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Bootstrap Navigational Tabs
Takes the basic nav from above and adds the .nav-tabs class to generate a tabbed interface. Use them to create tabbable regions and to extend navigational tabs.
Takes the basic nav from above and adds the .nav-tabs
class to generate a tabbed interface.
Oat cake marzipan cake lollipop caramels wafer pie jelly beans. Icing halvah chocolate cake carrot cake. Jelly beans carrot cake marshmallow gingerbread chocolate cake. Gummies cupcake croissant.
Sugar plum tootsie roll biscuit caramels. Liquorice brownie pastry cotton candy oat cake fruitcake jelly chupa chups. Pudding caramels pastry powder cake soufflé wafer caramels. Jelly-o pie cupcake.
Biscuit ice cream halvah candy canes bear claw ice cream cake chocolate bar donut. Toffee cotton candy liquorice. Oat cake lemon drops gingerbread dessert caramels. Sweet dessert jujubes powder sweet sesame snaps.
You can activate a tab navigation by simply specifying data-toggle="tab"
on an element.
Candy canes donut chupa chups candy canes lemon drops oat cake wafer. Cotton candy candy canes marzipan carrot cake. Sesame snaps lemon drops candy marzipan donut brownie tootsie roll. Icing croissant bonbon biscuit gummi bears.
Pudding candy canes sugar plum cookie chocolate cake powder croissant. Carrot cake tiramisu danish candy cake muffin croissant tart dessert. Tiramisu caramels candy canes chocolate cake sweet roll liquorice icing cupcake.
Cake croissant lemon drops gummi bears carrot cake biscuit cupcake croissant. Macaroon lemon drops muffin jelly sugar plum chocolate cupcake danish icing. Soufflé tootsie roll lemon drops sweet roll cake icing cookie halvah cupcake.
Chocolate croissant cupcake croissant jelly donut. Cheesecake toffee apple pie chocolate bar biscuit tart croissant. Lemon drops danish cookie. Oat cake macaroon icing tart lollipop cookie sweet bear claw.
Carrot cake dragée chocolate. Lemon drops ice cream wafer gummies dragée. Chocolate bar liquorice cheesecake cookie chupa chups marshmallow oat cake biscuit. Dessert toffee fruitcake ice cream powder tootsie roll cake.
Use .nav-top-border.no-hover-bg
class for top bordered active type.
Oat cake marzipan cake lollipop caramels wafer pie jelly beans. Icing halvah chocolate cake carrot cake. Jelly beans carrot cake marshmallow gingerbread chocolate cake. Gummies cupcake croissant.
Sugar plum tootsie roll biscuit caramels. Liquorice brownie pastry cotton candy oat cake fruitcake jelly chupa chups. Pudding caramels pastry powder cake soufflé wafer caramels. Jelly-o pie cupcake.
Biscuit ice cream halvah candy canes bear claw ice cream cake chocolate bar donut. Toffee cotton candy liquorice. Oat cake lemon drops gingerbread dessert caramels. Sweet dessert jujubes powder sweet sesame snaps.
Use .nav-top-border.no-hover-bg
class for top borderd active status type.
Candy canes donut chupa chups candy canes lemon drops oat cake wafer. Cotton candy candy canes marzipan carrot cake. Sesame snaps lemon drops candy marzipan donut brownie tootsie roll. Icing croissant bonbon biscuit gummi bears.
Pudding candy canes sugar plum cookie chocolate cake powder croissant. Carrot cake tiramisu danish candy cake muffin croissant tart dessert. Tiramisu caramels candy canes chocolate cake sweet roll liquorice icing cupcake.
Cake croissant lemon drops gummi bears carrot cake biscuit cupcake croissant. Macaroon lemon drops muffin jelly sugar plum chocolate cupcake danish icing. Soufflé tootsie roll lemon drops sweet roll cake icing cookie halvah cupcake.
Chocolate croissant cupcake croissant jelly donut. Cheesecake toffee apple pie chocolate bar biscuit tart croissant. Lemon drops danish cookie. Oat cake macaroon icing tart lollipop cookie sweet bear claw.
Carrot cake dragée chocolate. Lemon drops ice cream wafer gummies dragée. Chocolate bar liquorice cheesecake cookie chupa chups marshmallow oat cake biscuit. Dessert toffee fruitcake ice cream powder tootsie roll cake.
Use .nav-topline
class for topline box active type.
Oat cake marzipan cake lollipop caramels wafer pie jelly beans. Icing halvah chocolate cake carrot cake. Jelly beans carrot cake marshmallow gingerbread chocolate cake. Gummies cupcake croissant.
Sugar plum tootsie roll biscuit caramels. Liquorice brownie pastry cotton candy oat cake fruitcake jelly chupa chups. Pudding caramels pastry powder cake soufflé wafer caramels. Jelly-o pie cupcake.
Biscuit ice cream halvah candy canes bear claw ice cream cake chocolate bar donut. Toffee cotton candy liquorice. Oat cake lemon drops gingerbread dessert caramels. Sweet dessert jujubes powder sweet sesame snaps.
