AI Assistant Youtrack

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

  • Moral outsourcing

    Moral outsourcing

    Moral outsourcing is the placing of responsibility for ethical decision-making onto external entities, often algorithms. The term is often used in discussions of computer science and algorithmic fairness, but it can apply to any situation in which one appeals to outside agents in order to absolve themselves of responsibility for their actions. In this context, moral outsourcing specifically refers to the tendency of society to blame technology, rather than its creators or users, for any harm it may cause. == Definition == The term "moral outsourcing" was first coined by Dr. Rumman Chowdhury, a data scientist concerned with the overlap between artificial intelligence and social issues. Chowdhury used the term to describe looming fears of a so-called “Fourth Industrial Revolution” following the rise of artificial intelligence. Moral outsourcing is often applied by technologists to shrink away from their part in building offensive products. In her TED Talk, Chowdhury gives the example of a creator excusing their work by saying they were simply doing their job. This is a case of moral outsourcing and not taking ownership for the consequences of creation. When it comes to AI, moral outsourcing allows for creators to decide when the machine is human and when it is a computer - shifting the blame and responsibility of moral plights off of the technologists and onto the technology. Conversations around AI and bias and its impacts require accountability to bring change. It is difficult to address these biased systems if their creators use moral outsourcing to avoid taking any responsibility for the issue. One example of moral outsourcing is the anger that is directed at machines for “taking jobs away from humans” rather than companies for employing that technology and jeopardizing jobs in the first place. The term "moral outsourcing" refers to the concept of outsourcing, or enlisting an external operation to complete specific work for another organization. In the case of moral outsourcing, the work of resolving moral dilemmas or making choices according to an ethical code is supposed to be conducted by another entity. == Real-world applications == In the medical field, AI is increasingly involved in decision-making processes about which patients to treat, and how to treat them. The responsibility of the doctor to make informed decisions about what is best for their patients is outsourced to an algorithm. Sympathy is also noted to be an important part of medical practice; an aspect that artificial intelligence, glaringly, is missing. This form of moral outsourcing is a major concern in the medical community. Another field of technology in which moral outsourcing is frequently brought up is autonomous vehicles. California Polytechnic State University professor Keith Abney proposed an example scenario: "Suppose we have some [troublemaking] teenagers, and they see an autonomous vehicle, they drive right at it. They know the autonomous vehicle will swerve off the road and go off a cliff, but should it?" The decision of whether to sacrifice the autonomous vehicle (and any passengers inside) or the vehicle coming at it will be written into the algorithms defining the car's behavior. In the case of moral outsourcing, the responsibility of any damage caused by an accident may be attributed to the autonomous vehicle itself, rather than the creators who wrote the protocol the vehicle will use to "decide" what to do. Moral outsourcing is also used to delegate the consequences of predictive policing algorithms to technology, rather than the creators or the police. There are many ethical concerns with predictive policing due to the fact that it results in the over-policing of low income and minority communities. In the context of moral outsourcing, the positive feedback loop of sending disproportionate police forces into minority communities is attributed to the algorithm and the data being fed into this system--rather than the users and creators of the predictive policing technology. == Outside of technology == === Religion === Moral outsourcing is also commonly seen in appeals to religion to justify discrimination or harm. In his book What It Means to be Moral, sociologist Phil Zuckerman contradicts the popular religious notion that morality comes from God. Religion is oftentimes cited as a foundation for a moral stance without any tangible relation between the religious beliefs and personal stance. In these cases, religious individuals will "outsource" their personal beliefs and opinions by claiming that they are a result of their religious identification. This is seen where religion is cited as a factor for political beliefs, medical beliefs, and in extreme cases an excuse for violence. === Manufacturing === Moral outsourcing can also be seen in the business world in terms of manufacturing goods and avoiding environmental responsibility. Some companies in the United States will move their production process to foreign countries with more relaxed environmental policies to avoid the pollution laws that exist in the US. A study by the Harvard Business Review found that "in countries with tight environmental regulation, companies have 29% lower domestic emissions on average. On the other hand, such a tightening in regulation results in 43% higher emissions abroad." The consequences of higher pollution rates are then attributed to the loose regulations in these countries, rather than on the companies themselves who purposefully moved into these areas to avoid strict pollution policy.

