1,078 skills found · Page 28 of 36
bocaletto-luca / Synthesizer BasicSynthesizer Basic is a software application created by Luca Bocaletto that functions as a virtual MIDI synthesizer. This software is designed to receive MIDI input and generate sounds by emulating oscillators and different waveforms, allowing users to experiment and create music creatively.
turbomaze / CGP Evolutionary ArtRandom mathematical equations are used to generate appealing images, which are improved with a genetic algorithm through the use of a human fitness function. I used Cartesian genetic programming (CGP) to create the equations, and in this case, the CGP has access to around 20 functions, like addition, rounding, and exponentiation. A few project specific heuristics are used to obtain interesting pixel colors for each (x,y) location of the images. The images are mutated as per CGP, with the fitness function simply being the boolean value of whether or not an image was selected by the user.
MostafaEbrahiem / Realistic Face Images From SketchesA recent study has shown that around 75% of criminal investigations go unsolved. Only 1 out of 3 criminals gets arrested in America. According to recent statistics from Red Cross in 2018, the number of people who went missing worldwide are around 100,000. With so many people missing and so many cases going unsolved for not being able to fully identify the person’s face and facial characteristics. The Science of Deep learning offers a variety of solutions. Deep learning is a branch of machine learning which is completely based on artificial neural networks, as neural network is going to mimic the human brain so deep learning is also a kind of mimic of human brain. It works on datasets with huge amounts of data. Given a large dataset of input and output pairs, a deep learning algorithm will try to minimize the difference between its prediction and expected output. By doing this, it tries to learn the association/pattern between given inputs and outputs, this in turn allows a deep learning model to generalize to inputs that it hasn’t seen before. So, how do deep learning Algorithms learn? Deep Learn- 4 ing Algorithms use something called a neural network to find associations between a set of inputs and outputs. The “deep” part of deep learning refers to creating deep neural networks. This refers to a neural network with a large quantity of layers, with the addition of more weights and biases, the neural network improves its ability to approximate more complex functions. Which in return generates a more accurate output. So , This project aims to make an application that takes a sketch image as an input and generates a realistic face image as an output. Also Creating realistic human face images from sketches can be used for various applications including criminal investigation, character design, educational training, etc.
Lhagawajaw / 11 36 00 PM Build Ready To Start 11 36 02 PM Build Image Version 72a309a113b53ef075815b129953617811:36:00 PM: Build ready to start 11:36:02 PM: build-image version: 72a309a113b53ef075815b129953617827965e48 (focal) 11:36:02 PM: build-image tag: v4.8.2 11:36:02 PM: buildbot version: 72ebfe61ef7a5152002962d9129cc52f5b1bb560 11:36:02 PM: Fetching cached dependencies 11:36:02 PM: Failed to fetch cache, continuing with build 11:36:02 PM: Starting to prepare the repo for build 11:36:02 PM: No cached dependencies found. Cloning fresh repo 11:36:02 PM: git clone https://github.com/netlify-templates/gatsby-ecommerce-theme 11:36:03 PM: Preparing Git Reference refs/heads/main 11:36:04 PM: Parsing package.json dependencies 11:36:05 PM: Starting build script 11:36:05 PM: Installing dependencies 11:36:05 PM: Python version set to 2.7 11:36:06 PM: v16.15.1 is already installed. 