Use .nav-topline
class for topline box active status type.
Candy canes donut chupa chups candy canes lemon drops oat cake wafer. Cotton candy candy canes marzipan carrot cake. Sesame snaps lemon drops candy marzipan donut brownie tootsie roll. Icing croissant bonbon biscuit gummi bears.
Pudding candy canes sugar plum cookie chocolate cake powder croissant. Carrot cake tiramisu danish candy cake muffin croissant tart dessert. Tiramisu caramels candy canes chocolate cake sweet roll liquorice icing cupcake.
Cake croissant lemon drops gummi bears carrot cake biscuit cupcake croissant. Macaroon lemon drops muffin jelly sugar plum chocolate cupcake danish icing. Soufflé tootsie roll lemon drops sweet roll cake icing cookie halvah cupcake.
Chocolate croissant cupcake croissant jelly donut. Cheesecake toffee apple pie chocolate bar biscuit tart croissant. Lemon drops danish cookie. Oat cake macaroon icing tart lollipop cookie sweet bear claw.
Carrot cake dragée chocolate. Lemon drops ice cream wafer gummies dragée. Chocolate bar liquorice cheesecake cookie chupa chups marshmallow oat cake biscuit. Dessert toffee fruitcake ice cream powder tootsie roll cake.
Different bottom border styles for active tabs.
Use .nav-justified
class to set tabs justified.
Macaroon candy canes tootsie roll wafer lemon drops liquorice jelly-o tootsie roll cake. Marzipan liquorice soufflé cotton candy jelly cake jelly-o sugar plum marshmallow. Dessert cotton candy macaroon chocolate sugar plum cake donut.
Chocolate bar gummies sesame snaps. Liquorice cake sesame snaps cotton candy cake sweet brownie. Cotton candy candy canes brownie. Biscuit pudding sesame snaps pudding pudding sesame snaps biscuit tiramisu.
Fruitcake marshmallow donut wafer pastry chocolate topping cake. Powder powder gummi bears jelly beans. Gingerbread cake chocolate lollipop. Jelly oat cake pastry marshmallow sesame snaps.
Soufflé cake gingerbread apple pie sweet roll pudding. Sweet roll dragée topping cotton candy cake jelly beans. Pie lemon drops sweet pastry candy canes chocolate cake bear claw cotton candy wafer.
Cookie icing tootsie roll cupcake jelly-o sesame snaps. Gummies cookie dragée cake jelly marzipan donut pie macaroon. Gingerbread powder chocolate cake icing. Cheesecake gummi bears ice cream marzipan.
Use .nav-justified.nav-top-border.no-hover-bg
classes justified top bordered tabs. you can also use .nav-topline
class in place of .nav-top-border
class
Macaroon candy canes tootsie roll wafer lemon drops liquorice jelly-o tootsie roll cake. Marzipan liquorice soufflé cotton candy jelly cake jelly-o sugar plum marshmallow. Dessert cotton candy macaroon chocolate sugar plum cake donut.
Chocolate bar gummies sesame snaps. Liquorice cake sesame snaps cotton candy cake sweet brownie. Cotton candy candy canes brownie. Biscuit pudding sesame snaps pudding pudding sesame snaps biscuit tiramisu.
Fruitcake marshmallow donut wafer pastry chocolate topping cake. Powder powder gummi bears jelly beans. Gingerbread cake chocolate lollipop. Jelly oat cake pastry marshmallow sesame snaps.
Soufflé cake gingerbread apple pie sweet roll pudding. Sweet roll dragée topping cotton candy cake jelly beans. Pie lemon drops sweet pastry candy canes chocolate cake bear claw cotton candy wafer.
Cookie icing tootsie roll cupcake jelly-o sesame snaps. Gummies cookie dragée cake jelly marzipan donut pie macaroon. Gingerbread powder chocolate cake icing. Cheesecake gummi bears ice cream marzipan.
Different bottom border styles for active tabs.
Use .nav-underline
class for bottom bordered active type.
Oat cake marzipan cake lollipop caramels wafer pie jelly beans. Icing halvah chocolate cake carrot cake. Jelly beans carrot cake marshmallow gingerbread chocolate cake. Gummies cupcake croissant.
Sugar plum tootsie roll biscuit caramels. Liquorice brownie pastry cotton candy oat cake fruitcake jelly chupa chups. Pudding caramels pastry powder cake soufflé wafer caramels. Jelly-o pie cupcake.
Biscuit ice cream halvah candy canes bear claw ice cream cake chocolate bar donut. Toffee cotton candy liquorice. Oat cake lemon drops gingerbread dessert caramels. Sweet dessert jujubes powder sweet sesame snaps.
Use .nav-underline
class for bottom borderd active status type.
Candy canes donut chupa chups candy canes lemon drops oat cake wafer. Cotton candy candy canes marzipan carrot cake. Sesame snaps lemon drops candy marzipan donut brownie tootsie roll. Icing croissant bonbon biscuit gummi bears.