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  • PlantUML

    PlantUML

    PlantUML is an open-source tool allowing users to create diagrams from a plain text language. Besides various UML diagrams, PlantUML has support for various other software development related formats (such as Archimate, Block diagram, BPMN, C4, Computer network diagram, ERD, Gantt chart, Mind map, and WBD), as well as visualisation of JSON and YAML files. The language of PlantUML is an example of a domain-specific language. Besides its own DSL, PlantUML also understands AsciiMath, Creole, DOT, and LaTeX. It uses Graphviz software to lay out its diagrams and Tikz for LaTeX support. Images can be output as PNG, SVG, LaTeX and even ASCII art. PlantUML has also been used to allow blind people to design and read UML diagrams. == Applications that use PlantUML == There are various extensions or add-ons that incorporate PlantUML. Atom has a community maintained PlantUML syntax highlighter and viewer. Confluence wiki has a PlantUML plug-in for Confluence Server, which renders diagrams on-the-fly during a page reload. There is an additional PlantUML plug-in for Confluence Cloud. Doxygen integrates diagrams for which sources are provided after the startuml command. Eclipse has a PlantUML plug-in. Google Docs has an add-on called PlantUML Gizmo that works with the PlantUML.com server. IntelliJ IDEA can create and display diagrams embedded into Markdown (built-in) or in standalone files (using a plugin). LaTeX using the Tikz package has limited support for PlantUML. LibreOffice has Libo_PlantUML extension to use PlantUML diagrams. MediaWiki has a PlantUML plug-in which renders diagrams in pages as SVG or PNG. Microsoft Word can use PlantUML diagrams via a Word Template Add-in. There is an additional Visual Studio Tools for Office add-in called PlantUML Gizmo that works in a similar fashion. NetBeans has a PlantUML plug-in. Notepad++ has a PlantUML plug-in. Obsidian has a PlantUML plug-in. Org-mode has a PlantUML org-babel support. Rider has a PlantUML plug-in. Sublime Text has a PlantUML package called PlantUmlDiagrams for Sublime Text 2 and 3. Visual Studio Code has various PlantUML extensions on its marketplace, most popular being PlantUML by jebbs. Vnote open source notetaking markdown application has built in PlantUML support. Xcode has a community maintained Source Editor Extension to generate and view PlantUML class diagrams from Swift source code. == Text format to communicate UML at source code level == PlantUML uses well-formed and human-readable code to render the diagrams. There are other text formats for UML modelling, but PlantUML supports many diagram types, and does not need an explicit layout, though it is possible to tweak the diagrams if necessary. +--------------------------------------+ | TEDx Talks Recommendation | | System | +--------------------------------------+ | +----------------------------------+ | | | Visitor | | | +----------------------------------+ | | | + View Recommended Talks | | | | + Search Talks | | | +----------------------------------+ | +--------------------------------------+ | | V +--------------------------------------+ | Authenticated User | +--------------------------------------+ | +----------------------------------+ | | | User | | | +----------------------------------+ | | | + View Recommended Talks | | | | + Search Talks | | | | + Save Favorite Talks | | | +----------------------------------+ | +--------------------------------------+ | | V +--------------------------------------+ | Admin | +--------------------------------------+ | +----------------------------------+ | | | Admin | | | +----------------------------------+ | | | + CRUD Talks | | | | + Manage Users | | | +----------------------------------+ | +--------------------------------------+

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  • Scikit-learn

    Scikit-learn

    scikit-learn (formerly scikits.learn and also known as sklearn) is a free and open-source machine learning library for the Python programming language. It features various classification, regression and clustering algorithms including support-vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy. Scikit-learn is a NumFOCUS fiscally sponsored project. == Overview == The scikit-learn project started as scikits.learn, a Google Summer of Code project by French data scientist David Cournapeau. The name of the project derives from its role as a "scientific toolkit for machine learning", originally developed and distributed as a third-party extension to SciPy. The original codebase was later rewritten by other developers. In 2010, contributors Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort and Vincent Michel, from the French Institute for Research in Computer Science and Automation in Saclay, France, took leadership of the project and released the first public version of the library on February 1, 2010. In November 2012, scikit-learn as well as scikit-image were described as two of the "well-maintained and popular" scikits libraries. In 2019, it was noted that scikit-learn is one of the most popular machine learning libraries on GitHub. At that time, the project had over 1,400 contributors and the documentation received 42 million visits in 2018. According to a 2022 Kaggle survey of nearly 24,000 respondents from 173 countries, scikit-learn was identified as the most widely used machine learning framework. == Features == Large catalogue of well-established machine