11:36:06 PM: Now using node v16.15.1 (npm v8.11.0) 11:36:06 PM: Started restoring cached build plugins 11:36:06 PM: Finished restoring cached build plugins 11:36:06 PM: Attempting ruby version 2.7.2, read from environment 11:36:08 PM: Using ruby version 2.7.2 11:36:08 PM: Using PHP version 8.0 11:36:08 PM: No npm workspaces detected 11:36:08 PM: Started restoring cached node modules 11:36:08 PM: Finished restoring cached node modules 11:36:09 PM: Installing NPM modules using NPM version 8.11.0 11:36:09 PM: npm WARN config tmp This setting is no longer used. npm stores temporary files in a special 11:36:09 PM: npm WARN config location in the cache, and they are managed by 11:36:09 PM: npm WARN config [`cacache`](http://npm.im/cacache). 11:36:09 PM: npm WARN config tmp This setting is no longer used. npm stores temporary files in a special 11:36:09 PM: npm WARN config location in the cache, and they are managed by 11:36:09 PM: npm WARN config [`cacache`](http://npm.im/cacache). 11:36:24 PM: npm WARN deprecated source-map-url@0.4.1: See https://github.com/lydell/source-map-url#deprecated 11:36:25 PM: npm WARN deprecated source-map-resolve@0.5.3: See https://github.com/lydell/source-map-resolve#deprecated 11:36:26 PM: npm WARN deprecated uuid@3.4.0: Please upgrade to version 7 or higher. Older versions may use Math.random() in certain circumstances, which is known to be problematic. See https://v8.dev/blog/math-random for details. 11:36:28 PM: npm WARN deprecated querystring@0.2.1: The querystring API is considered Legacy. new code should use the URLSearchParams API instead. 11:36:33 PM: npm WARN deprecated subscriptions-transport-ws@0.9.19: The `subscriptions-transport-ws` package is no longer maintained. We recommend you use `graphql-ws` instead. For help migrating Apollo software to `graphql-ws`, see https://www.apollographql.com/docs/apollo-server/data/subscriptions/#switching-from-subscriptions-transport-ws For general help using `graphql-ws`, see https://github.com/enisdenjo/graphql-ws/blob/master/README.md 11:36:36 PM: npm WARN deprecated async-cache@1.1.0: No longer maintained. Use [lru-cache](http://npm.im/lru-cache) version 7.6 or higher, and provide an asynchronous `fetchMethod` option. 11:36:37 PM: npm WARN deprecated babel-eslint@10.1.0: babel-eslint is now @babel/eslint-parser. This package will no longer receive updates. 11:36:41 PM: npm WARN deprecated devcert@1.2.0: critical regex denial of service bug fixed in 1.2.1 patch 11:36:42 PM: npm WARN deprecated debug@4.1.1: Debug versions >=3.2.0 <3.2.7 || >=4 <4.3.1 have a low-severity ReDos regression when used in a Node.js environment. It is recommended you upgrade to 3.2.7 or 4.3.1. (https://github.com/visionmedia/debug/issues/797) 11:36:45 PM: npm WARN deprecated urix@0.1.0: Please see https://github.com/lydell/urix#deprecated 11:36:45 PM: npm WARN deprecated resolve-url@0.2.1: https://github.com/lydell/resolve-url#deprecated 11:36:53 PM: npm WARN deprecated puppeteer@7.1.0: Version no longer supported. Upgrade to @latest 11:37:30 PM: added 2044 packages, and audited 2045 packages in 1m 11:37:30 PM: 208 packages are looking for funding 11:37:30 PM: run `npm fund` for details 11:37:30 PM: 41 vulnerabilities (13 moderate, 25 high, 3 critical) 11:37:30 PM: To address issues that do not require attention, run: 11:37:30 PM: npm audit fix 11:37:30 PM: To address all issues possible (including breaking changes), run: 11:37:30 PM: npm audit fix --force 11:37:30 PM: Some issues need review, and may require choosing 11:37:30 PM: a different dependency. 11:37:30 PM: Run `npm audit` for details. 11:37:30 PM: NPM modules installed 11:37:31 PM: npm WARN config tmp This setting is no longer used. npm stores temporary files in a special 11:37:31 PM: npm WARN config location in the cache, and they are managed by 11:37:31 PM: npm WARN config [`cacache`](http://npm.im/cacache). 