NOTICE This repository contains the public FTC SDK for the SKYSTONE (2019-2020) competition season. If you are looking for the current season's FTC SDK software, please visit the new and permanent home of the public FTC SDK: FtcRobotController repository Welcome! This GitHub repository contains the source code that is used to build an Android app to control a FIRST Tech Challenge competition robot. To use this SDK, download/clone the entire project to your local computer. Getting Started If you are new to robotics or new to the FIRST Tech Challenge, then you should consider reviewing the FTC Blocks Tutorial to get familiar with how to use the control system: FTC Blocks Online Tutorial Even if you are an advanced Java programmer, it is helpful to start with the FTC Blocks tutorial, and then migrate to the OnBot Java Tool or to Android Studio afterwards. Downloading the Project If you are an Android Studio programmer, there are several ways to download this repo. Note that if you use the Blocks or OnBot Java Tool to program your robot, then you do not need to download this repository. If you are a git user, you can clone the most current version of the repository: git clone https://github.com/FIRST-Tech-Challenge/SKYSTONE.git Or, if you prefer, you can use the "Download Zip" button available through the main repository page. Downloading the project as a .ZIP file will keep the size of the download manageable. You can also download the project folder (as a .zip or .tar.gz archive file) from the Downloads subsection of the Releases page for this repository. Once you have downloaded and uncompressed (if needed) your folder, you can use Android Studio to import the folder ("Import project (Eclipse ADT, Gradle, etc.)"). Getting Help User Documentation and Tutorials FIRST maintains online documentation with information and tutorials on how to use the FIRST Tech Challenge software and robot control system. You can access this documentation using the following link: SKYSTONE Online Documentation Note that the online documentation is an "evergreen" document that is constantly being updated and edited. It contains the most current information about the FIRST Tech Challenge software and control system. Javadoc Reference Material The Javadoc reference documentation for the FTC SDK is now available online. Click on the following link to view the FTC SDK Javadoc documentation as a live website: FTC Javadoc Documentation Documentation for the FTC SDK is also included with this repository. There is a subfolder called "doc" which contains several subfolders: The folder "apk" contains the .apk files for the FTC Driver Station and FTC Robot Controller apps. The folder "javadoc" contains the JavaDoc user documentation for the FTC SDK. Online User Forum For technical questions regarding the Control System or the FTC SDK, please visit the FTC Technology forum: FTC Technology Forum Release Information Version 5.5 (20200824-090813) Version 5.5 requires Android Studio 4.0 or later. New features Adds support for calling custom Java classes from Blocks OpModes (fixes SkyStone issue #161). Classes must be in the org.firstinspires.ftc.teamcode package. Methods must be public static and have no more than 21 parameters. Parameters declared as OpMode, LinearOpMode, Telemetry, and HardwareMap are supported and the argument is provided automatically, regardless of the order of the parameters. On the block, the sockets for those parameters are automatically filled in. Parameters declared as char or java.lang.Character will accept any block that returns text and will only use the first character in the text. Parameters declared as boolean or java.lang.Boolean will accept any block that returns boolean. Parameters declared as byte, java.lang.Byte, short, java.lang.Short, int, java.lang.Integer, long, or java.lang.Long, will accept any block that returns a number and will round that value to the nearest whole number. Parameters declared as float, java.lang.Float, double, java.lang.Double will accept any block that returns a number. Adds telemetry API method for setting display format Classic Monospace HTML (certain tags only) Adds blocks support for switching cameras. Adds Blocks support for TensorFlow Object Detection with a custom model. Adds support for uploading a custom TensorFlow Object Detection model in the Manage page, which is especially useful for Blocks and OnBotJava users. Shows new Control Hub blink codes when the WiFi band is switched using the Control Hub's button (only possible on Control Hub OS 1.1.2) Adds new warnings which can be disabled in the Advanced RC Settings Mismatched app versions warning Unnecessary 2.4 GHz WiFi usage warning REV Hub is running outdated firmware (older than version 1.8.2) Adds support for Sony PS4 gamepad, and reworks how gamepads work on the Driver Station Removes preference which sets gamepad type based on driver position. Replaced with menu which allows specifying type for gamepads with unknown VID and PID Attempts to auto-detect gamepad type based on USB VID and PID If gamepad VID and PID is not known, use type specified by user for that VID and PID If gamepad VID and PID is not known AND the user has not specified a type for that VID and PID, an educated guess is made about how to map the gamepad Driver Station will now attempt to automatically recover from a gamepad disconnecting, and re-assign it to the position it was assigned to when it dropped If only one gamepad is assigned and it drops: it can be recovered If two gamepads are assigned, and have different VID/PID signatures, and only one drops: it will be recovered If two gamepads are assigned, and have different VID/PID signatures, and BOTH drop: both will be recovered If two gamepads are assigned, and have the same VID/PID signatures, and only one drops: it will be recovered If two gamepads are assigned, and have the same VID/PID signatures, and BOTH drop: neither will be recovered, because of the ambiguity of the gamepads when they re-appear on the USB bus. There is currently one known edge case: if there are two gamepads with the same VID/PID signature plugged in, but only one is assigned, and they BOTH drop, it's a 50-50 chance of which one will be chosen for automatic recovery to the assigned position: it is determined by whichever one is re-enumerated first by the USB bus controller. Adds landscape user interface to Driver Station New feature: practice timer with audio cues New feature (Control Hub only): wireless network connection strength indicator (0-5 bars) New feature (Control Hub only): tapping on the ping/channel display will switch to an alternate display showing radio RX dBm and link speed (tap again to switch back) The layout will NOT autorotate. You can switch the layout from the Driver Station's settings menu. Breaking changes Removes support for Android versions 4.4 through 5.1 (KitKat and Lollipop). The minSdkVersion is now 23. Removes the deprecated LinearOpMode methods waitOneFullHardwareCycle() and waitForNextHardwareCycle() Enhancements Handles RS485 address of Control Hub automatically The Control Hub is automatically given a reserved address Existing configuration files will continue to work All addresses in the range of 1-10 are still available for Expansion Hubs The Control Hub light will now normally be solid green, without blinking to indicate the address The Control Hub will not be shown on the Expansion Hub Address Change settings page Improves REV Hub firmware updater The user can now choose between all available firmware update files Version 1.8.2 of the REV Hub firmware is bundled into the Robot Controller app. Text was added to clarify that Expansion Hubs can only be updated via USB. Firmware update speed was reduced to improve reliability Allows REV Hub firmware to be updated directly from the Manage webpage Improves log viewer on Robot Controller Horizontal scrolling support (no longer word wrapped) Supports pinch-to-zoom Uses a monospaced font Error messages are highlighted New color scheme Attempts to force-stop a runaway/stuck OpMode without restarting the entire app Not all types of runaway conditions are stoppable, but if the user code attempts to talk to hardware during the runaway, the system should be able to capture it. Makes various tweaks to the Self Inspect screen Renames "OS version" entry to "Android version" Renames "WiFi Direct Name" to "WiFi Name" Adds Control Hub OS version, when viewing the report of a Control Hub Hides the airplane mode entry, when viewing the report of a Control Hub Removes check for ZTE Speed Channel Changer Shows firmware version for all Expansion and Control Hubs Reworks network settings portion of Manage page All network settings are now applied with a single click The WiFi Direct channel of phone-based Robot Controllers can now be changed from the Manage page WiFi channels are filtered by band (2.4 vs 5 GHz) and whether they overlap with other channels The current WiFi channel is pre-selected on phone-based Robot Controllers, and Control Hubs running OS 1.1.2 or later. On Control Hubs running OS 1.1.2 or later, you can choose to have the system automatically select a channel on the 5 GHz band Improves OnBotJava New light and dark themes replace the old themes (chaos, github, chrome,...) the new default theme is light and will be used when you first update to this version OnBotJava now has a tabbed editor Read-only offline mode Improves function of "exit" menu item on Robot Controller and Driver Station Now guaranteed to be fully stopped and unloaded from memory Shows a warning message if a LinearOpMode exists prematurely due to failure to monitor for the start condition Improves error message shown when the Driver Station and Robot Controller are incompatible with each other Driver Station OpMode Control Panel now disabled while a Restart Robot is in progress Disables advanced settings related to WiFi direct when the Robot Controller is a Control Hub. Tint phone battery icons on Driver Station when low/critical. Uses names "Control Hub Portal" and "Control Hub" (when appropriate) in new configuration files Improve I2C read performance Very large improvement on Control Hub; up to ~2x faster with small (e.g. 6 byte) reads Not as apparent on Expansion Hubs connected to a phone Update/refresh build infrastructure Update to 'androidx' support library from 'com.android.support:appcompat', which is end-of-life Update targetSdkVersion and compileSdkVersion to 28 Update Android Studio's Android plugin to latest Fix reported build timestamp in 'About' screen Add sample illustrating manual webcam use: ConceptWebcam Bug fixes Fixes SkyStone issue #248 Fixes SkyStone issue #232 and modifies bulk caching semantics to allow for cache-preserving MANUAL/AUTO transitions. Improves performance when REV 2M distance sensor is unplugged Improves readability of Toast messages on certain devices Allows a Driver Station to connect to a Robot Controller after another has disconnected Improves generation of fake serial numbers for UVC cameras which do not provide a real serial number Previously some devices would assign such cameras a serial of 0:0 and fail to open and start streaming Fixes ftc_app issue #638. Fixes a slew of bugs with the Vuforia camera monitor including: Fixes bug where preview could be displayed with a wonky aspect ratio Fixes bug where preview could be cut off in landscape Fixes bug where preview got totally messed up when rotating phone Fixes bug where crosshair could drift off target when using webcams Fixes issue in UVC driver on some devices (ftc_app 681) if streaming was started/stopped multiple times in a row Issue manifested as kernel panic on devices which do not have this kernel patch. On affected devices which do have the patch, the issue was manifest as simply a failure to start streaming. The Tech Team believes that the root cause of the issue is a bug in the Linux kernel XHCI driver. A workaround was implemented in the SDK UVC driver. Fixes bug in UVC driver where often half the frames from the camera would be dropped (e.g. only 15FPS delivered during a streaming session configured for 30FPS). Fixes issue where TensorFlow Object Detection would show results whose confidence was lower than the minimum confidence parameter. Fixes a potential exploitation issue of CVE-2019-11358 in OnBotJava Fixes changing the address of an Expansion Hub with additional Expansion Hubs connected to it Preserves the Control Hub's network connection when "Restart Robot" is selected Fixes issue where device scans would fail while the Robot was restarting Fix RenderScript usage Use androidx.renderscript variant: increased compatibility Use RenderScript in Java mode, not native: simplifies build Fixes webcam-frame-to-bitmap conversion problem: alpha channel wasn't being initialized, only R, G, & B Fixes possible arithmetic overflow in Deadline Fixes deadlock in Vuforia webcam support which could cause 5-second delays when stopping OpMode Version 5.4 (20200108-101156) Fixes SkyStone issue #88 Adds an inspection item that notes when a robot controller (Control Hub) is using the factory default password. Fixes SkyStone issue #61 Fixes SkyStone issue #142 Fixes ftc_app issue #417 by adding more current and voltage monitoring capabilities for REV Hubs. Fixes a crash sometimes caused by OnBotJava activity Improves OnBotJava autosave functionality ftc_app #738 Fixes system responsiveness issue when an Expansion Hub is disconnected Fixes issue where IMU initialization could prevent Op Modes from stopping Fixes issue where AndroidTextToSpeech.speak() would fail if it was called too early Adds telemetry.speak() methods and blocks, which cause the Driver Station (if also updated) to speak text Adds and improves Expansion Hub-related warnings Improves Expansion Hub low battery warning Displays the warning immediately after the hub reports it Specifies whether the condition is current or occurred temporarily during an OpMode run Displays which hubs reported low battery Displays warning when hub loses and regains power during an OpMode run Fixes the hub's LED pattern after this condition Displays warning when Expansion Hub is not responding to commands Specifies whether the condition is current or occurred temporarily during an OpMode run Clarifies warning when Expansion Hub is not present at startup Specifies that this condition requires a Robot Restart before the hub can be used. The hub light will now accurately reflect this state Improves logging and reduces log spam during these conditions Syncs the Control Hub time and timezone to a connected web browser programming the robot, if a Driver Station is not available. Adds bulk read functionality for REV Hubs A bulk caching mode must be set at the Hub level with LynxModule#setBulkCachingMode(). This applies to all relevant SDK hardware classes that reference that Hub. The following following Hub bulk caching modes are available: BulkCachingMode.OFF (default): All hardware calls operate as usual. Bulk data can read through LynxModule#getBulkData() and processed manually. BulkCachingMode.AUTO: Applicable hardware calls are served from a bulk read cache that is cleared/refreshed automatically to ensure identical commands don't hit the same cache. The cache can also be cleared manually with LynxModule#clearBulkCache(), although this is not recommended. (advanced users) BulkCachingMode.MANUAL: Same as BulkCachingMode.AUTO except the cache is never cleared automatically. To avoid getting stale data, the cache must be manually cleared at the beginning of each loop body or as the user deems appropriate. Removes PIDF Annotation values added in Rev 5.3 (to AndyMark, goBILDA and TETRIX motor configurations). The new motor types will still be available but their Default control behavior will revert back to Rev 5.2 Adds new ConceptMotorBulkRead sample Opmode to demonstrate and compare Motor Bulk-Read modes for reducing I/O latencies. Version 5.3 (20191004-112306) Fixes external USB/UVC webcam support Makes various bugfixes and improvements to Blocks page, including but not limited to: Many visual tweaks Browser zoom and window resize behave better Resizing the Java preview pane works better and more consistently across browsers The Java preview pane consistently gets scrollbars when needed The Java preview pane is hidden by default on phones Internet Explorer 11 should work Large dropdown lists display properly on lower res screens Disabled buttons are now visually identifiable as disabled A warning is shown if a user selects a TFOD sample, but their device is not compatible Warning messages in a Blocks op mode are now visible by default. Adds goBILDA 5201 and 5202 motors to Robot Configurator Adds PIDF Annotation values to AndyMark, goBILDA and TETRIX motor configurations. This has the effect of causing the RUN_USING_ENCODERS and RUN_TO_POSITION modes to use PIDF vs PID closed loop control on these motors. This should provide more responsive, yet stable, speed control. PIDF adds Feedforward control to the basic PID control loop. Feedforward is useful when controlling a motor's speed because it "anticipates" how much the control voltage must change to achieve a new speed set-point, rather than requiring the integrated error to change sufficiently. The PIDF values were chosen to provide responsive, yet stable, speed control on a lightly loaded motor. The more heavily a motor is loaded (drag or friction), the more noticable the PIDF improvement will be. Fixes startup crash on Android 10 Fixes ftc_app issue #712 (thanks to FROGbots-4634) Fixes ftc_app issue #542 Allows "A" and lowercase letters when naming device through RC and DS apps. Version 5.2 (20190905-083277) Fixes extra-wide margins on settings activities, and placement of the new configuration button Adds Skystone Vuforia image target data. Includes sample Skystone Vuforia Navigation op modes (Java). Includes sample Skystone Vuforia Navigation op modes (Blocks). Adds TensorFlow inference model (.tflite) for Skystone game elements. Includes sample Skystone TensorFlow op modes (Java). Includes sample Skystone TensorFlow op modes (Blocks). Removes older (season-specific) sample op modes. Includes 64-bit support (to comply with Google Play requirements). Protects against Stuck OpModes when a Restart Robot is requested. (Thanks to FROGbots-4634) (ftc_app issue #709) Blocks related changes: Fixes bug with blocks generated code when hardware device name is a java or javascript reserved word. Shows generated java code for blocks, even when hardware items are missing from the active configuration. Displays warning icon when outdated Vuforia and TensorFlow blocks are used (SkyStone issue #27) Version 5.1 (20190820-222104) Defines default PIDF parameters for the following motors: REV Core Hex Motor REV 20:1 HD Hex Motor REV 40:1 HD Hex Motor Adds back button when running on a device without a system back button (such as a Control Hub) Allows a REV Control Hub to update the firmware on a REV Expansion Hub via USB Fixes SkyStone issue #9 Fixes ftc_app issue #715 Prevents extra DS User clicks by filtering based on current state. Prevents incorrect DS UI state changes when receiving new OpMode list from RC Adds support for REV Color Sensor V3 Adds a manual-refresh DS Camera Stream for remotely viewing RC camera frames. To show the stream on the DS, initialize but do not run a stream-enabled opmode, select the Camera Stream option in the DS menu, and tap the image to refresh. This feature is automatically enabled when using Vuforia or TFOD—no additional RC configuration is required for typical use cases. To hide the stream, select the same menu item again. Note that gamepads are disabled and the selected opmode cannot be started while the stream is open as a safety precaution. To use custom streams, consult the API docs for CameraStreamServer#setSource and CameraStreamSource. Adds many Star Wars sounds to RobotController resources. Added SKYSTONE Sounds Chooser Sample Program. Switches out startup, connect chimes, and error/warning sounds for Star Wars sounds Updates OnBot Java to use a WebSocket for communication with the robot The OnBot Java page no longer has to do a full refresh when a user switches from editing one file to another Known issues: Camera Stream The Vuforia camera stream inherits the issues present in the phone preview (namely ftc_app issue #574). This problem does not affect the TFOD camera stream even though it receives frames from Vuforia. The orientation of the stream frames may not always match the phone preview. For now, these frames may be rotated manually via a custom CameraStreamSource if desired. OnBotJava Browser back button may not always work correctly It's possible for a build to be queued, but not started. The OnBot Java build console will display a warning if this occurs. A user might not realize they are editing a different file if the user inadvertently switches from one file to another since this switch is now seamless. The name of the currently open file is displayed in the browser tab. Version 5.0 (built on 19.06.14) Support for the REV Robotics Control Hub. Adds a Java preview pane to the Blocks editor. Adds a new offline export feature to the Blocks editor. Display wifi channel in Network circle on Driver Station. Adds calibration for Logitech C270 Updates build tooling and target SDK. Compliance with Google's permissions infrastructure (Required after build tooling update). Keep Alives to mitigate the Motorola wifi scanning problem. Telemetry substitute no longer necessary. Improves Vuforia error reporting. Fixes ftctechnh/ftc_app issues 621, 713. Miscellaneous bug fixes and improvements. Version 4.3 (built on 18.10.31) Includes missing TensorFlow-related libraries and files. Version 4.2 (built on 18.10.30) Includes fix to avoid deadlock situation with WatchdogMonitor which could result in USB communication errors. Comm error appeared to require that user disconnect USB cable and restart the Robot Controller app to recover. robotControllerLog.txt would have error messages that included the words "E RobotCore: lynx xmit lock: #### abandoning lock:" Includes fix to correctly list the parent module address for a REV Robotics Expansion Hub in a configuration (.xml) file. Bug in versions 4.0 and 4.1 would incorrect list the address module for a parent REV Robotics device as "1". If the parent module had a higher address value than the daisy-chained module, then this bug would prevent the Robot Controller from communicating with the downstream Expansion Hub. Added requirement for ACCESS_COARSE_LOCATION to allow a Driver Station running Android Oreo to scan for Wi-Fi Direct devices. Added google() repo to build.gradle because aapt2 must be downloaded from the google() repository beginning with version 3.2 of the Android Gradle Plugin. Important Note: Android Studio users will need to be connected to the Internet the first time build the ftc_app project. Internet connectivity is required for the first build so the appropriate files can be downloaded from the Google repository. Users should not need to be connected to the Internet for subsequent builds. This should also fix buid issue where Android Studio would complain that it "Could not find com.android.tools.lint:lint-gradle:26.1.4" (or similar). Added support for REV Spark Mini motor controller as part of the configuration menu for a servo/PWM port on the REV Expansion Hub. Provide examples for playing audio files in an Op Mode. Block Development Tool Changes Includes a fix for a problem with the Velocity blocks that were reported in the FTC Technology forum (Blocks Programming subforum). Change the "Save completed successfully." message to a white color so it will contrast with a green background. Fixed the "Download image" feature so it will work if there are text blocks in the op mode. Introduce support for Google's TensorFlow Lite technology for object detetion for 2018-2019 game. TensorFlow lite can recognize Gold Mineral and Silver Mineral from 2018-2019 game. Example Java and Block op modes are included to show how to determine the relative position of the gold block (left, center, right). Version 4.1 (released on 18.09.24) Changes include: Fix to prevent crash when deprecated configuration annotations are used. Change to allow FTC Robot Controller APK to be auto-updated using FIRST Global Control Hub update scripts. Removed samples for non supported / non legal hardware. Improvements to Telemetry.addData block with "text" socket. Updated Blocks sample op mode list to include Rover Ruckus Vuforia example. Update SDK library version number. Version 4.0 (released on 18.09.12) Changes include: Initial support for UVC compatible cameras If UVC camera has a unique serial number, RC will detect and enumerate by serial number. If UVC camera lacks a unique serial number, RC will only support one camera of that type connected. Calibration settings for a few cameras are included (see TeamCode/src/main/res/xml/teamwebcamcalibrations.xml for details). User can upload calibration files from Program and Manage web interface. UVC cameras seem to draw a fair amount of electrical current from the USB bus. This does not appear to present any problems for the REV Robotics Control Hub. This does seem to create stability problems when using some cameras with an Android phone-based Robot Controller. FTC Tech Team is investigating options to mitigate this issue with the phone-based Robot Controllers. Updated sample Vuforia Navigation and VuMark Op Modes to demonstrate how to use an internal phone-based camera and an external UVC webcam. Support for improved motor control. REV Robotics Expansion Hub firmware 1.8 and greater will support a feed forward mechanism for closed loop motor control. FTC SDK has been modified to support PIDF coefficients (proportional, integral, derivative, and feed forward). FTC Blocks development tool modified to include PIDF programming blocks. Deprecated older PID-related methods and variables. REV's 1.8.x PIDF-related changes provide a more linear and accurate way to control a motor. Wireless Added 5GHz support for wireless channel changing for those devices that support it. Tested with Moto G5 and E4 phones. Also tested with other (currently non-approved) phones such as Samsung Galaxy S8. Improved Expansion Hub firmware update support in Robot Controller app Changes to make the system more robust during the firmware update process (when performed through Robot Controller app). User no longer has to disconnect a downstream daisy-chained Expansion Hub when updating an Expansion Hub's firmware. If user is updating an Expansion Hub's firmware through a USB connection, he/she does not have to disconnect RS485 connection to other Expansion Hubs. The user still must use a USB connection to update an Expansion Hub's firmware. The user cannot update the Expansion Hub firmware for a downstream device that is daisy chained through an RS485 connection. If an Expansion Hub accidentally gets "bricked" the Robot Controller app is now more likely to recognize the Hub when it scans the USB bus. Robot Controller app should be able to detect an Expansion Hub, even if it accidentally was bricked in a previous update attempt. Robot Controller app should be able to install the firmware onto the Hub, even if if accidentally was bricked in a previous update attempt. Resiliency FTC software can detect and enable an FTDI reset feature that is available with REV Robotics v1.8 Expansion Hub firmware and greater. When enabled, the Expansion Hub can detect if it hasn't communicated with the Robot Controller over the FTDI (USB) connection. If the Hub hasn't heard from the Robot Controller in a while, it will reset the FTDI connection. This action helps system recover from some ESD-induced disruptions. Various fixes to improve reliability of FTC software. Blocks Fixed errors with string and list indices in blocks export to java. Support for USB connected UVC webcams. Refactored optimized Blocks Vuforia code to support Rover Ruckus image targets. Added programming blocks to support PIDF (proportional, integral, derivative and feed forward) motor control. Added formatting options (under Telemetry and Miscellaneous categories) so user can set how many decimal places to display a numerical value. Support to play audio files (which are uploaded through Blocks web interface) on Driver Station in addition to the Robot Controller. Fixed bug with Download Image of Blocks feature. Support for REV Robotics Blinkin LED Controller. Support for REV Robotics 2m Distance Sensor. Added support for a REV Touch Sensor (no longer have to configure as a generic digital device). Added blocks for DcMotorEx methods. These are enhanced methods that you can use when supported by the motor controller hardware. The REV Robotics Expansion Hub supports these enhanced methods. Enhanced methods include methods to get/set motor velocity (in encoder pulses per second), get/set PIDF coefficients, etc.. Modest Improvements in Logging Decrease frequency of battery checker voltage statements. Removed non-FTC related log statements (wherever possible). Introduced a "Match Logging" feature. Under "Settings" a user can enable/disable this feature (it's disabled by default). If enabled, user provides a "Match Number" through the Driver Station user interface (top of the screen). The Match Number is used to create a log file specifically with log statements from that particular Op Mode run. Match log files are stored in /sdcard/FIRST/matlogs on the Robot Controller. Once an op mode run is complete, the Match Number is cleared. This is a convenient way to create a separate match log with statements only related to a specific op mode run. New Devices Support for REV Robotics Blinkin LED Controller. Support for REV Robotics 2m Distance Sensor. Added configuration option for REV 20:1 HD Hex Motor. Added support for a REV Touch Sensor (no longer have to configure as a generic digital device). Miscellaneous Fixed some errors in the definitions for acceleration and velocity in our javadoc documentation. Added ability to play audio files on Driver Station When user is configuring an Expansion Hub, the LED on the Expansion Hub will change blink pattern (purple-cyan) to indicate which Hub is currently being configured. Renamed I2cSensorType to I2cDeviceType. Added an external sample Op Mode that demonstrates localization using 2018-2019 (Rover Ruckus presented by QualComm) Vuforia targets. Added an external sample Op Mode that demonstrates how to use the REV Robotics 2m Laser Distance Sensor. Added an external sample Op Mode that demonstrates how to use the REV Robotics Blinkin LED Controller. Re-categorized external Java sample Op Modes to "TeleOp" instead of "Autonomous". Known issues: Initial support for UVC compatible cameras UVC cameras seem to draw significant amount of current from the USB bus. This does not appear to present any problems for the REV Robotics Control Hub. This does seem to create stability problems when using some cameras with an Android phone-based Robot Controller. FTC Tech Team is investigating options to mitigate this issue with the phone-based Robot Controllers. There might be a possible deadlock which causes the RC to become unresponsive when using a UVC webcam with a Nougat Android Robot Controller. Wireless When user selects a wireless channel, this channel does not necessarily persist if the phone is power cycled. Tech Team is hoping to eventually address this issue in a future release. Issue has been present since apps were introduced (i.e., it is not new with the v4.0 release). Wireless channel is not currently displayed for WiFi Direct connections. Miscellaneous The blink indication feature that shows which Expansion Hub is currently being configured does not work for a newly created configuration file. User has to first save a newly created configuration file and then close and re-edit the file in order for blink indicator to work. Version 3.6 (built on 17.12.18) Changes include: Blocks Changes Uses updated Google Blockly software to allow users to edit their op modes on Apple iOS devices (including iPad and iPhone). Improvement in Blocks tool to handle corrupt op mode files. Autonomous op modes should no longer get switched back to tele-op after re-opening them to be edited. The system can now detect type mismatches during runtime and alert the user with a message on the Driver Station. Updated javadoc documentation for setPower() method to reflect correct range of values (-1 to +1). Modified VuforiaLocalizerImpl to allow for user rendering of frames Added a user-overrideable onRenderFrame() method which gets called by the class's renderFrame() method. Version 3.5 (built on 17.10.30) Changes with version 3.5 include: Introduced a fix to prevent random op mode stops, which can occur after the Robot Controller app has been paused and then resumed (for example, when a user temporarily turns off the display of the Robot Controller phone, and then turns the screen back on). Introduced a fix to prevent random op mode stops, which were previously caused by random peer disconnect events on the Driver Station. Fixes issue where log files would be closed on pause of the RC or DS, but not re-opened upon resume. Fixes issue with battery handler (voltage) start/stop race. Fixes issue where Android Studio generated op modes would disappear from available list in certain situations. Fixes problem where OnBot Java would not build on REV Robotics Control Hub. Fixes problem where OnBot Java would not build if the date and time on the Robot Controller device was "rewound" (set to an earlier date/time). Improved error message on OnBot Java that occurs when renaming a file fails. Removed unneeded resources from android.jar binaries used by OnBot Java to reduce final size of Robot Controller app. Added MR_ANALOG_TOUCH_SENSOR block to Blocks Programming Tool. Version 3.4 (built on 17.09.06) Changes with version 3.4 include: Added telemetry.update() statement for BlankLinearOpMode template. Renamed sample Block op modes to be more consistent with Java samples. Added some additional sample Block op modes. Reworded OnBot Java readme slightly. Version 3.3 (built on 17.09.04) This version of the software includes improves for the FTC Blocks Programming Tool and the OnBot Java Programming Tool. Changes with verion 3.3 include: Android Studio ftc_app project has been updated to use Gradle Plugin 2.3.3. Android Studio ftc_app project is already using gradle 3.5 distribution. Robot Controller log has been renamed to /sdcard/RobotControllerLog.txt (note that this change was actually introduced w/ v3.2). Improvements in I2C reliability. Optimized I2C read for REV Expansion Hub, with v1.7 firmware or greater. Updated all external/samples (available through OnBot and in Android project folder). Vuforia Added support for VuMarks that will be used for the 2017-2018 season game. Blocks Update to latest Google Blockly release. Sample op modes can be selected as a template when creating new op mode. Fixed bug where the blocks would disappear temporarily when mouse button is held down. Added blocks for Range.clip and Range.scale. User can now disable/enable Block op modes. Fix to prevent occasional Blocks deadlock. OnBot Java Significant improvements with autocomplete function for OnBot Java editor. Sample op modes can be selected as a template when creating new op mode. Fixes and changes to complete hardware setup feature. Updated (and more useful) onBot welcome message. Known issues: Android Studio After updating to the new v3.3 Android Studio project folder, if you get error messages indicating "InvalidVirtualFileAccessException" then you might need to do a File->Invalidate Caches / Restart to clear the error. OnBot Java Sometimes when you push the build button to build all op modes, the RC returns an error message that the build failed. If you press the build button a second time, the build typically suceeds. Version 3.2 (built on 17.08.02) This version of the software introduces the "OnBot Java" Development Tool. Similar to the FTC Blocks Development Tool, the FTC OnBot Java Development Tool allows a user to create, edit and build op modes dynamically using only a Javascript-enabled web browser. The OnBot Java Development Tool is an integrated development environment (IDE) that is served up by the Robot Controller. Op modes are created and edited using a Javascript-enabled browser (Google Chromse is recommended). Op modes are saved on the Robot Controller Android device directly. The OnBot Java Development Tool provides a Java programming environment that does NOT need Android Studio. Changes with version 3.2 include: Enhanced web-based development tools Introduction of OnBot Java Development Tool. Web-based programming and management features are "always on" (user no longer needs to put Robot Controller into programming mode). Web-based management interface (where user can change Robot Controller name and also easily download Robot Controller log file). OnBot Java, Blocks and Management features available from web based interface. Blocks Programming Development Tool: Changed "LynxI2cColorRangeSensor" block to "REV Color/range sensor" block. Fixed tooltip for ColorSensor.isLightOn block. Added blocks for ColorSensor.getNormalizedColors and LynxI2cColorRangeSensor.getNormalizedColors. Added example op modes for digital touch sensor and REV Robotics Color Distance sensor. User selectable color themes. Includes many minor enhancements and fixes (too numerous to list). Known issues: Auto complete function is incomplete and does not support the following (for now): Access via this keyword Access via super keyword Members of the super cloass, not overridden by the class Any methods provided in the current class Inner classes Can't handle casted objects Any objects coming from an parenthetically enclosed expression Version 3.10 (built on 17.05.09) This version of the software provides support for the REV Robotics Expansion Hub. This version also includes improvements in the USB communication layer in an effort to enhance system resiliency. If you were using a 2.x version of the software previously, updating to version 3.1 requires that you also update your Driver Station software in addition to updating the Robot Controller software. Also note that in version 3.10 software, the setMaxSpeed and getMaxSpeed methods are no longer available (not deprecated, they have been removed from the SDK). Also note that the the new 3.x software incorporates motor profiles that a user can select as he/she configures the robot. Changes include: Blocks changes Added VuforiaTrackableDefaultListener.getPose and Vuforia.trackPose blocks. Added optimized blocks support for Vuforia extended tracking. Added atan2 block to the math category. Added useCompetitionFieldTargetLocations parameter to Vuforia.initialize block. If set to false, the target locations are placed at (0,0,0) with target orientation as specified in https://github.com/gearsincorg/FTCVuforiaDemo/blob/master/Robot_Navigation.java tutorial op mode. Incorporates additional improvements to USB comm layer to improve system resiliency (to recover from a greater number of communication disruptions). Additional Notes Regarding Version 3.00 (built on 17.04.13) In addition to the release changes listed below (see section labeled "Version 3.00 (built on 17.04.013)"), version 3.00 has the following important changes: Version 3.00 software uses a new version of the FTC Robocol (robot protocol). If you upgrade to v3.0 on the Robot Controller and/or Android Studio side, you must also upgrade the Driver Station software to match the new Robocol. Version 3.00 software removes the setMaxSpeed and getMaxSpeed methods from the DcMotor class. If you have an op mode that formerly used these methods, you will need to remove the references/calls to these methods. Instead, v3.0 provides the max speed information through the use of motor profiles that are selected by the user during robot configuration. Version 3.00 software currently does not have a mechanism to disable extra i2c sensors. We hope to re-introduce this function with a release in the near future. Version 3.00 (built on 17.04.13) *** Use this version of the software at YOUR OWN RISK!!! *** This software is being released as an "alpha" version. Use this version at your own risk! This pre-release software contains SIGNIFICANT changes, including changes to the Wi-Fi Direct pairing mechanism, rewrites of the I2C sensor classes, changes to the USB/FTDI layer, and the introduction of support for the REV Robotics Expansion Hub and the REV Robotics color-range-light sensor. These changes were implemented to improve the reliability and resiliency of the FTC control system. Please note, however, that version 3.00 is considered "alpha" code. This code is being released so that the FIRST community will have an opportunity to test the new REV Expansion Hub electronics module when it becomes available in May. The developers do not recommend using this code for critical applications (i.e., competition use). *** Use this version of the software at YOUR OWN RISK!!! *** Changes include: Major rework of sensor-related infrastructure. Includes rewriting sensor classes to implement synchronous I2C communication. Fix to reset Autonomous timer back to 30 seconds. Implementation of specific motor profiles for approved 12V motors (includes Tetrix, AndyMark, Matrix and REV models). Modest improvements to enhance Wi-Fi P2P pairing. Fixes telemetry log addition race. Publishes all the sources (not just a select few). Includes Block programming improvements Addition of optimized Vuforia blocks. Auto scrollbar to projects and sounds pages. Fixed blocks paste bug. Blocks execute after while-opModeIsActive loop (to allow for cleanup before exiting op mode). Added gyro integratedZValue block. Fixes bug with projects page for Firefox browser. Added IsSpeaking block to AndroidTextToSpeech. Implements support for the REV Robotics Expansion Hub Implements support for integral REV IMU (physically installed on I2C bus 0, uses same Bosch BNO055 9 axis absolute orientation sensor as Adafruit 9DOF abs orientation sensor). - Implements support for REV color/range/light sensor. Provides support to update Expansion Hub firmware through FTC SDK. Detects REV firmware version and records in log file. Includes support for REV Control Hub (note that the REV Control Hub is not yet approved for FTC use). Implements FTC Blocks programming support for REV Expansion Hub and sensor hardware. Detects and alerts when I2C device disconnect. Version 2.62 (built on 17.01.07) Added null pointer check before calling modeToByte() in finishModeSwitchIfNecessary method for ModernRoboticsUsbDcMotorController class. Changes to enhance Modern Robotics USB protocol robustness. Version 2.61 (released on 16.12.19) Blocks Programming mode changes: Fix to correct issue when an exception was thrown because an OpticalDistanceSensor object appears twice in the hardware map (the second time as a LightSensor). Version 2.6 (released on 16.12.16) Fixes for Gyro class: Improve (decrease) sensor refresh latency. fix isCalibrating issues. Blocks Programming mode changes: Blocks now ignores a device in the configuration xml if the name is empty. Other devices work in configuration work fine. Version 2.5 (internal release on released on 16.12.13) Blocks Programming mode changes: Added blocks support for AdafruitBNO055IMU. Added Download Op Mode button to FtcBocks.html. Added support for copying blocks in one OpMode and pasting them in an other OpMode. The clipboard content is stored on the phone, so the programming mode server must be running. Modified Utilities section of the toolbox. In Programming Mode, display information about the active connections. Fixed paste location when workspace has been scrolled. Added blocks support for the android Accelerometer. Fixed issue where Blocks Upload Op Mode truncated name at first dot. Added blocks support for Android SoundPool. Added type safety to blocks for Acceleration. Added type safety to blocks for AdafruitBNO055IMU.Parameters. Added type safety to blocks for AnalogInput. Added type safety to blocks for AngularVelocity. Added type safety to blocks for Color. Added type safety to blocks for ColorSensor. Added type safety to blocks for CompassSensor. Added type safety to blocks for CRServo. Added type safety to blocks for DigitalChannel. Added type safety to blocks for ElapsedTime. Added type safety to blocks for Gamepad. Added type safety to blocks for GyroSensor. Added type safety to blocks for IrSeekerSensor. Added type safety to blocks for LED. Added type safety to blocks for LightSensor. Added type safety to blocks for LinearOpMode. Added type safety to blocks for MagneticFlux. Added type safety to blocks for MatrixF. Added type safety to blocks for MrI2cCompassSensor. Added type safety to blocks for MrI2cRangeSensor. Added type safety to blocks for OpticalDistanceSensor. Added type safety to blocks for Orientation. Added type safety to blocks for Position. Added type safety to blocks for Quaternion. Added type safety to blocks for Servo. Added type safety to blocks for ServoController. Added type safety to blocks for Telemetry. Added type safety to blocks for Temperature. Added type safety to blocks for TouchSensor. Added type safety to blocks for UltrasonicSensor. Added type safety to blocks for VectorF. Added type safety to blocks for Velocity. Added type safety to blocks for VoltageSensor. Added type safety to blocks for VuforiaLocalizer.Parameters. Added type safety to blocks for VuforiaTrackable. Added type safety to blocks for VuforiaTrackables. Added type safety to blocks for enums in AdafruitBNO055IMU.Parameters. Added type safety to blocks for AndroidAccelerometer, AndroidGyroscope, AndroidOrientation, and AndroidTextToSpeech. Version 2.4 (released on 16.11.13) Fix to avoid crashing for nonexistent resources. Blocks Programming mode changes: Added blocks to support OpenGLMatrix, MatrixF, and VectorF. Added blocks to support AngleUnit, AxesOrder, AxesReference, CameraDirection, CameraMonitorFeedback, DistanceUnit, and TempUnit. Added blocks to support Acceleration. Added blocks to support LinearOpMode.getRuntime. Added blocks to support MagneticFlux and Position. Fixed typos. Made blocks for ElapsedTime more consistent with other objects. Added blocks to support Quaternion, Velocity, Orientation, AngularVelocity. Added blocks to support VuforiaTrackables, VuforiaTrackable, VuforiaLocalizer, VuforiaTrackableDefaultListener. Fixed a few blocks. Added type checking to new blocks. Updated to latest blockly. Added default variable blocks to navigation and matrix blocks. Fixed toolbox entry for openGLMatrix_rotation_withAxesArgs. When user downloads Blocks-generated op mode, only the .blk file is downloaded. When user uploads Blocks-generated op mode (.blk file), Javascript code is auto generated. Added DbgLog support. Added logging when a blocks file is read/written. Fixed bug to properly render blocks even if missing devices from configuration file. Added support for additional characters (not just alphanumeric) for the block file names (for download and upload). Added support for OpMode flavor (“Autonomous” or “TeleOp”) and group. Changes to Samples to prevent tutorial issues. Incorporated suggested changes from public pull 216 (“Replace .. paths”). Remove Servo Glitches when robot stopped. if user hits “Cancels” when editing a configuration file, clears the unsaved changes and reverts to original unmodified configuration. Added log info to help diagnose why the Robot Controller app was terminated (for example, by watch dog function). Added ability to transfer log from the controller. Fixed inconsistency for AngularVelocity Limit unbounded growth of data for telemetry. If user does not call telemetry.update() for LinearOpMode in a timely manner, data added for telemetry might get lost if size limit is exceeded. Version 2.35 (released on 16.10.06) Blockly programming mode - Removed unnecesary idle() call from blocks for new project. Version 2.30 (released on 16.10.05) Blockly programming mode: Mechanism added to save Blockly op modes from Programming Mode Server onto local device To avoid clutter, blocks are displayed in categorized folders Added support for DigitalChannel Added support for ModernRoboticsI2cCompassSensor Added support for ModernRoboticsI2cRangeSensor Added support for VoltageSensor Added support for AnalogInput Added support for AnalogOutput Fix for CompassSensor setMode block Vuforia Fix deadlock / make camera data available while Vuforia is running. Update to Vuforia 6.0.117 (recommended by Vuforia and Google to close security loophole). Fix for autonomous 30 second timer bug (where timer was in effect, even though it appeared to have timed out). opModeIsActive changes to allow cleanup after op mode is stopped (with enforced 2 second safety timeout). Fix to avoid reading i2c twice. Updated sample Op Modes. Improved logging and fixed intermittent freezing. Added digital I/O sample. Cleaned up device names in sample op modes to be consistent with Pushbot guide. Fix to allow use of IrSeekerSensorV3. Version 2.20 (released on 16.09.08) Support for Modern Robotics Compass Sensor. Support for Modern Robotics Range Sensor. Revise device names for Pushbot templates to match the names used in Pushbot guide. Fixed bug so that IrSeekerSensorV3 device is accessible as IrSeekerSensor in hardwareMap. Modified computer vision code to require an individual Vuforia license (per legal requirement from PTC). Minor fixes. Blockly enhancements: Support for Voltage Sensor. Support for Analog Input. Support for Analog Output. Support for Light Sensor. Support for Servo Controller. Version 2.10 (released on 16.09.03) Support for Adafruit IMU. Improvements to ModernRoboticsI2cGyro class Block on reset of z axis. isCalibrating() returns true while gyro is calibration. Updated sample gyro program. Blockly enhancements support for android.graphics.Color. added support for ElapsedTime. improved look and legibility of blocks. support for compass sensor. support for ultrasonic sensor. support for IrSeeker. support for LED. support for color sensor. support for CRServo prompt user to configure robot before using programming mode. Provides ability to disable audio cues. various bug fixes and improvements. Version 2.00 (released on 16.08.19) This is the new release for the upcoming 2016-2017 FIRST Tech Challenge Season. Channel change is enabled in the FTC Robot Controller app for Moto G 2nd and 3rd Gen phones. Users can now use annotations to register/disable their Op Modes. Changes in the Android SDK, JDK and build tool requirements (minsdk=19, java 1.7, build tools 23.0.3). Standardized units in analog input. Cleaned up code for existing analog sensor classes. setChannelMode and getChannelMode were REMOVED from the DcMotorController class. This is important - we no longer set the motor modes through the motor controller. setMode and getMode were added to the DcMotor class. ContinuousRotationServo class has been added to the FTC SDK. Range.clip() method has been overloaded so it can support this operation for int, short and byte integers. Some changes have been made (new methods added) on how a user can access items from the hardware map. Users can now set the zero power behavior for a DC motor so that the motor will brake or float when power is zero. Prototype Blockly Programming Mode has been added to FTC Robot Controller. Users can place the Robot Controller into this mode, and then use a device (such as a laptop) that has a Javascript enabled browser to write Blockly-based Op Modes directly onto the Robot Controller. Users can now configure the robot remotely through the FTC Driver Station app. Android Studio project supports Android Studio 2.1.x and compile SDK Version 23 (Marshmallow). Vuforia Computer Vision SDK integrated into FTC SDK. Users can use sample vision targets to get localization information on a standard FTC field. Project structure has been reorganized so that there is now a TeamCode package that users can use to place their local/custom Op Modes into this package. Inspection function has been integrated into the FTC Robot Controller and Driver Station Apps (Thanks Team HazMat… 9277 & 10650!). Audio cues have been incorporated into FTC SDK. Swap mechanism added to FTC Robot Controller configuration activity. For example, if you have two motor controllers on a robot, and you misidentified them in your configuration file, you can use the Swap button to swap the devices within the configuration file (so you do not have to manually re-enter in the configuration info for the two devices). Fix mechanism added to all user to replace an electronic module easily. For example, suppose a servo controller dies on your robot. You replace the broken module with a new module, which has a different serial number from the original servo controller. You can use the Fix button to automatically reconfigure your configuration file to use the serial number of the new module. Improvements made to fix resiliency and responsiveness of the system. For LinearOpMode the user now must for a telemetry.update() to update the telemetry data on the driver station. This update() mechanism ensures that the driver station gets the updated data properly and at the same time. The Auto Configure function of the Robot Controller is now template based. If there is a commonly used robot configuration, a template can be created so that the Auto Configure mechanism can be used to quickly configure a robot of this type. The logic to detect a runaway op mode (both in the LinearOpMode and OpMode types) and to abort the run, then auto recover has been improved/implemented. Fix has been incorporated so that Logitech F310 gamepad mappings will be correct for Marshmallow users. Release 16.07.08 For the ftc_app project, the gradle files have been modified to support Android Studio 2.1.x. Release 16.03.30 For the MIT App Inventor, the design blocks have new icons that better represent the function of each design component. Some changes were made to the shutdown logic to ensure the robust shutdown of some of our USB services. A change was made to LinearOpMode so as to allow a given instance to be executed more than once, which is required for the App Inventor. Javadoc improved/updated. Release 16.03.09 Changes made to make the FTC SDK synchronous (significant change!) waitOneFullHardwareCycle() and waitForNextHardwareCycle() are no longer needed and have been deprecated. runOpMode() (for a LinearOpMode) is now decoupled from the system's hardware read/write thread. loop() (for an OpMode) is now decoupled from the system's hardware read/write thread. Methods are synchronous. For example, if you call setMode(DcMotorController.RunMode.RESET_ENCODERS) for a motor, the encoder is guaranteed to be reset when the method call is complete. For legacy module (NXT compatible), user no longer has to toggle between read and write modes when reading from or writing to a legacy device. Changes made to enhance reliability/robustness during ESD event. Changes made to make code thread safe. Debug keystore added so that user-generated robot controller APKs will all use the same signed key (to avoid conflicts if a team has multiple developer laptops for example). Firmware version information for Modern Robotics modules are now logged. Changes made to improve USB comm reliability and robustness. Added support for voltage indicator for legacy (NXT-compatible) motor controllers. Changes made to provide auto stop capabilities for op modes. A LinearOpMode class will stop when the statements in runOpMode() are complete. User does not have to push the stop button on the driver station. If an op mode is stopped by the driver station, but there is a run away/uninterruptible thread persisting, the app will log an error message then force itself to crash to stop the runaway thread. Driver Station UI modified to display lowest measured voltage below current voltage (12V battery). Driver Station UI modified to have color background for current voltage (green=good, yellow=caution, red=danger, extremely low voltage). javadoc improved (edits and additional classes). Added app build time to About activity for driver station and robot controller apps. Display local IP addresses on Driver Station About activity. Added I2cDeviceSynchImpl. Added I2cDeviceSync interface. Added seconds() and milliseconds() to ElapsedTime for clarity. Added getCallbackCount() to I2cDevice. Added missing clearI2cPortActionFlag. Added code to create log messages while waiting for LinearOpMode shutdown. Fix so Wifi Direct Config activity will no longer launch multiple times. Added the ability to specify an alternate i2c address in software for the Modern Robotics gyro. Release 16.02.09 Improved battery checker feature so that voltage values get refreshed regularly (every 250 msec) on Driver Station (DS) user interface. Improved software so that Robot Controller (RC) is much more resilient and “self-healing” to USB disconnects: If user attempts to start/restart RC with one or more module missing, it will display a warning but still start up. When running an op mode, if one or more modules gets disconnected, the RC & DS will display warnings,and robot will keep on working in spite of the missing module(s). If a disconnected module gets physically reconnected the RC will auto detect the module and the user will regain control of the recently connected module. Warning messages are more helpful (identifies the type of module that’s missing plus its USB serial number). Code changes to fix the null gamepad reference when users try to reference the gamepads in the init() portion of their op mode. NXT light sensor output is now properly scaled. Note that teams might have to readjust their light threshold values in their op modes. On DS user interface, gamepad icon for a driver will disappear if the matching gamepad is disconnected or if that gamepad gets designated as a different driver. Robot Protocol (ROBOCOL) version number info is displayed in About screen on RC and DS apps. Incorporated a display filter on pairing screen to filter out devices that don’t use the “-“ format. This filter can be turned off to show all WiFi Direct devices. Updated text in License file. Fixed formatting error in OpticalDistanceSensor.toString(). Fixed issue on with a blank (“”) device name that would disrupt WiFi Direct Pairing. Made a change so that the WiFi info and battery info can be displayed more quickly on the DS upon connecting to RC. Improved javadoc generation. Modified code to make it easier to support language localization in the future. Release 16.01.04 Updated compileSdkVersion for apps Prevent Wifi from entering power saving mode removed unused import from driver station Corrrected "Dead zone" joystick code. LED.getDeviceName and .getConnectionInfo() return null apps check for ROBOCOL_VERSION mismatch Fix for Telemetry also has off-by-one errors in its data string sizing / short size limitations error User telemetry output is sorted. added formatting variants to DbgLog and RobotLog APIs code modified to allow for a long list of op mode names. changes to improve thread safety of RobocolDatagramSocket Fix for "missing hardware leaves robot controller disconnected from driver station" error fix for "fast tapping of Init/Start causes problems" (toast is now only instantiated on UI thread). added some log statements for thread life cycle. moved gamepad reset logic inside of initActiveOpMode() for robustness changes made to mitigate risk of race conditions on public methods. changes to try and flag when WiFi Direct name contains non-printable characters. fix to correct race condition between .run() and .close() in ReadWriteRunnableStandard. updated FTDI driver made ReadWriteRunnableStanard interface public. fixed off-by-one errors in Command constructor moved specific hardware implmentations into their own package. moved specific gamepad implemnatations to the hardware library. changed LICENSE file to new BSD version. fixed race condition when shutting down Modern Robotics USB devices. methods in the ColorSensor classes have been synchronized. corrected isBusy() status to reflect end of motion. corrected "back" button keycode. the notSupported() method of the GyroSensor class was changed to protected (it should not be public). Release 15.11.04.001 Added Support for Modern Robotics Gyro. The GyroSensor class now supports the MR Gyro Sensor. Users can access heading data (about Z axis) Users can also access raw gyro data (X, Y, & Z axes). Example MRGyroTest.java op mode included. Improved error messages More descriptive error messages for exceptions in user code. Updated DcMotor API Enable read mode on new address in setI2cAddress Fix so that driver station app resets the gamepads when switching op modes. USB-related code changes to make USB comm more responsive and to display more explicit error messages. Fix so that USB will recover properly if the USB bus returns garbage data. Fix USB initializtion race condition. Better error reporting during FTDI open. More explicit messages during USB failures. Fixed bug so that USB device is closed if event loop teardown method was not called. Fixed timer UI issue Fixed duplicate name UI bug (Legacy Module configuration). Fixed race condition in EventLoopManager. Fix to keep references stable when updating gamepad. For legacy Matrix motor/servo controllers removed necessity of appending "Motor" and "Servo" to controller names. Updated HT color sensor driver to use constants from ModernRoboticsUsbLegacyModule class. Updated MR color sensor driver to use constants from ModernRoboticsUsbDeviceInterfaceModule class. Correctly handle I2C Address change in all color sensors Updated/cleaned up op modes. Updated comments in LinearI2cAddressChange.java example op mode. Replaced the calls to "setChannelMode" with "setMode" (to match the new of the DcMotor method). Removed K9AutoTime.java op mode. Added MRGyroTest.java op mode (demonstrates how to use MR Gyro Sensor). Added MRRGBExample.java op mode (demonstrates how to use MR Color Sensor). Added HTRGBExample.java op mode (demonstrates how to use HT legacy color sensor). Added MatrixControllerDemo.java (demonstrates how to use legacy Matrix controller). Updated javadoc documentation. Updated release .apk files for Robot Controller and Driver Station apps. Release 15.10.06.002 Added support for Legacy Matrix 9.6V motor/servo controller. Cleaned up build.gradle file. Minor UI and bug fixes for driver station and robot controller apps. Throws error if Ultrasonic sensor (NXT) is not configured for legacy module port 4 or 5. Release 15.08.03.001 New user interfaces for FTC Driver Station and FTC Robot Controller apps. An init() method is added to the OpMode class. For this release, init() is triggered right before the start() method. Eventually, the init() method will be triggered when the user presses an "INIT" button on driver station. The init() and loop() methods are now required (i.e., need to be overridden in the user's op mode). The start() and stop() methods are optional. A new LinearOpMode class is introduced. Teams can use the LinearOpMode mode to create a linear (not event driven) program model. Teams can use blocking statements like Thread.sleep() within a linear op mode. The API for the Legacy Module and Core Device Interface Module have been updated. Support for encoders with the Legacy Module is now working. The hardware loop has been updated for better performance.