learning algorithms and data pre-processing methods (i.e. feature engineering) Utility methods for common data-science tasks, such as splitting data into train and test sets, cross-validation and grid search Consistent way of running machine learning models (estimator.fit() and estimator.predict()), which libraries can implement Declarative way of structuring a data science process (the Pipeline), including data pre-processing and model fitting == Examples == Fitting a random forest classifier: == Implementation == scikit-learn is largely written in Python, and uses NumPy extensively for high-performance linear algebra and array operations. Furthermore, some core algorithms are written in Cython to improve performance. Support vector machines are implemented by a Cython wrapper around LIBSVM; logistic regression and linear support vector machines by a similar wrapper around LIBLINEAR. In such cases, extending these methods with Python may not be possible. scikit-learn integrates well with many other Python libraries, such as Matplotlib and plotly for plotting, NumPy for array vectorization, Pandas dataframes, SciPy, and many more. == History == scikit-learn was initially developed by David Cournapeau as a Google Summer of Code project in 2007. Later that year, Matthieu Brucher joined the project and started to use it as a part of his thesis work. In 2010, INRIA, the French Institute for Research in Computer Science and Automation, got involved and the first public release (v0.1 beta) was published in late January 2010. The project released its first stable version, 1.0.0, on September 24, 2021. The release was the result of over 2,100 merged pull requests, approximately 800 of which were dedicated to improving documentation. Development continues to focus on bug fixes, efficiency and feature expansion. The latest version, 1.8, was released on December 10, 2025. This update introduced native Array API support, enabling the library to perform GPU computations by directly using PyTorch and CuPy arrays. This version also included bug fixes, improvements and new features, such as efficiency improvements to the fit time of linear models. == Applications == Scikit-learn is widely used across industries for a variety of machine learning tasks such as classification, regression, clustering, and model selection. The following are real-world applications of the library: === Finance and Insurance === AXA uses scikit-learn to speed up the compensation process for car accidents and to detect insurance fraud. Zopa, a peer-to-peer lending platform, employs scikit-learn for credit risk modelling, fraud detection, marketing segmentation, and loan pricing. BNP Paribas Cardif uses scikit-learn to improve the dispatching of incoming mail and manage internal model risk governance through pipelines that reduce operational and overfitting risks. J.P. Morgan reports broad usage of scikit-learn across the bank for classification tasks and predictive analytics in financial decision-making. === Retail and E-Commerce === Booking.com uses scikit-learn for hotel and destination recommendation systems, fraudulent reservation detection, and workforce scheduling for customer support agents. HowAboutWe uses it to predict user engagement and preferences on a dating platform. Lovely leverages the library to understand user behaviour and detect fraudulent activity on its platform. Data Publica uses it for customer segmentation based on the success of past partnerships. Otto Group integrates scikit-learn throughout its data science stack, particularly in logistics optimization and product recommendations. === Media, Marketing, and Social Platforms === Spotify applies scikit-learn in its recommendation systems. Betaworks uses the library for both recommendation systems (e.g., for Digg) and dynamic subspace clustering applied to weather forecasting data. PeerIndex used scikit-learn for missing data imputation, tweet classification, and community clustering in social media analytics. Bestofmedia Group employs it for spam detection and ad click prediction. Machinalis utilizes scikit-learn for click-through rate prediction and relational information extraction for content classification and advertising optimization. Change.org applies scikit-learn for targeted email outreach based on user behaviour. === Technology === AWeber uses scikit-learn to extract features from emails and build pipelines for managing large-scale email campaigns. Solido applies it to semiconductor design tasks such as rare-event estimation and worst-case verification using statistical learning. Evernote, Dataiku, and other tech companies employ scikit-learn in prototyping and production workflows due to its consistent API and integration with the Python ecosystem. === Academia === Télécom ParisTech integrates scikit-learn in hands-on coursework and assignments as part of its machine learning curriculum. == Awards == 2019 Inria-French Academy of Sciences-Dassault Systèmes Innovation Prize: Awarded in recognition of scikit-learn's impact as a major free software breakthrough in machine learning and its role in the digital transformation of science and industry. 2022 Open Science Award for Open Source Research Software: Awarded by the French Ministry of Higher Education and Research as part of the second National Plan for Open Science. The project was recognized in the "Community" category for its technical quality, its large international contributor network, and the quality of its documentation.