11:37:31 PM: Started restoring cached go cache 11:37:31 PM: Finished restoring cached go cache 11:37:31 PM: Installing Go version 1.17 (requested 1.17) 11:37:36 PM: unset GOOS; 11:37:36 PM: unset GOARCH; 11:37:36 PM: export GOROOT='/opt/buildhome/.gimme/versions/go1.17.linux.amd64'; 11:37:36 PM: export PATH="/opt/buildhome/.gimme/versions/go1.17.linux.amd64/bin:${PATH}"; 11:37:36 PM: go version >&2; 11:37:36 PM: export GIMME_ENV="/opt/buildhome/.gimme/env/go1.17.linux.amd64.env" 11:37:37 PM: go version go1.17 linux/amd64 11:37:37 PM: Installing missing commands 11:37:37 PM: Verify run directory 11:37:38 PM: 11:37:38 PM: ──────────────────────────────────────────────────────────────── 11:37:38 PM: Netlify Build 11:37:38 PM: ──────────────────────────────────────────────────────────────── 11:37:38 PM: 11:37:38 PM: ❯ Version 11:37:38 PM: @netlify/build 27.3.0 11:37:38 PM: 11:37:38 PM: ❯ Flags 11:37:38 PM: baseRelDir: true 11:37:38 PM: buildId: 62b9ce60232d3454599e9b1c 11:37:38 PM: deployId: 62b9ce60232d3454599e9b1e 11:37:38 PM: 11:37:38 PM: ❯ Current directory 11:37:38 PM: /opt/build/repo 11:37:38 PM: 11:37:38 PM: ❯ Config file 11:37:38 PM: /opt/build/repo/netlify.toml 11:37:38 PM: 11:37:38 PM: ❯ Context 11:37:38 PM: production 11:37:38 PM: 11:37:38 PM: ❯ Loading plugins 11:37:38 PM: - @netlify/plugin-gatsby@3.2.4 from netlify.toml and package.json 11:37:38 PM: - netlify-plugin-cypress@2.2.0 from netlify.toml and package.json 11:37:40 PM: 11:37:40 PM: ──────────────────────────────────────────────────────────────── 11:37:40 PM: 1. @netlify/plugin-gatsby (onPreBuild event) 11:37:40 PM: ──────────────────────────────────────────────────────────────── 11:37:40 PM: 11:37:40 PM: No Gatsby cache found. Building fresh. 11:37:40 PM: 11:37:40 PM: (@netlify/plugin-gatsby onPreBuild completed in 17ms) 11:37:40 PM: 11:37:40 PM: ──────────────────────────────────────────────────────────────── 11:37:40 PM: 2. netlify-plugin-cypress (onPreBuild event) 11:37:40 PM: ──────────────────────────────────────────────────────────────── 11:37:40 PM: 11:37:41 PM: [STARTED] Task without title. 11:37:44 PM: [SUCCESS] Task without title. 11:37:46 PM: [2266:0627/153746.716704:ERROR:zygote_host_impl_linux.cc(263)] Failed to adjust OOM score of renderer with pid 2420: Permission denied (13) 11:37:46 PM: [2420:0627/153746.749095:ERROR:sandbox_linux.cc(377)] InitializeSandbox() called with multiple threads in process gpu-process. 11:37:46 PM: [2420:0627/153746.764711:ERROR:gpu_memory_buffer_support_x11.cc(44)] dri3 extension not supported. 11:37:46 PM: Displaying Cypress info... 11:37:46 PM: Detected no known browsers installed 11:37:46 PM: Proxy Settings: none detected 11:37:46 PM: Environment Variables: 11:37:46 PM: CYPRESS_CACHE_FOLDER: ./node_modules/.cache/CypressBinary 11:37:46 PM: Application Data: /opt/buildhome/.config/cypress/cy/development 11:37:46 PM: Browser Profiles: /opt/buildhome/.config/cypress/cy/development/browsers 11:37:46 PM: Binary Caches: /opt/build/repo/node_modules/.cache/CypressBinary 11:37:46 PM: Cypress Version: 10.2.0 (stable) 11:37:46 PM: System Platform: linux (Ubuntu - 20.04) 11:37:46 PM: System Memory: 32.8 GB free 27.9 GB 11:37:47 PM: 11:37:47 PM: (netlify-plugin-cypress onPreBuild completed in 6.2s) 11:37:47 PM: 11:37:47 PM: ──────────────────────────────────────────────────────────────── 11:37:47 PM: 3. build.command from netlify.toml 11:37:47 PM: ──────────────────────────────────────────────────────────────── 11:37:47 PM: 11:37:47 PM: $ gatsby build 11:37:49 PM: success open and validate gatsby-configs, load plugins - 0.298s 11:37:49 PM: success onPreInit - 0.003s 11:37:49 PM: success initialize cache - 0.107s 11:37:49 PM: success copy gatsby files - 0.044s 11:37:49 PM: success Compiling Gatsby Functions - 0.251s 11:37:49 PM: success onPreBootstrap - 0.259s 11:37:50 PM: success createSchemaCustomization - 0.000s 11:37:50 PM: success Checking for changed pages - 0.000s 11:37:50 PM: success source and transform nodes - 0.154s 11:37:50 PM: info Writing GraphQL type definitions to /opt/build/repo/.cache/schema.gql 11:37:50 PM: success building schema - 0.402s 11:37:50 PM: success createPages - 0.000s 11:37:50 PM: success createPagesStatefully - 0.312s 11:37:50 PM: info Total nodes: 49, SitePage nodes: 26 (use --verbose for breakdown) 11:37:50 PM: success Checking for changed pages - 0.000s 11:37:50 PM: success onPreExtractQueries - 0.000s 11:37:54 PM: success extract queries from components - 3.614s 11:37:54 PM: success write out redirect data - 0.006s 11:37:54 PM: success Build manifest and related icons - 0.468s 11:37:54 PM: success onPostBootstrap - 0.469s 11:37:54 PM: info bootstrap finished - 7.967s 11:37:54 PM: success write out requires - 0.009s 11:38:19 PM: success Building production JavaScript and CSS bundles - 24.472s 11:38:38 PM: <w> [webpack.cache.PackFileCacheStrategy] Skipped not serializable cache item 'mini-css-extract-plugin /opt/build/repo/node_modules/css-loader/dist/cjs.js??ruleSet[1].rules[10].oneOf[0].use[1]!/opt/build/repo/node_modules/postcss-loader/dist/cjs.js??ruleSet[1].rules[10].oneOf[0].use[2]!/opt/build/repo/src/components/Footer/Footer.module.css|0|Compilation/modules|/opt/build/repo/node_modules/css-loader/dist/cjs.js??ruleSet[1].rules[10].oneOf[0].use[1]!/opt/build/repo/node_modules/postcss-loader/dist/cjs.js??ruleSet[1].rules[10].oneOf[0].use[2]!/opt/build/repo/src/components/Footer/Footer.module.css': No serializer registered for Warning 11:38:38 PM: <w> while serializing webpack/lib/cache/PackFileCacheStrategy.PackContentItems -> webpack/lib/NormalModule -> Array { 1 items } -> webpack/lib/ModuleWarning -> Warning 11:38:38 PM: <w> [webpack.cache.PackFileCacheStrategy] Skipped not serializable cache item 'mini-css-extract-plugin /opt/build/repo/node_modules/css-loader/dist/cjs.js??ruleSet[1].rules[10].oneOf[0].use[1]!/opt/build/repo/node_modules/postcss-loader/dist/cjs.js??ruleSet[1].rules[10].oneOf[0].use[2]!/opt/build/repo/src/components/Header/Header.module.css|0|Compilation/modules|/opt/build/repo/node_modules/css-loader/dist/cjs.js??ruleSet[1].rules[10].oneOf[0].use[1]!/opt/build/repo/node_modules/postcss-loader/dist/cjs.js??ruleSet[1].rules[10].oneOf[0].use[2]!/opt/build/repo/src/components/Header/Header.module.css': No serializer registered for Warning 11:38:38 PM: <w> while serializing webpack/lib/cache/PackFileCacheStrategy.PackContentItems -> webpack/lib/NormalModule -> Array { 1 items } -> webpack/lib/ModuleWarning -> Warning 11:38:39 PM: <w> [webpack.cache.PackFileCacheStrategy] Skipped not serializable cache item 'Compilation/modules|/opt/build/repo/node_modules/css-loader/dist/cjs.js??ruleSet[1].rules[9].oneOf[0].use[0]!/opt/build/repo/node_modules/postcss-loader/dist/cjs.js??ruleSet[1].rules[9].oneOf[0].use[1]!/opt/build/repo/src/components/Footer/Footer.module.css': No serializer registered for Warning 11:38:39 PM: <w> while serializing webpack/lib/cache/PackFileCacheStrategy.PackContentItems -> webpack/lib/NormalModule -> Array { 1 items } -> webpack/lib/ModuleWarning -> Warning 11:38:39 PM: <w> [webpack.cache.PackFileCacheStrategy] Skipped not serializable cache item 'Compilation/modules|/opt/build/repo/node_modules/css-loader/dist/cjs.js??ruleSet[1].rules[9].oneOf[0].use[0]!/opt/build/repo/node_modules/postcss-loader/dist/cjs.js??ruleSet[1].rules[9].oneOf[0].use[1]!/opt/build/repo/src/components/Header/Header.module.css': No serializer registered for Warning 11:38:39 PM: <w> while serializing webpack/lib/cache/PackFileCacheStrategy.PackContentItems -> webpack/lib/NormalModule -> Array { 1 items } -> webpack/lib/ModuleWarning -> Warning 11:38:41 PM: success Building HTML renderer - 21.648s 11:38:41 PM: success Execute page configs - 0.024s 11:38:41 PM: success Caching Webpack compilations - 0.000s 11:38:41 PM: success run queries in workers - 0.042s - 26/26 621.26/s 11:38:41 PM: success Merge worker state - 0.001s 11:38:41 PM: success Rewriting compilation hashes - 0.001s 11:38:41 PM: success Writing page-data.json files to public directory - 0.014s - 26/26 1818.92/s 11:38:45 PM: success Building static HTML for pages - 4.353s - 26/26 5.97/s 11:38:45 PM: info [gatsby-plugin-netlify] Creating SSR/DSG redirects... 11:38:45 PM: info [gatsby-plugin-netlify] Created 0 SSR/DSG redirects... 