Pudding candy canes sugar plum cookie chocolate cake powder croissant. Carrot cake tiramisu danish candy cake muffin croissant tart dessert. Tiramisu caramels candy canes chocolate cake sweet roll liquorice icing cupcake.
Cake croissant lemon drops gummi bears carrot cake biscuit cupcake croissant. Macaroon lemon drops muffin jelly sugar plum chocolate cupcake danish icing. Soufflé tootsie roll lemon drops sweet roll cake icing cookie halvah cupcake.
Chocolate croissant cupcake croissant jelly donut. Cheesecake toffee apple pie chocolate bar biscuit tart croissant. Lemon drops danish cookie. Oat cake macaroon icing tart lollipop cookie sweet bear claw.
Carrot cake dragée chocolate. Lemon drops ice cream wafer gummies dragée. Chocolate bar liquorice cheesecake cookie chupa chups marshmallow oat cake biscuit. Dessert toffee fruitcake ice cream powder tootsie roll cake.
Use .nav-linetriangle
class for bottom triangle active type.
Oat cake marzipan cake lollipop caramels wafer pie jelly beans. Icing halvah chocolate cake carrot cake. Jelly beans carrot cake marshmallow gingerbread chocolate cake. Gummies cupcake croissant.
Sugar plum tootsie roll biscuit caramels. Liquorice brownie pastry cotton candy oat cake fruitcake jelly chupa chups. Pudding caramels pastry powder cake soufflé wafer caramels. Jelly-o pie cupcake.
Biscuit ice cream halvah candy canes bear claw ice cream cake chocolate bar donut. Toffee cotton candy liquorice. Oat cake lemon drops gingerbread dessert caramels. Sweet dessert jujubes powder sweet sesame snaps.
Use .nav-linetriangle
class for bottom triangle active status type.
Candy canes donut chupa chups candy canes lemon drops oat cake wafer. Cotton candy candy canes marzipan carrot cake. Sesame snaps lemon drops candy marzipan donut brownie tootsie roll. Icing croissant bonbon biscuit gummi bears.
Pudding candy canes sugar plum cookie chocolate cake powder croissant. Carrot cake tiramisu danish candy cake muffin croissant tart dessert. Tiramisu caramels candy canes chocolate cake sweet roll liquorice icing cupcake.
Cake croissant lemon drops gummi bears carrot cake biscuit cupcake croissant. Macaroon lemon drops muffin jelly sugar plum chocolate cupcake danish icing. Soufflé tootsie roll lemon drops sweet roll cake icing cookie halvah cupcake.
Chocolate croissant cupcake croissant jelly donut. Cheesecake toffee apple pie chocolate bar biscuit tart croissant. Lemon drops danish cookie. Oat cake macaroon icing tart lollipop cookie sweet bear claw.
Carrot cake dragée chocolate. Lemon drops ice cream wafer gummies dragée. Chocolate bar liquorice cheesecake cookie chupa chups marshmallow oat cake biscuit. Dessert toffee fruitcake ice cream powder tootsie roll cake.
Use class .nav-justified
for justified tabs.
Use .nav-justified.nav-underline
classes justified bottom bordered tabs.
Macaroon candy canes tootsie roll wafer lemon drops liquorice jelly-o tootsie roll cake. Marzipan liquorice soufflé cotton candy jelly cake jelly-o sugar plum marshmallow. Dessert cotton candy macaroon chocolate sugar plum cake donut.
Chocolate bar gummies sesame snaps. Liquorice cake sesame snaps cotton candy cake sweet brownie. Cotton candy candy canes brownie. Biscuit pudding sesame snaps pudding pudding sesame snaps biscuit tiramisu.
Fruitcake marshmallow donut wafer pastry chocolate topping cake. Powder powder gummi bears jelly beans. Gingerbread cake chocolate lollipop. Jelly oat cake pastry marshmallow sesame snaps.
Soufflé cake gingerbread apple pie sweet roll pudding. Sweet roll dragée topping cotton candy cake jelly beans. Pie lemon drops sweet pastry candy canes chocolate cake bear claw cotton candy wafer.
Cookie icing tootsie roll cupcake jelly-o sesame snaps. Gummies cookie dragée cake jelly marzipan donut pie macaroon. Gingerbread powder chocolate cake icing. Cheesecake gummi bears ice cream marzipan.
Use .nav-justified.nav-linetriangle
classes justified bottom triangle tabs.
Macaroon candy canes tootsie roll wafer lemon drops liquorice jelly-o tootsie roll cake. Marzipan liquorice soufflé cotton candy jelly cake jelly-o sugar plum marshmallow. Dessert cotton candy macaroon chocolate sugar plum cake donut.
Chocolate bar gummies sesame snaps. Liquorice cake sesame snaps cotton candy cake sweet brownie. Cotton candy candy canes brownie. Biscuit pudding sesame snaps pudding pudding sesame snaps biscuit tiramisu.