using Neural Networks (SSD) on Tensorflow. This repo documents steps and scripts used to train a hand detector using Tensorflow (Object Detection API). As with any DNN based task, the most expensive (and riskiest) part of the process has to do with finding or creating the right (annotated) dataset. I was interested mainly in detecting hands on a table (egocentric view point). I experimented first with the [Oxford Hands Dataset](http://www.robots.ox.ac.uk/~vgg/data/hands/) (the results were not good). I then tried the [Egohands Dataset](http://vision.soic.indiana.edu/projects/egohands/) which was a much better fit to my requirements. The goal of this repo/post is to demonstrate how neural networks can be applied to the (hard) problem of tracking hands (egocentric and other views). Better still, provide code that can be adapted to other uses cases. If you use this tutorial or models in your research or project, please cite [this](#citing-this-tutorial). Here is the detector in action. <img src="images/hand1.gif" width="33.3%"><img src="images/hand2.gif" width="33.3%"><img src="images/hand3.gif" width="33.3%"> Realtime detection on video stream from a webcam . <img src="images/chess1.gif" width="33.3%"><img src="images/chess2.gif" width="33.3%"><img src="images/chess3.gif" width="33.3%"> Detection on a Youtube video. Both examples above were run on a macbook pro **CPU** (i7, 2.5GHz, 16GB). Some fps numbers are: | FPS | Image Size | Device| Comments| | ------------- | ------------- | ------------- | ------------- | | 21 | 320 * 240 | Macbook pro (i7, 2.5GHz, 16GB) | Run without visualizing results| | 16 | 320 * 240 | Macbook pro (i7, 2.5GHz, 16GB) | Run while visualizing results (image above) | | 11 | 640 * 480 | Macbook pro (i7, 2.5GHz, 16GB) | Run while visualizing results (image above) | > Note: The code in this repo is written and tested with Tensorflow `1.4.0-rc0`. Using a different version may result in [some errors](https://github.com/tensorflow/models/issues/1581). You may need to [generate your own frozen model](https://pythonprogramming.net/testing-custom-object-detector-tensorflow-object-detection-api-tutorial/?completed=/training-custom-objects-tensorflow-object-detection-api-tutorial/) graph using the [model checkpoints](model-checkpoint) in the repo to fit your TF version. **Content of this document** - Motivation - Why Track/Detect hands with Neural Networks - Data preparation and network training in Tensorflow (Dataset, Import, Training) - Training the hand detection Model - Using the Detector to Detect/Track hands - Thoughts on Optimizations. > P.S if you are using or have used the models provided here, feel free to reach out on twitter ([@vykthur](https://twitter.com/vykthur)) and share your work! ## Motivation - Why Track/Detect hands with Neural Networks? There are several existing approaches to tracking hands in the computer vision domain. Incidentally, many of these approaches are rule based (e.g extracting background based on texture and boundary features, distinguishing between hands and background using color histograms and HOG classifiers,) making them not very robust. For example, these algorithms might get confused if the background is unusual or in situations where sharp changes in lighting conditions cause sharp changes in skin color or the tracked object becomes occluded.(see [here for a review](https://www.cse.unr.edu/~bebis/handposerev.pdf) paper on hand pose estimation from the HCI perspective) With sufficiently large datasets, neural networks provide opportunity to train models that perform well and address challenges of existing object tracking/detection algorithms - varied/poor lighting, noisy environments, diverse viewpoints and even occlusion. The main drawbacks to usage for real-time tracking/detection is that they can be complex, are relatively slow compared to tracking-only algorithms and it can be quite expensive to assemble a good dataset. But things are changing with advances in fast neural networks. Furthermore, this entire area of work has been made more approachable by deep learning frameworks (such as the tensorflow object detection api) that simplify the process of training a model for custom object detection. More importantly, the advent of fast neural network models like ssd, faster r-cnn, rfcn (see [here](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md#coco-trained-models-coco-models) ) etc make neural networks an attractive candidate for real-time detection (and tracking) applications. Hopefully, this repo demonstrates this. > If you are not interested in the process of training the detector, you can skip straight to applying the [pretrained model I provide in detecting hands](#detecting-hands). Training a model is a multi-stage process (assembling dataset, cleaning, splitting into training/test partitions and generating an inference graph). While I lightly touch on the details of these parts, there are a few other tutorials cover training a custom object detector using the tensorflow object detection api in more detail[ see [here](https://pythonprogramming.net/training-custom-objects-tensorflow-object-detection-api-tutorial/) and [here](https://towardsdatascience.com/how-to-train-your-own-object-detector-with-tensorflows-object-detector-api-bec72ecfe1d9) ]. I recommend you walk through those if interested in training a custom object detector from scratch. ## Data preparation and network training in Tensorflow (Dataset, Import, Training) **The Egohands Dataset** The hand detector model is built using data from the [Egohands Dataset](http://vision.soic.indiana.edu/projects/egohands/) dataset. This dataset works well for several reasons. It contains high quality, pixel level annotations (>15000 ground truth labels) where hands are located across 4800 images. All images are captured from an egocentric view (Google glass) across 48 different environments (indoor, outdoor) and activities (playing cards, chess, jenga, solving puzzles etc). <img src="images/egohandstrain.jpg" width="100%"> If you will be using the Egohands dataset, you can cite them as follows: > Bambach, Sven, et al. "Lending a hand: Detecting hands and recognizing activities in complex egocentric interactions." Proceedings of the IEEE International Conference on Computer Vision. 2015. The Egohands dataset (zip file with labelled data) contains 48 folders of locations where video data was collected (100 images per folder). ``` -- LOCATION_X -- frame_1.jpg -- frame_2.jpg ... -- frame_100.jpg -- polygons.mat // contains annotations for all 100 images in current folder -- LOCATION_Y -- frame_1.jpg -- frame_2.jpg ... -- frame_100.jpg -- polygons.mat // contains annotations for all 100 images in current folder ``` **Converting data to Tensorflow Format** Some initial work needs to be done to the Egohands dataset to transform it into the format (`tfrecord`) which Tensorflow needs to train a model. This repo contains `egohands_dataset_clean.py` a script that will help you generate these csv files. - Downloads the egohands datasets - Renames all files to include their directory names to ensure each filename is unique - Splits the dataset into train (80%), test (10%) and eval (10%) folders. - Reads in `polygons.mat` for each folder, generates bounding boxes and visualizes them to ensure correctness (see image above). - Once the script is done running, you should have an images folder containing three folders - train, test and eval. Each of these folders should also contain a csv label document each - `train_labels.csv`, `test_labels.csv` that can be used to generate `tfrecords` Note: While the egohands dataset provides four separate labels for hands (own left, own right, other left, and other right), for my purpose, I am only interested in the general `hand` class and label all training data as `hand`. You can modify the data prep script to generate `tfrecords` that support 4 labels. Next: convert your dataset + csv files to tfrecords. A helpful guide on this can be found [here](https://pythonprogramming.net/creating-tfrecord-files-tensorflow-object-detection-api-tutorial/).For each folder, you should be able to generate `train.record`, `test.record` required in the training process. ## Training the hand detection Model Now that the dataset has been assembled (and your tfrecords), the next task is to train a model based on this. With neural networks, it is possible to use a process called [transfer learning](https://www.tensorflow.org/tutorials/image_retraining) to shorten the amount of time needed to train the entire model. This means we can take an existing model (that has been trained well on a related domain (here image classification) and retrain its final layer(s) to detect hands for us. Sweet!. Given that neural networks sometimes have thousands or millions of parameters that can take weeks or months to train, transfer learning helps shorten training time to possibly hours. Tensorflow does offer a few models (in the tensorflow [model zoo](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md#coco-trained-models-coco-models)) and I chose to use the `ssd_mobilenet_v1_coco` model as my start point given it is currently (one of) the fastest models (read the SSD research [paper here](https://arxiv.org/pdf/1512.02325.pdf)). The training process can be done locally on your CPU machine which may take a while or better on a (cloud) GPU machine (which is what I did). For reference, training on my macbook pro (tensorflow compiled from source to take advantage of the mac's cpu architecture) the maximum speed I got was 5 seconds per step as opposed to the ~0.5 seconds per step I got with a GPU. For reference it would take about 12 days to run 200k steps on my mac (i7, 2.5GHz, 16GB) compared to ~5hrs on a GPU. > **Training on your own images**: Please use the [guide provided by Harrison from pythonprogramming](https://pythonprogramming.net/training-custom-objects-tensorflow-object-detection-api-tutorial/) on how to generate tfrecords given your label csv files and your images. The guide also covers how to start the training process if training locally. [see [here] (https://pythonprogramming.net/training-custom-objects-tensorflow-object-detection-api-tutorial/)]. If training in the cloud using a service like GCP, see the [guide here](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/running_on_cloud.md). As the training process progresses, the expectation is that total loss (errors) gets reduced to its possible minimum (about a value of 1 or thereabout). By observing the tensorboard graphs for total loss(see image below), it should be possible to get an idea of when the training process is complete (total loss does not decrease with further iterations/steps). I ran my training job for 200k steps (took about 5 hours) and stopped at a total Loss (errors) value of 2.575.(In retrospect, I could have stopped the training at about 50k steps and gotten a similar total loss value). With tensorflow, you can also run an evaluation concurrently that assesses your model to see how well it performs on the test data. A commonly used metric for performance is mean average precision (mAP) which is single number used to summarize the area under the precision-recall curve. mAP is a measure of how well the model generates a bounding box that has at least a 50% overlap with the ground truth bounding box in our test dataset. For the hand detector trained here, the mAP value was **0.9686@0.5IOU**. mAP values range from 0-1, the higher the better. <img src="images/accuracy.jpg" width="100%"> Once training is completed, the trained inference graph (`frozen_inference_graph.pb`) is then exported (see the earlier referenced guides for how to do this) and saved in the `hand_inference_graph` folder. Now its time to do some interesting detection. ## Using the Detector to Detect/Track hands If you have not done this yet, please following the guide on installing [Tensorflow and the Tensorflow object detection api](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/installation.md). This will walk you through setting up the tensorflow framework, cloning the tensorflow github repo and a guide on - Load the `frozen_inference_graph.pb` trained on the hands dataset as well as the corresponding label map. In this repo, this is done in the `utils/detector_utils.py` script by the `load_inference_graph` method. ```python detection_graph = tf.Graph() with detection_graph.as_default(): od_graph_def = tf.GraphDef() with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid: serialized_graph = fid.read() od_graph_def.ParseFromString(serialized_graph) tf.import_graph_def(od_graph_def, name='') sess = tf.Session(graph=detection_graph) print("> ====== Hand Inference graph loaded.") ``` - Detect hands. In this repo, this is done in the `utils/detector_utils.py` script by the `detect_objects` method. ```python (boxes, scores, classes, num) = sess.run( [detection_boxes, detection_scores, detection_classes, num_detections], feed_dict={image_tensor: image_np_expanded}) ``` - Visualize detected bounding detection_boxes. In this repo, this is done in the `utils/detector_utils.py` script by the `draw_box_on_image` method. This repo contains two scripts that tie all these steps together. - detect_multi_threaded.py : A threaded implementation for reading camera video input detection and detecting. Takes a set of command line flags to set parameters such as `--display` (visualize detections), image parameters `--width` and `--height`, videe `--source` (0 for camera) etc. - detect_single_threaded.py : Same as above, but single threaded. This script works for video files by setting the video source parameter videe `--source` (path to a video file). ```cmd # load and run detection on video at path "videos/chess.mov" python detect_single_threaded.py --source videos/chess.mov ``` > Update: If you do have errors loading the frozen inference graph in this repo, feel free to generate a new graph that fits your TF version from the model-checkpoint in this repo. Use the [export_inference_graph.py](https://github.com/tensorflow/models/blob/master/research/object_detection/export_inference_graph.py) script provided in the tensorflow object detection api repo. More guidance on this [here](https://pythonprogramming.net/testing-custom-object-detector-tensorflow-object-detection-api-tutorial/?completed=/training-custom-objects-tensorflow-object-detection-api-tutorial/). ## Thoughts on Optimization. A few things that led to noticeable performance increases. - Threading: Turns out that reading images from a webcam is a heavy I/O event and if run on the main application thread can slow down the program. I implemented some good ideas from [Adrian Rosebuck](https://www.pyimagesearch.com/2017/02/06/faster-video-file-fps-with-cv2-videocapture-and-opencv/) on parrallelizing image capture across multiple worker threads. This mostly led to an FPS increase of about 5 points. - For those new to Opencv, images from the `cv2.read()` method return images in [BGR format](https://www.learnopencv.com/why-does-opencv-use-bgr-color-format/). Ensure you convert to RGB before detection (accuracy will be much reduced if you dont). ```python cv2.cvtColor(image_np, cv2.COLOR_BGR2RGB) ``` - Keeping your input image small will increase fps without any significant accuracy drop.(I used about 320 x 240 compared to the 1280 x 720 which my webcam provides). - Model Quantization. Moving from the current 32 bit to 8 bit can achieve up to 4x reduction in memory required to load and store models. One way to further speed up this model is to explore the use of [8-bit fixed point quantization](https://heartbeat.fritz.ai/8-bit-quantization-and-tensorflow-lite-speeding-up-mobile-inference-with-low-precision-a882dfcafbbd). Performance can also be increased by a clever combination of tracking algorithms with the already decent detection and this is something I am still experimenting with. Have ideas for optimizing better, please share! <img src="images/general.jpg" width="100%"> Note: The detector does reflect some limitations associated with the training set. This includes non-egocentric viewpoints, very noisy backgrounds (e.g in a sea of hands) and sometimes skin tone. There is opportunity to improve these with additional data. ## Integrating Multiple DNNs. One way to make things more interesting is to integrate our new knowledge of where "hands" are with other detectors trained to recognize other objects. Unfortunately, while our hand detector can in fact detect hands, it cannot detect other objects (a factor or how it is trained). To create a detector that classifies multiple different objects would mean a long involved process of assembling datasets for each class and a lengthy training process. > Given the above, a potential strategy is to explore structures that allow us **efficiently** interleave output form multiple pretrained models for various object classes and have them detect multiple objects on a single image. An example of this is with my primary use case where I am interested in understanding the position of objects on a table with respect to hands on same table. I am currently doing some work on a threaded application that loads multiple detectors and outputs bounding boxes on a single image. More on this soon.