11:38:45 PM: success onPostBuild - 0.011s 11:38:45 PM: 11:38:45 PM: Pages 11:38:45 PM: ┌ src/pages/404.js 11:38:45 PM: │ ├ /404/ 11:38:45 PM: │ └ /404.html 11:38:45 PM: ├ src/pages/about.js 11:38:45 PM: │ └ /about/ 11:38:45 PM: ├ src/pages/accountSuccess.js 11:38:45 PM: │ └ /accountSuccess/ 11:38:45 PM: ├ src/pages/cart.js 11:38:45 PM: │ └ /cart/ 11:38:45 PM: ├ src/pages/faq.js 11:38:45 PM: │ └ /faq/ 11:38:45 PM: ├ src/pages/forgot.js 11:38:45 PM: │ └ /forgot/ 11:38:45 PM: ├ src/pages/how-to-use.js 11:38:45 PM: │ └ /how-to-use/ 11:38:45 PM: ├ src/pages/index.js 11:38:45 PM: │ └ / 11:38:45 PM: ├ src/pages/login.js 11:38:45 PM: │ └ /login/ 11:38:45 PM: ├ src/pages/orderConfirm.js 11:38:45 PM: │ └ /orderConfirm/ 11:38:45 PM: ├ src/pages/search.js 11:38:45 PM: │ └ /search/ 11:38:45 PM: ├ src/pages/shop.js 11:38:45 PM: │ └ /shop/ 11:38:45 PM: ├ src/pages/shopV2.js 11:38:45 PM: │ └ /shopV2/ 11:38:45 PM: ├ src/pages/signup.js 11:38:45 PM: │ └ /signup/ 11:38:45 PM: ├ src/pages/styling.js 11:38:45 PM: │ └ /styling/ 11:38:45 PM: ├ src/pages/support.js 11:38:45 PM: │ └ /support/ 11:38:45 PM: ├ src/pages/account/address.js 11:38:45 PM: │ └ /account/address/ 11:38:45 PM: ├ src/pages/account/favorites.js 11:38:45 PM: │ └ /account/favorites/ 11:38:45 PM: ├ src/pages/account/index.js 11:38:45 PM: │ └ /account/ 11:38:45 PM: ├ src/pages/account/orders.js 11:38:45 PM: │ └ /account/orders/ 11:38:45 PM: ├ src/pages/account/settings.js 11:38:45 PM: │ └ /account/settings/ 11:38:45 PM: ├ src/pages/account/viewed.js 11:38:45 PM: │ └ /account/viewed/ 11:38:45 PM: ├ src/pages/blog/index.js 11:38:45 PM: │ └ /blog/ 11:38:45 PM: ├ src/pages/blog/sample.js 11:38:45 PM: │ └ /blog/sample/ 11:38:45 PM: └ src/pages/product/sample.js 11:38:45 PM: └ /product/sample/ 11:38:45 PM: ╭────────────────────────────────────────────────────────────────────╮ 11:38:45 PM: │ │ 11:38:45 PM: │ (SSG) Generated at build time │ 11:38:45 PM: │ D (DSG) Deferred static generation - page generated at runtime │ 11:38:45 PM: │ ∞ (SSR) Server-side renders at runtime (uses getServerData) │ 11:38:45 PM: │ λ (Function) Gatsby function │ 11:38:45 PM: │ │ 11:38:45 PM: ╰────────────────────────────────────────────────────────────────────╯ 11:38:45 PM: info Done building in 58.825944508 sec 11:38:46 PM: 11:38:46 PM: (build.command completed in 59s) 11:38:46 PM: 11:38:46 PM: ──────────────────────────────────────────────────────────────── 11:38:46 PM: 4. @netlify/plugin-gatsby (onBuild event) 11:38:46 PM: ──────────────────────────────────────────────────────────────── 11:38:46 PM: 11:38:46 PM: Skipping Gatsby Functions and SSR/DSG support 11:38:46 PM: 11:38:46 PM: (@netlify/plugin-gatsby onBuild completed in 9ms) 11:38:46 PM: 11:38:46 PM: ──────────────────────────────────────────────────────────────── 11:38:46 PM: 5. Functions bundling 11:38:46 PM: ──────────────────────────────────────────────────────────────── 11:38:46 PM: 11:38:46 PM: The Netlify Functions setting targets a non-existing directory: netlify/functions 11:38:46 PM: 11:38:46 PM: (Functions bundling completed in 3ms) 11:38:46 PM: 11:38:46 PM: ──────────────────────────────────────────────────────────────── 11:38:46 PM: 6. @netlify/plugin-gatsby (onPostBuild event) 11:38:46 PM: ──────────────────────────────────────────────────────────────── 11:38:46 PM: 11:38:47 PM: Skipping Gatsby Functions and SSR/DSG support 11:38:47 PM: 11:38:47 PM: (@netlify/plugin-gatsby onPostBuild completed in 1.4s) 11:38:47 PM: 11:38:47 PM: ──────────────────────────────────────────────────────────────── 11:38:47 PM: 7. netlify-plugin-cypress (onPostBuild event) 11:38:47 PM: ──────────────────────────────────────────────────────────────── 11:38:47 PM: 11:38:49 PM: [2557:0627/153849.751277:ERROR:zygote_host_impl_linux.cc(263)] Failed to adjust OOM score of renderer with pid 2711: Permission denied (13) 11:38:49 PM: [2711:0627/153849.770005:ERROR:sandbox_linux.cc(377)] InitializeSandbox() called with multiple threads in process gpu-process. 