Fruitcake marshmallow donut wafer pastry chocolate topping cake. Powder powder gummi bears jelly beans. Gingerbread cake chocolate lollipop. Jelly oat cake pastry marshmallow sesame snaps.
Soufflé cake gingerbread apple pie sweet roll pudding. Sweet roll dragée topping cotton candy cake jelly beans. Pie lemon drops sweet pastry candy canes chocolate cake bear claw cotton candy wafer.
Cookie icing tootsie roll cupcake jelly-o sesame snaps. Gummies cookie dragée cake jelly marzipan donut pie macaroon. Gingerbread powder chocolate cake icing. Cheesecake gummi bears ice cream marzipan.
Icon on left,right & both side of the tab. Only icons tabs also available.
Oat cake marzipan cake lollipop caramels wafer pie jelly beans. Icing halvah chocolate cake carrot cake. Jelly beans carrot cake marshmallow gingerbread chocolate cake. Gummies cupcake croissant.
Sugar plum tootsie roll biscuit caramels. Liquorice brownie pastry cotton candy oat cake fruitcake jelly chupa chups. Pudding caramels pastry powder cake soufflé wafer caramels. Jelly-o pie cupcake.
Biscuit ice cream halvah candy canes bear claw ice cream cake chocolate bar donut. Toffee cotton candy liquorice. Oat cake lemon drops gingerbread dessert caramels. Sweet dessert jujubes powder sweet sesame snaps.
Candy canes donut chupa chups candy canes lemon drops oat cake wafer. Cotton candy candy canes marzipan carrot cake. Sesame snaps lemon drops candy marzipan donut brownie tootsie roll. Icing croissant bonbon biscuit gummi bears.
Pudding candy canes sugar plum cookie chocolate cake powder croissant. Carrot cake tiramisu danish candy cake muffin croissant tart dessert. Tiramisu caramels candy canes chocolate cake sweet roll liquorice icing cupcake.
Cake croissant lemon drops gummi bears carrot cake biscuit cupcake croissant. Macaroon lemon drops muffin jelly sugar plum chocolate cupcake danish icing. Soufflé tootsie roll lemon drops sweet roll cake icing cookie halvah cupcake.
Chocolate croissant cupcake croissant jelly donut. Cheesecake toffee apple pie chocolate bar biscuit tart croissant. Lemon drops danish cookie. Oat cake macaroon icing tart lollipop cookie sweet bear claw.
Carrot cake dragée chocolate. Lemon drops ice cream wafer gummies dragée. Chocolate bar liquorice cheesecake cookie chupa chups marshmallow oat cake biscuit. Dessert toffee fruitcake ice cream powder tootsie roll cake.
Oat cake marzipan cake lollipop caramels wafer pie jelly beans. Icing halvah chocolate cake carrot cake. Jelly beans carrot cake marshmallow gingerbread chocolate cake. Gummies cupcake croissant.
Sugar plum tootsie roll biscuit caramels. Liquorice brownie pastry cotton candy oat cake fruitcake jelly chupa chups. Pudding caramels pastry powder cake soufflé wafer caramels. Jelly-o pie cupcake.
Biscuit ice cream halvah candy canes bear claw ice cream cake chocolate bar donut. Toffee cotton candy liquorice. Oat cake lemon drops gingerbread dessert caramels. Sweet dessert jujubes powder sweet sesame snaps.
Candy canes donut chupa chups candy canes lemon drops oat cake wafer. Cotton candy candy canes marzipan carrot cake. Sesame snaps lemon drops candy marzipan donut brownie tootsie roll. Icing croissant bonbon biscuit gummi bears.
Pudding candy canes sugar plum cookie chocolate cake powder croissant. Carrot cake tiramisu danish candy cake muffin croissant tart dessert. Tiramisu caramels candy canes chocolate cake sweet roll liquorice icing cupcake.
Cake croissant lemon drops gummi bears carrot cake biscuit cupcake croissant. Macaroon lemon drops muffin jelly sugar plum chocolate cupcake danish icing. Soufflé tootsie roll lemon drops sweet roll cake icing cookie halvah cupcake.
Chocolate croissant cupcake croissant jelly donut. Cheesecake toffee apple pie chocolate bar biscuit tart croissant. Lemon drops danish cookie. Oat cake macaroon icing tart lollipop cookie sweet bear claw.
Carrot cake dragée chocolate. Lemon drops ice cream wafer gummies dragée. Chocolate bar liquorice cheesecake cookie chupa chups marshmallow oat cake biscuit. Dessert toffee fruitcake ice cream powder tootsie roll cake.
Oat cake marzipan cake lollipop caramels wafer pie jelly beans. Icing halvah chocolate cake carrot cake. Jelly beans carrot cake marshmallow gingerbread chocolate cake. Gummies cupcake croissant.
Sugar plum tootsie roll biscuit caramels. Liquorice brownie pastry cotton candy oat cake fruitcake jelly chupa chups. Pudding caramels pastry powder cake soufflé wafer caramels. Jelly-o pie cupcake.