# AD-Prediction Convolutional Neural Networks for Alzheimer's Disease Prediction Using Brain MRI Image ## Abstract Alzheimers disease (AD) is characterized by severe memory loss and cognitive impairment. It associates with significant brain structure changes, which can be measured by magnetic resonance imaging (MRI) scan. The observable preclinical structure changes provides an opportunity for AD early detection using image classification tools, like convolutional neural network (CNN). However, currently most AD related studies were limited by sample size. Finding an efficient way to train image classifier on limited data is critical. In our project, we explored different transfer-learning methods based on CNN for AD prediction brain structure MRI image. We find that both pretrained 2D AlexNet with 2D-representation method and simple neural network with pretrained 3D autoencoder improved the prediction performance comparing to a deep CNN trained from scratch. The pretrained 2D AlexNet performed even better (**86%**) than the 3D CNN with autoencoder (**77%**). ## Method #### 1. Data In this project, we used public brain MRI data from **Alzheimers Disease Neuroimaging Initiative (ADNI)** Study. ADNI is an ongoing, multicenter cohort study, started from 2004. It focuses on understanding the diagnostic and predictive value of Alzheimers disease specific biomarkers. The ADNI study has three phases: ADNI1, ADNI-GO, and ADNI2. Both ADNI1 and ADNI2 recruited new AD patients and normal control as research participants. Our data included a total of 686 structure MRI scans from both ADNI1 and ADNI2 phases, with 310 AD cases and 376 normal controls. We randomly derived the total sample into training dataset (n = 519), validation dataset (n = 100), and testing dataset (n = 67). #### 2. Image preprocessing Image preprocessing were conducted using Statistical Parametric Mapping (SPM) software, version 12. The original MRI scans were first skull-stripped and segmented using segmentation algorithm based on 6-tissue probability mapping and then normalized to the International Consortium for Brain Mapping template of European brains using affine registration. Other configuration includes: bias, noise, and global intensity normalization. The standard preprocessing process output 3D image files with an uniform size of 121x145x121. Skull-stripping and normalization ensured the comparability between images by transforming the original brain image into a standard image space, so that same brain substructures can be aligned at same image coordinates for different participants. Diluted or enhanced intensity was used to compensate the structure changes. the In our project, we used both whole brain (including both grey matter and white matter) and grey matter only. #### 3. AlexNet and Transfer Learning Convolutional Neural Networks (CNN) are very similar to ordinary Neural Networks. A CNN consists of an input and an output layer, as well as multiple hidden layers. The hidden layers are either convolutional, pooling or fully connected. ConvNet architectures make the explicit assumption that the inputs are images, which allows us to encode certain properties into the architecture. These then make the forward function more efficient to implement and vastly reduce the amount of parameters in the network. #### 3.1. AlexNet The net contains eight layers with weights; the first five are convolutional and the remaining three are fully connected. The overall architecture is shown in Figure 1. The output of the last fully-connected layer is fed to a 1000-way softmax which produces a distribution over the 1000 class labels. AlexNet maximizes the multinomial logistic regression objective, which is equivalent to maximizing the average across training cases of the log-probability of the correct label under the prediction distribution. The kernels of the second, fourth, and fifth convolutional layers are connected only to those kernel maps in the previous layer which reside on the same GPU (as shown in Figure1). The kernels of the third convolutional layer are connected to all kernel maps in the second layer. The neurons in the fully connected layers are connected to all neurons in the previous layer. Response-normalization layers follow the first and second convolutional layers. Max-pooling layers follow both response-normalization layers as well as the fifth convolutional layer. The ReLU non-linearity is applied to the output of every convolutional and fully-connected layer.  The first convolutional layer filters the 224x224x3 input image with 96 kernels of size 11x11x3 with a stride of 4 pixels (this is the distance between the receptive field centers of neighboring neurons in a kernel map). The second convolutional layer takes as input the (response-normalized and pooled) output of the first convolutional layer and filters it with 256 kernels of size 5x5x48. The third, fourth, and fifth convolutional layers are connected to one another without any intervening pooling or normalization layers. The third convolutional layer has 384 kernels of size 3x3x256 connected to the (normalized, pooled) outputs of the second convolutional layer. The fourth convolutional layer has 384 kernels of size 3x3x192 , and the fifth convolutional layer has 256 kernels of size 3x3x192. The fully-connected layers have 4096 neurons each. #### 3.2. Transfer Learning Training an entire Convolutional Network from scratch (with random initialization) is impractical[14] because it is relatively rare to have a dataset of sufficient size. An alternative is to pretrain a Conv-Net on a very large dataset (e.g. ImageNet), and then use the ConvNet either as an initialization or a fixed feature extractor for the task of interest. Typically, there are three major transfer learning scenarios: **ConvNet as fixed feature extractor:** We can take a ConvNet pretrained on ImageNet, and remove the last fully-connected layer, then treat the rest structure as a fixed feature extractor for the target dataset. In AlexNet, this would be a 4096-D vector. Usually, we call these features as CNN codes. Once we get these features, we can train a linear classifier (e.g. linear SVM or Softmax classifier) for our target dataset. **Fine-tuning the ConvNet:** Another idea is not only replace the last fully-connected layer in the classifier, but to also fine-tune the parameters of the pretrained network. Due to overfitting concerns, we can only fine-tune some higher-level part of the network. This suggestion is motivated by the observation that earlier features in a ConvNet contains more generic features (e.g. edge detectors or color blob detectors) that can be useful for many kind of tasks. But the later layer of the network becomes progressively more specific to the details of the classes contained in the original dataset. **Pretrained models:** The released pretrained model is usually the final ConvNet checkpoint. So it is common to see people use the network for fine-tuning. #### 4. 3D Autoencoder and Convolutional Neural Network We take a two-stage approach where we first train a 3D sparse autoencoder to learn filters for convolution operations, and then build a convolutional neural network whose first layer uses the filters learned with the autoencoder.  #### 4.1. Sparse Autoencoder An autoencoder is a 3-layer neural network that is used to extract features from an input such as an image. Sparse representations can provide a simple interpretation of the input data in terms of a small number of \parts by extracting the structure hidden in the data. The autoencoder has an input layer, a hidden layer and an output layer, and the input and output layers have same number of units, while the hidden layer contains more units for a sparse and overcomplete representation. The encoder function maps input x to representation h, and the decoder function maps the representation h to the output x. In our problem, we extract 3D patches from scans as the input to the network. The decoder function aims to reconstruct the input form the hidden representation h. #### 4.2. 3D Convolutional Neural Network Training the 3D convolutional neural network(CNN) is the second stage. The CNN we use in this project has one convolutional layer, one pooling layer, two linear layers, and finally a log softmax layer. After training the sparse autoencoder, we take the weights and biases of the encoder from trained model, and use them a 3D filter of a 3D convolutional layer of the 1-layer convolutional neural network. Figure 2 shows the architecture of the network. #### 5. Tools In this project, we used Nibabel for MRI image processing and PyTorch Neural Networks implementation.

