may require visitors to present valid identification cards or valid access keys. Office workers may be required to obey "clear desk" policies, protecting documents and other storage media (including portable IT devices) by tidying them away out of sight (for example in locked drawers, filing cabinets, safes or a Bank vault). Workers may be required to memorize their passwords or use a password manager instead of writing passwords on paper. Computers are vulnerable to outages caused by power cuts, accidental disconnection, flat batteries, brown-outs, surges, spikes, electrical interference and electronic failures. Physical information security controls to address the associated risks include: fuses, no-break battery-backed power supplies, electrical generators, redundant power sources and cabling, "Do not remove" warning signs on plugs, surge protectors, power quality monitoring, spare batteries, professional design and installation of power circuits plus regular inspections/tests and preventive maintenance.

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  • Random (software)

    Random (software)

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

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