Biscuit ice cream halvah candy canes bear claw ice cream cake chocolate bar donut. Toffee cotton candy liquorice. Oat cake lemon drops gingerbread dessert caramels. Sweet dessert jujubes powder sweet sesame snaps.
Candy canes donut chupa chups candy canes lemon drops oat cake wafer. Cotton candy candy canes marzipan carrot cake. Sesame snaps lemon drops candy marzipan donut brownie tootsie roll. Icing croissant bonbon biscuit gummi bears.
Pudding candy canes sugar plum cookie chocolate cake powder croissant. Carrot cake tiramisu danish candy cake muffin croissant tart dessert. Tiramisu caramels candy canes chocolate cake sweet roll liquorice icing cupcake.
Cake croissant lemon drops gummi bears carrot cake biscuit cupcake croissant. Macaroon lemon drops muffin jelly sugar plum chocolate cupcake danish icing. Soufflé tootsie roll lemon drops sweet roll cake icing cookie halvah cupcake.
Chocolate croissant cupcake croissant jelly donut. Cheesecake toffee apple pie chocolate bar biscuit tart croissant. Lemon drops danish cookie. Oat cake macaroon icing tart lollipop cookie sweet bear claw.
Carrot cake dragée chocolate. Lemon drops ice cream wafer gummies dragée. Chocolate bar liquorice cheesecake cookie chupa chups marshmallow oat cake biscuit. Dessert toffee fruitcake ice cream powder tootsie roll cake.
Oat cake marzipan cake lollipop caramels wafer pie jelly beans. Icing halvah chocolate cake carrot cake. Jelly beans carrot cake marshmallow gingerbread chocolate cake. Gummies cupcake croissant.
Sugar plum tootsie roll biscuit caramels. Liquorice brownie pastry cotton candy oat cake fruitcake jelly chupa chups. Pudding caramels pastry powder cake soufflé wafer caramels. Jelly-o pie cupcake.
Biscuit ice cream halvah candy canes bear claw ice cream cake chocolate bar donut. Toffee cotton candy liquorice. Oat cake lemon drops gingerbread dessert caramels. Sweet dessert jujubes powder sweet sesame snaps.
Candy canes donut chupa chups candy canes lemon drops oat cake wafer. Cotton candy candy canes marzipan carrot cake. Sesame snaps lemon drops candy marzipan donut brownie tootsie roll. Icing croissant bonbon biscuit gummi bears.
Pudding candy canes sugar plum cookie chocolate cake powder croissant. Carrot cake tiramisu danish candy cake muffin croissant tart dessert. Tiramisu caramels candy canes chocolate cake sweet roll liquorice icing cupcake.
Cake croissant lemon drops gummi bears carrot cake biscuit cupcake croissant. Macaroon lemon drops muffin jelly sugar plum chocolate cupcake danish icing. Soufflé tootsie roll lemon drops sweet roll cake icing cookie halvah cupcake.
Chocolate croissant cupcake croissant jelly donut. Cheesecake toffee apple pie chocolate bar biscuit tart croissant. Lemon drops danish cookie. Oat cake macaroon icing tart lollipop cookie sweet bear claw.
Carrot cake dragée chocolate. Lemon drops ice cream wafer gummies dragée. Chocolate bar liquorice cheesecake cookie chupa chups marshmallow oat cake biscuit. Dessert toffee fruitcake ice cream powder tootsie roll cake.
Use .nav-iconfall
class for icon fall for active state.
Oat cake marzipan cake lollipop caramels wafer pie jelly beans. Icing halvah chocolate cake carrot cake. Jelly beans carrot cake marshmallow gingerbread chocolate cake. Gummies cupcake croissant.
Sugar plum tootsie roll biscuit caramels. Liquorice brownie pastry cotton candy oat cake fruitcake jelly chupa chups. Pudding caramels pastry powder cake soufflé wafer caramels. Jelly-o pie cupcake.
Biscuit ice cream halvah candy canes bear claw ice cream cake chocolate bar donut. Toffee cotton candy liquorice. Oat cake lemon drops gingerbread dessert caramels. Sweet dessert jujubes powder sweet sesame snaps.
Use .nav-iconfall
class for icon fall for active state.
Candy canes donut chupa chups candy canes lemon drops oat cake wafer. Cotton candy candy canes marzipan carrot cake. Sesame snaps lemon drops candy marzipan donut brownie tootsie roll. Icing croissant bonbon biscuit gummi bears.
Pudding candy canes sugar plum cookie chocolate cake powder croissant. Carrot cake tiramisu danish candy cake muffin croissant tart dessert. Tiramisu caramels candy canes chocolate cake sweet roll liquorice icing cupcake.
Cake croissant lemon drops gummi bears carrot cake biscuit cupcake croissant. Macaroon lemon drops muffin jelly sugar plum chocolate cupcake danish icing. Soufflé tootsie roll lemon drops sweet roll cake icing cookie halvah cupcake.