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  • Yahoo Groups

    Yahoo Groups

    Yahoo! Groups was a free-to-use system of electronic mailing lists offered by Yahoo!. Prior to February 2020, Yahoo! Groups was one of the world's largest collections of online discussion boards. It allowed members to subscribe to various groups, read subscribed discussions online, view and share photos, files and bookmarks within a group, access a group calendar, create polls for group members, and receive email notifications of new discussion topics. Some groups were simply announcement boards, to which only the group moderators could post, while others were discussion forums. Depending on each group's settings, membership could be open to everyone or only to invited or approved people. On February 1, 2020, Yahoo! removed online access to discussions and all other features except simple membership management, essentially turning all groups into mailing lists, and on October 13, 2020, it announced that Yahoo Groups would shut down completely on December 15, 2020. == History == In 1998 Yahoo! Clubs was launched as an extension of services developed by Yahoo! Messenger. In August 2000 Yahoo acquired eGroups.com. Yahoo! Groups was launched in early 2001 as an integration of technology from eGroups.com and community groups from both eGroups.com and Yahoo! Clubs. In 2001 Yahoo! deleted adult groups from its search directory, making it very difficult to locate Yahoo! groups with adult content. The Groups Updates Email feature was introduced in 2010. It summarized, in a single email, all the updates that occurred every twenty-four hours in all groups. In September 2010, a major facelift was rolled out, making Yahoo! Groups look very similar to Facebook. In December, Yahoo! Groups Japan emailed its users and posted a notice on its homepage, to announce that its service, which commenced in February 2004, would be closing on May 28, 2014. In October 2019, Yahoo! announced that all content that had been posted to Yahoo! Groups will be deleted on December 14, 2019; that date was later amended to January 31, 2020. Yahoo! announced that adding new content would be blocked on October 28, 2019. Once the content was deleted, users of Yahoo! Groups were only able to browse the group directory, request invitations and, if members of a group, send messages to that group. On October 13, 2020, Yahoo! announced they would be shutting down Yahoo! Groups on December 15, 2020. The site was closed down a few days after the advertised date, displaying a message that the service was officially shut down. This message stopped appearing at the end of January 2021 and the Yahoo! Groups web address began redirecting to the main Yahoo! page. === Criticism and controversy === On August 31, 2010, Yahoo! Groups started rolling out a major software change, which was denounced by a large number of users. The re-model was completely abandoned on January 12, 2011. == Site statistics == In August 2008, Yahoo! Group staff reported that there were 113 million users, and nine million Groups using 22 languages. In July 2010, the web analytics website Quantcast reported around 915 thousand unique visitors daily to the Yahoo! Groups website (US). In January 2011, that number had increased to 933 thousand unique visitors daily. The number did not include Yahoo! Group members who accessed the Groups site via email. In September 2010, at its "Product Runway" event, Yahoo! told reporters that Yahoo! Groups had 115 million group members and that there were 10 million Yahoo! groups. == Archives ==