11:38:49 PM: [2711:0627/153849.773016:ERROR:gpu_memory_buffer_support_x11.cc(44)] dri3 extension not supported. 11:38:52 PM: Couldn't find tsconfig.json. tsconfig-paths will be skipped 11:38:52 PM: tput: No value for $TERM and no -T specified 11:38:52 PM: ==================================================================================================== 11:38:52 PM: (Run Starting) 11:38:52 PM: ┌────────────────────────────────────────────────────────────────────────────────────────────────┐ 11:38:52 PM: │ Cypress: 10.2.0 │ 11:38:52 PM: │ Browser: Custom Chromium 90 (headless) │ 11:38:52 PM: │ Node Version: v16.15.1 (/opt/buildhome/.nvm/versions/node/v16.15.1/bin/node) │ 11:38:52 PM: │ Specs: 1 found (basic.cy.js) │ 11:38:52 PM: │ Searched: cypress/e2e/**/*.cy.{js,jsx,ts,tsx} │ 11:38:52 PM: └────────────────────────────────────────────────────────────────────────────────────────────────┘ 11:38:52 PM: ──────────────────────────────────────────────────────────────────────────────────────────────────── 11:38:52 PM: Running: basic.cy.js (1 of 1) 11:38:56 PM: 11:38:56 PM: sample render test 11:38:58 PM: ✓ displays the title text (2517ms) 11:38:58 PM: 1 passing (3s) 11:39:00 PM: (Results) 11:39:00 PM: ┌────────────────────────────────────────────────────────────────────────────────────────────────┐ 11:39:00 PM: │ Tests: 1 │ 11:39:00 PM: │ Passing: 1 │ 11:39:00 PM: │ Failing: 0 │ 11:39:00 PM: │ Pending: 0 │ 11:39:00 PM: │ Skipped: 0 │ 11:39:00 PM: │ Screenshots: 0 │ 11:39:00 PM: │ Video: true │ 11:39:00 PM: │ Duration: 2 seconds │ 11:39:00 PM: │ Spec Ran: basic.cy.js │ 11:39:00 PM: └────────────────────────────────────────────────────────────────────────────────────────────────┘ 11:39:00 PM: (Video) 11:39:00 PM: - Started processing: Compressing to 32 CRF 11:39:01 PM: - Finished processing: /opt/build/repo/cypress/videos/basic.cy.js.mp4 (1 second) 11:39:01 PM: tput: No value for $TERM and no -T specified 11:39:01 PM: ==================================================================================================== 11:39:01 PM: (Run Finished) 11:39:01 PM: Spec Tests Passing Failing Pending Skipped 11:39:01 PM: ┌────────────────────────────────────────────────────────────────────────────────────────────────┐ 11:39:01 PM: Creating deploy upload records 11:39:01 PM: │ ✔ basic.cy.js 00:02 1 1 - - - │ 11:39:01 PM: └────────────────────────────────────────────────────────────────────────────────────────────────┘ 11:39:01 PM: ✔ All specs passed! 00:02 1 1 - - - 11:39:01 PM: 11:39:01 PM: (netlify-plugin-cypress onPostBuild completed in 14s) 11:39:01 PM: 11:39:01 PM: ──────────────────────────────────────────────────────────────── 11:39:01 PM: 8. Deploy site 11:39:01 PM: ──────────────────────────────────────────────────────────────── 11:39:01 PM: 11:39:01 PM: Starting to deploy site from 'public' 11:39:01 PM: Creating deploy tree 11:39:01 PM: 0 new files to upload 11:39:01 PM: 0 new functions to upload 11:39:02 PM: Starting post processing 11:39:02 PM: Incorrect TOML configuration format: Key inputs is already used as table key 11:39:02 PM: Post processing - HTML 11:39:02 PM: Incorrect TOML configuration format: Key inputs is already used as table key 11:39:03 PM: Incorrect TOML configuration format: Key inputs is already used as table key 11:39:03 PM: Post processing - header rules 11:39:03 PM: Incorrect TOML configuration format: Key inputs is already used as table key 11:39:03 PM: Post processing - redirect rules 11:39:03 PM: Incorrect TOML configuration format: Key inputs is already used as table key 11:39:03 PM: Post processing done 11:39:07 PM: Site is live ✨ 11:39:07 PM: Finished waiting for live deploy in 6.137803722s 11:39:07 PM: Site deploy was successfully initiated 11:39:07 PM: 11:39:07 PM: (Deploy site completed in 6.4s) 11:39:07 PM: 11:39:07 PM: ──────────────────────────────────────────────────────────────── 11:39:07 PM: 9. @netlify/plugin-gatsby (onSuccess event) 11:39:07 PM: ──────────────────────────────────────────────────────────────── 11:39:07 PM: 11:39:07 PM: 11:39:07 