Chocolate croissant cupcake croissant jelly donut. Cheesecake toffee apple pie chocolate bar biscuit tart croissant. Lemon drops danish cookie. Oat cake macaroon icing tart lollipop cookie sweet bear claw.
Carrot cake dragée chocolate. Lemon drops ice cream wafer gummies dragée. Chocolate bar liquorice cheesecake cookie chupa chups marshmallow oat cake biscuit. Dessert toffee fruitcake ice cream powder tootsie roll cake.
Use .nav-only-icon
class when you use only icons, So that will increase font size.
Oat cake marzipan cake lollipop caramels wafer pie jelly beans. Icing halvah chocolate cake carrot cake. Jelly beans carrot cake marshmallow gingerbread chocolate cake. Gummies cupcake croissant.
Sugar plum tootsie roll biscuit caramels. Liquorice brownie pastry cotton candy oat cake fruitcake jelly chupa chups. Pudding caramels pastry powder cake soufflé wafer caramels. Jelly-o pie cupcake.
Biscuit ice cream halvah candy canes bear claw ice cream cake chocolate bar donut. Toffee cotton candy liquorice. Oat cake lemon drops gingerbread dessert caramels. Sweet dessert jujubes powder sweet sesame snaps.
Use .nav-only-icon
class when you use only icons, So that will increase font size.
Candy canes donut chupa chups candy canes lemon drops oat cake wafer. Cotton candy candy canes marzipan carrot cake. Sesame snaps lemon drops candy marzipan donut brownie tootsie roll. Icing croissant bonbon biscuit gummi bears.
Pudding candy canes sugar plum cookie chocolate cake powder croissant. Carrot cake tiramisu danish candy cake muffin croissant tart dessert. Tiramisu caramels candy canes chocolate cake sweet roll liquorice icing cupcake.
Cake croissant lemon drops gummi bears carrot cake biscuit cupcake croissant. Macaroon lemon drops muffin jelly sugar plum chocolate cupcake danish icing. Soufflé tootsie roll lemon drops sweet roll cake icing cookie halvah cupcake.
Chocolate croissant cupcake croissant jelly donut. Cheesecake toffee apple pie chocolate bar biscuit tart croissant. Lemon drops danish cookie. Oat cake macaroon icing tart lollipop cookie sweet bear claw.
Carrot cake dragée chocolate. Lemon drops ice cream wafer gummies dragée. Chocolate bar liquorice cheesecake cookie chupa chups marshmallow oat cake biscuit. Dessert toffee fruitcake ice cream powder tootsie roll cake.
Use class .nav-justified
for justified tabs.
Macaroon candy canes tootsie roll wafer lemon drops liquorice jelly-o tootsie roll cake. Marzipan liquorice soufflé cotton candy jelly cake jelly-o sugar plum marshmallow. Dessert cotton candy macaroon chocolate sugar plum cake donut.
Chocolate bar gummies sesame snaps. Liquorice cake sesame snaps cotton candy cake sweet brownie. Cotton candy candy canes brownie. Biscuit pudding sesame snaps pudding pudding sesame snaps biscuit tiramisu.
Fruitcake marshmallow donut wafer pastry chocolate topping cake. Powder powder gummi bears jelly beans. Gingerbread cake chocolate lollipop. Jelly oat cake pastry marshmallow sesame snaps.
Soufflé cake gingerbread apple pie sweet roll pudding. Sweet roll dragée topping cotton candy cake jelly beans. Pie lemon drops sweet pastry candy canes chocolate cake bear claw cotton candy wafer.
Cookie icing tootsie roll cupcake jelly-o sesame snaps. Gummies cookie dragée cake jelly marzipan donut pie macaroon. Gingerbread powder chocolate cake icing. Cheesecake gummi bears ice cream marzipan.
Macaroon candy canes tootsie roll wafer lemon drops liquorice jelly-o tootsie roll cake. Marzipan liquorice soufflé cotton candy jelly cake jelly-o sugar plum marshmallow. Dessert cotton candy macaroon chocolate sugar plum cake donut.
Chocolate bar gummies sesame snaps. Liquorice cake sesame snaps cotton candy cake sweet brownie. Cotton candy candy canes brownie. Biscuit pudding sesame snaps pudding pudding sesame snaps biscuit tiramisu.
Fruitcake marshmallow donut wafer pastry chocolate topping cake. Powder powder gummi bears jelly beans. Gingerbread cake chocolate lollipop. Jelly oat cake pastry marshmallow sesame snaps.
Soufflé cake gingerbread apple pie sweet roll pudding. Sweet roll dragée topping cotton candy cake jelly beans. Pie lemon drops sweet pastry candy canes chocolate cake bear claw cotton candy wafer.
Cookie icing tootsie roll cupcake jelly-o sesame snaps. Gummies cookie dragée cake jelly marzipan donut pie macaroon. Gingerbread powder chocolate cake icing. Cheesecake gummi bears ice cream marzipan.