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  • Autonomous agent

    Autonomous agent

    An autonomous agent is an artificial intelligence (AI) system that can perform complex tasks independently. == Definitions == There are various definitions of autonomous agent. According to Brustoloni (1991): "Autonomous agents are systems capable of autonomous, purposeful action in the real world." According to Maes (1995): "Autonomous agents are computational systems that inhabit some complex dynamic environment, sense and act autonomously in this environment, and by doing so realize a set of goals or tasks for which they are designed." Franklin and Graesser (1997) review different definitions and propose their definition: "An autonomous agent is a system situated within and a part of an environment that senses that environment and acts on it, over time, in pursuit of its own agenda and so as to effect what it senses in the future." They explain that: "Humans and some animals are at the high end of being an agent, with multiple, conflicting drives, multiples senses, multiple possible actions, and complex sophisticated control structures. At the low end, with one or two senses, a single action, and an absurdly simple control structure we find a thermostat." == Agent appearance == Lee et al. (2015) post safety issue from how the combination of external appearance and internal autonomous agent have impact on human reaction about autonomous vehicles. Their study explores the human-like appearance agent and high level of autonomy are strongly correlated with social presence, intelligence, safety and trustworthiness. In specific, appearance impacts most on affective trust while autonomy impacts most on both affective and cognitive domain of trust where cognitive trust is characterized by knowledge-based factors and affective trust is largely emotion driven. == Applications == Agentic AI systems: Advanced AI agents that can scope out projects and complete them with necessary tools, representing a significant evolution from simple task-oriented systems. Internet of things (IoT) Integration: Autonomous agents increasingly interact with IoT devices, enabling smart home systems, industrial monitoring, and urban infrastructure management. Collaborative software development: Tools like Cognition AI's Devin aim to create autonomous software engineers capable of complex reasoning, planning, and completing engineering tasks requiring thousands of decisions. Enterprise automation: Business process automation platforms like Salesforce's Agentforce provide autonomous bots for various service functions. == Challenges and considerations == Uncertainty and incomplete information: Autonomous agents must make decisions with limited or uncertain information about their environment and future states. Integration complexity: Incorporating autonomous agents into existing systems and workflows can be technically challenging and resource-intensive. Scalability: As systems become more complex and more agents are used, maintaining coordination and avoiding conflicts becomes increasingly difficult. Trust: Research has shown the combination of external appearance and internal autonomous capabilities significantly impacts human reactions and trust. Lee et al. (2015) found that human-like appearance and high levels of autonomy are strongly correlated with social presence, intelligence, safety, and trustworthiness perceptions. Specifically, appearance impacts affective trust most significantly, while autonomy affects both affective and cognitive trust domains, where affective trust is emotionally driven, and cognitive trust is characterized by knowledge-based factors. Vulnerability to manipulation: Researchers from Harvard, MIT and other educational institutions found that AI agents could become vulnerable to manipulation and could perform detrimental actions in the process of being helpful. == Ethical and regulatory concerns == Accountability: Determining responsibility when autonomous agents make incorrect or harmful decisions remains a complex issue. Privacy and security: autonomous agents often require access to sensitive data, raising concerns about data protection and system security.

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  • Easyrec

    Easyrec

    easyrec is an open-source program that provides personalized recommendations using RESTful Web services to be integrated into Web enabled applications. It is distributed under the GNU General Public License by the Studio Smart Agent Technologies and hosted at SourceForge. It is written in Java, uses a MySQL database and comes with an administration tool. == History == The development of easyrec, an implementation of the Adaptive Personalization approach, started in the course of several research and development projects conducted by the Studio Smart Agent Technologies in close cooperation with international companies. During the year of 2008 the core functionality of easyrec was developed forming the basis of research prototypes focusing on the music domain (e.g. MusicExplorer). In June 2009 a beta version of easyrec, containing basic administration features, was integrated into a movie streaming portal for evaluation purposes. Furthermore, in September 2009 easyrec was awarded a special recognition in the category “Award for Innovations – IT Innovations for an economic upswing” by the jury of the Austrian state prize for multimedia and e-business. After a comprehensive refactoring phase and the integration of the evaluation results easyrec was published on SourceForge on 18 February 2010. In course of the CeBIT tradeshow 2011 in Hanover easyrec has been awarded the German “INNOVATIONSPREIS-IT 2011”. == Principles == The following five primary goals guided the development of easyrec. It should be a ready-to-use application, not another algorithmic framework It should be easy to use, concerning installation, integration and administration It should be robust and scalable for serving real world applications It should be free of charge, so that anyone can profit from personalization features It should rely on a community-driven development == Uses == Although easyrec is a domain-agnostic, general purpose personalization system, the current Web service API is customized for providing online shops with item recommendations. Especially for small and medium enterprises, easyrec provides a low barrier entrance to personalization. == Features == A major feature of easyrec is a set of usage statistics and other business relevant information presented via an administration and management interface. Furthermore, the easyrec administrator is supported by a variety of administration and configuration functions including the manual import or adaptation of business rules. Integrators or developers benefit from the lightweight Web service APIs (REST and SOAP) as well as from the guided installation wizard. Concerning personalization functionality easyrec is providing the following services unpersonalized recommendations of the form "other users also bought/viewed/...", etc. personalized recommendation depending on individual preferences rankings such as "most bought items", "most viewed...", etc. Additionally, as an integration showcase, a MediaWiki extension was developed and is bundled with the application. Currently additional features like further recommender algorithms and a plugin-system are evaluated and prepared for integration into the easyrec system. == Architecture == The underlying architecture of easyrec is designed to be robust and scalable—separating time-consuming computations from the task of online assembling of recommendations. easyrec is designed as a multi-layer system consisting of a database layer as storage of user actions and pre-calculated business rules an application layer for hosting online and offline recommendation services and an API layer for various Web service interfaces. Moreover, the generator server contains different item association generators which create business rules that define a relation between two items.