W3C Strategic Highlights September 2019 This report was prepared for the September 2019 W3C Advisory Committee Meeting (W3C Member link). See the accompanying W3C Fact Sheet — September 2019. For the previous edition, see the April 2019 W3C Strategic Highlights. For future editions of this report, please consult the latest version. A Chinese translation is available. ☰ Contents Introduction Future Web Standards Meeting Industry Needs Web Payments Digital Publishing Media and Entertainment Web & Telecommunications Real-Time Communications (WebRTC) Web & Networks Automotive Web of Things Strengthening the Core of the Web HTML CSS Fonts SVG Audio Performance Web Performance WebAssembly Testing Browser Testing and Tools WebPlatform Tests Web of Data Web for All Security, Privacy, Identity Internationalization (i18n) Web Accessibility Outreach to the world W3C Developer Relations W3C Training Translations W3C Liaisons Introduction This report highlights recent work of enhancement of the existing landscape of the Web platform and innovation for the growth and strength of the Web. 33 working groups and a dozen interest groups enable W3C to pursue its mission through the creation of Web standards, guidelines, and supporting materials. We track the tremendous work done across the Consortium through homogeneous work-spaces in Github which enables better monitoring and management. We are in the middle of a period where we are chartering numerous working groups which demonstrate the rapid degree of change for the Web platform: After 4 years, we are nearly ready to publish a Payment Request API Proposed Recommendation and we need to soon charter follow-on work. In the last year we chartered the Web Payment Security Interest Group. In the last year we chartered the Web Media Working Group with 7 specifications for next generation Media support on the Web. We have Accessibility Guidelines under W3C Member review which includes Silver, a new approach. We have just launched the Decentralized Identifier Working Group which has tremendous potential because Decentralized Identifier (DID) is an identifier that is globally unique, resolveable with high availability, and cryptographically verifiable. We have Privacy IG (PING) under W3C Member review which strengthens our focus on the tradeoff between privacy and function. We have a new CSS charter under W3C Member review which maps the group's work for the next three years. In this period, W3C and the WHATWG have succesfully completed the negotiation of a Memorandum of Understanding rooted in the mutual belief that that having two distinct specifications claiming to be normative is generally harmful for the Web community. The MOU, signed last May, describes how the two organizations are to collaborate on the development of a single authoritative version of the HTML and DOM specifications. W3C subsequently rechartered the HTML Working Group to assist the W3C community in raising issues and proposing solutions for the HTML and DOM specifications, and for the production of W3C Recommendations from WHATWG Review Drafts. As the Web evolves continuously, some groups are looking for ways for specifications to do so as well. So-called "evergreen recommendations" or "living standards" aim to track continuous development (and maintenance) of features, on a feature-by-feature basis, while getting review and patent commitments. We see the maturation and further development of an incredible number of new technologies coming to the Web. Continued progress in many areas demonstrates the vitality of the W3C and the Web community, as the rest of the report illustrates. Future Web Standards W3C has a variety of mechanisms for listening to what the community thinks could become good future Web standards. These include discussions with the Membership, discussions with other standards bodies, the activities of thousands of participants in over 300 community groups, and W3C Workshops. There are lots of good ideas. The W3C strategy team has been identifying promising topics and invites public participation. Future, recent and under consideration Workshops include: Inclusive XR (5-6 November 2019, Seattle, WA, USA) to explore existing and future approaches on making Virtual and Augmented Reality experiences more inclusive, including to people with disabilities; W3C Workshop on Data Models for Transportation (12-13 September 2019, Palo Alto, CA, USA) W3C Workshop on Web Games (27-28 June 2019, Redmond, WA, USA), view report Second W3C Workshop on the Web of Things (3-5 June 2019, Munich, Germany) W3C Workshop on Web Standardization for Graph Data; Creating Bridges: RDF, Property Graph and SQL (4-6 March 2019, Berlin, Germany), view report Web & Machine Learning. The Strategy Funnel documents the staff's exploration of potential new work at various phases: Exploration and Investigation, Incubation and Evaluation, and eventually to the chartering of a new standards group. The Funnel view is a GitHub Project where new area are issues represented by “cards” which move through the columns, usually from left to right. Most cards start in Exploration and move towards Chartering, or move out of the funnel. Public input is welcome at any stage but particularly once Incubation has begun. This helps W3C identify work that is sufficiently incubated to warrant standardization, to review the ecosystem around the work and indicate interest in participating in its standardization, and then to draft a charter that reflects an appropriate scope. Ongoing feedback can speed up the overall standardization process. Since the previous highlights document, W3C has chartered a number of groups, and started discussion on many more: Newly Chartered or Rechartered Web Application Security WG (03-Apr) Web Payment Security IG (17-Apr) Patent and Standards IG (24-Apr) Web Applications WG (14-May) Web & Networks IG (16-May) Media WG (23-May) Media and Entertainment IG (06-Jun) HTML WG (06-Jun) Decentralized Identifier WG (05-Sep) Extended Privacy IG (PING) (30-Sep) Verifiable Claims WG (30-Sep) Service Workers WG (31-Dec) Dataset Exchange WG (31-Dec) Web of Things Working Group (31-Dec) Web Audio Working Group (31-Dec) Proposed charters / Advance Notice Accessibility Guidelines WG Privacy IG (PING) RDF Literal Direction WG Timed Text WG CSS WG Web Authentication WG Closed Internationalization Tag Set IG Meeting Industry Needs Web Payments All Web Payments specifications W3C's payments standards enable a streamlined checkout experience, enabling a consistent user experience across the Web with lower front end development costs for merchants. Users can store and reuse information and more quickly and accurately complete online transactions. The Web Payments Working Group has republished Payment Request API as a Candidate Recommendation, aiming to publish a Proposed Recommendation in the Fall 2019, and is discussing use cases and features for Payment Request after publication of the 1.0 Recommendation. Browser vendors have been finalizing implementation of features added in the past year (view the implementation report). As work continues on the Payment Handler API and its implementation (currently in Chrome and Edge Canary), one focus in 2019 is to increase adoption in other browsers. Recently, Mastercard demonstrated the use of Payment Request API to carry out EMVCo's Secure Remote Commerce (SRC) protocol whose payment method definition is being developed with active participation by Visa, Mastercard, American Express, and Discover. Payment method availability is a key factor in merchant considerations about adopting Payment Request API. The ability to get uniform adoption of a new payment method such as Secure Remote Commerce (SRC) also depends on the availability of the Payment Handler API in browsers, or of proprietary alternatives. Web Monetization, which the Web Payments Working Group will discuss again at its face-to-face meeting in September, can be used to enable micropayments as an alternative revenue stream to advertising. Since the beginning of 2019, Amazon, Brave Software, JCB, Certus Cybersecurity Solutions and Netflix have joined the Web Payments Working Group. In April, W3C launched the Web Payment Security Group to enable W3C, EMVCo, and the FIDO Alliance to collaborate on a vision for Web payment security and interoperability. Participants will define areas of collaboration and identify gaps between existing technical specifications in order to increase compatibility among different technologies, such as: How do SRC, FIDO, and Payment Request relate? The Payment Services Directive 2 (PSD2) regulations in Europe are scheduled to take effect in September 2019. What is the role of EMVCo, W3C, and FIDO technologies, and what is the current state of readiness for the deadline? How can we improve privacy on the Web at the same time as we meet industry requirements regarding user identity? Digital Publishing All Digital Publishing specifications, Publication milestones The Web is the universal publishing platform. Publishing is increasingly impacted by the Web, and the Web increasingly impacts Publishing. Topic of particular interest to Publishing@W3C include typography and layout, accessibility, usability, portability, distribution, archiving, offline access, print on demand, and reliable cross referencing. And the diverse publishing community represented in the groups consist of the traditional "trade" publishers, ebook reading system manufacturers, but also publishers of audio book, scholarly journals or educational materials, library scientists or browser developers. The Publishing Working Group currently concentrates on Audiobooks which lack a comprehensive standard, thus incurring extra costs and time to publish in this booming market. Active development is ongoing on the future standard: Publication Manifest Audiobook profile for Web Publications Lightweight Packaging Format The BD Comics Manga Community Group, the Synchronized Multimedia for Publications Community Group, the Publishing Community Group and a future group on archival, are companions to the working group where specific work is developed and incubated. The Publishing Community Group is a recently launched incubation channel for Publishing@W3C. The goal of the group is to propose, document, and prototype features broadly related to: publications on the Web reading modes and systems and the user experience of publications The EPUB 3 Community Group has successfully completed the revision of EPUB 3.2. The Publishing Business Group fosters ongoing participation by members of the publishing industry and the overall ecosystem in the development of Web infrastructure to better support the needs of the industry. The Business Group serves as an additional conduit to the Publishing Working Group and several Community Groups for feedback between the publishing ecosystem and W3C. The Publishing BG has played a vital role in fostering and advancing the adoption and continued development of EPUB 3. In particular the BG provided critical support to the update of EPUBCheck to validate EPUB content to the new EPUB 3.2 specification. This resulted in the development, in conjunction with the EPUB3 Community Group, of a new generation of EPUBCheck, i.e., EPUBCheck 4.2 production-ready release. Media and Entertainment All Media specifications The Media and Entertainment vertical tracks media-related topics and features that create immersive experiences for end users. HTML5 brought standard audio and video elements to the Web. Standardization activities since then have aimed at turning the Web into a professional platform fully suitable for the delivery of media content and associated materials, enabling missing features to stream video content on the Web such as adaptive streaming and content protection. Together with Microsoft, Comcast, Netflix and Google, W3C received an Technology & Engineering Emmy Award in April 2019 for standardization of a full TV experience on the Web. Current goals are to: Reinforce core media technologies: Creation of the Media Working Group, to develop media-related specifications incubated in the WICG (e.g. Media Capabilities, Picture-in-picture, Media Session) and maintain maintain/evolve Media Source Extensions (MSE) and Encrypted Media Extensions (EME). Improve support for Media Timed Events: data cues incubation. Enhance color support (HDR, wide gamut), in scope of the CSS WG and in the Color on the Web CG. Reduce fragmentation: Continue annual releases of a common and testable baseline media devices, in scope of the Web Media APIs CG and in collaboration with the CTA WAVE Project. Maintain the Road-map of Media Technologies for the Web which highlights Web technologies that can be used to build media applications and services, as well as known gaps to enable additional use cases. Create the future: Discuss perspectives for Media and Entertainment for the Web. Bring the power of GPUs to the Web (graphics, machine learning, heavy processing), under incubation in the GPU for the Web CG. Transition to a Working Group is under discussion. Determine next steps after the successful W3C Workshop on Web Games of June 2019. View the report. Timed Text The Timed Text Working Group develops and maintains formats used for the representation of text synchronized with other timed media, like audio and video, and notably works on TTML, profiles of TTML, and WebVTT. Recent progress includes: A robust WebVTT implementation report poises the specification for publication as a proposed recommendation. Discussions around re-chartering, notably to add a TTML Profile for Audio Description deliverable to the scope of the group, and clarify that rendering of captions within XR content is also in scope. Immersive Web Hardware that enables Virtual Reality (VR) and Augmented Reality (AR) applications are now broadly available to consumers, offering an immersive computing platform with both new opportunities and challenges. The ability to interact directly with immersive hardware is critical to ensuring that the web is well equipped to operate as a first-class citizen in this environment. The Immersive Web Working Group has been stabilizing the WebXR Device API while the companion Immersive Web Community Group incubates the next series of features identified as key for the future of the Immersive Web. W3C plans a workshop focused on the needs and benefits at the intersection of VR & Accessibility (Inclusive XR), on 5-6 November 2019 in Seattle, WA, USA, to explore existing and future approaches on making Virtual and Augmented Reality experiences more inclusive. Web & Telecommunications The Web is the Open Platform for Mobile. Telecommunication service providers and network equipment providers have long been critical actors in the deployment of Web technologies. As the Web platform matures, it brings richer and richer capabilities to extend existing services to new users and devices, and propose new and innovative services. Real-Time Communications (WebRTC) All Real-Time Communications specifications WebRTC has reshaped the whole communication landscape by making any connected device a potential communication end-point, bringing audio and video communications anywhere, on any network, vastly expanding the ability of operators to reach their customers. WebRTC serves as the corner-stone of many online communication and collaboration services. The WebRTC Working Group aims to bringing WebRTC 1.0 (and companion specification Media Capture and Streams) to Recommendation by the end of 2019. Intense efforts are focused on testing (supported by a dedicated hackathon at IETF 104) and interoperability. The group is considering pushing features that have not gotten enough traction to separate modules or to a later minor revision of the spec. Beyond WebRTC 1.0, the WebRTC Working Group will focus its efforts on WebRTC NV which the group has started documenting by identifying use cases. Web & Networks Recently launched, in the wake of the May 2018 Web5G workshop, the Web & Networks Interest Group is chaired by representatives from AT&T, China Mobile and Intel, with a goal to explore solutions for web applications to achieve better performance and resource allocation, both on the device and network. The group's first efforts are around use cases, privacy & security requirements and liaisons. Automotive All Automotive specifications To create a rich application ecosystem for vehicles and other devices allowed to connect to the vehicle, the W3C Automotive Working Group is delivering a service specification to expose all common vehicle signals (engine temperature, fuel/charge level, range, tire pressure, speed, etc.) The Vehicle Information Service Specification (VISS), which is a Candidate Recommendation, is seeing more implementations across the industry. It provides the access method to a common data model for all the vehicle signals –presently encapsulating a thousand or so different data elements– and will be growing to accommodate the advances in automotive such as autonomous and driver assist technologies and electrification. The group is already working on a successor to VISS, leveraging the underlying data model and the VIWI submission from Volkswagen, for a more robust means of accessing vehicle signals information and the same paradigm for other automotive needs including location-based services, media, notifications and caching content. The Automotive and Web Platform Business Group acts as an incubator for prospective standards work. One of its task forces is using W3C VISS in performing data sampling and off-boarding the information to the cloud. Access to the wealth of information that W3C's auto signals standard exposes is of interest to regulators, urban planners, insurance companies, auto manufacturers, fleet managers and owners, service providers and others. In addition to components needed for data sampling and edge computing, capturing user and owner consent, information collection methods and handling of data are in scope. The upcoming W3C Workshop on Data Models for Transportation (September 2019) is expected to focus on the need of additional ontologies around transportation space. Web of Things All Web of Things specifications W3C's Web of Things work is designed to bridge disparate technology stacks to allow devices to work together and achieve scale, thus enabling the potential of the Internet of Things by eliminating fragmentation and fostering interoperability. Thing descriptions expressed in JSON-LD cover the behavior, interaction affordances, data schema, security configuration, and protocol bindings. The Web of Things complements existing IoT ecosystems to reduce the cost and risk for suppliers and consumers of applications that create value by combining multiple devices and information services. There are many sectors that will benefit, e.g. smart homes, smart cities, smart industry, smart agriculture, smart healthcare and many more. The Web of Things Working Group is finishing the initial Web of Things standards, with support from the Web of Things Interest Group: Web of Things Architecture Thing Descriptions Strengthening the Core of the Web HTML The HTML Working Group was chartered early June to assist the W3C community in raising issues and proposing solutions for the HTML and DOM specifications, and to produce W3C Recommendations from WHATWG Review Drafts. A few days before, W3C and the WHATWG signed a Memorandum of Understanding outlining the agreement to collaborate on the development of a single version of the HTML and DOM specifications. Issues and proposed solutions for HTML and DOM done via the newly rechartered HTML Working Group in the WHATWG repositories The HTML Working Group is targetting November 2019 to bring HTML and DOM to Candidate Recommendations. CSS All CSS specifications CSS is a critical part of the Open Web Platform. The CSS Working Group gathers requirements from two large groups of CSS users: the publishing industry and application developers. Within W3C, those groups are exemplified by the Publishing groups and the Web Platform Working Group. The former requires things like better pagination support and advanced font handling, the latter needs intelligent (and fast!) scrolling and animations. What we know as CSS is actually a collection of almost a hundred specifications, referred to as ‘modules’. The current state of CSS is defined by a snapshot, updated once a year. The group also publishes an index defining every term defined by CSS specifications. Fonts All Fonts specifications The Web Fonts Working Group develops specifications that allow the interoperable deployment of downloadable fonts on the Web, with a focus on Progressive Font Enrichment as well as maintenance of WOFF Recommendations. Recent and ongoing work includes: Early API experiments by Adobe and Monotype have demonstrated the feasibility of a font enrichment API, where a server delivers a font with minimal glyph repertoire and the client can query the full repertoire and request additional subsets on-the-fly. In other experiments, the Brotli compression used in WOFF 2 was extended to support shared dictionaries and patch update. Metrics to quantify improvement are a current hot discussion topic. The group will meet at ATypi 2019 in Japan, to gather requirements from the international typography community. The group will first produce a report summarizing the strengths and weaknesses of each prototype solution by Q2 2020. SVG All SVG specifications SVG is an important and widely-used part of the Open Web Platform. The SVG Working Group focuses on aligning the SVG 2.0 specification with browser implementations, having split the specification into a currently-implemented 2.0 and a forward-looking 2.1. Current activity is on stabilization, increased integration with the Open Web Platform, and test coverage analysis. The Working Group was rechartered in March 2019. A new work item concerns native (non-Web-browser) uses of SVG as a non-interactive, vector graphics format. Audio The Web Audio Working Group was extended to finish its work on the Web Audio API, expecting to publish it as a Recommendation by year end. The specification enables synthesizing audio in the browser. Audio operations are performed with audio nodes, which are linked together to form a modular audio routing graph. Multiple sources — with different types of channel layout — are supported. This modular design provides the flexibility to create complex audio functions with dynamic effects. The first version of Web Audio API is now feature complete and is implemented in all modern browsers. Work has started on the next version, and new features are being incubated in the Audio Community Group. Performance Web Performance All Web Performance specifications There are currently 18 specifications in development in the Web Performance Working Group aiming to provide methods to observe and improve aspects of application performance of user agent features and APIs. The W3C team is looking at related work incubated in the W3C GPU for the Web (WebGPU) Community Group which is poised to transition to a W3C Working Group. A preliminary draft charter is available. WebAssembly All WebAssembly specifications WebAssembly improves Web performance and power by being a virtual machine and execution environment enabling loaded pages to run native (compiled) code. It is deployed in Firefox, Edge, Safari and Chrome. The specification will soon reach Candidate Recommendation. WebAssembly enables near-native performance, optimized load time, and perhaps most importantly, a compilation target for existing code bases. While it has a small number of native types, much of the performance increase relative to Javascript derives from its use of consistent typing. WebAssembly leverages decades of optimization for compiled languages and the byte code is optimized for compactness and streaming (the web page starts executing while the rest of the code downloads). Network and API access all occurs through accompanying Javascript libraries -- the security model is identical to that of Javascript. Requirements gathering and language development occur in the Community Group while the Working Group manages test development, community review and progression of specifications on the Recommendation Track. Testing Browser testing plays a critical role in the growth of the Web by: Improving the reliability of Web technology definitions; Improving the quality of implementations of these technologies by helping vendors to detect bugs in their products; Improving the data available to Web developers on known bugs and deficiencies of Web technologies by publishing results of these tests. Browser Testing and Tools The Browser Testing and Tools Working Group is developing WebDriver version 2, having published last year the W3C Recommendation of WebDriver. WebDriver acts as a remote control interface that enables introspection and control of user agents, provides a platform- and language-neutral wire protocol as a way for out-of-process programs to remotely instruct the behavior of Web, and emulates the actions of a real person using the browser. WebPlatform Tests The WebPlatform Tests project now provides a mechanism which allows to fully automate tests that previously needed to be run manually: TestDriver. TestDriver enables sending trusted key and mouse events, sending complex series of trusted pointer and key interactions for things like in-content drag-and-drop or pinch zoom, and even file upload. Since 2014 W3C began work on this coordinated open-source effort to build a cross-browser test suite for the Web Platform, which WHATWG, and all major browsers adopted. Web of Data All Data specifications There have been several great success stories around the standardization of data on the web over the past year. Verifiable Claims seems to have significant uptake. It is also significant that the Distributed Identifier WG charter has received numerous favorable reviews, and was just recently launched. JSON-LD has been a major success with the large deployment on Web sites via schema.org. JSON-LD 1.1 completed technical work, about to transition to CR More than 25% of websites today include schema.org data in JSON-LD The Web of Things description is in CR since May, making use of JSON-LD Verifiable Credentials data model is in CR since July, also making use of JSON-LD Continued strong interest in decentralized identifiers Engagement from the TAG with reframing core documents, such as Ethical Web Principles, to include data on the web within their scope Data is increasingly important for all organizations, especially with the rise of IoT and Big Data. W3C has a mature and extensive suite of standards relating to data that were developed over two decades of experience, with plans for further work on making it easier for developers to work with graph data and knowledge graphs. Linked Data is about the use of URIs as names for things, the ability to dereference these URIs to get further information and to include links to other data. There are ever-increasing sources of open Linked Data on the Web, as well as data services that are restricted to the suppliers and consumers of those services. The digital transformation of industry is seeking to exploit advanced digital technologies. This will facilitate businesses to integrate horizontally along the supply and value chains, and vertically from the factory floor to the office floor. W3C is seeking to make it easier to support enterprise-wide data management and governance, reflecting the strategic importance of data to modern businesses. Traditional approaches to data have focused on tabular databases (SQL/RDBMS), Comma Separated Value (CSV) files, and data embedded in PDF documents and spreadsheets. We're now in midst of a major shift to graph data with nodes and labeled directed links between them. Graph data is: Faster than using SQL and associated JOIN operations More favorable to integrating data from heterogeneous sources Better suited to situations where the data model is evolving In the wake of the recent W3C Workshop on Graph Data we are in the process of launching a Graph Standardization Business Group to provide a business perspective with use cases and requirements, to coordinate technical standards work and liaisons with external organizations. Web for All Security, Privacy, Identity All Security specifications, all Privacy specifications Authentication on the Web As the WebAuthn Level 1 W3C Recommendation published last March is seeing wide implementation and adoption of strong cryptographic authentication, work is proceeding on Level 2. The open standard Web API gives native authentication technology built into native platforms, browsers, operating systems (including mobile) and hardware, offering protection against hacking, credential theft, phishing attacks, thus aiming to end the era of passwords as a security construct. You may read more in our March press release. Privacy An increasing number of W3C specifications are benefitting from Privacy and Security review; there are security and privacy aspects to every specification. Early review is essential. Working with the TAG, the Privacy Interest Group has updated the Self-Review Questionnaire: Security and Privacy. Other recent work of the group includes public blogging further to the exploration of anti-patterns in standards and permission prompts. Security The Web Application Security Working Group adopted Feature Policy, aiming to allow developers to selectively enable, disable, or modify the behavior of some of these browser features and APIs within their application; and Fetch Metadata, aiming to provide servers with enough information to make a priori decisions about whether or not to service a request based on the way it was made, and the context in which it will be used. The Web Payment Security Interest Group, launched last April, convenes members from W3C, EMVCo, and the FIDO Alliance to discuss cooperative work to enhance the security and interoperability of Web payments (read more about payments). Internationalization (i18n) All Internationalization specifications, educational articles related to Internationalization, spec developers checklist Only a quarter or so current Web users use English online and that proportion will continue to decrease as the Web reaches more and more communities of limited English proficiency. If the Web is to live up to the "World Wide" portion of its name, and for the Web to truly work for stakeholders all around the world engaging with content in various languages, it must support the needs of worldwide users as they engage with content in the various languages. The growth of epublishing also brings requirements for new features and improved typography on the Web. It is important to ensure the needs of local communities are captured. The W3C Internationalization Initiative was set up to increase in-house resources dedicated to accelerating progress in making the World Wide Web "worldwide" by gathering user requirements, supporting developers, and education & outreach. For an overview of current projects see the i18n radar. W3C's Internationalization efforts progressed on a number of fronts recently: Requirements: New African and European language groups will work on the gap analysis, errata and layout requirements. Gap analysis: Japanese, Devanagari, Bengali, Tamil, Lao, Khmer, Javanese, and Ethiopic updated in the gap-analysis documents. Layout requirements document: notable progress tracked in the Southeast Asian Task Force while work continues on Chinese layout requirements. Developer support: Spec reviews: the i18n WG continues active review of specifications of the WHATWG and other W3C Working Groups. Short review checklist: easy way to begin a self-review to help spec developers understand what aspects of their spec are likely to need attention for internationalization, and points them to more detailed checklists for the relevant topics. It also helps those reviewing specs for i18n issues. Strings on the Web: Language and Direction Metadata lays out issues and discusses potential solutions for passing information about language and direction with strings in JSON or other data formats. The document was rewritten for clarity, and expanded. The group is collaborating with the JSON-LD and Web Publishing groups to develop a plan for updating RDF, JSON-LD and related specifications to handle metadata for base direction of text (bidi). User-friendly test format: a new format was developed for Internationalization Test Suite tests, which displays helpful information about how the test works. This particularly useful because those tests are pointed to by educational materials and gap-analysis documents. Web Platform Tests: a large number of tests in the i18n test suite have been ported to the WPT repository, including: css-counter-styles, css-ruby, css-syntax, css-test, css-text-decor, css-writing-modes, and css-pseudo. Education & outreach: (for all educational materials, see the HTML & CSS Authoring Techniques) Web Accessibility All Accessibility specifications, WAI resources The Web Accessibility Initiative supports W3C's Web for All mission. Recent achievements include: Education and training: Inaccessibility of CAPTCHA updated to bring our analysis and recommendations up to date with CAPTCHA practice today, concluding two years of extensive work and invaluable input from the public (read more on the W3C Blog Learn why your web content and applications should be accessible. The Education and Outreach Working Group has completed revision and updating of the Business Case for Digital Accessibility. Accessibility guidelines: The Accessibility Guidelines Working Group has continued to update WCAG Techniques and Understanding WCAG 2.1; and published a Candidate Recommendation of Accessibility Conformance Testing Rules Format 1.0 to improve inter-rater reliability when evaluating conformance of web content to WCAG An updated charter is being developed to host work on "Silver", the next generation accessibility guidelines (WCAG 2.2) There are accessibility aspects to most specifications. Check your work with the FAST checklist. Outreach to the world W3C Developer Relations To foster the excellent feedback loop between Web Standards development and Web developers, and to grow participation from that diverse community, recent W3C Developer Relations activities include: @w3cdevs tracks the enormous amount of work happening across W3C W3C Track during the Web Conference 2019 in San Francisco Tech videos: W3C published the 2019 Web Games Workshop videos The 16 September 2019 Developer Meetup in Fukuoka, Japan, is open to all and will combine a set of technical demos prepared by W3C groups, and a series of talks on a selected set of W3C technologies and projects W3C is involved with Mozilla, Google, Samsung, Microsoft and Bocoup in the organization of ViewSource 2019 in Amsterdam (read more on the W3C Blog) W3C Training In partnership with EdX, W3C's MOOC training program, W3Cx offers a complete "Front-End Web Developer" (FEWD) professional certificate program that consists of a suite of five courses on the foundational languages that power the Web: HTML5, CSS and JavaScript. We count nearly 900K students from all over the world. Translations Many Web users rely on translations of documents developed at W3C whose official language is English. W3C is extremely grateful to the continuous efforts of its community in ensuring our various deliverables in general, and in our specifications in particular, are made available in other languages, for free, ensuring their exposure to a much more diverse set of readers. Last Spring we developed a more robust system, a new listing of translations of W3C specifications and updated the instructions on how to contribute to our translation efforts. W3C Liaisons Liaisons and coordination with numerous organizations and Standards Development Organizations (SDOs) is crucial for W3C to: make sure standards are interoperable coordinate our respective agenda in Internet governance: W3C participates in ICANN, GIPO, IGF, the I* organizations (ICANN, IETF, ISOC, IAB). ensure at the government liaison level that our standards work is officially recognized when important to our membership so that products based on them (often done by our members) are part of procurement orders. W3C has ARO/PAS status with ISO. W3C participates in the EU MSP and Rolling Plan on Standardization ensure the global set of Web and Internet standards form a compatible stack of technologies, at the technical and policy level (patent regime, fragmentation, use in policy making) promote Standards adoption equally by the industry, the public sector, and the public at large Coralie Mercier, Editor, W3C Marketing & Communications $Id: Overview.html,v 1.60 2019/10/15 12:05:52 coralie Exp $ Copyright © 2019 W3C ® (MIT, ERCIM, Keio, Beihang) Usage policies apply.

Prosto is a data processing toolkit radically changing how data is processed by heavily relying on functions and operations with functions - an alternative to map-reduce and join-groupby

An ANN is a model based on a collection of connected units or nodes called "artificial neurons", which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit information, a "signal", from one artificial neuron to another. An artificial neuron that receives a signal can process it and then signal additional artificial neurons connected to it. In common ANN implementations, the signal at a connection between artificial neurons is a real number, and the output of each artificial neuron is computed by some non-linear function of the sum of its inputs. The connections between artificial neurons are called "edges". Artificial neurons and edges typically have a weight that adjusts as learning proceeds. The weight increases or decreases the strength of the signal at a connection. Artificial neurons may have a threshold such that the signal is only sent if the aggregate signal crosses that threshold. Typically, artificial neurons are aggregated into layers. Different layers may perform different kinds of transformations on their inputs. Signals travel from the first layer (the input layer) to the last layer (the output layer), possibly after traversing the layers multiple times. The original goal of the ANN approach was to solve problems in the same way that a human brain would. However, over time, attention moved to performing specific tasks, leading to deviations from biology. Artificial neural networks have been used on a variety of tasks, including computer vision, speech recognition, machine translation, social network filtering, playing board and video games and medical diagnosis. Deep learning consists of multiple hidden layers in an artificial neural network. This approach tries to model the way the human brain processes light and sound into vision and hearing. Some successful applications of deep learning are computer vision and speech recognition.[68] Decision trees Main article: Decision tree learning Decision tree learning uses a decision tree as a predictive model to go from observations about an item (represented in the branches) to conclusions about the item's target value (represented in the leaves). It is one of the predictive modeling approaches used in statistics, data mining, and machine learning. Tree models where the target variable can take a discrete set of values are called classification trees; in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. Decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. In decision analysis, a decision tree can be used to visually and explicitly represent decisions and decision making. In data mining, a decision tree describes data, but the resulting classification tree can be an input for decision making. Support vector machines Main article: Support vector machines Support vector machines (SVMs), also known as support vector networks, are a set of related supervised learning methods used for classification and regression. Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that predicts whether a new example falls into one category or the other.[69] An SVM training algorithm is a non-probabilistic, binary, linear classifier, although methods such as Platt scaling exist to use SVM in a probabilistic classification setting. In addition to performing linear classification, SVMs can efficiently perform a non-linear classification using what is called the kernel trick, implicitly mapping their inputs into high-dimensional feature spaces. Illustration of linear regression on a data set. Regression analysis Main article: Regression analysis Regression analysis encompasses a large variety of statistical methods to estimate the relationship between input variables and their associated features. Its most common form is linear regression, where a single line is drawn to best fit the given data according to a mathematical criterion such as ordinary least squares. The latter is often extended by regularization (mathematics) methods to mitigate overfitting and bias, as in ridge regression. When dealing with non-linear problems, go-to models include polynomial regression (for example, used for trendline fitting in Microsoft Excel[70]), logistic regression (often used in statistical classification) or even kernel regression, which introduces non-linearity by taking advantage of the kernel trick to implicitly map input variables to higher-dimensional space. Bayesian networks Main article: Bayesian network A simple Bayesian network. Rain influences whether the sprinkler is activated, and both rain and the sprinkler influence whether the grass is wet. A Bayesian network, belief network, or directed acyclic graphical model is a probabilistic graphical model that represents a set of random variables and their conditional independence with a directed acyclic graph (DAG). For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms. Given symptoms, the network can be used to compute the probabilities of the presence of various diseases. Efficient algorithms exist that perform inference and learning. Bayesian networks that model sequences of variables, like speech signals or protein sequences, are called dynamic Bayesian networks. Generalizations of Bayesian networks that can represent and solve decision problems under uncertainty are called influence diagrams. Genetic algorithms Main article: Genetic algorithm A genetic algorithm (GA) is a search algorithm and heuristic technique that mimics the process of natural selection, using methods such as mutation and crossover to generate new genotypes in the hope of finding good solutions to a given problem. In machine learning, genetic algorithms were used in the 1980s and 1990s.[71][72] Conversely, machine learning techniques have been used to improve the performance of genetic and evolutionary algorithms.[73] Training models Usually, machine learning models require a lot of data in order for them to perform well. Usually, when training a machine learning model, one needs to collect a large, representative sample of data from a training set. Data from the training set can be as varied as a corpus of text, a collection of images, and data collected from individual users of a service. Overfitting is something to watch out for when training a machine learning model. Federated learning Main article: Federated learning Federated learning is an adapted form of distributed artificial intelligence to training machine learning models that decentralizes the training process, allowing for users' privacy to be maintained by not needing to send their data to a centralized server. This also increases efficiency by decentralizing the training process to many devices. For example, Gboard uses federated machine learning to train search query prediction models on users' mobile phones without having to send individual searches back to Google.[74] Applications There are many applications for machine learning, including: Agriculture Anatomy Adaptive websites Affective computing Banking Bioinformatics Brain–machine interfaces Cheminformatics Citizen science Computer networks Computer vision Credit-card fraud detection Data quality DNA sequence classification Economics Financial market analysis[75] General game playing Handwriting recognition Information retrieval Insurance Internet fraud detection Linguistics Machine learning control Machine perception Machine translation Marketing Medical diagnosis Natural language processing Natural language understanding Online advertising Optimization Recommender systems Robot locomotion Search engines Sentiment analysis Sequence mining Software engineering Speech recognition Structural health monitoring Syntactic pattern recognition Telecommunication Theorem proving Time series forecasting User behavior analytics In 2006, the media-services provider Netflix held the first "Netflix Prize" competition to find a program to better predict user preferences and improve the accuracy of its existing Cinematch movie recommendation algorithm by at least 10%. A joint team made up of researchers from AT&T Labs-Research in collaboration with the teams Big Chaos and Pragmatic Theory built an ensemble model to win the Grand Prize in 2009 for $1 million.[76] Shortly after the prize was awarded, Netflix realized that viewers' ratings were not the best indicators of their viewing patterns ("everything is a recommendation") and they changed their recommendation engine accordingly.[77] In 2010 The Wall Street Journal wrote about the firm Rebellion Research and their use of machine learning to predict the financial crisis.[78] In 2012, co-founder of Sun Microsystems, Vinod Khosla, predicted that 80% of medical doctors' jobs would be lost in the next two decades to automated machine learning medical diagnostic software.[79] In 2014, it was reported that a machine learning algorithm had been applied in the field of art history to study fine art paintings and that it may have revealed previously unrecognized influences among artists.[80] In 2019 Springer Nature published the first research book created using machine learning.[81] Limitations Although machine learning has been transformative in some fields, machine-learning programs often fail to deliver expected results.[82][83][84] Reasons for this are numerous: lack of (suitable) data, lack of access to the data, data bias, privacy problems, badly chosen tasks and algorithms, wrong tools and people, lack of resources, and evaluation problems.[85] In 2018, a self-driving car from Uber failed to detect a pedestrian, who was killed after a collision.[86] Attempts to use machine learning in healthcare with the IBM Watson system failed to deliver even after years of time and billions of dollars invested.[87][88] Bias Main article: Algorithmic bias Machine learning approaches in particular can suffer from different data biases. A machine learning system trained on current customers only may not be able to predict the needs of new customer groups that are not represented in the training data. When trained on man-made data, machine learning is likely to pick up the same constitutional and unconscious biases already present in society.[89] Language models learned from data have been shown to contain human-like biases.[90][91] Machine learning systems used for criminal risk assessment have been found to be biased against black people.[92][93] In 2015, Google photos would often tag black people as gorillas,[94] and in 2018 this still was not well resolved, but Google reportedly was still using the workaround to remove all gorillas from the training data, and thus was not able to recognize real gorillas at all.[95] Similar issues with recognizing non-white people have been found in many other systems.[96] In 2016, Microsoft tested a chatbot that learned from Twitter, and it quickly picked up racist and sexist language.[97] Because of such challenges, the effective use of machine learning may take longer to be adopted in other domains.[98] Concern for fairness in machine learning, that is, reducing bias in machine learning and propelling its use for human good is increasingly expressed by artificial intelligence scientists, including Fei-Fei Li, who reminds engineers that "There’s nothing artificial about AI...It’s inspired by people, it’s created by people, and—most importantly—it impacts people. It is a powerful tool we are only just beginning to understand, and that is a profound responsibility.”[99] Model assessments Classification of machine learning models can be validated by accuracy estimation techniques like the holdout method, which splits the data in a training and test set (conventionally 2/3 training set and 1/3 test set designation) and evaluates the performance of the training model on the test set. In comparison, the K-fold-cross-validation method randomly partitions the data into K subsets and then K experiments are performed each respectively considering 1 subset for evaluation and the remaining K-1 subsets for training the model. In addition to the holdout and cross-validation methods, bootstrap, which samples n instances with replacement from the dataset, can be used to assess model accuracy.[100] In addition to overall accuracy, investigators frequently report sensitivity and specificity meaning True Positive Rate (TPR) and True Negative Rate (TNR) respectively. Similarly, investigators sometimes report the false positive rate (FPR) as well as the false negative rate (FNR). However, these rates are ratios that fail to reveal their numerators and denominators. The total operating characteristic (TOC) is an effective method to express a model's diagnostic ability. TOC shows the numerators and denominators of the previously mentioned rates, thus TOC provides more information than the commonly used receiver operating characteristic (ROC) and ROC's associated area under the curve (AUC).[101] Ethics Machine learning poses a host of ethical questions. Systems which are trained on datasets collected with biases may exhibit these biases upon use (algorithmic bias), thus digitizing cultural prejudices.[102] For example, using job hiring data from a firm with racist hiring policies may lead to a machine learning system duplicating the bias by scoring job applicants against similarity to previous successful applicants.[103][104] Responsible collection of data and documentation of algorithmic rules used by a system thus is a critical part of machine learning. Because human languages contain biases, machines trained on language corpora will necessarily also learn these biases.[105][106] Other forms of ethical challenges, not related to personal biases, are more seen in health care. There are concerns among health care professionals that these systems might not be designed in the public's interest but as income-generating machines. This is especially true in the United States where there is a long-standing ethical dilemma of improving health care, but also increasing profits. For example, the algorithms could be designed to provide patients with unnecessary tests or medication in which the algorithm's proprietary owners hold stakes. There is huge potential for machine learning in health care to provide professionals a great tool to diagnose, medicate, and even plan recovery paths for patients, but this will not happen until the personal biases mentioned previously, and these "greed" biases are addressed.[107] Hardware Since the 2010s, advances in both machine learning algorithms and computer hardware have led to more efficient methods for training deep neural networks (a particular narrow subdomain of machine learning) that contain many layers of non-linear hidden units.[108] By 2019, graphic processing units (GPUs), often with AI-specific enhancements, had displaced CPUs as the dominant method of training large-scale commercial cloud AI.[109] OpenAI estimated the hardware compute used in the largest deep learning projects from AlexNet (2012) to AlphaZero (2017), and found a 300,000-fold increase in the amount of compute required, with a doubling-time trendline of 3.4 months.[110][111] Software Software suites containing a variety of machine learning algorithms include the following: Free and open-source so

