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.
CAMeL-View TestRig
CAMeL-View is a software application, which is used for the model based design of mechatronic systems (multi-body simulation, block diagrams, pneumatic systems, hydraulic systems, general simulation, linear analysis and Hardware-in-the-Loop). CAMeL-View enables object-oriented model creation of mechatronic systems through the use of graphic blocks. The basic elements of multi-body system dynamics, control technology, hydraulics and hardware connectivity support the modeling process. The user’s proprietary C-Code can also be integrated into the models, which allows CAMeL-View TestRig to be implemented in all phases of the model based design process ( modeling, physical testing and prototyping), and lends itself especially well to mechatronic system design. The model’s structure is described and displayed with the help of directional connectors. Physical connections (such as mechanical or hydraulic linkages) as well as input and output connections (signal flow) are also available. The input of equations is done via mathematical expressions, e.g. the input of constitutive differential equations in vector and matrix form. Based on the model’s structure, the descriptive equations are converted into non-linear state space representations and converted into executable C-Code. CAMeL-View supports the simulation process with a configurable “experiment environment” (for simulator and instrumentation components) which allows the user to apply simulation models to supported targets (MPC5200, TriCore, X86, etc.) without the need for additional software tools for Hardware-in-the-Loop applications. In addition, the generation of so-called S-Functions for use in Simulink and the generation of ANSI C-Code for use in stand-alone simulators is also supported. A particularly noteworthy feature in CAMeL-View TestRig is the way in which the descriptive equations for multi-body system models are created. All multi-body simulation formalisms used for code generation create their equations in the form of typical explicit differential equations (ODE). This is especially important in Hardware-in-the-Loop applications where the calculation of simulation results within a specific, defined time frame must be assured. Only then is it possible to implement complex multi-body simulation models for Hardware-in-the-Loop applications under stringent real-time conditions. These constraints cannot be met when using DAE-based methods. Additional Toolboxes are available for linear analysis (Eigenvalues, pole-zero analysis, frequency response, etc.) of VRML-based animation. Development of CAMeL-View began in 1991 in the Paderborn Mechatronic Laboratory of Professor Dr. Ing. J. Lückel. The software was based on predecessors that had been developed there since 1986. The name stands for Computer Aided Mechatronic Laboratory – Virtual Engineering Workbench and describes the basic intent of one of the specific demands placed on development engineers in the computer lab.
Best AI Pair Programmers in 2026
Shopping for the best AI pair programmer? An AI pair programmer is software that uses machine learning to help you get more done — it keeps getting smarter as the underlying models improve. Pricing, accuracy, and the size of the model behind the tool are the three factors that most affect daily usefulness. Whether you are a beginner or a pro, the right AI pair programmer slots into your workflow and pays for itself fast. Below we compare features, pricing, and real output so you can choose with confidence.
Top 10 AI Analytics Tools Compared (2026)
Shopping for the best AI analytics tool? An AI analytics tool is software that uses machine learning to help you get more done — it keeps getting smarter as the underlying models improve. Pricing, accuracy, and the size of the model behind the tool are the three factors that most affect daily usefulness. Whether you are a beginner or a pro, the right AI analytics tool slots into your workflow and pays for itself fast. Below we compare features, pricing, and real output so you can choose with confidence.