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  • AIX Toolbox for Linux Applications

    AIX Toolbox for Linux Applications

    The AIX Toolbox for Linux Applications is a collection of GNU tools for IBM AIX. These tools are available for installation using Red Hat's RPM format. == Licensing == Each of these packages includes its own licensing information and while IBM has made the code available to AIX users, the code is provided as is and has not been thoroughly tested. The Toolbox is meant to provide a core set of some of the most common development tools and libraries along with the more popular GNU packages.

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  • List of C software and tools

    List of C software and tools

    This is a list of software and programming tools for the C programming language, including libraries, debuggers, compilers, integrated development environments (IDEs), and other related development tools and utilities. == Libraries and tools == Adns — asynchronous DNS resolver library Advanced Linux Sound Architecture — API for sound card device drivers Allegro — cross-platform software library for video game development Apache Portable Runtime — Apache web server tool set of APIs that map to the underlying operating system Argon2 — memory-hard password hashing library Berkeley DB — embedded database software library for key/value data Binary File Descriptor library — binary file manipulation library in the GNU toolchain Boehm garbage collector – conservative garbage collector Borland Graphics Interface — graphics library for Borland compilers BSAFE — FIPS 140-2 validated cryptography library Chipmunk — 2D real-time rigid body physics engine C POSIX library — specification of a C standard library for POSIX systems C standard library – standard library for the C programming language Cairo – vector graphics library API for software developers CFD General Notation System (CGNS) — data format and library for computational fluid dynamics cJSON — lightweight JSON parser CLIPS — public-domain software tool for building expert systems Core Audio — low-level API for dealing with sound in Apple's macOS and iOS operating systems Core Foundation — API for macOS and iOS and other Apple operating systems Core Image — GPU accelerated image processing technology for Apple operating systems with Quartz graphics rendering layer. Core Text — text layout and font rendering API for macOS and iOS. Cryptlib — portable cryptography library cURL / libcurl — CLI app for uploading and downloading individual files, such as a URL from a web server over HTTP. DevIL — cross-platform image library for loading and converting file formats DirectFB — graphics acceleration and input device handling library Dld — dynamic loading library Expat — stream-oriented XML 1.0 parser library, written in C99. FFmpeg — multimedia framework for audio/video processing Fontconfig — font customization and configuration library FreeTDS — database library for Sybase and Microsoft SQL Server FreeType — render text onto bitmaps with a font rasterization engine GD Graphics Library — image creation and manipulation library GDK — graphics abstraction layer for GTK GEGL — graph-based image processing framework GIO — I/O and virtual file system library in GLib GLib — utility library providing data structures, event loops, and portability functions. glibc — GNU implementation of the C standard library GLFW — library for OpenGL contexts, windows, and input device handling GNet — networking library for GLib GNU Libtool — Library management tool GNU portability library — collection of portability routines for GNU software GNU Portable Threads — POSIX/ANSI-C based user space thread library for UNIX for scheduling multithreading GNU Readline — command-line editing library GnuTLS — secure communications (TLS/SSL) library GObject — object system library for GNOME GTK — widget toolkit for creating graphical user interfaces GTK Scene Graph Kit (GSK) — scene graph and rendering toolkit for GTK HDF — file format and library for managing large datasets Integrated Performance Primitives — Intel library of optimized multimedia and data processing routines IUP — portable GUI toolkit J2K-Codec — JPEG 2000 image codec JasPer — reference implementation of the codec specified in the JPEG-2000 Part-1 standard LDAP API — API for interacting with Lightweight Directory Access Protocol LZO — lossless compression library Liba52 — decoder for A/52 (AC-3) audio streams libarchive — reading and writing various archive and compression formats Libart — 2D graphics library Libavcodec — codec library from FFmpeg Libavdevice — library for handling multimedia devices Libavfilter — audio and video filter library Libavformat — library for muxing and demuxing multimedia Libpcap — packet capture library Libdca — decoder for DTS audio Libdvdcss — access to encrypted DVD-Video discs libevent — asynchronous event notification callbacks libffi — foreign function interface libfuse — userspace filesystem Libgegl — programming interface to GEGL image processing libgcrypt — cryptography Libgimp — plug-in development library for GIMP Libhybris — compatibility layer for running Android libraries on Linux Libinput — input device library for Wayland and X.Org libjpeg — JPEG image library libLAS — reading and writing geospatial data encoded in the ASPRS laser (LAS) file format libmicrohttpd — small C library for embedding HTTP server functionality Libmpcodecs — media player codec library from MPlayer Libmpdemux — demultiplexing library from MPlayer libpng — PNG image format Libpostproc — video post-processing library from FFmpeg libpq — PostgreSQL client LibreSSL — fork of OpenSSL for TLS Librsb — parallel library for sparse matrix computations Librsvg — SVG rendering library libsndfile — reading and writing audio files libsodium — easy-to-use cryptography library Libswscale — image scaling and colorspace conversion library LibTIFF — TIFF image handling library Libusb — USB device access library Libuv — asynchronous I/O and event loop library LibVLC — media player engine from VLC LibVNCServer — implementation of the VNC server protocol Libvpx — VP8 and VP9 video codec library Libwww — early World Wide Web protocol library from W3C libxml2 — XML parsing Libxslt — XSLT library for the GNOME Project libzip — ZIP archives Lightning Memory-Mapped Database — fast key–value database engine LittleCMS — open-source color management system LZ4 — fast lossless compression algorithm LZFSE — compression library developed by Apple MatrixSSL — lightweight TLS implementation Mbed TLS — portable cryptography and TLS library MediaLib — Sun Microsystems library for multimedia processing Mesa — OpenGL and Vulkan graphics library Microwindows — small windowing system for embedded devices Ming — library for generating SWF (Flash) files Mongoose — embedded web server and networking library Mpg123 — MP3 audio decoding library MPIR — multiple-precision arithmetic library MsQuic — Microsoft implementation of the QUIC transport protocol MuJoCo — physics engine for robotics and control Mustache — logic-less templating library Ncurses — terminal control library Nettle — low-level cryptography library Newt — text-based user interface library Netpbm — graphics conversion and processing library Nghttp2 — implementation of the HTTP/2 protocol Oniguruma — regular expression library Open Asset Import Library — library to import/export 3D model formats OpenCL — parallel computing API/library OpenCV — computer vision OpenGL — API for rendering 2D and 3D vector graphics OpenGL Utility Library — OpenGL utility functions OpenJPEG — JPEG 2000 image codec OpenSSL — SSL and TLS protocols and cryptography library Pango — layout engine library which works with the HarfBuzz shaping engine for displaying multi-language text perf (Linux) — performance analyzing tool PCRE — regular expression library PROJ — library for map projections and coordinate transforms Quartz 2D — 2D graphics rendering API for macOS and iOS platforms, part of the Core Graphics framework. Raylib — simple library for games and multimedia Redland RDF Application Framework — RDF data storage library S2n-tls — TLS implementation from AWS Setcontext — context switching library functions SDL — Simple DirectMedia Layer systemd — system and service manager libraries for Linux Tk — GUI widgets for building graphical user interfaces VDPAU — video decoding acceleration API Vorbis — audio compression codec library VTD-XML — high-performance XML parser Wimlib — library for handling Windows Imaging Format disk images Windows.h — base Windows API header file WolfSSH — lightweight SSH library WolfSSL — lightweight SSL/TLS library X Toolkit Intrinsics — toolkit library for the X Window System x264 — H.264 video codec library XCB — C binding for the X Window System protocol Xft — font rendering library using FreeType Xlib — low-level X Window System API XMDF — eXtensible Model Data Format for scientific data XMLStarlet — XML command-line toolkit zlib — data compression Zopfli — data compression library that performs deflate, gzip and zlib data encoding. Zstd — fast data compression library == Integrated development environments == Anjuta — GNOME IDE CLion — cross-platform commercial IDE from JetBrains Code::Blocks — cross-platform open-source IDE CodeLite — open-source IDE Dev-C++ Eclipse CDT Geany — text editor with IDE features KDevelop — KDE IDE NetBeans Qt Creator SlickEdit Visual Studio Xcode === Online IDEs === CodeSandbox — online IDE primarily for web development with some C support via containers GitHub Codespaces — cloud-based online IDE developed by GitHub Google Cloud Shell — browser-based shell and editor that can comp