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SpreadCoin October 5, 2014 Introduction In proof-of-work cryptocurrencies new coins are generated by the network through the process of mining. One of the purposes of mining is to protect network from double spending attacks and history rewriting. Miners generate new blocks and check contents of the blocks generated by other peers for conformation to the network rules. However, many miners now delegate all the checking work crucial to cryptocurrency security to pools. This means that pool operators do not have any large hashing power but have control over generation of new blocks. This brings unnecessary centralization to otherwise decentralized system. Controlling more than 50% of mining power allows to perform double-spending attacks with 100% chance of success but even with less than 50% control it is possible to perform attacks which have chances to succeed1 . The core idea of SpreadCoin is to prevent creation of pools and thus make mining more decentralized and the whole system more secure. Pool Prevention In pooled mining miners perform only the work which is necessary to fulfill the proof-of-work requirements and pools take care of block generation and broadcasting and distribute reward among miners according to the shares they submit. In this scheme miner has two alternatives: 1. Solo mining. In this case miner cannot send shares to the pool because they will not be accepted. 2. Pooled mining. Miner’s shares will be accepted by the pool but in the case miner will actually generate a new block its reward will go to the pool which will redistribute it to all miners. This allows organization of pools because miners has no way to cheat and steal generated money. To prevent creation of pools we must remove this possibility so that if pool will be created than miner can mine in a pool, submit shares as usual and get reward for them but in the case of actually finding a block miner can send it directly to the network instead of the pool and get full reward for it. In SpreadCoin mining is organized in such way that miner must know the following things: 1. Private key corresponding to the coinbase transaction. 2. Whole block, not only its header. This ensures that miner can broadcast mined block and spend coins generated in that block. It may seem that it is necessary to know only the private key to spend coinbase transaction. If two conflicting transactions will appear on the network then the one that was broadcasted first will have much higher probability to be included in a block because each peer remembers and retransmits only the first one of the conflicting transactions. If both miner and pool know private key but only pool knows the content of the block than pool can generate and broadcast spending transaction earlier than miner. If both miner 1 Double-spending. Bitcoin Wiki. https://en.bitcoin.it/wiki/Double-spending and pool know content of the block than miner will be the first one who can broadcast block and spending transaction. To prove knowledge of the private key and whole block there are two new fields in the block header: MinerSignature and hashWholeBlock. MinerSignature is a digital signature of all fields of the block header except for the hashWholeBlock. Changing any information in the block requires regeneration of this signature which means that it is necessary to recalculate it during each iteration of the mining process. This implies that miner must be able to sign any arbitrary data. hashWholeBlock is a SHA-256 hash of the block data arranged as follows: Padding ensures that there is no incentive to mine empty blocks without transactions. Padding values are computed using simple algorithm which initializes last 32 bytes (8 uint32) with hashPrevBlock and then goes backward and computes remaining uint32 values using the following recursive formula: 𝐼𝑖 = 𝐼𝑖+3 ∙ 𝐼𝑖+7. This algorithm ensures that there is no efficient way to compute padding values on the fly during hash computation which otherwise could potentially give some advantage to mine empty blocks in certain computing environments. It is important that block is hashed twice. If it was hashed only once then pool could hash the beginning of the block and send resulting hash state to the miners. Each miner would then modify some information in the end of the block and recalculate the hash based on the known state without actual knowledge about what is contained in the beginning of the block. Appending block data to itself make it necessary to know the whole block to recalculate hashWholeBlock. Pool may detect and ban cheating miners. However, many miners may still prefer to cheat so that pool will be completely unusable for honest miners. Miners that have low probability of finding a block will get more profit by stealing reward for accidentally found block even if pool will ban them thereafter. Miners that have enough mining power to find blocks consistently can still connect to a pool and submit shares for some time but steal the first found block. This way they can get both reward for their shares and the actual mined block. Given all this it is expected that no one will create a pool. But even if someone will than it can be countered by releasing stealing miner software which many miners will switch to. Compact Transactions SpreadCoin as well as Bitcoin uses ECDSA signatures. Each address in Bitcoin is a hash of an ECDSA public key. To spend coins sent to an address it is necessary to provide public key matching to that hash and a signature. This results in 139 or 107 bytes for each transaction input script (scriptSig) depending on Block Padding MAX_BLOCK_SIZE Block Padding whether compact public key is used. However, it is possible to recover public key from the signature2 which means that it is not necessary to provide it in transaction input. Together with using compact representation of the signature3 it allows to reduce size of transaction input script from 139 or 107 bytes in Bitcoin to 67 bytes in SpreadCoin. Recovering public key has almost no extra CPU cost compared to the usual signature verification process used in Bitcoin. This is important because the CPU cost of ECDSA signature verification is a bottleneck for Bitcoin transaction processing. Usual output script (scriptPubKey) in Bitcoin looks as follows: OP_DUP OP_HASH160 5bd18804e4bb43a4bb8b6bc88408970bafaf4a38 OP_EQUALVERIFY OP_CHECKSIG In SpreadCoin the semantics of the OP_CHECKSIG instruction was changed to checking signature by hash of the public key (it recovers public key and compares its hash with the provided one). This results in a much simpler script in SpreadCoin: 5bd18804e4bb43a4bb8b6bc88408970bafaf4a38 OP_CHECKSIG This results in additional minor space saving because this script is 3 bytes smaller. Smooth Supply Block reward in Bitcoin is computed using the following formula: 𝑅ℎ = 𝑅0 ∙ 2 −⌊ ℎ 𝑝 ⌋ , where ℎ – block height, 𝑝 – reward halving period, 𝑅0 – initial reward, 𝑅ℎ – reward for block ℎ, ⌊ ⌋ – floor function. This method results in abrupt reward changes near halving points. SpreadCoin uses simple linear interpolation between halving points to make reward decrease much smother. This is achieved by modifying reward using the following formula: 𝑅ℎ ′ = 4 3 (𝑅ℎ − 𝑅ℎ ∙ ℎ mod 𝑝 2𝑝 ). SpreadCoin uses 𝑝 = 2 ∙ 106 as its reward halving period. 2 ECDSA Signatures allow recovery of the public key. Bitcoin Forum. https://bitcointalk.org/?topic=6430.0%29%3F 3 Why the signature is always 65 (1+32+32) bytes long? Bitcoin Stack Exchange. https://bitcoin.stackexchange.com/questions/12554/why-the-signature-is-always-65-13232-bytes-long | NO YEAR 2106 PROBLEM The time stamp field in the block header is now 64 bit instead of 32 bit (Bitcoin) so that much farther date times are possible (>Year 2106) Upcoming features that are in development and will be introduced over the next weeks and months: SERVICENODES A servicenode is a node which runs continuously (24/7) on a server and which provides services within the spreadcoin network. You have to pay a collateral to be able to install a servernode (in return your servicenode will earn a steady income). This collateral is determined by a free market price discovery. (No fix collateral. The price is allowed to fluctuate over time.) COMPETITIVE COLLATERAL Furthermore, to introduce a competitive nature to the servicenodes there will only ever be a limited number of allowed servicenodes worldwide. Since the collateral isn't set in stone, but the amount of servicenodes is fixed, the price of a servicenode will be determined by the participants themselves. It is expected that the price will vary widely over time, which exposes it to the same market forces that hashrate and currency value are exposed to too. SERVICE APPS There are a number of decentralized applications that will run on servicenodes. Most likely those apps will include: 1) "Spread the message" (an in-wallet encrypted messaging system, which allows you to send a message to an SPR address) 2) "Spread the Search" (A decentralized search engine that lets the servicenodes crawl and map the entire internet.) . SPREADX11 SpreadX11 is different from plain X11 by introducing a sophisticated pool prevention mechanism. With SpreadX11 every block header contains additional information (MinerSignature and hashWholeBlock). With the help of this information the protocol ensures that the miner of a new block is always also the first one to know the content of the whole block and the private key to spend the coinbase transaction. (contrary to pool mining where the pool operator is the first one to know those things) So when a miner finds a block, he must himself sign and transmit the block to the network (like solo mining), instead of having a pool handle this for him. This effectively prevents pools by making their rules non-enforceable, since any miner in any assumed pool can always just steal the block reward instead of following the rules set up by the pool. COMPACT TRANSACTIONS SpreadCoin uses a more compact representation for signatures in transactions. SpreadCoin as well as Bitcoin uses ECDSA signatures. While bitcoin keeps a copy of the public key of the corresponding signature around, SpreadCoin ommits this by recovering the public key on the fly directly from the signature. This way it is not necessary to keep the public key of every ECDSA signature in the blockchain, so this leads to *smaller transactions and hence a smaller blockchain (at the cost of a few CPU cycles more). (*reduction in size of transaction from 139 or 107 bytes in Bitcoin to 67 bytes in SpreadCoin.) SMOOTH HALVING Unlike Bitcoin, there are no abrupt reward halvings in SpreadCoin. Block reward is smoothly decreasing over time. UNIQUE DESIGN WITH IN-WALLET VANITYGEN One of the first apps to be built into the wallet is the vanity generator (or vanity gen) which allows anyone to create personalised payment addresses. The easy to use wallet lets you search through trillions of payment addresses allowing you to find one or multiple vanity addresses, which are then stored safely along with the private keys on your own computer - and nowhere else. Searching using the vanity gen is probabilistic, so the amount of time required to find your chosen address patterns depends on how complex the pattern is, the speed of your computer, and a little bit of luck. You can use the vanity gen for a bit of fun, to make your address standout from the crowd or to create a link to a brand, business or other organisation. You can even search for addresses that others might be willing to buy from you. SpreadCoin is a new cryptocurrency which is more decentralized than Bitcoin. It prevents centralization of hashing power in pools, which is one of the main concerns of Bitcoin security. SpreadCoin was fairly launched on 29 July 2014, 9:00 UTC with no premine.

Prepare to Technical Skills Here are the essential skills that a Machine Learning Engineer needs, as mentioned Read me files. Within each group are topics that you should be familiar with. Study Tip: Copy and paste this list into a document and save to your computer for easy referral. Computer Science Fundamentals and Programming Topics Data structures: Lists, stacks, queues, strings, hash maps, vectors, matrices, classes & objects, trees, graphs, etc. Algorithms: Recursion, searching, sorting, optimization, dynamic programming, etc. Computability and complexity: P vs. NP, NP-complete problems, big-O notation, approximate algorithms, etc. Computer architecture: Memory, cache, bandwidth, threads & processes, deadlocks, etc. Probability and Statistics Topics Basic probability: Conditional probability, Bayes rule, likelihood, independence, etc. Probabilistic models: Bayes Nets, Markov Decision Processes, Hidden Markov Models, etc. Statistical measures: Mean, median, mode, variance, population parameters vs. sample statistics etc. Proximity and error metrics: Cosine similarity, mean-squared error, Manhattan and Euclidean distance, log-loss, etc. Distributions and random sampling: Uniform, normal, binomial, Poisson, etc. Analysis methods: ANOVA, hypothesis testing, factor analysis, etc. Data Modeling and Evaluation Topics Data preprocessing: Munging/wrangling, transforming, aggregating, etc. Pattern recognition: Correlations, clusters, trends, outliers & anomalies, etc. Dimensionality reduction: Eigenvectors, Principal Component Analysis, etc. Prediction: Classification, regression, sequence prediction, etc.; suitable error/accuracy metrics. Evaluation: Training-testing split, sequential vs. randomized cross-validation, etc. Applying Machine Learning Algorithms and Libraries Topics Models: Parametric vs. nonparametric, decision tree, nearest neighbor, neural net, support vector machine, ensemble of multiple models, etc. Learning procedure: Linear regression, gradient descent, genetic algorithms, bagging, boosting, and other model-specific methods; regularization, hyperparameter tuning, etc. Tradeoffs and gotchas: Relative advantages and disadvantages, bias and variance, overfitting and underfitting, vanishing/exploding gradients, missing data, data leakage, etc. Software Engineering and System Design Topics Software interface: Library calls, REST APIs, data collection endpoints, database queries, etc. User interface: Capturing user inputs & application events, displaying results & visualization, etc. Scalability: Map-reduce, distributed processing, etc. Deployment: Cloud hosting, containers & instances, microservices, etc. Move on to the final lesson of this course to find lots of sample practice questions for each topic!

# All paths in this configuration file are relative to Dynmap's data-folder: minecraft_server/dynmap/ # All map templates are defined in the templates directory # To use the HDMap very-low-res (2 ppb) map templates as world defaults, set value to vlowres # The definitions of these templates are in normal-vlowres.txt, nether-vlowres.txt, and the_end-vlowres.txt # To use the HDMap low-res (4 ppb) map templates as world defaults, set value to lowres # The definitions of these templates are in normal-lowres.txt, nether-lowres.txt, and the_end-lowres.txt # To use the HDMap hi-res (16 ppb) map templates (these can take a VERY long time for initial fullrender), set value to hires # The definitions of these templates are in normal-hires.txt, nether-hires.txt, and the_end-hires.txt # To use the HDMap low-res (4 ppb) map templates, with support for boosting resolution selectively to hi-res (16 ppb), set value to low_boost_hi # The definitions of these templates are in normal-low_boost_hi.txt, nether-low_boost_hi.txt, and the_end-low_boost_hi.txt # To use the HDMap hi-res (16 ppb) map templates, with support for boosting resolution selectively to vhi-res (32 ppb), set value to hi_boost_vhi # The definitions of these templates are in normal-hi_boost_vhi.txt, nether-hi_boost_vhi.txt, and the_end-hi_boost_vhi.txt # To use the HDMap hi-res (16 ppb) map templates, with support for boosting resolution selectively to xhi-res (64 ppb), set value to hi_boost_xhi # The definitions of these templates are in normal-hi_boost_xhi.txt, nether-hi_boost_xhi.txt, and the_end-hi_boost_xhi.txt deftemplatesuffix: lowres # Map storage scheme: only uncommoent one 'type' value # filetree: classic and default scheme: tree of files, with all map data under the directory indicated by 'tilespath' setting # sqlite: single SQLite database file (this can get VERY BIG), located at 'dbfile' setting (default is file dynmap.db in data directory) # mysql: MySQL database, at hostname:port in database, accessed via userid with password # mariadb: MariaDB database, at hostname:port in database, accessed via userid with password # postgres: PostgreSQL database, at hostname:port in database, accessed via userid with password storage: # Filetree storage (standard tree of image files for maps) type: filetree # SQLite db for map storage (uses dbfile as storage location) #type: sqlite #dbfile: dynmap.db # MySQL DB for map storage (at 'hostname':'port' in database 'database' using user 'userid' password 'password' and table prefix 'prefix' #type: mysql #hostname: localhost #port: 3306 #database: dynmap #userid: dynmap #password: dynmap #prefix: "" components: - class: org.dynmap.ClientConfigurationComponent - class: org.dynmap.InternalClientUpdateComponent sendhealth: true sendposition: true allowwebchat: true webchat-interval: 5 hidewebchatip: false trustclientname: false includehiddenplayers: false # (optional) if true, color codes in player display names are used use-name-colors: false # (optional) if true, player login IDs will be used for web chat when their IPs match use-player-login-ip: true # (optional) if use-player-login-ip is true, setting this to true will cause chat messages not matching a known player IP to be ignored require-player-login-ip: false # (optional) block player login IDs that are banned from chatting block-banned-player-chat: true # Require login for web-to-server chat (requires login-enabled: true) webchat-requires-login: false # If set to true, users must have dynmap.webchat permission in order to chat webchat-permissions: false # Limit length of single chat messages chatlengthlimit: 256 # # Optional - make players hidden when they are inside/underground/in shadows (#=light level: 0=full shadow,15=sky) # hideifshadow: 4 # # Optional - make player hidden when they are under cover (#=sky light level,0=underground,15=open to sky) # hideifundercover: 14 # # (Optional) if true, players that are crouching/sneaking will be hidden hideifsneaking: false # If true, player positions/status is protected (login with ID with dynmap.playermarkers.seeall permission required for info other than self) protected-player-info: false # If true, hide players with invisibility potion effects active hide-if-invisiblity-potion: true # If true, player names are not shown on map, chat, list hidenames: false #- class: org.dynmap.JsonFileClientUpdateComponent # writeinterval: 1 # sendhealth: true # sendposition: true # allowwebchat: true # webchat-interval: 5 # hidewebchatip: false # includehiddenplayers: false # use-name-colors: false # use-player-login-ip: false # require-player-login-ip: false # block-banned-player-chat: true # hideifshadow: 0 # hideifundercover: 0 # hideifsneaking: false # # Require login for web-to-server chat (requires login-enabled: true) # webchat-requires-login: false # # If set to true, users must have dynmap.webchat permission in order to chat # webchat-permissions: false # # Limit length of single chat messages # chatlengthlimit: 256 # hide-if-invisiblity-potion: true # hidenames: false - class: org.dynmap.SimpleWebChatComponent allowchat: true # If true, web UI users can supply name for chat using 'playername' URL parameter. 'trustclientname' must also be set true. allowurlname: false # Note: this component is needed for the dmarker commands, and for the Marker API to be available to other plugins - class: org.dynmap.MarkersComponent type: markers showlabel: false enablesigns: false # Default marker set for sign markers default-sign-set: markers # (optional) add spawn point markers to standard marker layer showspawn: true spawnicon: world spawnlabel: "Spawn" # (optional) layer for showing offline player's positions (for 'maxofflinetime' minutes after logoff) showofflineplayers: false offlinelabel: "Offline" offlineicon: offlineuser offlinehidebydefault: true offlineminzoom: 0 maxofflinetime: 30 # (optional) layer for showing player's spawn beds showspawnbeds: false spawnbedlabel: "Spawn Beds" spawnbedicon: bed spawnbedhidebydefault: true spawnbedminzoom: 0 spawnbedformat: "%name%'s bed" # (optional) Show world border (vanilla 1.8+) showworldborder: true worldborderlabel: "Border" - class: org.dynmap.ClientComponent type: chat allowurlname: false - class: org.dynmap.ClientComponent type: chatballoon focuschatballoons: false - class: org.dynmap.ClientComponent type: chatbox showplayerfaces: true messagettl: 5 # Optional: set number of lines in scrollable message history: if set, messagettl is not used to age out messages #scrollback: 100 # Optional: set maximum number of lines visible for chatbox #visiblelines: 10 # Optional: send push button sendbutton: false - class: org.dynmap.ClientComponent type: playermarkers showplayerfaces: true showplayerhealth: true # If true, show player body too (only valid if showplayerfaces=true showplayerbody: false # Option to make player faces small - don't use with showplayerhealth smallplayerfaces: false # Optional - make player faces layer hidden by default hidebydefault: false # Optional - ordering priority in layer menu (low goes before high - default is 0) layerprio: 0 # Optional - label for player marker layer (default is 'Players') label: "Players" #- class: org.dynmap.ClientComponent # type: digitalclock - class: org.dynmap.ClientComponent type: link - class: org.dynmap.ClientComponent type: timeofdayclock showdigitalclock: true #showweather: true # Mouse pointer world coordinate display - class: org.dynmap.ClientComponent type: coord label: "Location" hidey: false show-mcr: false show-chunk: false # Note: more than one logo component can be defined #- class: org.dynmap.ClientComponent # type: logo # text: "Dynmap" # #logourl: "images/block_surface.png" # linkurl: "http://forums.bukkit.org/threads/dynmap.489/" # # Valid positions: top-left, top-right, bottom-left, bottom-right # position: bottom-right #- class: org.dynmap.ClientComponent # type: inactive # timeout: 1800 # in seconds (1800 seconds = 30 minutes) # redirecturl: inactive.html # #showmessage: 'You were inactive for too long.' #- class: org.dynmap.TestComponent # stuff: "This is some configuration-value" # Treat hiddenplayers.txt as a whitelist for players to be shown on the map? (Default false) display-whitelist: false # How often a tile gets rendered (in seconds). renderinterval: 1 # How many tiles on update queue before accelerate render interval renderacceleratethreshold: 60 # How often to render tiles when backlog is above renderacceleratethreshold renderaccelerateinterval: 0.2 # How many update tiles to work on at once (if not defined, default is 1/2 the number of cores) tiles-rendered-at-once: 2 # If true, use normal priority threads for rendering (versus low priority) - this can keep rendering # from starving on busy Windows boxes (Linux JVMs pretty much ignore thread priority), but may result # in more competition for CPU resources with other processes usenormalthreadpriority: true # Save and restore pending tile renders - prevents their loss on server shutdown or /reload saverestorepending: true # Save period for pending jobs (in seconds): periodic saving for crash recovery of jobs save-pending-period: 900 # Zoom-out tile update period - how often to scan for and process tile updates into zoom-out tiles (in seconds) zoomoutperiod: 30 # Control whether zoom out tiles are validated on startup (can be needed if zoomout processing is interrupted, but can be expensive on large maps) initial-zoomout-validate: true # Default delay on processing of updated tiles, in seconds. This can reduce potentially expensive re-rendering # of frequently updated tiles (such as due to machines, pistons, quarries or other automation). Values can # also be set on individual worlds and individual maps. tileupdatedelay: 30 # Tile hashing is used to minimize tile file updates when no changes have occurred - set to false to disable enabletilehash: true # Optional - hide ores: render as normal stone (so that they aren't revealed by maps) #hideores: true # Optional - enabled BetterGrass style rendering of grass and snow block sides #better-grass: true # Optional - enable smooth lighting by default on all maps supporting it (can be set per map as lighting option) smooth-lighting: true # Optional - use world provider lighting table (good for custom worlds with custom lighting curves, like nether) # false=classic Dynmap lighting curve use-brightness-table: true # Optional - render specific block names using the textures and models of another block name: can be used to hide/disguise specific # blocks (e.g. make ores look like stone, hide chests) or to provide simple support for rendering unsupported custom blocks block-alias: # "minecraft:quartz_ore": "stone" # "diamond_ore": "coal_ore" # Default image format for HDMaps (png, jpg, jpg-q75, jpg-q80, jpg-q85, jpg-q90, jpg-q95, jpg-q100, webp, webp-q75, webp-q80, webp-q85, webp-q90, webp-q95, webp-q100), # Note: any webp format requires the presence of the 'webp command line tools' (cwebp, dwebp) (https://developers.google.com/speed/webp/download) # # Has no effect on maps with explicit format settings image-format: jpg-q90 # If cwebp or dwebp are not on the PATH, use these settings to provide their full path. Do not use these settings if the tools are on the PATH # For Windows, include .exe # #cwebpPath: /usr/bin/cwebp #dwebpPath: /usr/bin/dwebp # use-generated-textures: if true, use generated textures (same as client); false is static water/lava textures # correct-water-lighting: if true, use corrected water lighting (same as client); false is legacy water (darker) # transparent-leaves: if true, leaves are transparent (lighting-wise): false is needed for some Spout versions that break lighting on leaf blocks use-generated-textures: true correct-water-lighting: true transparent-leaves: true # ctm-support: if true, Connected Texture Mod (CTM) in texture packs is enabled (default) ctm-support: true # custom-colors-support: if true, Custom Colors in texture packs is enabled (default) custom-colors-support: true # Control loading of player faces (if set to false, skins are never fetched) #fetchskins: false # Control updating of player faces, once loaded (if faces are being managed by other apps or manually) #refreshskins: false # Customize URL used for fetching player skins (%player% is macro for name) skin-url: "http://skins.minecraft.net/MinecraftSkins/%player%.png" # Control behavior for new (1.0+) compass orientation (sunrise moved 90 degrees: east is now what used to be south) # default is 'newrose' (preserve pre-1.0 maps, rotate rose) # 'newnorth' is used to rotate maps and rose (requires fullrender of any HDMap map - same as 'newrose' for FlatMap or KzedMap) compass-mode: newnorth # Triggers for automatic updates : blockupdate-with-id is debug for breaking down updates by ID:meta # To disable, set just 'none' and comment/delete the rest render-triggers: - blockupdate #- blockupdate-with-id #- lightingupdate - chunkpopulate - chunkgenerate #- none # Title for the web page - if not specified, defaults to the server's name (unless it is the default of 'Unknown Server') #webpage-title: "My Awesome Server Map" # The path where the tile-files are placed. tilespath: web/tiles # The path where the web-files are located. webpath: web # The path were the /dynmapexp command exports OBJ ZIP files exportpath: export # The network-interface the webserver will bind to (0.0.0.0 for all interfaces, 127.0.0.1 for only local access). # If not set, uses same setting as server in server.properties (or 0.0.0.0 if not specified) #webserver-bindaddress: 0.0.0.0 # The TCP-port the webserver will listen on. webserver-port: 8123 # Maximum concurrent session on internal web server - limits resources used in Bukkit server max-sessions: 30 # Disables Webserver portion of Dynmap (Advanced users only) disable-webserver: false # Enable/disable having the web server allow symbolic links (true=compatible with existing code, false=more secure (default)) allow-symlinks: true # Enable login support login-enabled: false # Require login to access website (requires login-enabled: true) login-required: false # Period between tile renders for fullrender, in seconds (non-zero to pace fullrenders, lessen CPU load) timesliceinterval: 0.0 # Maximum chunk loads per server tick (1/20th of a second) - reducing this below 90 will impact render performance, but also will reduce server thread load maxchunkspertick: 200 # Progress report interval for fullrender/radiusrender, in tiles. Must be 100 or greater progressloginterval: 100 # Parallel fullrender: if defined, number of concurrent threads used for fullrender or radiusrender # Note: setting this will result in much more intensive CPU use, some additional memory use. Caution should be used when # setting this to equal or exceed the number of physical cores on the system. #parallelrendercnt: 4 # Interval the browser should poll for updates. updaterate: 2000 # If nonzero, server will pause fullrender/radiusrender processing when 'fullrenderplayerlimit' or more users are logged in fullrenderplayerlimit: 0 # If nonzero, server will pause update render processing when 'updateplayerlimit' or more users are logged in updateplayerlimit: 0 # Target limit on server thread use - msec per tick per-tick-time-limit: 50 # If TPS of server is below this setting, update renders processing is paused update-min-tps: 18.0 # If TPS of server is below this setting, full/radius renders processing is paused fullrender-min-tps: 18.0 # If TPS of server is below this setting, zoom out processing is paused zoomout-min-tps: 18.0 showplayerfacesinmenu: true # Control whether players that are hidden or not on current map are grayed out (true=yes) grayplayerswhenhidden: true # Set sidebaropened: 'true' to pin menu sidebar opened permanently, 'pinned' to default the sidebar to pinned, but allow it to unpin #sidebaropened: true # Customized HTTP response headers - add 'id: value' pairs to all HTTP response headers (internal web server only) #http-response-headers: # Access-Control-Allow-Origin: "my-domain.com" # X-Custom-Header-Of-Mine: "MyHeaderValue" # Trusted proxies for web server - which proxy addresses are trusted to supply valid X-Forwarded-For fields trusted-proxies: - "127.0.0.1" - "0:0:0:0:0:0:0:1" joinmessage: "%playername% joined" quitmessage: "%playername% quit" spammessage: "You may only chat once every %interval% seconds." # format for messages from web: %playername% substitutes sender ID (typically IP), %message% includes text webmsgformat: "&color;2[WEB] %playername%: &color;f%message%" # Control whether layer control is presented on the UI (default is true) showlayercontrol: true # Enable checking for banned IPs via banned-ips.txt (internal web server only) check-banned-ips: true # Default selection when map page is loaded defaultzoom: 0 defaultworld: world defaultmap: flat # (optional) Zoom level and map to switch to when following a player, if possible #followzoom: 3 #followmap: surface # If true, make persistent record of IP addresses used by player logins, to support web IP to player matching persist-ids-by-ip: true # If true, map text to cyrillic cyrillic-support: false # Messages to customize msg: maptypes: "Map Types" players: "Players" chatrequireslogin: "Chat Requires Login" chatnotallowed: "You are not permitted to send chat messages" hiddennamejoin: "Player joined" hiddennamequit: "Player quit" # URL for client configuration (only need to be tailored for proxies or other non-standard configurations) url: # configuration URL #configuration: "up/configuration" # update URL #update: "up/world/{world}/{timestamp}" # sendmessage URL #sendmessage: "up/sendmessage" # login URL #login: "up/login" # register URL #register: "up/register" # tiles base URL #tiles: "tiles/" # markers base URL #markers: "tiles/" # Snapshot cache size, in chunks snapshotcachesize: 500 # Snapshot cache uses soft references (true), else weak references (false) soft-ref-cache: true # Player enter/exit title messages for map markers # # Processing period - how often to check player positions vs markers - default is 1000ms (1 second) #enterexitperiod: 1000 # Title message fade in time, in ticks (0.05 second intervals) - default is 10 (1/2 second) #titleFadeIn: 10 # Title message stay time, in ticks (0.05 second intervals) - default is 70 (3.5 seconds) #titleStay: 70 # Title message fade out time, in ticks (0.05 seocnd intervals) - default is 20 (1 second) #titleFadeOut: 20 # Enter/exit messages use on screen titles (true - default), if false chat messages are sent instead #enterexitUseTitle: true # Set true if new enter messages should supercede pending exit messages (vs being queued in order), default false #enterReplacesExits: true # Set to true to enable verbose startup messages - can help with debugging map configuration problems # Set to false for a much quieter startup log verbose: false # Enables debugging. #debuggers: # - class: org.dynmap.debug.LogDebugger # Debug: dump blocks missing render data dump-missing-blocks: false

a Hadoop Map Reduce application that retrieves data/articles related to sports from sources like NY Times, Commoncrawl, and Twitter and creates a word cloud of most frequently occurring words. Python scripts are developed for gathering data and processing on a Hadoop MR infrastructure. Angular with D3.js is used to create an interactive web app that displays the word cloud

Map Reduce Project that works on weather data and process it , the final outcome of the project can be processed further to find similarities on different weather stations :-)

MapReduce大数据实验课程资料

I had the data set which was an anonymized Web server log file from a public relations company whose clients were DVD distributors. My goal was to write my Mappers and Reducers from scratch using Python and to answer to some questions about this dataset. I did the data processing on my your pseudo-distributed cluster (I used a virtual machine).