PM: (@netlify/plugin-gatsby onSuccess completed in 5ms) 11:39:07 PM: 11:39:07 PM: ──────────────────────────────────────────────────────────────── 11:39:07 PM: 10. netlify-plugin-cypress (onSuccess event) 11:39:07 PM: ──────────────────────────────────────────────────────────────── 11:39:07 PM: 11:39:07 PM: 11:39:07 PM: (netlify-plugin-cypress onSuccess completed in 6ms) 11:39:08 PM: 11:39:08 PM: ──────────────────────────────────────────────────────────────── 11:39:08 PM: Netlify Build Complete 11:39:08 PM: ──────────────────────────────────────────────────────────────── 11:39:08 PM: 11:39:08 PM: (Netlify Build completed in 1m 29.4s) 11:39:08 PM: Caching artifacts 11:39:08 PM: Started saving node modules 11:39:08 PM: Finished saving node modules 11:39:08 PM: Started saving build plugins 11:39:08 PM: Finished saving build plugins 11:39:08 PM: Started saving pip cache 11:39:08 PM: Finished saving pip cache 11:39:08 PM: Started saving emacs cask dependencies 11:39:08 PM: Finished saving emacs cask dependencies 11:39:08 PM: Started saving maven dependencies 11:39:08 PM: Finished saving maven dependencies 11:39:08 PM: Started saving boot dependencies 11:39:08 PM: Finished saving boot dependencies 11:39:08 PM: Started saving rust rustup cache 11:39:08 PM: Finished saving rust rustup cache 11:39:08 PM: Started saving go dependencies 11:39:08 PM: Finished saving go dependencies 11:39:10 PM: Build script success 11:39:10 PM: Pushing to repository git@github.com:Lhagawajaw/hymd-baraa 11:40:32 PM: Finished processing build request in 4m30.278982258s
Aryia-Behroziuan / Robot LearningIn developmental robotics, robot learning algorithms generate their own sequences of learning experiences, also known as a curriculum, to cumulatively acquire new skills through self-guided exploration and social interaction with humans. These robots use guidance mechanisms such as active learning, maturation, motor synergies and imitation. Association rules Main article: Association rule learning See also: Inductive logic programming Association rule learning is a rule-based machine learning method for discovering relationships between variables in large databases. It is intended to identify strong rules discovered in databases using some measure of "interestingness".[60] Rule-based machine learning is a general term for any machine learning method that identifies, learns, or evolves "rules" to store, manipulate or apply knowledge. The defining characteristic of a rule-based machine learning algorithm is the identification and utilization of a set of relational rules that collectively represent the knowledge captured by the system. This is in contrast to other machine learning algorithms that commonly identify a singular model that can be universally applied to any instance in order to make a prediction.[61] Rule-based machine learning approaches include learning classifier systems, association rule learning, and artificial immune systems. Based on the concept of strong rules, Rakesh Agrawal, Tomasz Imieliński and Arun Swami introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (POS) systems in supermarkets.[62] For example, the rule {\displaystyle \{\mathrm {onions,potatoes} \}\Rightarrow \{\mathrm {burger} \}}\{{\mathrm {onions,potatoes}}\}\Rightarrow \{{\mathrm {burger}}\} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. Such information can be used as the basis for decisions about marketing activities such as promotional pricing or product placements. In addition to market basket analysis, association rules are employed today in application areas including Web usage mining, intrusion detection, continuous production, and bioinformatics. In contrast with sequence mining, association rule learning typically does not consider the order of items either within a transaction or across transactions. Learning classifier systems (LCS) are a family of rule-based machine learning algorithms that combine a discovery component, typically a genetic algorithm, with a learning component, performing either supervised learning, reinforcement learning, or unsupervised learning. They seek to identify a set of context-dependent rules that collectively store and apply knowledge in a piecewise manner in order to make predictions.