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  • Kdan Mobile

    Kdan Mobile

    Kdan Mobile Software Limited is a software application development company based in Tainan City, Taiwan. Kdan also has branches in Taipei, Changsha, Irvine, California, Japan, and South Korea. The company was founded in 2009 by Kenny Su, the company's CEO. == History == Kdan Mobile was founded in 2009 by Kenny Su (蘇柏州) and develops an application for PDF documents. Su previously worked at the Industrial Technology Research Institute (ITRI) . In 2018, the company completed its Series B round of fundraising, in which it raised 16 million USD in total. Four global firms, Dattoz Partners (South Korea), WI Harper Group (U.S.), Taiwania Capital (Taiwan), and Golden Asia Fund Mitsubishi UFJ Capital (Japan), made up the Series B investment. Kdan previously raised 5 million USD in its Series A round in 2018.

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  • Box blur

    Box blur

    A box blur (also known as a box linear filter) is a spatial domain linear filter in which each pixel in the resulting image has a value equal to the average value of its neighboring pixels in the input image. It is a form of low-pass ("blurring") filter. A 3 by 3 box blur ("radius 1") can be written as matrix 1 9 [ 1 1 1 1 1 1 1 1 1 ] . {\displaystyle {\frac {1}{9}}{\begin{bmatrix}1&1&1\\1&1&1\\1&1&1\end{bmatrix}}.} Due to its property of using equal weights, it can be implemented using a much simpler accumulation algorithm, which is significantly faster than using a sliding-window algorithm. Box blurs are frequently used to approximate a Gaussian blur. By the central limit theorem, repeated application of a box blur will approximate a Gaussian blur. In the frequency domain, a box blur has zeros and negative components. That is, a sine wave with a period equal to the size of the box will be blurred away entirely, and wavelengths shorter than the size of the box may be phase-reversed, as seen when two bokeh circles touch to form a bright spot where there would be a dark spot between two bright spots in the original image. == Extensions == Gwosdek, et al. has extended Box blur to take a fractional radius: the edges of the 1-D filter are expanded with a fraction. It makes slightly better gaussian approximation possible due to the elimination of integer-rounding error. Mario Klingemann has a "stack blur" that tries to better emulate gaussian's look in one pass by stacking weights: 1 9 [ 1 2 3 2 1 ] {\displaystyle {\frac {1}{9}}{\begin{bmatrix}1&2&3&2&1\end{bmatrix}}} The triangular impulse response it forms decomposes to two rounds of box blur. Stacked Integral Image by Bhatia et al. takes the weighted average of a few box blurs to fit the gaussian response curve. == Implementation == The following pseudocode implements a 3x3 box blur. The example does not handle the edges of the image, which would not fit inside the kernel, so that these areas remain unblurred. In practice, the issue is better handled by: Introducing an alpha channel to represent the absence of colors; Extending the boundary by filling in values, ranked by quality: Fill in a mirrored image at the border Fill in a constant color extending from the last pixel Pad in a fixed color A number of optimizations can be applied when implementing the box blur of a radius r and N pixels: The box blur is a separable filter, so that only two 1D passes of averaging 2 r + 1 pixels will be needed, one horizontal and one vertical, for each pixel. This lowers the complexity from O(Nr2) to O(Nr). In digital signal processing terminology, each pass is a moving-average filter. Accumulation. Instead of discarding the sum for each pixel, the algorithm re-uses the previous sum, and updates it by subtracting away the old pixel and adding the new pixel in the blurring range. A summed-area table can be used similarly. This lowers the complexity from O(Nr) to O(N). When being used in multiple passes to approximate a Gaussian blur, the cascaded integrator–comb filter construction allows for doing the equivalent operation in a single pass.

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  • WiPay

    WiPay

    WiPay is a Caribbean-based payment technology company that specializes in electronic payments for businesses. WiPay was founded in 2016 by Aldwyn Wayne Jr., a Trinidadian businessman and graduate of Georgia Tech Institute. In September 2019, WiPay partnered with MasterCard. As a result, WiPay became the only licensed Payment Facilitator (PAYFAC) on both the MasterCard and Visa networks in the region.

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  • Web Intents

    Web Intents

    Web Intents was an experimental framework for web-based inter-application communication and service discovery. Web Intents consists of a discovery mechanism and a very light-weight RPC system between web applications, modelled after the Intents system in Android. In the context of the framework an Intent equals an action to be performed by a provider. Web Intents allow two web applications to communicate with each other, without either of them having to actually know what the other one is. == Support == === Client === Google Chrome versions 18 to 23 natively supported Web Intents. This support was disabled in version 24, citing the existence of a "number of areas for development in both the API and specific user experience in Chrome". There is a JavaScript shim with support for IE 8, IE 9, Opera, Safari, Firefox 3+ and Chrome 3+. === Server === There are some Web Intents proxy pages that make available some real services that don't yet support intents. AddThis supports Web Intents by their sharing tools regardless of browser support. == History == Paul Kinlan of Google announced the Web Intents project in December 2010. He soon released a prototype API to GitHub. In August 2011 Google announced that Chrome would support Web Intents. Google and Mozilla have started co-operating to unify Web Intents and Mozilla's Web Activities (which tries to solve the same problem) into one proposal. In November 2012, Greg Billock of Google announced that experimental support of Web Intents had been removed from Chrome.