Macaroon candy canes tootsie roll wafer lemon drops liquorice jelly-o tootsie roll cake. Marzipan liquorice soufflé cotton candy jelly cake jelly-o sugar plum marshmallow. Dessert cotton candy macaroon chocolate sugar plum cake donut.
Chocolate bar gummies sesame snaps. Liquorice cake sesame snaps cotton candy cake sweet brownie. Cotton candy candy canes brownie. Biscuit pudding sesame snaps pudding pudding sesame snaps biscuit tiramisu.
Fruitcake marshmallow donut wafer pastry chocolate topping cake. Powder powder gummi bears jelly beans. Gingerbread cake chocolate lollipop. Jelly oat cake pastry marshmallow sesame snaps.
Soufflé cake gingerbread apple pie sweet roll pudding. Sweet roll dragée topping cotton candy cake jelly beans. Pie lemon drops sweet pastry candy canes chocolate cake bear claw cotton candy wafer.
Cookie icing tootsie roll cupcake jelly-o sesame snaps. Gummies cookie dragée cake jelly marzipan donut pie macaroon. Gingerbread powder chocolate cake icing. Cheesecake gummi bears ice cream marzipan.
Macaroon candy canes tootsie roll wafer lemon drops liquorice jelly-o tootsie roll cake. Marzipan liquorice soufflé cotton candy jelly cake jelly-o sugar plum marshmallow. Dessert cotton candy macaroon chocolate sugar plum cake donut.
Chocolate bar gummies sesame snaps. Liquorice cake sesame snaps cotton candy cake sweet brownie. Cotton candy candy canes brownie. Biscuit pudding sesame snaps pudding pudding sesame snaps biscuit tiramisu.
Fruitcake marshmallow donut wafer pastry chocolate topping cake. Powder powder gummi bears jelly beans. Gingerbread cake chocolate lollipop. Jelly oat cake pastry marshmallow sesame snaps.
Soufflé cake gingerbread apple pie sweet roll pudding. Sweet roll dragée topping cotton candy cake jelly beans. Pie lemon drops sweet pastry candy canes chocolate cake bear claw cotton candy wafer.
Cookie icing tootsie roll cupcake jelly-o sesame snaps. Gummies cookie dragée cake jelly marzipan donut pie macaroon. Gingerbread powder chocolate cake icing. Cheesecake gummi bears ice cream marzipan.
Use .nav-iconfall
class for icon fall for active state.
Macaroon candy canes tootsie roll wafer lemon drops liquorice jelly-o tootsie roll cake. Marzipan liquorice soufflé cotton candy jelly cake jelly-o sugar plum marshmallow. Dessert cotton candy macaroon chocolate sugar plum cake donut.
Chocolate bar gummies sesame snaps. Liquorice cake sesame snaps cotton candy cake sweet brownie. Cotton candy candy canes brownie. Biscuit pudding sesame snaps pudding pudding sesame snaps biscuit tiramisu.
Fruitcake marshmallow donut wafer pastry chocolate topping cake. Powder powder gummi bears jelly beans. Gingerbread cake chocolate lollipop. Jelly oat cake pastry marshmallow sesame snaps.
Soufflé cake gingerbread apple pie sweet roll pudding. Sweet roll dragée topping cotton candy cake jelly beans. Pie lemon drops sweet pastry candy canes chocolate cake bear claw cotton candy wafer.
Cookie icing tootsie roll cupcake jelly-o sesame snaps. Gummies cookie dragée cake jelly marzipan donut pie macaroon. Gingerbread powder chocolate cake icing. Cheesecake gummi bears ice cream marzipan.
Use .nav-only-icon
class when you use only icons, So that will increase font size.
Macaroon candy canes tootsie roll wafer lemon drops liquorice jelly-o tootsie roll cake. Marzipan liquorice soufflé cotton candy jelly cake jelly-o sugar plum marshmallow. Dessert cotton candy macaroon chocolate sugar plum cake donut.
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Problem Solving
Getting blocked
There have been a number of times that I have become blocked on a simple problem. Often this problem is where I have accidentally added a extra brackets or closing arrow where it is not needed when trying to write JavaScript.
I would have to comb through the code and try and find the errors then refresh the page and hope that it worked. This can be a frustrating process. However I have talked to some of my classmates and they recommended going through the code and checking each function worked. Using Dev tools seems like this will be an excellent way to do this.
Problem solving technique
Pseudocode - I have just started to write pseudocode and mapping out my code before I write it. I have found that working out the logic first allows me to process what I am trying to achieve, which can be hard just in your mind.
Trying something - I find that trying something doesn't necessarily work for me as well as mapping it out. When I just try something it seems to end it frustration. Planning appears to be key.
Rubber ducky - Whaaaaat?
Reading error messages - This has been super helpful especially in the exercises to see where I have gone wrong. I sometimes have to google the message though to see what it means.
Console.logging - My classmates put me onto this as a great way to work through JavaScript line by line to see where the error is. I think I will be able to get a similar result with Dev tools.
Googling - A man's best friend is not his dog, it is Google.