HOCR
hOCR is an open standard of data representation for formatted text obtained from optical character recognition (OCR). The definition encodes text, style, layout information, recognition confidence metrics and other information using Extensible Markup Language (XML) in the form of Hypertext Markup Language (HTML) or XHTML. == Software == The following OCR software can output the recognition result as hOCR file: OCRopus Tesseract Cuneiform ghostscript HebOCR gcv2hocr gImageReader == Example == The following example is an extract of an hOCR file: The recognized text is stored in normal text nodes of the HTML file. The distribution into separate lines and words is here given by the surrounding span tags. Moreover, the usual HTML entities are used, for example the p tag for a paragraph. Additional information is given in the properties such as: different layout elements such as "ocr_par", "ocr_line", "ocrx_word" geometric information for each element with a bounding box "bbox" language information "lang" some confidence values "x_wconf" == bbox == === General === The Layout of the Bounding Box Object or bbox Object is Grammar. property-name = "bbox" property-value = uint uint uint uint ==== Example ==== bbox 0 0 100 200 The bbox - short for "bounding box" - of an element is a rectangular box around this element, which is defined by the upper-left corner (x0, y0) and the lower-right corner (x1, y1). the values are with reference to the top-left corner of the document image and measured in pixels the order of the values are x0 y0 x1 y1 = "left top right bottom" ===== Usage ===== Use x_bboxes below for character bounding boxes. Do not use bbox unless the bounding box of the layout component is, in fact, rectangular, some non-rectangular layout components may have rectangular bounding boxes if the non-rectangularity is caused by floating elements around which text flows. The bounding box bbox of this line is shown in blue and it is span by the upper-left corner (10, 20) and the lower-right corner (160, 30). All coordinates are measured with reference to the top-left corner of the document image which border is drawn in black. == Searchable PDF files == The hOCR format is most commonly used in order to make searchable PDF files or as an extracted metadata of the PDF file. In order to create searchable PDF files we can use a scanned document image and a .hocr file of the particular image. We can use the following open source tools in order to achieve that. === hocr-tools === Source: hocr-tools is an open source library written in Python. It has a command-line utility attached in the scripts called hocr-pdf that enables us to convert standard hocr files to a searchable PDF file. It is also worth noting that the version for dealing with hocr files in RTL or non-Latin scripts like Arabic, we need to use the GitHub repository at the moment. hocr-pdf We can use the hocr-pdf utility using the following basic syntax. hocr-pdf—savefile final.pdf folder_images_and_hocr The folder_images_and_hocr must contain the respective .jpg and .hocr format files with their file extensions changed. ==== Known issues ==== Some of the known issues of hocr-pdf script in PyPI installation are the following. Not up to date with GitHub repository. hocr-pdf is broken on line 134 due to decodebytes() depreciated after Python 3.1 ==== Known fixes ==== Compile hocr-tools using latest GitHub repository. === hocr2pdf === hocr2pdf is another library that supports the conversion of hocr files. It is written in C++ and is cross-compatible with other libraries. It also has support for UTF-8 languages but that may require some additional debugging and browsing through some google conversation records to achieve that. According to Ubuntu Manpages,ExactImage is a fast C++ image processing library. Unlike many other library frameworks it allows operation in several color spaces and bit depths natively, resulting in low memory and computational requirements. hocr2pdf creates well layouted, searchable PDF files from hOCR (annotated HTML) input obtained from an OCR system. == hOCR to PDF attempts == In addition to the following discussed and stable libraries there have been many contributions to the hOCR format over the years with support from many of the early adopters of this format. You can get access to inlaying text on an Image with hOCR and converting that in a PDF file using Python 2 with this 12-year-old script as of 2021. This script can also be updated and made functional by converting that Python 2 Source code to Python 3 Supported Context. - HOCRConverter by jbrinley (Documentation) === HOCRConverter === The HOCRConverter is a script written in Python 2.x that can used in order to convert a hOCR file with a specified image file in order to convert it to a searchable PDF file. You can see the documentation using the link above. ==== Known issues ==== Has not been tested. Does not natively support Python 3.x
SAP NetWeaver Visual Composer
SAP NetWeaver Visual Composer is SAP’s web-based software modelling tool. It enables business process specialists and developers to create business application components, without coding. Visual Composer produces applications in a declarative form, enabling code-free execution mode for multiple runtime environments. It provides application lifecycle support by maintaining the connection between an application and its model throughout its lifecycle. Visual Composer is designed with an open architecture, which enables developers to extend its design-time environment and modelling language, as well as to integrate external data services. The tool aims to increase productivity by reducing development effort time, and narrowing the gap between application definition and implementation. Starting with a blank canvas, the Visual Composer user, typically a business process specialist, draws the application in Visual Composer Storyboard (workspace), without writing code, to prototype, design and produce applications. A typical workflow for creating, deploying and running an application using Visual Composer is: Create a model Discover data services and add them to the model Select necessary UI elements and add them to the model Connect model elements to define the model logic and data flow Edit the layout Arranging the UI elements and the controls of the application on forms and tables. Deploy the model This step includes compilation, validation and deployment to a selected environment. Run the application The application can run using different runtime environment (such as Adobe Flex and HTML). In 2014 a runtime environment was introduced that is utilizing HTML5 capabilities of SAPUI5.
BFR algorithm
The BFR algorithm, named after its inventors Bradley, Fayyad and Reina, is a variant of k-means algorithm that is designed to cluster data in a high-dimensional Euclidean space. It makes a very strong assumption about the shape of clusters: they must be normally distributed about a centroid. The mean and standard deviation for a cluster may differ for different dimensions, but the dimensions must be independent. In other words, the data must take the shape of axis-aligned ellipses.