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  • Adobe PhotoDeluxe

    Adobe PhotoDeluxe

    PhotoDeluxe was a consumer-oriented image editing software line published by Adobe Systems from 1996 until July 8, 2002. At that time it was replaced by Adobe's newly launched consumer-oriented image editing software Photoshop Elements. Adobe no longer provides technical support for the PhotoDeluxe software line. PhotoDeluxe had a range of image processing capabilities for the home photographer and image handler. These included removing red-eye, cropping, and adjusting brightness, contrast, and sharpness. It also included software to extract pictures from an image scanner. Among the functionality included was the ability to dynamically resize photos and export them in a wide range of formats. It also had a range of printing options including printing multiple copies of an image on the same page. It was often bundled free with Epson scanners or as free software with new computers. == Features == Despite the critical concerns regarding the quality of the setup, Photo Deluxe supports layering, blurs, sharpening, cloning, gradient fills, color and background switches, color variations, resizing options, and many other features. Another drawback of PhotoDeluxe was that it was designed for Mac computers, so working on Windows PC was a problem for those who were unable to customize their preferences. == Versions == === Adobe PhotoDeluxe 1.0 === The first version was released in 1996 for Windows and Macintosh computers. In one year, it sold over one million copies. === Adobe PhotoDeluxe 2.0 === The new version was released in 1997 and had added features such as a Clone Tool, red-eye removal, and sample templates for making posters, cards, and calendars. It also had new special effect features. === Adobe PhotoDeluxe 3.0 === The 3rd version was released in 1998. The new features included customizable clipart settings, the ability to import photos on the web, enhanced repair activities following Guided Activities, and Adobe Connectables to add new activities. === Adobe PhotoDeluxe Home Edition (4.0) === Version 4.0 was created by the makers of Photoshop. It had advanced abilities such as tools to add animation, voice, and music to a picture. It also had features to restore photos to their original position. == History == Adobe PhotoDeluxe 1.0 was released in 1996 for Macintosh computers, initially retailing for an MSRP of $49. The software did quite well, reportedly selling over a million copies by February of the next year, primarily due to bundles with companies like Apple and Hewlett-Packard. PhotoDeluxe was primarily advertised to consumers as a way to do basic photo manipulation, such as cropping and rotating images, or creating simple cards and calendars. PhotoDeluxe 2.0 was released in 1997, and was the last version of PhotoDeluxe that Adobe made that worked on Macs. PhotoDeluxe 2.0 became the "number one selling consumer photo-editing software product in the world." PhotoDeluxe 3.0 was released in 1998, where it was rebranded as "3.0 Home Edition", as Adobe released PhotoDeluxe Business Edition later that year for a higher price. PhotoDeluxe Home Edition, unofficially called PhotoDeluxe 4.0, was released in 1999 and was the last version of PhotoDeluxe to be released. Adobe officially cancelled PhotoDeluxe on July 8, 2002, citing the presence of Photoshop and Photoshop Elements, with support being officially cancelled in mid-2003. No version of PhotoDeluxe is compatible with Windows 10, rendering the program obsolete. == Pricing == All home versions of PhotoDeluxe retailed for an MSRP of $49. PhotoDeluxe 2.0 and onwards allowed users to upgrade from a previous version of PhotoDeluxe or a competing piece of graphics software for $39. Additionally PhotoDeluxe Business Edition allowed a similar deal, allowing users to upgrade from other versions of PhotoDeluxe or a competing software for $59, instead of its normal price of $99. Adobe also offered a bundle allowing users of 1.0 or 2.0 to get 3.0 and Business Edition for $79.

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  • Gamma (app)

    Gamma (app)

    Gamma is a web-based software platform that uses artificial intelligence to generate presentations, documents, webpages, and other visual content. The platform allow users to create structured layouts and draft text based on prompts or uploaded material. It operates as an online application and provides tools for editing, organizing, and sharing content. == History == Gamma was established in the early 2020s by Grant Lee, James Fox, and Jon Noronha during a period of increased development in artificial intelligence–based productivity software. The platform was introduced as a web-based format designed to present information through structured visual layouts rather than traditional slide-based presentations. Its interface was developed to adapt content to different screen sizes and devices. In later updates, Gamma expanded its functionality to support additional formats, including documents and simple webpages. By November 2025, the company reported that the platform had reached approximately 70 million users. Gamma has raised venture capital funding from a number of technology-focused investors since its founding. == Features == Gamma allows users to create presentations, documents, and webpages by entering prompts, pasting text, or uploading source files. The platform uses artificial intelligence to generate draft text, organize information, and apply structured layouts. Users can edit generated material manually and adjust formatting, structure, and visual elements. The software also supports collaborative editing, allowing multiple users to contribute to and revise the same project. Instead of relying only on fixed slide-based formats, Gamma presents content in scrollable layouts designed for web viewing across different screen sizes. Projects created on the platform can be shared through web links or exported to formats compatible with other software. Gamma also provides integration options and developer access through an application programming interface (API). == Technology == Gamma uses generative artificial intelligence models to interpret user input and generate structured content. The software automates elements of layout selection, formatting, and visual presentation. As with other AI-assisted tools, output produced by the system may require human review and revision to ensure accuracy and appropriate context. == Funding == Gamma has raised venture capital funding from a number of technology-focused investors since its founding. In November 2025, the company announced a Series B funding round that raised $68 million at a reported valuation of approximately $2.1 billion. Investors in the round included Andreessen Horowitz, Accel, and Uncork Capital, among others. == Controversy == In 2025, cybersecurity researchers reported that Gamma had been used in a phishing campaign targeting Microsoft accounts. Attackers shared links to presentations hosted on the platform that redirected users to a spoofed Microsoft SharePoint login page intended to collect credentials. Researchers noted that the incident reflected the broader misuse of legitimate online services in phishing schemes.

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  • Color image pipeline

    Color image pipeline

    An image pipeline or video pipeline is the set of components commonly used between an image source (such as a camera, a scanner, or the rendering engine in a computer game), and an image renderer (such as a television set, a computer screen, a computer printer or cinema screen), or for performing any intermediate digital image processing consisting of two or more separate processing blocks. An image/video pipeline may be implemented as computer software, in a digital signal processor, on an FPGA, or as fixed-function ASIC. In addition, analog circuits can be used to do many of the same functions. Typical components include image sensor corrections (including debayering or applying a Bayer filter), noise reduction, image scaling, gamma correction, image enhancement, colorspace conversion (between formats such as RGB, YUV or YCbCr), chroma subsampling, framerate conversion, image compression/video compression (such as JPEG), and computer data storage/data transmission. Typical goals of an imaging pipeline may be perceptually pleasing end-results, colorimetric precision, a high degree of flexibility, low cost/low CPU utilization/long battery life, or reduction in bandwidth/file size. Some functions may be algorithmically linear. Mathematically, those elements can be connected in any order without changing the end-result. As digital computers use a finite approximation to numerical computing, this is in practice not true. Other elements may be non-linear or time-variant. For both cases, there is often one or a few sequences of components that makes sense for optimum precision and minimum hardware-cost/CPU-load.

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