This XML file does not appear to have any style information associated with it. The document tree is shown below. <xsd:schema xmlns="http://www.springframework.org/schema/mvc" xmlns:xsd="http://www.w3.org/2001/XMLSchema" xmlns:beans="http://www.springframework.org/schema/beans" xmlns:tool="http://www.springframework.org/schema/tool" targetNamespace="http://www.springframework.org/schema/mvc" elementFormDefault="qualified" attributeFormDefault="unqualified"> <xsd:import namespace="http://www.springframework.org/schema/beans" schemaLocation="https://www.springframework.org/schema/beans/spring-beans-4.3.xsd"/> <xsd:import namespace="http://www.springframework.org/schema/tool" schemaLocation="https://www.springframework.org/schema/tool/spring-tool-4.3.xsd"/> <xsd:element name="annotation-driven"> <xsd:annotation> <xsd:documentation source="java:org.springframework.web.servlet.mvc.method.annotation.RequestMappingHandlerAdapter"> <![CDATA[ Configures the annotation-driven Spring MVC Controller programming model. Note that this tag works in Web MVC only, not in Portlet MVC! See org.springframework.web.servlet.config.annotation.EnableWebMvc javadoc for details on code-based alternatives to enabling annotation-driven Spring MVC support. ]]> </xsd:documentation> </xsd:annotation> <xsd:complexType> <xsd:all minOccurs="0"> <xsd:element name="path-matching" minOccurs="0"> <xsd:annotation> <xsd:documentation> <![CDATA[ Configures the path matching part of the Spring MVC Controller programming model. Like annotation-driven, code-based alternatives are also documented in EnableWebMvc javadoc. ]]> </xsd:documentation> </xsd:annotation> <xsd:complexType> <xsd:attribute name="suffix-pattern" type="xsd:boolean"> <xsd:annotation> <xsd:documentation> <![CDATA[ Whether to use suffix pattern match (".*") when matching patterns to requests. If enabled a method mapped to "/users" also matches to "/users.*". The default value is true. ]]> </xsd:documentation> </xsd:annotation> </xsd:attribute> <xsd:attribute name="trailing-slash" type="xsd:boolean"> <xsd:annotation> <xsd:documentation> <![CDATA[ Whether to match to URLs irrespective of the presence of a trailing slash. If enabled a method mapped to "/users" also matches to "/users/". The default value is true. ]]> </xsd:documentation> </xsd:annotation> </xsd:attribute> <xsd:attribute name="registered-suffixes-only" type="xsd:boolean"> <xsd:annotation> <xsd:documentation> <![CDATA[ Whether suffix pattern matching should work only against path extensions explicitly registered when you configure content negotiation. This is generally recommended to reduce ambiguity and to avoid issues such as when a "." appears in the path for other reasons. The default value is false. ]]> </xsd:documentation> </xsd:annotation> </xsd:attribute> <xsd:attribute name="path-helper" type="xsd:string"> <xsd:annotation> <xsd:documentation> <![CDATA[ The bean name of the UrlPathHelper to use for resolution of lookup paths. Use this to override the default UrlPathHelper with a custom subclass, or to share common UrlPathHelper settings across multiple HandlerMappings and MethodNameResolvers. ]]> </xsd:documentation> </xsd:annotation> </xsd:attribute> <xsd:attribute name="path-matcher" type="xsd:string"> <xsd:annotation> <xsd:documentation> <![CDATA[ The bean name of the PathMatcher implementation to use for matching URL paths against registered URL patterns. Default is AntPathMatcher. ]]> </xsd:documentation> </xsd:annotation> </xsd:attribute> </xsd:complexType> </xsd:element> <xsd:element name="message-converters" minOccurs="0"> <xsd:annotation> <xsd:documentation> <![CDATA[ Configures one or more HttpMessageConverter types to use for converting @RequestBody method parameters and @ResponseBody method return values. Using this configuration element is optional. HttpMessageConverter registrations provided here will take precedence over HttpMessageConverter types registered by default. Also see the register-defaults attribute if you want to turn off default registrations entirely. ]]> </xsd:documentation> </xsd:annotation> <xsd:complexType> <xsd:sequence> <xsd:choice maxOccurs="unbounded"> <xsd:element ref="beans:bean"> <xsd:annotation> <xsd:documentation> <![CDATA[ An HttpMessageConverter bean definition. ]]> </xsd:documentation> </xsd:annotation> </xsd:element> <xsd:element ref="beans:ref"> <xsd:annotation> <xsd:documentation> <![CDATA[ A reference to an HttpMessageConverter bean. ]]> </xsd:documentation> </xsd:annotation> </xsd:element> </xsd:choice> </xsd:sequence> <xsd:attribute name="register-defaults" type="xsd:boolean" default="true"> <xsd:annotation> <xsd:documentation> <![CDATA[ Whether or not default HttpMessageConverter registrations should be added in addition to the ones provided within this element. ]]> </xsd:documentation> </xsd:annotation> </xsd:attribute> </xsd:complexType> </xsd:element> <xsd:element name="argument-resolvers" minOccurs="0"> <xsd:annotation> <xsd:documentation> <![CDATA[ Configures HandlerMethodArgumentResolver types to support custom controller method argument types. Using this option does not override the built-in support for resolving handler method arguments. To customize the built-in support for argument resolution configure RequestMappingHandlerAdapter directly. ]]> </xsd:documentation> </xsd:annotation> <xsd:complexType> <xsd:choice minOccurs="1" maxOccurs="unbounded"> <xsd:element ref="beans:bean" minOccurs="0" maxOccurs="unbounded"> <xsd:annotation> <xsd:documentation> <![CDATA[ The HandlerMethodArgumentResolver (or WebArgumentResolver for backwards compatibility) bean definition. ]]> </xsd:documentation> </xsd:annotation> </xsd:element> <xsd:element ref="beans:ref" minOccurs="0" maxOccurs="unbounded"> <xsd:annotation> <xsd:documentation> <![CDATA[ A reference to a HandlerMethodArgumentResolver bean definition. ]]> </xsd:documentation> <xsd:appinfo> <tool:annotation kind="ref"> <tool:expected-type type="java:org.springframework.web.method.support.HandlerMethodArgumentResolver"/> </tool:annotation> </xsd:appinfo> </xsd:annotation> </xsd:element> </xsd:choice> </xsd:complexType> </xsd:element> <xsd:element name="return-value-handlers" minOccurs="0"> <xsd:annotation> <xsd:documentation> <![CDATA[ Configures HandlerMethodReturnValueHandler types to support custom controller method return value handling. Using this option does not override the built-in support for handling return values. To customize the built-in support for handling return values configure RequestMappingHandlerAdapter directly. ]]> </xsd:documentation> </xsd:annotation> <xsd:complexType> <xsd:choice minOccurs="1" maxOccurs="unbounded"> <xsd:element ref="beans:bean" minOccurs="0" maxOccurs="unbounded"> <xsd:annotation> <xsd:documentation> <![CDATA[ The HandlerMethodReturnValueHandler bean definition. ]]> </xsd:documentation> </xsd:annotation> </xsd:element> <xsd:element ref="beans:ref" minOccurs="0" maxOccurs="unbounded"> <xsd:annotation> <xsd:documentation> <![CDATA[ A reference to a HandlerMethodReturnValueHandler bean definition. ]]> </xsd:documentation> <xsd:appinfo> <tool:annotation kind="ref"> <tool:expected-type type="java:org.springframework.web.method.support.HandlerMethodReturnValueHandler"/> </tool:annotation> </xsd:appinfo> </xsd:annotation> </xsd:element> </xsd:choice> </xsd:complexType> </xsd:element> <xsd:element name="async-support" minOccurs="0"> <xsd:annotation> <xsd:documentation> <![CDATA[ Configure options for asynchronous request processing. ]]> </xsd:documentation> </xsd:annotation> <xsd:complexType> <xsd:all minOccurs="0"> <xsd:element name="callable-interceptors" minOccurs="0"> <xsd:annotation> <xsd:documentation> <![CDATA[ The ordered set of interceptors that intercept the lifecycle of concurrently executed requests, which start after a controller returns a java.util.concurrent.Callable. ]]> </xsd:documentation> </xsd:annotation> <xsd:complexType> <xsd:sequence> <xsd:element ref="beans:bean" minOccurs="1" maxOccurs="unbounded"> <xsd:annotation> <xsd:documentation> <![CDATA[ Registers a CallableProcessingInterceptor. ]]> </xsd:documentation> </xsd:annotation> </xsd:element> </xsd:sequence> </xsd:complexType> </xsd:element> <xsd:element name="deferred-result-interceptors" minOccurs="0"> <xsd:annotation> <xsd:documentation> <![CDATA[ The ordered set of interceptors that intercept the lifecycle of concurrently executed requests, which start after a controller returns a DeferredResult. ]]> </xsd:documentation> </xsd:annotation> <xsd:complexType> <xsd:sequence> <xsd:element ref="beans:bean" minOccurs="1" maxOccurs="unbounded"> <xsd:annotation> <xsd:documentation> <![CDATA[ Registers a DeferredResultProcessingInterceptor. ]]> </xsd:documentation> </xsd:annotation> </xsd:element> </xsd:sequence> </xsd:complexType> </xsd:element> </xsd:all> <xsd:attribute name="task-executor" type="xsd:string"> <xsd:annotation> <xsd:documentation source="java:org.springframework.core.task.AsyncTaskExecutor"> <![CDATA[ The bean name of a default AsyncTaskExecutor to use when a controller method returns a {@link Callable}. Controller methods can override this default on a per-request basis by returning an AsyncTask. By default, a SimpleAsyncTaskExecutor is used which does not re-use threads and is not recommended for production. ]]> </xsd:documentation> <xsd:appinfo> <tool:annotation kind="ref"> <tool:expected-type type="java:org.springframework.core.task.AsyncTaskExecutor"/> </tool:annotation> </xsd:appinfo> </xsd:annotation> </xsd:attribute> <xsd:attribute name="default-timeout" type="xsd:long"> <xsd:annotation> <xsd:documentation> <![CDATA[ Specify the amount of time, in milliseconds, before asynchronous request handling times out. In Servlet 3, the timeout begins after the main request processing thread has exited and ends when the request is dispatched again for further processing of the concurrently produced result. If this value is not set, the default timeout of the underlying implementation is used, e.g. 10 seconds on Tomcat with Servlet 3. ]]> </xsd:documentation> </xsd:annotation> </xsd:attribute> </xsd:complexType> </xsd:element> </xsd:all> <xsd:attribute name="conversion-service" type="xsd:string"> <xsd:annotation> <xsd:documentation source="java:org.springframework.core.convert.ConversionService"> <![CDATA[ The bean name of the ConversionService that is to be used for type conversion during field binding. This attribute is not required, and only needs to be specified if custom converters need to be configured. If not specified, a default FormattingConversionService is registered with converters to/from common value types. ]]> </xsd:documentation> <xsd:appinfo> <tool:annotation kind="ref"> <tool:expected-type type="java:org.springframework.core.convert.ConversionService"/> </tool:annotation> </xsd:appinfo> </xsd:annotation> </xsd:attribute> <xsd:attribute name="validator" type="xsd:string"> <xsd:annotation> <xsd:documentation source="java:org.springframework.validation.Validator"> <![CDATA[ The bean name of the Validator that is to be used to validate Controller model objects. This attribute is not required, and only needs to be specified if a custom Validator needs to be configured. If not specified, JSR-303 validation will be installed if a JSR-303 provider is present on the classpath. ]]> </xsd:documentation> <xsd:appinfo> <tool:annotation kind="ref"> <tool:expected-type type="java:org.springframework.validation.Validator"/> </tool:annotation> </xsd:appinfo> </xsd:annotation> </xsd:attribute> <xsd:attribute name="content-negotiation-manager" type="xsd:string"> <xsd:annotation> <xsd:documentation source="java:org.springframework.web.accept.ContentNegotiationManager"> <![CDATA[ The bean name of a ContentNegotiationManager that is to be used to determine requested media types. If not specified, a default ContentNegotiationManager is configured that checks the request path extension first and the "Accept" header second where path extensions such as ".json", ".xml", ".atom", and ".rss" are recognized if Jackson, JAXB2, or the Rome libraries are available. As a fallback option, the path extension is also used to perform a lookup through the ServletContext and the Java Activation Framework (if available). ]]> </xsd:documentation> <xsd:appinfo> <tool:annotation kind="ref"> <tool:expected-type type="java:org.springframework.web.accept.ContentNegotiationManager"/> </tool:annotation> </xsd:appinfo> </xsd:annotation> </xsd:attribute> <xsd:attribute name="message-codes-resolver" type="xsd:string"> <xsd:annotation> <xsd:documentation> <![CDATA[ The bean name of a MessageCodesResolver to use to build message codes from data binding and validation error codes. This attribute is not required. If not specified the DefaultMessageCodesResolver is used. ]]> </xsd:documentation> <xsd:appinfo> <tool:annotation kind="ref"> <tool:expected-type type="java:org.springframework.validation.MessageCodesResolver"/> </tool:annotation> </xsd:appinfo> </xsd:annotation> </xsd:attribute> <xsd:attribute name="enable-matrix-variables" type="xsd:boolean"> <xsd:annotation> <xsd:documentation> <![CDATA[ Matrix variables can appear in any path segment, each matrix variable separated with a ";" (semicolon). For example "/cars;color=red;year=2012". By default, they're removed from the URL. If this property is set to true, matrix variables are not removed from the URL, and the request mapping pattern must use URI variable in path segments where matrix variables are expected. For example "/{cars}". Matrix variables can then be injected into a controller method with @MatrixVariable. ]]> </xsd:documentation> </xsd:annotation> </xsd:attribute> <xsd:attribute name="ignore-default-model-on-redirect" type="xsd:boolean"> <xsd:annotation> <xsd:documentation> <![CDATA[ By default, the content of the "default" model is used both during rendering and redirect scenarios. Alternatively a controller method can declare a RedirectAttributes argument and use it to provide attributes for a redirect. Setting this flag to true ensures the "default" model is never used in a redirect scenario even if a RedirectAttributes argument is not declared. Setting it to false means the "default" model may be used in a redirect if the controller method doesn't declare a RedirectAttributes argument. The default setting is false but new applications should consider setting it to true. ]]> </xsd:documentation> </xsd:annotation> </xsd:attribute> </xsd:complexType> </xsd:element> <xsd:complexType name="content-version-strategy"> <xsd:annotation> <xsd:documentation source="org.springframework.web.servlet.resource.ContentVersionStrategy"> <![CDATA[ A VersionStrategy that calculates an Hex MD5 hashes from the content of the resource and appends it to the file name, e.g. "styles/main-e36d2e05253c6c7085a91522ce43a0b4.css". ]]> </xsd:documentation> </xsd:annotation> <xsd:attribute name="patterns" type="xsd:string" use="required"/> </xsd:complexType> <xsd:complexType name="fixed-version-strategy"> <xsd:annotation> <xsd:documentation source="org.springframework.web.servlet.resource.FixedVersionStrategy"> <![CDATA[ A VersionStrategy that relies on a fixed version applied as a request path prefix, e.g. reduced SHA, version name, release date, etc. ]]> </xsd:documentation> </xsd:annotation> <xsd:attribute name="version" type="xsd:string" use="required"/> <xsd:attribute name="patterns" type="xsd:string" use="required"/> </xsd:complexType> <xsd:complexType name="resource-version-strategy"> <xsd:annotation> <xsd:documentation source="org.springframework.web.servlet.resource.VersionStrategy"> <![CDATA[ A strategy for extracting and embedding a resource version in its URL path. ]]> </xsd:documentation> </xsd:annotation> <xsd:choice minOccurs="1" maxOccurs="1"> <xsd:element ref="beans:bean"> <xsd:annotation> <xsd:documentation source="org.springframework.web.servlet.resource.VersionStrategy"> <![CDATA[ A VersionStrategy bean definition. ]]> </xsd:documentation> </xsd:annotation> </xsd:element> <xsd:element ref="beans:ref"> <xsd:annotation> <xsd:documentation source="org.springframework.web.servlet.resource.VersionStrategy"> <![CDATA[ A reference to a VersionStrategy bean. ]]> </xsd:documentation> </xsd:annotation> </xsd:element> </xsd:choice> <xsd:attribute name="patterns" type="xsd:string" use="required"/> </xsd:complexType> <xsd:complexType name="version-resolver"> <xsd:annotation> <xsd:documentation source="org.springframework.web.servlet.resource.VersionResourceResolver"> <![CDATA[ Resolves request paths containing a version string that can be used as part of an HTTP caching strategy in which a resource is cached with a far future date (e.g. 1 year) and cached until the version, and therefore the URL, is changed. ]]> </xsd:documentation> </xsd:annotation> <xsd:choice maxOccurs="unbounded"> <xsd:element type="content-version-strategy" name="content-version-strategy"/> <xsd:element type="fixed-version-strategy" name="fixed-version-strategy"/> <xsd:element type="resource-version-strategy" name="version-strategy"/> </xsd:choice> </xsd:complexType> <xsd:complexType name="resource-resolvers"> <xsd:annotation> <xsd:documentation source="org.springframework.web.servlet.resource.ResourceResolver"> <![CDATA[ A list of ResourceResolver beans definition and references. A ResourceResolver provides mechanisms for resolving an incoming request to an actual Resource and for obtaining the public URL path that clients should use when requesting the resource. ]]> </xsd:documentation> </xsd:annotation> <xsd:sequence> <xsd:choice maxOccurs="unbounded"> <xsd:element type="version-resolver" name="version-resolver"/> <xsd:element ref="beans:bean"> <xsd:annotation> <xsd:documentation source="org.springframework.web.servlet.resource.ResourceResolver"> <![CDATA[ A ResourceResolver bean definition. ]]> </xsd:documentation> </xsd:annotation> </xsd:element> <xsd:element ref="beans:ref"> <xsd:annotation> <xsd:documentation source="org.springframework.web.servlet.resource.ResourceResolver"> <![CDATA[ A reference to a ResourceResolver bean. ]]> </xsd:documentation> </xsd:annotation> </xsd:element> </xsd:choice> </xsd:sequence> </xsd:complexType> <xsd:complexType name="resource-transformers"> <xsd:annotation> <xsd:documentation source="org.springframework.web.servlet.resource.ResourceTransformer"> <![CDATA[ A list of ResourceTransformer beans definition and references. A ResourceTransformer provides mechanisms for transforming the content of a resource. ]]> </xsd:documentation> </xsd:annotation> <xsd:sequence> <xsd:choice maxOccurs="unbounded"> <xsd:element ref="beans:bean"> <xsd:annotation> <xsd:documentation source="org.springframework.web.servlet.resource.ResourceTransformer"> <![CDATA[ A ResourceTransformer bean definition. ]]> </xsd:documentation> </xsd:annotation> </xsd:element> <xsd:element ref="beans:ref"> <xsd:annotation> <xsd:documentation source="org.springframework.web.servlet.resource.ResourceTransformer"> <![CDATA[ A reference to a ResourceTransformer bean. ]]> </xsd:documentation> </xsd:annotation> </xsd:element> </xsd:choice> </xsd:sequence> </xsd:complexType> <xsd:complexType name="resource-chain"> <xsd:annotation> <xsd:documentation source="org.springframework.web.servlet.config.annotation.ResourceChainRegistration"> <![CDATA[ Assists with the registration of resource resolvers and transformers. Unless set to "false", the auto-registration adds default Resolvers (a PathResourceResolver) and Transformers (CssLinkResourceTransformer, if a VersionResourceResolver has been manually registered). The resource-cache attribute sets whether to cache the result of resource resolution/transformation; setting this to "true" is recommended for production (and "false" for development). A custom Cache can be configured if a CacheManager is provided as a bean reference in the "cache-manager" attribute, and the cache name provided in the "cache-name" attribute. ]]> </xsd:documentation> </xsd:annotation> <xsd:sequence> <xsd:element name="resolvers" type="resource-resolvers" minOccurs="0" maxOccurs="1"/> <xsd:element name="transformers" type="resource-transformers" minOccurs="0" maxOccurs="1"/> </xsd:sequence> <xsd:attribute name="resource-cache" type="xsd:boolean" use="required"> <xsd:annotation> <xsd:documentation> <![CDATA[ Whether the resource chain should cache resource resolution. Note that the resource content itself won't be cached, but rather Resource instances. ]]> </xsd:documentation> </xsd:annotation> </xsd:attribute> <xsd:attribute name="auto-registration" type="xsd:boolean" default="true" use="optional"> <xsd:annotation> <xsd:documentation> <![CDATA[ Whether to register automatically ResourceResolvers and ResourceTransformers. Setting this property to "false" means that it gives developers full control over the registration process. ]]> </xsd:documentation> </xsd:annotation> </xsd:attribute> <xsd:attribute name="cache-manager" type="xsd:string" use="optional"> <xsd:annotation> <xsd:documentation> <![CDATA[ The name of the Cache Manager to cache resource resolution. By default, a ConcurrentCacheMap will be used. Since Resources aren't serializable and can be dependent on the application host, one should not use a distributed cache but rather an in-memory cache. ]]> </xsd:documentation> </xsd:annotation> </xsd:attribute> <xsd:attribute name="cache-name" type="xsd:string" use="optional"> <xsd:annotation> <xsd:documentation> <![CDATA[ The cache name to use in the configured cache manager. Will use "spring-resource-chain-cache" by default. ]]> </xsd:documentation> </xsd:annotation> </xsd:attribute> </xsd:complexType> <xsd:complexType name="cache-control"> <xsd:annotation> <xsd:documentation source="org.springframework.web.cache.CacheControl"> <![CDATA[ Generates "Cache-Control" HTTP response headers. ]]> </xsd:documentation> </xsd:annotation> <xsd:attribute name="must-revalidate" type="xsd:boolean" use="optional"> <xsd:annotation> <xsd:documentation> <![CDATA[ Adds a "must-revalidate" directive in the Cache-Control header. This indicates that caches should revalidate the cached response when it's become stale. ]]> </xsd:documentation> </xsd:annotation> </xsd:attribute> <xsd:attribute name="no-cache" type="xsd:boolean" use="optional"> <xsd:annotation> <xsd:documentation> <![CDATA[ Adds a "no-cache" directive in the Cache-Control header. This indicates that caches should always revalidate cached response with the server. ]]> </xsd:documentation> </xsd:annotation> </xsd:attribute> <xsd:attribute name="no-store" type="xsd:boolean" use="optional"> <xsd:annotation> <xsd:documentation> <![CDATA[ Adds a "no-store" directive in the Cache-Control header. This indicates that caches should never cache the response. ]]> </xsd:documentation> </xsd:annotation> </xsd:attribute> <xsd:attribute name="no-transform" type="xsd:boolean" use="optional"> <xsd:annotation> <xsd:documentation> <![CDATA[ Adds a "no-transform" directive in the Cache-Control header. This indicates that caches should never transform (i.e. compress, optimize) the response content. ]]> </xsd:documentation> </xsd:annotation> </xsd:attribute> <xsd:attribute name="cache-public" type="xsd:boolean" use="optional"> <xsd:annotation> <xsd:documentation> <![CDATA[ Adds a "public" directive in the Cache-Control header. This indicates that any cache MAY store the response. ]]> </xsd:documentation> </xsd:annotation> </xsd:attribute> <xsd:attribute name="cache-private" type="xsd:boolean" use="optional"> <xsd:annotation> <xsd:documentation> <![CDATA[ Adds a "private" directive in the Cache-Control header. This indicates that the response is intended for a single user and may not be stored by shared caches. ]]> </xsd:documentation> </xsd:annotation> </xsd:attribute> <xsd:attribute name="proxy-revalidate" type="xsd:boolean" use="optional"> <xsd:annotation> <xsd:documentation> <![CDATA[ Adds a "proxy-revalidate" directive in the Cache-Control header. This directive has the same meaning as the "must-revalidate" directive, except it only applies to shared caches. ]]> </xsd:documentation> </xsd:annotation> </xsd:attribute> <xsd:attribute name="max-age" type="xsd:int" use="optional"> <xsd:annotation> <xsd:documentation> <![CDATA[ Adds a "max-age" directive in the Cache-Control header. This indicates that the response should be cached for the given number of seconds. ]]> </xsd:documentation> </xsd:annotation> </xsd:attribute> <xsd:attribute name="s-maxage" type="xsd:int" use="optional"> <xsd:annotation> <xsd:documentation> <![CDATA[ Adds a "s-maxage" directive in the Cache-Control header. This directive has the same meaning as the "max-age" directive, except it only applies to shared caches. ]]> </xsd:documentation> </xsd:annotation> </xsd:attribute> <xsd:attribute name="stale-while-revalidate" type="xsd:int" use="optional"> <xsd:annotation> <xsd:documentation> <![CDATA[ Adds a "stale-while-revalidate" directive in the Cache-Control header. This indicates that caches may serve the response after it becomes stale up to the given number of seconds. ]]> </xsd:documentation> </xsd:annotation> </xsd:attribute> <xsd:attribute name="stale-if-error" type="xsd:int" use="optional"> <xsd:annotation> <xsd:documentation> <![CDATA[ Adds a "stale-if-error" directive in the Cache-Control header. When an error is encountered, a cached stale response may be used for the given number of seconds. ]]> </xsd:documentation> </xsd:annotation> </xsd:attribute> </xsd:complexType> <xsd:element name="resources"> <xsd:annotation> <xsd:documentation source="java:org.springframework.web.servlet.resource.ResourceHttpRequestHandler"> <![CDATA[ Configures a handler for serving static resources such as images, js, and, css files with cache headers optimized for efficient loading in a web browser. Allows resources to be served out of any path that is reachable via Spring's Resource handling. ]]> </xsd:documentation> </xsd:annotation> <xsd:complexType> <xsd:sequence> <xsd:element name="cache-control" type="cache-control" minOccurs="0" maxOccurs="1"/> <xsd:element name="resource-chain" type="resource-chain" minOccurs="0" maxOccurs="1"/> </xsd:sequence> <xsd:attribute name="mapping" use="required" type="xsd:string"> <xsd:annotation> <xsd:documentation> <![CDATA[ The URL mapping pattern within the current Servlet context to use for serving resources from this handler, such as "/resources/**" ]]> </xsd:documentation> </xsd:annotation> </xsd:attribute> <xsd:attribute name="location" use="required" type="xsd:string"> <xsd:annotation> <xsd:documentation> <![CDATA[ The resource location from which to serve static content, specified at a Spring Resource pattern. Each location must point to a valid directory. Multiple locations may be specified as a comma-separated list, and the locations will be checked for a given resource in the order specified. For example, a value of "/, classpath:/META-INF/public-web-resources/" will allow resources to be served both from the web app root and from any JAR on the classpath that contains a /META-INF/public-web-resources/ directory, with resources in the web app root taking precedence. For URL-based resources (e.g. files, HTTP URLs, etc) this property supports a special prefix to indicate the charset associated with the URL so that relative paths appended to it can be encoded correctly, e.g. "[charset=Windows-31J]https://example.org/path". ]]> </xsd:documentation> </xsd:annotation> </xsd:attribute> <xsd:attribute name="cache-period" type="xsd:string"> <xsd:annotation> <xsd:documentation> <![CDATA[ Specifies the cache period for the resources served by this resource handler, in seconds. The default is to not send any cache headers but rather to rely on last-modified timestamps only. Set this to 0 in order to send cache headers that prevent caching, or to a positive number of seconds in order to send cache headers with the given max-age value. ]]> </xsd:documentation> </xsd:annotation> </xsd:attribute> <xsd:attribute name="order" type="xsd:token"> <xsd:annotation> <xsd:documentation> <![CDATA[ Specifies the order of the HandlerMapping for the resource handler. The default order is Ordered.LOWEST_PRECEDENCE - 1. ]]> </xsd:documentation> </xsd:annotation> </xsd:attribute> </xsd:complexType> </xsd:element> <xsd:element name="default-servlet-handler"> <xsd:annotation> <xsd:documentation source="java:org.springframework.web.servlet.resource.DefaultServletHttpRequestHandler"> <![CDATA[ Configures a handler for serving static resources by forwarding to the Servlet container's default Servlet. Use of this handler allows using a "/" mapping with the DispatcherServlet while still utilizing the Servlet container to serve static resources. This handler will forward all requests to the default Servlet. Therefore it is important that it remains last in the order of all other URL HandlerMappings. That will be the case if you use the "annotation-driven" element or alternatively if you are setting up your customized HandlerMapping instance be sure to set its "order" property to a value lower than that of the DefaultServletHttpRequestHandler, which is Integer.MAX_VALUE. ]]> </xsd:documentation> </xsd:annotation> <xsd:complexType> <xsd:attribute name="default-servlet-name" type="xsd:string"> <xsd:annotation> <xsd:documentation> <![CDATA[ The name of the default Servlet to forward to for static resource requests. The handler will try to autodetect the container's default Servlet at startup time using a list of known names. If the default Servlet cannot be detected because of using an unknown container or because it has been manually configured, the servlet name must be set explicitly. ]]> </xsd:documentation> </xsd:annotation> </xsd:attribute> </xsd:complexType> </xsd:element> <xsd:element name="interceptors"> <xsd:annotation> <xsd:documentation> <![CDATA[ The ordered set of interceptors that intercept HTTP Servlet Requests handled by Controllers. Interceptors allow requests to be pre/post processed before/after handling. Each interceptor must implement the org.springframework.web.servlet.HandlerInterceptor or org.springframework.web.context.request.WebRequestInterceptor interface. The interceptors in this set are automatically detected by every registered HandlerMapping. The URI paths each interceptor applies to are configurable. ]]> </xsd:documentation> </xsd:annotation> <xsd:complexType> <xsd:choice maxOccurs="unbounded"> <xsd:choice> <xsd:element ref="beans:bean"> <xsd:annotation> <xsd:documentation> <![CDATA[ Registers an interceptor that intercepts every request regardless of its URI path.. ]]> </xsd:documentation> </xsd:annotation> </xsd:element> <xsd:element ref="beans:ref"> <xsd:annotation> <xsd:documentation> <![CDATA[ Registers an interceptor that intercepts every request regardless of its URI path.. ]]> </xsd:documentation> </xsd:annotation> </xsd:element> </xsd:choice> <xsd:element name="interceptor"> <xsd:annotation> <xsd:documentation source="java:org.springframework.web.servlet.handler.MappedInterceptor"> <![CDATA[ Registers an interceptor that interceptors requests sent to one or more URI paths. ]]> </xsd:documentation> </xsd:annotation> <xsd:complexType> <xsd:sequence> <xsd:element name="mapping" maxOccurs="unbounded"> <xsd:complexType> <xsd:attribute name="path" type="xsd:string" use="required"> <xsd:annotation> <xsd:documentation> <![CDATA[ A path into the application intercepted by this interceptor. Exact path mapping URIs (such as "/myPath") are supported as well as Ant-stype path patterns (such as /myPath/**). ]]> </xsd:documentation> </xsd:annotation> </xsd:attribute> </xsd:complexType> </xsd:element> <xsd:element name="exclude-mapping" minOccurs="0" maxOccurs="unbounded"> <xsd:complexType> <xsd:attribute name="path" type="xsd:string" use="required"> <xsd:annotation> <xsd:documentation> <![CDATA[ A path into the application that should not be intercepted by this interceptor. Exact path mapping URIs (such as "/admin") are supported as well as Ant-stype path patterns (such as /admin/**). ]]> </xsd:documentation> </xsd:annotation> </xsd:attribute> </xsd:complexType> </xsd:element> <xsd:choice> <xsd:element ref="beans:bean"> <xsd:annotation> <xsd:documentation> <![CDATA[ The interceptor's bean definition. ]]> </xsd:documentation> </xsd:annotation> </xsd:element> <xsd:element ref="beans:ref"> <xsd:annotation> <xsd:documentation> <![CDATA[ A reference to an interceptor bean. ]]> </xsd:documentation> </xsd:annotation> </xsd:element> </xsd:choice> </xsd:sequence> </xsd:complexType> </xsd:element> </xsd:choice> <xsd:attribute name="path-matcher" type="xsd:string"> <xsd:annotation> <xsd:documentation source="java:org.springframework.util.PathMatcher"> <![CDATA[ The bean name of a PathMatcher implementation to use with nested interceptors. This is an optional, advanced property required only if using custom PathMatcher implementations that support mapping metadata other than the Ant path patterns supported by default. ]]> </xsd:documentation> <xsd:appinfo> <tool:annotation kind="ref"> <tool:expected-type type="java:org.springframework.util.PathMatcher"/> </tool:annotation> </xsd:appinfo> </xsd:annotation> </xsd:attribute> </xsd:complexType> </xsd:element> <xsd:element name="view-controller"> <xsd:annotation> <xsd:documentation source="java:org.springframework.web.servlet.mvc.ParameterizableViewController"> <![CDATA[ Map a simple (logic-less) view controller to a specific URL path (or pattern) in order to render a response with a pre-configured status code and view. ]]> </xsd:documentation> </xsd:annotation> <xsd:complexType> <xsd:attribute name="path" type="xsd:string" use="required"> <xsd:annotation> <xsd:documentation> <![CDATA[ The URL path (or pattern) the controller is mapped to. ]]> </xsd:documentation> </xsd:annotation> </xsd:attribute> <xsd:attribute name="view-name" type="xsd:string"> <xsd:annotation> <xsd:documentation> <![CDATA[ Set the view name to return. Optional. If not specified, the view controller will return null as the view name in which case the configured RequestToViewNameTranslator will select the view name. The DefaultRequestToViewNameTranslator for example translates "/foo/bar" to "foo/bar". ]]> </xsd:documentation> </xsd:annotation> </xsd:attribute> <xsd:attribute name="status-code" type="xsd:int"> <xsd:annotation> <xsd:documentation> <![CDATA[ Set the status code to set on the response. Optional. If not set the response status will be 200 (OK). ]]> </xsd:documentation> </xsd:annotation> </xsd:attribute> </xsd:complexType> </xsd:element> <xsd:element name="redirect-view-controller"> <xsd:annotation> <xsd:documentation source="java:org.springframework.web.servlet.mvc.ParameterizableViewController"> <![CDATA[ Map a simple (logic-less) view controller to the given URL path (or pattern) in order to redirect to another URL. ]]> </xsd:documentation> </xsd:annotation> <xsd:complexType> <xsd:attribute name="path" type="xsd:string" use="required"> <xsd:annotation> <xsd:documentation> <![CDATA[ The URL path (or pattern) the controller is mapped to. ]]> </xsd:documentation> </xsd:annotation> </xsd:attribute> <xsd:attribute name="redirect-url" type="xsd:string" use="required"> <xsd:annotation> <xsd:documentation> <![CDATA[ By default, the redirect URL is expected to be relative to the current ServletContext, i.e. as relative to the web application root. ]]> </xsd:documentation> </xsd:annotation> </xsd:attribute> <xsd:attribute name="status-code" type="xsd:int"> <xsd:annotation> <xsd:documentation> <![CDATA[ Set the specific redirect 3xx status code to use. If not set, org.springframework.web.servlet.view.RedirectView will select MOVED_TEMPORARILY (302) by default. ]]> </xsd:documentation> </xsd:annotation> </xsd:attribute> <xsd:attribute name="context-relative" type="xsd:boolean"> <xsd:annotation> <xsd:documentation> <![CDATA[ Whether to interpret a given redirect URL that starts with a slash ("/") as relative to the current ServletContext, i.e. as relative to the web application root. The default is "true". ]]> </xsd:documentation> </xsd:annotation> </xsd:attribute> <xsd:attribute name="keep-query-params" type="xsd:boolean"> <xsd:annotation> <xsd:documentation> <![CDATA[ Whether to propagate the query parameters of the current request through to the target redirect URL. The default is "false". ]]> </xsd:documentation> </xsd:annotation> </xsd:attribute> </xsd:complexType> </xsd:element> <xsd:element name="status-controller"> <xsd:annotation> <xsd:documentation source="java:org.springframework.web.servlet.mvc.ParameterizableViewController"> <![CDATA[ Map a simple (logic-less) controller to the given URL path (or pattern) in order to sets the response status to the given code without rendering a body. ]]> </xsd:documentation> </xsd:annotation> <xsd:complexType> <xsd:attribute name="path" type="xsd:string" use="required"> <xsd:annotation> <xsd:documentation> <![CDATA[ The URL path (or pattern) the controller is mapped to. ]]> </xsd:documentation> </xsd:annotation> </xsd:attribute> <xsd:attribute name="status-code" type="xsd:int" use="required"> <xsd:annotation> <xsd:documentation> <![CDATA[ The status code to set on the response. ]]> </xsd:documentation> </xsd:annotation> </xsd:attribute> </xsd:complexType> </xsd:element> <xsd:complexType name="contentNegotiationType"> <xsd:all> <xsd:element name="default-views" minOccurs="0"> <xsd:complexType> <xsd:sequence> <xsd:choice maxOccurs="unbounded"> <xsd:element ref="beans:bean"> <xsd:annotation> <xsd:documentation> <![CDATA[ A bean definition for an org.springframework.web.servlet.View class. ]]> </xsd:documentation> </xsd:annotation> </xsd:element> <xsd:element ref="beans:ref"> <xsd:annotation> <xsd:documentation> <![CDATA[ A reference to a bean for an org.springframework.web.servlet.View class. ]]> </xsd:documentation> </xsd:annotation> </xsd:element> </xsd:choice> </xsd:sequence> </xsd:complexType> </xsd:element> </xsd:all> <xsd:attribute name="use-not-acceptable" type="xsd:string"> <xsd:annotation> <xsd:documentation> <![CDATA[ Indicate whether a 406 Not Acceptable status code should be returned if no suitable view can be found. ]]> </xsd:documentation> </xsd:annotation> </xsd:attribute> </xsd:complexType> <xsd:complexType name="urlViewResolverType"> <xsd:attribute name="prefix" type="xsd:string"> <xsd:annotation> <xsd:documentation> <![CDATA[ The prefix that gets prepended to view names when building a URL. ]]> </xsd:documentation> </xsd:annotation> </xsd:attribute> <xsd:attribute name="suffix" type="xsd:string"> <xsd:annotation> <xsd:documentation> <![CDATA[ The suffix that gets appended to view names when building a URL. ]]> </xsd:documentation> </xsd:annotation> </xsd:attribute> <xsd:attribute name="cache-views" type="xsd:boolean"> <xsd:annotation> <xsd:documentation> <![CDATA[ Enable or disable thew caching of resolved views. Default is "true": caching is enabled. Disable this only for debugging and development. ]]> </xsd:documentation> </xsd:annotation> </xsd:attribute> <xsd:attribute name="view-class" type="xsd:string"> <xsd:annotation> <xsd:documentation> <![CDATA[ The view class that should be used to create views. Configure this if you want to provide a custom View implementation, typically a ub-class of the expected View type. ]]> </xsd:documentation> <xsd:appinfo> <tool:annotation kind="ref"> <tool:expected-type type="java:java.lang.Class"/> </tool:annotation> </xsd:appinfo> </xsd:annotation> </xsd:attribute> <xsd:attribute name="view-names" type="xsd:string"> <xsd:annotation> <xsd:documentation> <![CDATA[ Set the view names (or name patterns) that can be handled by this view resolver. View names can contain simple wildcards such that 'my*', '*Report' and '*Repo*' will all match the view name 'myReport'. ]]> </xsd:documentation> </xsd:annotation> </xsd:attribute> </xsd:complexType> <xsd:element name="view-resolvers"> <xsd:annotation> <xsd:documentation> <![CDATA[ Configure a chain of ViewResolver instances to resolve view names returned from controllers into actual view instances to use for rendering. All registered resolvers are wrapped in a single (composite) ViewResolver with its order property set to 0 so that other external resolvers may be ordere ]]> <![CDATA[ d before or after it. When content negotiation is enabled the order property is set to highest priority instead with the ContentNegotiatingViewResolver encapsulating all other registered view resolver instances. That way the resolvers registered through the MVC namespace form self-encapsulated resolver chain. ]]> </xsd:documentation> </xsd:annotation> <xsd:complexType> <xsd:choice minOccurs="1" maxOccurs="unbounded"> <xsd:element name="content-negotiation" type="contentNegotiationType"> <xsd:annotation> <xsd:documentation> <![CDATA[ Registers a ContentNegotiatingViewResolver with the list of all other registered ViewResolver instances used to set its "viewResolvers" property. See the javadoc of ContentNegotiatingViewResolver for more details. ]]> </xsd:documentation> </xsd:annotation> </xsd:element> <xsd:element name="jsp" type="urlViewResolverType"> <xsd:annotation> <xsd:documentation> <![CDATA[ Register an InternalResourceViewResolver bean for JSP rendering. By default, "/WEB-INF/" is registered as a view name prefix and ".jsp" as a suffix. ]]> </xsd:documentation> </xsd:annotation> </xsd:element> <xsd:element name="tiles" type="urlViewResolverType"> <xsd:annotation> <xsd:documentation> <![CDATA[ Register a TilesViewResolver based on Tiles 3.x. To configure Tiles you must also add a top-level <mvc:tiles-configurer> element or declare a TilesConfigurer bean. ]]> </xsd:documentation> </xsd:annotation> </xsd:element> <xsd:element name="freemarker" type="urlViewResolverType"> <xsd:annotation> <xsd:documentation> <![CDATA[ Register a FreeMarkerViewResolver. By default, ".ftl" is configured as a view name suffix. To configure FreeMarker you must also add a top-level <mvc:freemarker-configurer> element or declare a FreeMarkerConfigurer bean. ]]> </xsd:documentation> </xsd:annotation> </xsd:element> <xsd:element name="groovy" type="urlViewResolverType"> <xsd:annotation> <xsd:documentation> <![CDATA[ Register a GroovyMarkupViewResolver. By default, ".tpl" is configured as a view name suffix. To configure the Groovy markup template engine you must also add a top-level <mvc:groovy-configurer> element or declare a GroovyMarkupConfigurer bean. ]]> </xsd:documentation> </xsd:annotation> </xsd:element> <xsd:element name="script-template" type="urlViewResolverType"> <xsd:annotation> <xsd:documentation> <![CDATA[ Register a ScriptTemplateViewResolver. To configure the Script engine you must also add a top-level <mvc:script-template-configurer> element or declare a ScriptTemplateConfigurer bean. ]]> </xsd:documentation> </xsd:annotation> </xsd:element> <xsd:element name="bean-name" maxOccurs="1"> <xsd:annotation> <xsd:documentation> <![CDATA[ Register a BeanNameViewResolver bean. ]]> </xsd:documentation> </xsd:annotation> </xsd:element> <xsd:element ref="beans:bean"> <xsd:annotation> <xsd:documentation> <![CDATA[ Register a ViewResolver as a direct bean declaration. ]]> </xsd:documentation> </xsd:annotation> </xsd:element> <xsd:element ref="beans:ref"> <xsd:annotation> <xsd:documentation> <![CDATA[ Register a ViewResolver through references to an existing bean declaration. ]]> </xsd:documentation> </xsd:annotation> </xsd:element> </xsd:choice> <xsd:attribute name="order" type="xsd:int"> <xsd:annotation> <xsd:documentation> <![CDATA[ ViewResolver's registered through this element are encapsulated in an instance of org.springframework.web.servlet.view.ViewResolverComposite and follow the order of registration. This attribute determines the order of the ViewResolverComposite itself relative to any additional ViewResolver's (not registered through this element) present in the Spring configuration By default this property is not set, which means the resolver is ordered at Ordered.LOWEST_PRECEDENCE unless content negotiation is enabled in which case the order (if not set explicitly) is changed to Ordered.HIGHEST_PRECEDENCE. ]]> </xsd:documentation> </xsd:annotation> </xsd:attribute> </xsd:complexType> </xsd:element> <xsd:element name="tiles-configurer"> <xsd:annotation> <xsd:documentation> <![CDATA[ Configure Tiles 3.x by registering a TilesConfigurer bean. This is a shortcut alternative to declaring a TilesConfigurer bean directly. ]]> </xsd:documentation> </xsd:annotation> <xsd:complexType> <xsd:sequence> <xsd:element name="definitions" minOccurs="0" maxOccurs="unbounded"> <xsd:complexType> <xsd:attribute name="location" type="xsd:string" use="required"> <xsd:annotation> <xsd:documentation> <![CDATA[ The location of a file containing Tiles definitions (or a Spring resource pattern). If no Tiles definitions are registerd, then "/WEB-INF/tiles.xml" is expected to exists. ]]> </xsd:documentation> </xsd:annotation> </xsd:attribute> </xsd:complexType> </xsd:element> </xsd:sequence> <xsd:attribute name="check-refresh" type="xsd:boolean"> <xsd:annotation> <xsd:documentation> <![CDATA[ Whether to check Tiles definition files for a refresh at runtime. Default is "false". ]]> </xsd:documentation> </xsd:annotation> </xsd:attribute> <xsd:attribute name="validate-definitions" type="xsd:boolean"> <xsd:annotation> <xsd:documentation> <![CDATA[ Whether to validate the Tiles XML definitions. Default is "true". ]]> </xsd:documentation> </xsd:annotation> </xsd:attribute> <xsd:attribute name="definitions-factory" type="xsd:string"> <xsd:annotation> <xsd:documentation> <![CDATA[ The Tiles DefinitionsFactory class to use. Default is Tiles' default. ]]> </xsd:documentation> </xsd:annotation> </xsd:attribute> <xsd:attribute name="preparer-factory" type="xsd:string"> <xsd:annotation> <xsd:documentation> <![CDATA[ The Tiles PreparerFactory class to use. Default is Tiles' default. Consider "org.springframework.web.servlet.view.tiles3.SimpleSpringPreparerFactory" or "org.springframework.web.servlet.view.tiles3.SpringBeanPreparerFactory" (see javadoc). ]]> </xsd:documentation> </xsd:annotation> </xsd:attribute> </xsd:complexType> </xsd:element> <xsd:element name="freemarker-configurer"> <xsd:annotation> <xsd:documentation> <![CDATA[ Configure FreeMarker for view resolution by registering a FreeMarkerConfigurer bean. This is a shortcut alternative to declaring a FreeMarkerConfigurer bean directly. ]]> </xsd:documentation> </xsd:annotation> <xsd:complexType> <xsd:sequence> <xsd:element name="template-loader-path" minOccurs="0" maxOccurs="unbounded"> <xsd:complexType> <xsd:attribute name="location" type="xsd:string" use="required"> <xsd:annotation> <xsd:documentation> <![CDATA[ The location of a FreeMarker template loader path (or a Spring resource pattern). ]]> </xsd:documentation> </xsd:annotation> </xsd:attribute> </xsd:complexType> </xsd:element> </xsd:sequence> </xsd:complexType> </xsd:element> <xsd:element name="groovy-configurer"> <xsd:annotation> <xsd:documentation> <![CDATA[ Configure the Groovy markup template engine for view resolution by registering a GroovyMarkupConfigurer bean. This is a shortcut alternative to declaring a GroovyMarkupConfigurer bean directly. ]]> </xsd:documentation> </xsd:annotation> <xsd:complexType> <xsd:attribute name="auto-indent" type="xsd:boolean"> <xsd:annotation> <xsd:documentation> <![CDATA[ Whether you want the template engine to render indents automatically. ]]> </xsd:documentation> </xsd:annotation> </xsd:attribute> <xsd:attribute name="cache-templates" type="xsd:boolean"> <xsd:annotation> <xsd:documentation> <![CDATA[ If enabled templates are compiled once for each source (URL or File). It is recommended to keep this flag to true unless you are in development mode and want automatic reloading of templates. ]]> </xsd:documentation> </xsd:annotation> </xsd:attribute> <xsd:attribute name="resource-loader-path" type="xsd:string"> <xsd:annotation> <xsd:documentation> <![CDATA[ The Groovy markup template engine resource loader path via a Spring resource location. ]]> </xsd:documentation> </xsd:annotation> </xsd:attribute> </xsd:complexType> </xsd:element> <xsd:element name="script-template-configurer"> <xsd:annotation> <xsd:documentation> <![CDATA[ Configure the script engine for view resolution by registering a ScriptTemplateConfigurer bean. This is a shortcut alternative to declaring a ScriptTemplateConfigurer bean directly. ]]> </xsd:documentation> </xsd:annotation> <xsd:complexType> <xsd:sequence> <xsd:element name="script" minOccurs="0" maxOccurs="unbounded"> <xsd:complexType> <xsd:attribute name="location" type="xsd:string" use="required"> <xsd:annotation> <xsd:documentation> <![CDATA[ The location of the script to be loaded by the script engine (library or user provided). ]]> </xsd:documentation> </xsd:annotation> </xsd:attribute> </xsd:complexType> </xsd:element> </xsd:sequence> <xsd:attribute name="engine-name" type="xsd:string" use="required"> <xsd:annotation> <xsd:documentation> <![CDATA[ The script engine name to use by the view. The script engine must implement Invocable. ]]> </xsd:documentation> </xsd:annotation> </xsd:attribute> <xsd:attribute name="render-object" type="xsd:string"> <xsd:annotation> <xsd:documentation> <![CDATA[ The object where belong the render function. For example, in order to call Mustache.render(), renderObject should be set to Mustache and renderFunction to render. ]]> </xsd:documentation> </xsd:annotation> </xsd:attribute> <xsd:attribute name="render-function" type="xsd:string" use="required"> <xsd:annotation> <xsd:documentation> <![CDATA[ Set the render function name. ]]> </xsd:documentation> </xsd:annotation> </xsd:attribute> <xsd:attribute name="content-type" type="xsd:string"> <xsd:annotation> <xsd:documentation> <![CDATA[ Set the content type to use for the response (text/html by default). ]]> </xsd:documentation> </xsd:annotation> </xsd:attribute> <xsd:attribute name="charset" type="xsd:string"> <xsd:annotation> <xsd:documentation> <![CDATA[ Set the charset used to read script and template files (UTF-8 by default). ]]> </xsd:documentation> </xsd:annotation> </xsd:attribute> <xsd:attribute name="resource-loader-path" type="xsd:string"> <xsd:annotation> <xsd:documentation> <![CDATA[ The script engine resource loader path via a Spring resource location. ]]> </xsd:documentation> </xsd:annotation> </xsd:attribute> <xsd:attribute name="shared-engine" type="xsd:boolean"> <xsd:annotation> <xsd:documentation> <![CDATA[ When set to false, use thread-local ScriptEngine instances instead of one single shared instance. This flag should be set to false for those using non thread-safe script engines with templating libraries not designed for concurrency, like Handlebars or React running on Nashorn for example. In this case, Java 8u60 or greater is required due to this bug: https://bugs.openjdk.java.net/browse/JDK-8076099. ]]> </xsd:documentation> </xsd:annotation> </xsd:attribute> </xsd:complexType> </xsd:element> <xsd:element name="cors"> <xsd:annotation> <xsd:documentation> <![CDATA[ Configure cross origin requests processing. ]]> </xsd:documentation> </xsd:annotation> <xsd:complexType> <xsd:sequence> <xsd:element name="mapping" minOccurs="1" maxOccurs="unbounded"> <xsd:annotation> <xsd:documentation> <![CDATA[ Enable cross origin requests processing on the specified path pattern. By default, all origins, GET HEAD POST methods, all headers and credentials are allowed and max age is set to 30 minutes. ]]> </xsd:documentation> </xsd:annotation> <xsd:complexType> <xsd:attribute name="path" type="xsd:string" use="required"> <xsd:annotation> <xsd:documentation> <![CDATA[ A path into the application that should handle CORS requests. Exact path mapping URIs (such as "/admin") are supported as well as Ant-stype path patterns (such as /admin/**). ]]> </xsd:documentation> </xsd:annotation> </xsd:attribute> <xsd:attribute name="allowed-origins" type="xsd:string"> <xsd:annotation> <xsd:documentation> <![CDATA[ Comma-separated list of origins to allow, e.g. "https://domain1.com, https://domain2.com". The special value "*" allows all domains (default). Note that CORS checks use values from "Forwarded" (RFC 7239), "X-Forwarded-Host", "X-Forwarded-Port", and "X-Forwarded-Proto" headers, if present, in order to reflect the client-originated address. Consider using the ForwardedHeaderFilter in order to choose from a central place whether to extract and use such headers, or whether to discard them. See the Spring Framework reference for more on this filter. ]]> </xsd:documentation> </xsd:annotation> </xsd:attribute> <xsd:attribute name="allowed-methods" type="xsd:string"> <xsd:annotation> <xsd:documentation> <![CDATA[ Comma-separated list of HTTP methods to allow, e.g. "GET, POST". The special value "*" allows all method. By default GET, HEAD and POST methods are allowed. ]]> </xsd:documentation> </xsd:annotation> </xsd:attribute> <xsd:attribute name="allowed-headers" type="xsd:string"> <xsd:annotation> <xsd:documentation> <![CDATA[ Comma-separated list of headers that a pre-flight request can list as allowed for use during an actual request. The special value of "*" allows actual requests to send any header (default). ]]> </xsd:documentation> </xsd:annotation> </xsd:attribute> <xsd:attribute name="exposed-headers" type="xsd:string"> <xsd:annotation> <xsd:documentation> <![CDATA[ Comma-separated list of response headers other than simple headers (i.e. Cache-Control, Content-Language, Content-Type, Expires, Last-Modified, Pragma) that an actual response might have and can be exposed. Empty by default. ]]> </xsd:documentation> </xsd:annotation> </xsd:attribute> <xsd:attribute name="allow-credentials" type="xsd:boolean"> <xsd:annotation> <xsd:documentation> <![CDATA[ Whether user credentials are supported (true by default). ]]> </xsd:documentation> </xsd:annotation> </xsd:attribute> <xsd:attribute name="max-age" type="xsd:long"> <xsd:annotation> <xsd:documentation> <![CDATA[ How long, in seconds, the response from a pre-flight request can be cached by clients. 1800 seconds (30 minutes) by default. ]]> </xsd:documentation> </xsd:annotation> </xsd:attribute> </xsd:complexType> </xsd:element> </xsd:sequence> </xsd:complexType> </xsd:element> </xsd:schema>