[63] Inductive logic programming (ILP) is an approach to rule-learning using logic programming as a uniform representation for input examples, background knowledge, and hypotheses. Given an encoding of the known background knowledge and a set of examples represented as a logical database of facts, an ILP system will derive a hypothesized logic program that entails all positive and no negative examples. Inductive programming is a related field that considers any kind of programming language for representing hypotheses (and not only logic programming), such as functional programs. Inductive logic programming is particularly useful in bioinformatics and natural language processing. Gordon Plotkin and Ehud Shapiro laid the initial theoretical foundation for inductive machine learning in a logical setting.[64][65][66] Shapiro built their first implementation (Model Inference System) in 1981: a Prolog program that inductively inferred logic programs from positive and negative examples.[67] The term inductive here refers to philosophical induction, suggesting a theory to explain observed facts, rather than mathematical induction, proving a property for all members of a well-ordered set. Models Performing machine learning involves creating a model, which is trained on some training data and then can process additional data to make predictions. Various types of models have been used and researched for machine learning systems. Artificial neural networks Main article: Artificial neural network See also: Deep learning An artificial neural network is an interconnected group of nodes, akin to the vast network of neurons in a brain. Here, each circular node represents an artificial neuron and an arrow represents a connection from the output of one artificial neuron to the input of another. Artificial neural networks (ANNs), or connectionist systems, are computing systems vaguely inspired by the biological neural networks that constitute animal brains. Such systems "learn" to perform tasks by considering examples, generally without being programmed with any task-specific rules. 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]
johnmyleswhite / Computer MusicR functions for generating computer music.
cerbos / Protoc Gen Go HashpbGenerate hash functions for protocol buffer messages
rishabkumar7 / Azure Qr CodeServerless Azure Function that generates QR codes for provided URLs and stores them in Azure Blob Storage.
revelrylabs / React UniqueidProvider component and connect function for generating unique identifiers in React.
bzhanglab / Funmapgenerate gene co-function networks using omics data
yidas / Brute Force Attacker PhpBrute-force attack tool for generating all possible string and executing function
brennerm / RandompyCollection of functions to generate pseudo random values for emails, IP addresses, ...
abrightmoore / TrigonometryBotA Twitter Bot that generates images using mathematic functions
OlivierLDff / QtGeneratorCMakeCollection of CMake function to generate `qrc`, `qmldir` files for qt applications.
mokshyaprotocol / Pseudo Random GeneratorGenerates pseudo random using sha2_256 function
DanielSchuessler / Tuple ThTemplate Haskell functions for generating functions similar to those in Data.List for tuples of statically known size.
akc / HopsHOPS - Handy Operations on Power Series
frankadrian / Lambda Sitemap GeneratorNodeJS Lambda Function to generate set of urls from mysql
makerbase-mks / Cura15.04.6 MKSBased on Ultimaker Cura15.04.6, adding supporting MKS WIFI control function and generating MKS-TFT gcode file function, which can implement model preview
Zaccheuss / MapMakerAn application that uses noise functions to procedurally generate two-dimensional maps.