A set of tools for processing big data sets using Map Reduce frameworks. Free for research and educational purpose

<h1>hare krishna</h1> Here’s an overview of our goals for you in the course. After completing this course you should be able to: - Describe the Big Data landscape including examples of real world big data problems including the three key sources of Big Data: people, organizations, and sensors. - Explain the V’s of Big Data (volume, velocity, variety, veracity, valence, and value) and why each impacts data collection, monitoring, storage, analysis and reporting. - Get value out of Big Data by using a 5-step process to structure your analysis. - Identify what are and what are not big data problems and be able to recast big data problems as data science questions. - Provide an explanation of the architectural components and programming models used for scalable big data analysis. - Summarize the features and value of core Hadoop stack components including the YARN resource and job management system, the HDFS file system and the MapReduce programming model. - Install and run a program using Hadoop! Throughout the course, we offer you various ways to engage and test your proficiency with these goals. Required quizzes seek to give you the opportunity to retrieve core definitions, terms, and key take-away points. We know from research that the first step in gaining proficiency with something involves repeated practice to solidify long-term memory. But, we also offer a number of optional discussion prompts where we encourage you to think about the concepts covered as they might impact your life or business. We encourage you to both contribute to these discussions and to read and respond to the posts of others. This opportunity to consider the application of new concepts to problems in your own life really helps deepen your understanding and ability to utilize the new knowledge you have learned. Finally, we know this is an introductory course, but we offer you one problem solving opportunity to give you practice in applying the Map Reduce process. Map Reduce is a core programming model for Big Data analysis and there’s no better way to make sure you really understand it than by trying it out for yourself! We hope that you will find this course both accessible, but also capable of helping you deepen your thinking about the core concepts of Big Data. Remember, this is just the start to our specialization -- but it’s also a great time to take a step back and think about why the challenges of Big Data now exist and how you might see them impacting your world -- or the world in the future!

An adoption of the map-reduce paradigm based on the concept of coroutines to the world of stream data processing.

Song recommendation and year prediction application based on the data processing of Million Song Dataset from Amazon PDS with map reduce and data mining.

In the development of any country democracy plays a vital role. Democracy system runs by a leader of the country who is selected by citizen of a country. Citizens have right to choose leader through election. Process of election consumes lots of manpower as well as resources and preparation is started many days before commencement of the election. During this preparation it may happen that involved people make an illegal arrangement with each other and in the existing system there are certain drawbacks such as damage of machines, dummy voting and problem of proper monitoring. Human face recognition plays an important role in applications such as video surveillance, human computer interface, and face image database management. The algorithm constructs eye, mouth, and boundary maps for verifying each face candidate. Face recognition also refers to the psychological process by which humans locate and attend to faces in a visual scene.. It is analogous to image detection in which the image of a person is matched bit by bit. Image matches with the image stores in database. Any facial feature changes in the database will invalidate the matching process. Through this paper we are aiming that voters do not need to wait for longer period of time as they do not have to wait in a queue and there is no time constraint; this system provides mobility for voters. The advantages of this proposed system is that,it is less time consuming compared to existing system. With this system man power can be reduced and there is no need to apply ink on the fingers. The proposed system is highly secure and no chances of data lost and also unlimited number of candidate information is being stored. A voter can vote only once, so voting multiple times or dummy voting shall be prohibited.

Digital assaults assurance or discovery is by and by among the most testing research subjects of data security, while dramatically expanding in number of close to nothing, remote based associated gadgets ready to send individual data to the Web it is sitting idle yet causing the fight between the included individuals. Thus, this assurance gets significant with ordinary Internet of Things arrangement, as it oftentimes incorporates numerous IOT based information assets speaking with actual world inside the different application spaces, similar to horticulture, medical care, home computerization, and so on Lamentably, contemporary IoT based gadgets frequently offer an extremely restricted security determinations, exposing themselves to always new and more confounded assaults and furthermore hindering anticipated worldwide selection of the IoT innovations, also the great many the IoT gadgets previously delivered with no equipment security uphold. In this unique circumstance, it is fundamental to improve devices which can identify such digital assaults Interruption location is the way toward observing the functions happening in a PC framework or network and investigating them for indications of interruption. It expects to ensure the privacy, honesty, and accessibility of basic arranged data frameworks. Interruption location framework (IDS) is a framework that assembles and investigates data from different regions inside a PC or an organization to distinguish assaults made against these parts. The IDS utilizes various conventional strategies for checking the misuses of weaknesses. Present day, airplanes are made sure about by solid and wellbeing properties, prepared administrators, measure based safety efforts. Be that as it may, considering late development in the inflight administration towards the expanded network, the asset sharing and progressed amusement functionalities, along with increment of dangers focusing on installed frameworks, the possible malignant alteration of an airplane framework must be truly considered for future frameworks. In this specific circumstance, numerous arrangements can be produced for airplane security. Specifically, Host based Intrusion Detection Systems are appropriate to manage the focused on dangers like an insider-assault Intrusion detection systems are almost absolutely necessary in all types of networks to provide protection from intruders. Intrusion detection systems (IDS) have to process a lot of packets to detect any intrusion which causes a delay in detection and mitigation. A host-based IDS with rule structure generation and pattern matching algorithm sets the rule structure for the unknown attack by using association rule mining in the map reduce framework. It occurs in two different stages. An intellectual method is used to generate an efficacious rule in the first stage and a pattern matching algorithm is brute forced in the second stage of this proposed framework. Log reviewing and auditing is required to find any malicious activity. Windows is the most popular operating system in the world for personal computing needs. So, there are a large number of attacks happening every day on these systems and the built-in signaturebased detection methods are not suitable for detection of zero-day and stealth attacks. Intrusion based system based on anomaly Unfortunately, a comprehensive dataset that can identify surface operations and attacks are not available. To solve this, we are going to use Australian Defense Force Academy Windows Data Set with a Stealth Attacks Addendum (ADFA-WD: SAA). To make use of this dataset a highly intelligent host based intrusion detection system is required

MapReduce is a programming model that involves two steps. The first, the map step, takes an input set I and groups it into N equivalence classes I0, I1, I2, ..., IN-1. I can be thought of as a set of tuples <key, data>, and the function map maps I into the equivalence classes based on the value of key. In the second reduce step, the equivalence classes are processed, and the set of tuples in an equivalence class Ij are reduced into a single value. MapReduce has become very popular in part because of its use by Google, but is an old parallel programming model. It is surprisingly general. To perform a parallel MapReduce, the input is spread across the available processors. Each processor runs one or more instances of map, followed by executing one or more instances of reduce. Each instance of map will potentially form equivalence classes I0, I1, I2, ..., IN-1. Consider the word counting problem, which can be solved in parallel using MapReduce. Given a list of words, the output should consist of how many times each word appeared in the list (or text). Viewing the input as tuples, the word is the key, and the data is the constant 1. A naive map function would collect all instances of a word into an equivalence class. Each equivalence class would then be assigned to a process pr, and process pr would determine the cardinality of the equivalence classes from all maps, which would be the word count. A more intelligent map function would form singleton equivalence classes Iword, where the only element is <word, count>. The process pr that reduces Iword would receive the Iword equivalence classes from all of the map functions, and would perform a reduction on the class. In Google terminology, the function that performs this optimization is called a combiner and executes on the same process as the map. This is important since its function is to combine many members of an equivalence class into a single member so as to decrease the volume of communicated data sent form the needed between the map and reduce stages. A second optimization that can be performed is to group multiple equivalence classes together to be sent together to the same reducer. Thus, the records for “cat”, “dog”, “test” and “homework” might be sent by different mappers to the same reducer. This enables all of the to be sent by a single communication operation, improving the efficiency of the communication. The question then becomes, how do we decide which equivalence classes to group together. This decision is done using a hash function H. Let’s say we will have R reducers. Then having a function 0 ≤ H(key) ≤ R-1 will group the equivalence classes into R groups to be sent to the R reducers. What we will program We will program a map reduce that executes on a distributed memory machine and uses OpenMP on each core to compute the map reduce. The project will be done in three steps: The OpenMP version and a wordcount map reduce (20% of the project grade) The MPI version that uses the OpenMP version to perform node-local computation with a wordcount map reduce (20% of the project grade) Final turn-in. (60% of the project grade) Details are given below. Note that even though I use OpenMP you can use Pthreads, Java or other code that supports multithreading to write the shared memory version. Note that if you use Java you will need to use Java isolates to communicate between nodes/processes. General information: The text for the map reduce will be distributed across FI input text files, where FI > Nmpi*C, where Nmpiis the number of nodes (machines and processes) used by MPI and C is the number of cores on each processor. OpenMP code (i.e. OpenMP code on a node). There will be four kinds of threads: Reader threads, which read files and put the data read (or created by self-initialization) into a work queue. For wordcount each work item will be a word. For the numerical problem, each entry can be a section of the array that a thread should work on; Mapper threads, which execute in parallel with Reader threads (at least until the Reader threads finish) and create combined records of words. I.e., if there are 2045 instances of “cat” in the files read by the program, the final output of the mapper threads will be a record that looks like <“cat”,2045>; Reducer threads that operate on work queue entries created by mapper threads and combine (reduce) them to a single record. Thus, for the word “cat”, there is potentially a <“cat”,counti> record sent by every mapper thread ti in the system and it will sum all of the counts and place it on a work queue. For each word there is exactly one Reducer thread in the system that handles it. Writer threads that take a sum from the work queue and write it to a file. Note that each process can write its results to a separate file. You may not need threads for each of these but only different work queue entries. Thus, Reader and Writer threads run at different times. Mapper and Reducer threads, within a node, can be made to run at different times. These threads can be made to do different tasks by pulling different work out of work queues. This is not mandatory, i.e., you can have different groups of threads to perform different tasks, thus you might have reader, mapper, reducer and writer threads. A work queue for each reducer thread. Mapper threads will put work items into this queue. For load balance purposes it is desirable that the range of function H that determines which reducer will get a work item be from 0 to R where R = k⋅numMappers, and k is some constant. You need to have mechanisms to ensure that Mapper threads wait until all Readers have finished before considering themselves complete, i.e. the work queue from which Mapper threads get their work may be empty at some point in time, but have data at a later point in time because an unfinished Reader thread put data in it. Mappers will need to put their data on a reducer’s work queue based on the key (word) for that data: As mentioned above, the reducer of a key should be determined by some sort of hash function g = H(key). All keys that map onto reducer g should be added to g’s work queue. Each process can assume it will be receiving data from every other node. This will simplify the communication structure of your program when you go to the MPI version. A node that sends no data should send an “empty” record letting the other process no it will get no data from it. As each process finishes its reduce work, it should write its results to an output file, close it, and notify the master thread that it is finished so that it can terminate the job, and then terminate itself. MPI version: The MPI version will use multiple nodes. Each node will run a copy of the OpenMP code above to perform local computations. A few changes need to be made to the OpenMP process on a node to communicate with the OpenMP processes running on other nodes. Instead of mappers putting their results onto a reducers work queue, they should put them onto a list to be sent to other nodes. A sender thread should be used to send the results of reducers in these lists to the appropriate node. Each node should have a receiver thread that obtains data sent to it by sender threads in other nodes The receiver thread for a node will place its received data onto work queues in the node for each reducer. Each node will read some portion of the FI > Nmpi*C input files. We could statically define the files each node will process, but this could lead to some nodes getting many big files and other nodes getting many small files. Instead, each node should request a file from a master node which will either send a filename back to the node or an “all done” that indicates that all files have been or are being processed. Performance data and tuning: You should collect performance data showing: What the bottlenecks are in the code. This might involve time Mapper threads are waiting for work from Reader threads, how long I/O takes vs. Mapping (not counting waiting for I/O on mapping) and data to support this other numbers below. How much load imbalance there is within a node. How much load imbalance there is across nodes (i.e. the difference in time between the first map node is ready to send its data and the latest/last map node is ready to send its data to be reduced. You should experiment with different numbers of Reader threads Step deliverables: For the OpenMP version: speedup numbers when using 1, 2, 4, . . . , #cores Mapper and Reader threads; For the MPI version: speedup numbers when using 1, 2, 4, …, #nodes to run the program, with Mapper and Reader threads for each core on a node (i.e. you don’t need to experiment with various numbers of nodes and cores For the final turn-in version: A paper not longer than ten pages that describes your overall strategy, performance bottlenecks, Performance numbers and implementation positives and negatives (what you are happy about, what you would like to change.) A full set of performance numbers either the word-count problem, and scaling by number of nodes, and dataset size, for the matrix multiply problem. Speedups and efficiencies for 2, 4, 8 and 16 processors. Do the Karp-Flatt analysis on 2, 4, 8 and 16 processors. Curves showing the number of Reader threads and performance, and the number of map and reduce threads and performance. Overall performance of the different parts of the map reduce, and the entire map reduce. For baseline “serial” numbers, use a system with one thread for each of the tasks above. Performance numbers for different numbers of nodes along with the various speedup metrics (speedup, efficiency and Karp-Flatt). An explanation of why you are getting the speedups you are getting. I may have a meeting with each group to have you demonstrate your code. This would likely happen during dead week. The point distribution will be 40% for a working parallel project with any speedup; 40% for the paper and presentation of your results and explanation of your results, 20% for acceptable speedups or non-trivial explanations of unacceptable speedups.

python script for filtering a set of xpaths out of an xml document, and producing json data for them; includes option for parallel map-reduce processing with mrjob

Part I - WeatherPy In this example, you’ll be creating a Python script to visualize the weather of 500+ cities across the world of varying distance from the equator. To accomplish this, you’ll be utilizing a simple Python library, the OpenWeatherMap API, and a little common sense to create a representative model of weather across world cities. Your first objective is to build a series of scatter plots to showcase the following relationships: Temperature (F) vs. Latitude Humidity (%) vs. Latitude Cloudiness (%) vs. Latitude Wind Speed (mph) vs. Latitude After each plot add a sentence or too explaining what the code is and analyzing. Your next objective is to run linear regression on each relationship, only this time separating them into Northern Hemisphere (greater than or equal to 0 degrees latitude) and Southern Hemisphere (less than 0 degrees latitude): Northern Hemisphere - Temperature (F) vs. Latitude Southern Hemisphere - Temperature (F) vs. Latitude Northern Hemisphere - Humidity (%) vs. Latitude Southern Hemisphere - Humidity (%) vs. Latitude Northern Hemisphere - Cloudiness (%) vs. Latitude Southern Hemisphere - Cloudiness (%) vs. Latitude Northern Hemisphere - Wind Speed (mph) vs. Latitude Southern Hemisphere - Wind Speed (mph) vs. Latitude After each pair of plots explain what the linear regression is modelling such as any relationships you notice and any other analysis you may have. Your final notebook must: Randomly select at least 500 unique (non-repeat) cities based on latitude and longitude. Perform a weather check on each of the cities using a series of successive API calls. Include a print log of each city as it’s being processed with the city number and city name. Save a CSV of all retrieved data and a PNG image for each scatter plot. Part II - VacationPy Now let’s use your skills in working with weather data to plan future vacations. Use jupyter-gmaps and the Google Places API for this part of the assignment. Note: if you having trouble displaying the maps try running jupyter nbextension enable --py gmaps in your environment and retry. Create a heat map that displays the humidity for every city from the part I of the homework. heatmap Narrow down the DataFrame to find your ideal weather condition. For example: A max temperature lower than 80 degrees but higher than 70. Wind speed less than 10 mph. Zero cloudiness. Drop any rows that don’t contain all three conditions. You want to be sure the weather is ideal. Note: Feel free to adjust to your specifications but be sure to limit the number of rows returned by your API requests to a reasonable number. Using Google Places API to find the first hotel for each city located within 5000 meters of your coordinates. Plot the hotels on top of the humidity heatmap with each pin containing the Hotel Name, City, and Country. hotel map As final considerations: Create a new GitHub repository for this project called API-Challenge (note the kebab-case). Do not add to an existing repo You must complete your analysis using a Jupyter notebook. You must use the Matplotlib or Pandas plotting libraries. For Part I, you must include a written description of three observable trends based on the data. You must use proper labeling of your plots, including aspects like: Plot Titles (with date of analysis) and Axes Labels. For max intensity in the heat map, try setting it to the highest humidity found in the data set. Hints and Considerations The city data you generate is based on random coordinates as well as different query times; as such, your outputs will not be an exact match to the provided starter notebook. You may want to start this assignment by refreshing yourself on the geographic coordinate system. Next, spend the requisite time necessary to study the OpenWeatherMap API. Based on your initial study, you should be able to answer basic questions about the API: Where do you request the API key? Which Weather API in particular will you need? What URL endpoints does it expect? What JSON structure does it respond with? Before you write a line of code, you should be aiming to have a crystal clear understanding of your intended outcome. A starter code for Citipy has been provided. However, if you’re craving an extra challenge, push yourself to learn how it works: citipy Python library. Before you try to incorporate the library into your analysis, start by creating simple test cases outside your main script to confirm that you are using it correctly. Too often, when introduced to a new library, students get bogged down by the most minor of errors – spending hours investigating their entire code – when, in fact, a simple and focused test would have shown their basic utilization of the library was wrong from the start. Don’t let this be you! Part of our expectation in this challenge is that you will use critical thinking skills to understand how and why we’re recommending the tools we are. What is Citipy for? Why would you use it in conjunction with the OpenWeatherMap API? How would you do so? In building your script, pay attention to the cities you are using in your query pool. Are you getting coverage of the full gamut of latitudes and longitudes? Or are you simply choosing 500 cities concentrated in one region of the world? Even if you were a geographic genius, simply rattling 500 cities based on your human selection would create a biased dataset. Be thinking of how you should counter this. (Hint: Consider the full range of latitudes). Once you have computed the linear regression for one chart, the process will be similar for all others. As a bonus, try to create a function that will create these charts based on different parameters. Remember that each coordinate will trigger a separate call to the Google API. If you’re creating your own criteria to plan your vacation, try to reduce the results in your DataFrame to 10 or fewer cities. Lastly, remember – this is a challenging activity. Push yourself! If you complete this task, then you can safely say that you’ve gained a strong mastery of the core foundations of data analytics and it will only go better from here. Good luck!

# Trybe This repository contains all the learning activities developed by João V.S Dias https://www.linkedin.com/in/diasvictorj/_ while studying at [Trybe](https://www.betrybe.com/) :rocket: _"Trybe is a school of the future for anyone who wants to improve their life and build a successful career in technology, where people only pay when they get a good job."_ The program has more than 1,500 hours of classroom and online classes, covers an introduction to software development, front-end, back-end, computer science, software engineering, agile methodologies and behavioral skills. ## Web Development Fundamentals :white_check_mark: ##### Block 1: Introduction - Unix & Shell - [ ] 1-3: _Unix & Shell- Part 1_ - [ ] 1-4: _Unix & Shell- Part 2_ ##### Block 2: Git & GitHub - [ ] 2-1: _What is it and what is it for?_ - [ ] 2-2: _Understanding the commands_ - [ ] 2-3: _Internet - Understanding how it works_ ##### Block 3: Introduction - HTML & CSS - [ ] 3-1: _HTML & CSS - Page Structures_ - [ ] 3-2: _HTML & CSS - Getting Started with CSS_ - [ ] 3-3: _HTML & CSS - Selectors and positioning_ - [ ] 3-4: _Semantic HTML_ - [ ] 3-5: _[Project - HTML & CSS]()_ ##### Block 4: Introduction - JavaScript - [ ] 4-1: _JavaScript - First steps_ - [ ] 4-2: _JavaScript - Array and For_ loop - [ ] 4-3: _JavaScript - Programming Logic and Algorithms_ - [ ] 4-4: _JavaScript - Objects and Functions_ - [ ] 4-5: _[Project - Playground Functions]()_ ##### Block 5: Introduction - JavaScript - Projects - [ ] 5-1: _JavaScript - DOM and selectors_ - [ ] 5-2: _JavaScript - Working with elements_ - [ ] 5-3: _JavaScript - Events_ - [ ] 5-4: _JavaScript - Web Storage_ - [ ] 5-5: _[Project - Meme Generator]()_ - [ ] 5-6: _[Project - Pixel Art]()_ - [ ] 5-7: _[Project - Task List]()_ - [ ] 5-7: _[Project - Guess the Color]()_ - [ ] 5-7: _[Project - Mysterious Letter]()_ ##### Block 6: Advanced HTML & CSS - [ ] 6-1: _HTML & CSS - Forms_ - [ ] 6-2: _JavaScript Libraries and CSS Frameworks_ - [ ] 6-3: _CSS Flexbox - Part 1_ - [ ] 6-4: _CSS Flexbox - Part 2_ - [ ] 6-5: _CSS Responsive - Mobile First_ - [ ] 6-6: _[Project - Facebook homepage]()_ ##### Block 7: JavaScript ES6 & Unit Tests - [ ] 7-1: _JavaScript ES6 - let, const, arrow functions and template literals_ - [ ] 7-2: _JavaScript ES6 - Objects_ - [ ] 7-3: _Unit tests in JavaScript_ - [ ] 7-4: _[Project - JavaScript Unit Tests]()_ ##### Block 8: JavaScript ES6 - [ ] 8-1: _JavaScript ES6 - Higher Order Functions - forEach, find, some, every, sort_ - [ ] 8-2: _JavaScript ES6 - Higher Order Functions - map and filter_ - [ ] 8-3: _JavaScript ES6 - Higher Order Functions - reduce_ - [ ] 8-4: _JavaScript ES6 - spread operator, rest parameter, destructuring and more_ - [ ] 8-5: _[Project - Zoo functions]()_ ##### Block 9: Asynchronicity & Callbacks - [ ] 9-1: _Asynchronous JavaScript and Callbacks_ - [ ] 9-2: _JavaScript Promises_ - [ ] 9-3: _[Project - Shopping Cart]()_ ##### Block 10: Jest - [ ] 10-1: _First steps in Jest_ - [ ] 10-2: _Jest - Asynchronous Tests_ - [ ] 10-3: _Jest - Simulating behavior_ - [ ] 10-4: _[Project - Asynchronous Jest and Mocking]()_ ## Front-end development :hourglass_flowing_sand: ##### Block 11: Introduction - React - [ ] 11-1: _'Hello, world!' on React!_ - [ ] 11-2: _React Components_ - [ ] 11-3: _[Project - Movie Cards Library]()_ ##### Block 12: React - [ ] 12-1: _Components with status_ - [ ] 12-2: _Events and forms in React_ - [ ] 12-3: _[Project - Movie Cards Library Stateful]()_ ##### Block 13: React - [ ] 13-1: _Improving component reuse: props.children and PropTypes_ - [ ] 13-2: _Component lifecycle in React_ - [ ] 13-3: _React Router_ - [ ] 13-4: _[Project - Movie Cards Library CRUD]()_ ##### Block 14: Agile Methodologies - [ ] 14-1: _Agile Methodologies_ - [ ] 14-2: _[Project - Frontend Online Store]()_ ##### Block 15: Tests in React - [ ] 15-1: _Testing React with the React Testing Library_ - [ ] 15-2: _Testing React with the React Testing Library - Part 2_ - [ ] 15-3: _[Project - Tests in React]()_ ##### Block 16: Introduction to Redux - [ ] 16-1: _Introduction to Redux_ - [ ] 16-2: _React with Redux - Part 1_ - [ ] 16-3: _React with Redux - Practice_ - [ ] 16-4: _React with Redux - Part 2_ - [ ] 16-5: _Synchronous tests with React-Redux_ - [ ] 16-6: _[Project - Table with data filters]()_ ##### Block 17: Project React - [ ] 17-1: _[Project - Trivia Game]()_ ##### Block 18: React & Context API - [ ] 18-1: _Context API of React_ - [ ] 18-2: _React Hooks - useState and useContext_ - [ ] 18-3: _React Hooks - useEffect and Custom Hooks_ - [ ] 18-4: _[Project - StarWars Datatable with Context API and Hooks]()_ ##### Block 19: Front-end Final Project - [ ] 19-1: _[Project - Recipe App]()_ ## Back-end Development :hourglass_flowing_sand: ##### Block 20: Introduction - Relational Databases - [ ] 20-1: _SQL Database_ - [ ] 20-2: _Finding data in a database_ - [ ] 20-3: _Filtering data specifically_ - [ ] 20-4: _Manipulating tables_ - [ ] 20-5: _[Project - All For One]()_ ##### Block 21: Relational Databases - [ ] 21-1: _Most used functions in SQL_ - [ ] 21-2: _Uncomplicating JOINs and UNIONs_ - [ ] 21-3: _Stored Routines & Subqueries_ - [ ] 21-4: _[Project - Vocabulary Booster]()_ ##### Block 22: Relational Databases - [ ] 22-1: _Transforming ideas into a database model_ - [ ] 22-2: _Normalization, Normal Forms and Dumps_ - [ ] 22-2: _Turning ideas into a database model - Part 2_ - [ ] 22-3: _[Project - One For All]()_ ##### Block 23: Introduction - NoSQL - [ ] 23-1: _MongoDB - Introduction_ - [ ] 23-2: _Filter Operators_ - [ ] 23-3: _[Project - Date Flights]()_ ##### Block 24: Updates - [ ] 24-1: _Simple Updates_ - [ ] 24-2: _Complex Updates - Arrays - Part 1_ - [ ] 24-3: _Complex Updates - Arrays - Part 2_ - [ ] 24-4: _[Project - Commerce]()_ ##### Block 25: Aggregation Framework - [ ] 25-1: _Aggregation Framework - Part 1_ - [ ] 25-2: _Aggregation Framework - Part 2_ - [ ] 25-3: _[Project - Aggregations]()_ ##### Block 26: Intro - NodeJS - [ ] 26-1: _NodeJS - Introduction_ - [ ] 26-2: _NodeJS - Asynchronous Flow_ - [ ] 26-3: _NodeJS - Architecture_ - [ ] 26-4: _[Project - A CLI of Ice and Fire]()_ ##### Block 27: NodeJS - [ ] 27-1: _Express: HTTP with Node.js_ - [ ] 27-2: _Software Architecture - Introduction to MVC_ - [ ] 27-3: _[Project - Cookmaster]()_ ##### Block 28: NodeJS - [ ] 28-1: _Software Architecture - Service Layer_ - [ ] 28-2: _Web Architecture - Rest and Restful_ - [ ] 28-3: _[Project - Store Manager]()_ ##### Block 29: NodeJS - [ ] 29-1: _NodeJS - JWT - (JSON Web Token)_ - [ ] 29-2: _NodeJS - Upload files with Multer_ - [ ] 29-3: _[Project - Cookmaster V2]()_ ##### Block 30: Introduction - Deploy - [ ] 30-1: _Infrastructure - Deploy with Heroku_ - [ ] 30-2: _Deploy - Process Managers_ - [ ] 30-3: _[Project - Stranger Things]()_ ##### Block 31: Project - [ ] 31-1: _[Project - Trybeer]()_ ##### Block 32: Software Architecture - [ ] 32-1: _Architecture - SOLID Principles_ - [ ] 32-2: _ORM - Application interface with the database_ - [ ] 32-3: _Software Architecture - DDD_ - [ ] 32-4: _Good practice writing tests_ - [ ] 32-3: _[Project - Blogs API]()_ ##### Block 33: Sockets - [ ] 30-1: _Sockets - TCP/UDP & NET_ - [ ] 30-2: _Sockets - Socket.io_ - [ ] 30-3: _[Project - Webchat]()_ ##### Block 34: Project - [ ] 34-1: _[Project - Trybeer V2]()_ ## Computer Science :hourglass_flowing_sand: ##### Block 35: Introduction - Computer Science - [ ] 35-1: _Computer Architecture_ - [ ] 35-2: _Network architecture_ - [ ] 35-3: _Computer networks, tools and security_ - [ ] 35-4: _[Project - Exploring the protocols]()_ ##### Block 36: Python - [ ] 35-1: _Learning Python_ - [ ] 35-2: _Tests and Exceptions_ - [ ] 35-3: _Data Input and Output_ - [ ] 36-4: _Data Input and Output_ - [ ] 35-5: _[Project - Tech news]()_ # [...]

BITCOIN ANALYSIS USING BIG DATA JOBS

BigData&Hadoop. The project involved cross referencing the Common Crawl database with UK company information from Companies House to produce an index of UK company web sites. The Companies House information is freely available, but does not include web site information. The project needs to search through the Common Crawl database to find likely matches to the Company House information that could be company web sites and then filter to produce an acceptably reliable index from official Company Number to web site URL. To do this project It has been using Amazon EMR and S3 to process and analyze the data by doing repeated map-reduce java codes.

Linear regression is one of the easiest and most popular Machine Learning algorithms. It is a statistical method that is used for predictive analysis. Linear regression makes predictions for continuous/real or numeric variables such as sales, salary, age, product price, etc. Linear regression algorithm shows a linear relationship between a dependent (y) and one or more independent (y) variables, hence called as linear regression. Since linear regression shows the linear relationship, which means it finds how the value of the dependent variable is changing according to the value of the independent variable. The linear regression model provides a sloped straight line representing the relationship between the variables. Consider the below image: Linear Regression in Machine Learning Mathematically, we can represent a linear regression as: y= a0+a1x+ ε Here, Y= Dependent Variable (Target Variable) X= Independent Variable (predictor Variable) a0= intercept of the line (Gives an additional degree of freedom) a1 = Linear regression coefficient (scale factor to each input value). ε = random error The values for x and y variables are training datasets for Linear Regression model representation. Types of Linear Regression Linear regression can be further divided into two types of the algorithm: Simple Linear Regression: If a single independent variable is used to predict the value of a numerical dependent variable, then such a Linear Regression algorithm is called Simple Linear Regression. Multiple Linear regression: If more than one independent variable is used to predict the value of a numerical dependent variable, then such a Linear Regression algorithm is called Multiple Linear Regression. Linear Regression Line A linear line showing the relationship between the dependent and independent variables is called a regression line. A regression line can show two types of relationship: Positive Linear Relationship: If the dependent variable increases on the Y-axis and independent variable increases on X-axis, then such a relationship is termed as a Positive linear relationship. Linear Regression in Machine Learning Negative Linear Relationship: If the dependent variable decreases on the Y-axis and independent variable increases on the X-axis, then such a relationship is called a negative linear relationship. Linear Regression in Machine Learning Finding the best fit line: When working with linear regression, our main goal is to find the best fit line that means the error between predicted values and actual values should be minimized. The best fit line will have the least error. The different values for weights or the coefficient of lines (a0, a1) gives a different line of regression, so we need to calculate the best values for a0 and a1 to find the best fit line, so to calculate this we use cost function. Cost function- The different values for weights or coefficient of lines (a0, a1) gives the different line of regression, and the cost function is used to estimate the values of the coefficient for the best fit line. Cost function optimizes the regression coefficients or weights. It measures how a linear regression model is performing. We can use the cost function to find the accuracy of the mapping function, which maps the input variable to the output variable. This mapping function is also known as Hypothesis function. For Linear Regression, we use the Mean Squared Error (MSE) cost function, which is the average of squared error occurred between the predicted values and actual values. It can be written as: For the above linear equation, MSE can be calculated as: Linear Regression in Machine Learning Where, N=Total number of observation Yi = Actual value (a1xi+a0)= Predicted value. Residuals: The distance between the actual value and predicted values is called residual. If the observed points are far from the regression line, then the residual will be high, and so cost function will high. If the scatter points are close to the regression line, then the residual will be small and hence the cost function. Gradient Descent: Gradient descent is used to minimize the MSE by calculating the gradient of the cost function. A regression model uses gradient descent to update the coefficients of the line by reducing the cost function. It is done by a random selection of values of coefficient and then iteratively update the values to reach the minimum cost function. Model Performance: The Goodness of fit determines how the line of regression fits the set of observations. The process of finding the best model out of various models is called optimization. It can be achieved by below method: 1. R-squared method: R-squared is a statistical method that determines the goodness of fit. It measures the strength of the relationship between the dependent and independent variables on a scale of 0-100%. The high value of R-square determines the less difference between the predicted values and actual values and hence represents a good model. It is also called a coefficient of determination, or coefficient of multiple determination for multiple regression. It can be calculated from the below formula: Linear Regression in Machine Learning Assumptions of Linear Regression Below are some important assumptions of Linear Regression. These are some formal checks while building a Linear Regression model, which ensures to get the best possible result from the given dataset. Linear relationship between the features and target: Linear regression assumes the linear relationship between the dependent and independent variables. Small or no multicollinearity between the features: Multicollinearity means high-correlation between the independent variables. Due to multicollinearity, it may difficult to find the true relationship between the predictors and target variables. Or we can say, it is difficult to determine which predictor variable is affecting the target variable and which is not. So, the model assumes either little or no multicollinearity between the features or independent variables. Homoscedasticity Assumption: Homoscedasticity is a situation when the error term is the same for all the values of independent variables. With homoscedasticity, there should be no clear pattern distribution of data in the scatter plot. Normal distribution of error terms: Linear regression assumes that the error term should follow the normal distribution pattern. If error terms are not normally distributed, then confidence intervals will become either too wide or too narrow, which may cause difficulties in finding coefficients. It can be checked using the q-q plot. If the plot shows a straight line without any deviation, which means the error is normally distributed. No autocorrelations: The linear regression model assumes no autocorrelation in error terms. If there will be any correlation in the error term, then it will drastically reduce the accuracy of the model. Autocorrelation usually occurs if there is a dependency between residual errors.

Bistro is a light-weight column-oriented data processing engine which changes the way data is being processed. It is based on a new data model and is an alternative to conventional SQL-like languages, map-reduce and other set-oriented approaches. Bistro can be applied to many problems like data integration, data migration, extract-transform-load (ETL), big data processing, stream analytics, big data processing, IoT analytics.

CS6240 - Map Reduce