Asking your peers for help - This was good but as I have fallen a week behind people have moved on so not so helpful. When I was in Wellington with a couple of the team this was very helpful though.
Coaches - Asking coaches for help is great if you have some code to show them but if you are stumped from the beginning it may be better to ask your peers first.
Reflection - This is a very valuable tool. Thinking about what has worked and trying to to continue to make the same mistakes can make a real difference.
Define these
.map() - This calls a function on all the elements in an array then creates a new array with the values that have been outputed.
.filter() - Checks what elements are true in an array and creates a new array with them
.reduce() - Applys a function (e.g + -) against an accumulator (from left to right) to reduce the array to a single value.


AI-RFX Procurement Framework v1.0
Machine Learning Maturity Model, AI & Machine Learning Solutions
0 - Introduction
0.1 - Overview
This “Machine Learning Maturity Model v1.0” is part of the AI-RFX Procurement Framework, and it is the core of all the templates including the ”AI Request for Proposal Template” & the “AI Tender Competition Template”.
The Machine Learning Maturity Model is an extension of The Principles for Responsible Machine Learning, which aims to convert the high level Responsible ML Principles into a practical checklist-style assessment criteria. This “checklist” goes beyond the machine learning algorithms themselves, and provides an assessment criteria to evaluate the maturity of the infrastructure and processes around the algorithms. The concept of “Maturity” is not just defined as a matter of technical excellence, scientific rigor, and robust products. It also essentially involves responsible innovation and development processes, with sensitivity to the relevant domains of expert knowledge and consideration of all relevant direct and indirect stakeholders.
The Machine Learning Maturity Model should be a subset of the overall assessment criteria required to evaluate a proposed solution, and it is specific to the machine learning piece. It should be complemented with a traditional assessment of other areas such as the specific features requested, services needed, and more domain-specific areas.
Each of the criteria was designed to be linked to each one of the Principles for Responsible Machine Learning, and consists of the following:
|
# |
Assessment Criteria |
Responsible ML Principle |
|
#1 |
Practical benchmarks |
|
|
#2 |
Explainability by justification |
|
|
#3 |
Infrastructure for reproducible operations |
|
|
#4 |
Data and model assessment processes |
|
|
#5 |
Privacy enforcing infrastructure |
|
|
#6 |
Operational process design |
|
|
#7 |
Change management capabilities |
|
|
#8 |
Security risk processes |
0.2 - About Us
The Institute for Ethical AI & Machine Learning is a UK-based research centre that carries out world class research into responsible machine learning systems. We are formed by cross functional teams of applied STEM researchers, philosophers, industry experts, data scientists and software engineers.
Our vision is to mitigate risks of AI and unlock its full potential through frameworks that ensure ethical and conscientious development of intelligent systems across industrial sectors. We are building the Bell Labs of the 21st Century by delivering breakthrough contributions through applied AI research. You can find more information about us at https://ethical.institute.
0.3 - Motivation
There is currently a growing number of companies that are working towards introducing machine learning systems to automate critical processes at scale. This has required the “productisation” of machine learning models, which introduces new complexities. This complexity revolves around a new set of roles that fall under the umbrella of “Machine Learning Engineering”. This new set of roles fall in the intersection between DevOps, data science and software engineering.
To make things harder, the deployment of machine learning solutions in industry introduces an even bigger complexity. This involves the intersection of the new abstract “Machine Learning Engineering” roles, together with the industry domain experts and policy makers.
Because of this, there is a strong need to set the AI & ML standards, so practitioners are empowered to raise the bar for safety, quality and performance around AI solutions. The AI-RFX Procurement Framework aims to achieve the first steps towards this.
0.4 - How to use this document
0.4.1 - Using as reference
Many procurement managers may already own internally-approved assessment criteria. If that is the case, this document can be treated as a reference to obtain insights on key areas that should be taken into consideration when procuring and evaluation an AI / Machine Learning solution.
0.4.2 - Structure
Each subsection below consists of a detailed explanation of the criteria. It is followed by an summary overview of the requirements expected by the suppliers. Finally it contains a set of detailed questions that the supplier is expected to answer whether explicitly or implicitly in their proposal, together with red flags to look out for in each of the detailed questions.
0.4.3 - Example
The Machine Learning Maturity Model was used to build the ”AI Request for Proposal Template” & the “AI Tender Competition Template”, which are part of the AI-RFX Procurement Framework.
0.4.4 - When to use
This template is relevant only for the procurement of machine learning systems, and hence it is only suitable when looking to automate a process that involves data analysis that is too complex to be tackled using simple RPA tools or rule-based systems.
0.5 - Template vs Reality
This document should serve as a guide, and doesn’t require everything to be completed exactly as it’s stated. Especially for smaller projects, the level of detail required may vary significantly, and some sections can be left out as required. This template attempts to to provide a high level overview on each chapter (and respective sections) so the procurement manager and suppliers can provide as much content as reasonable.
0.6 - Open Source License - Free as in freedom
0.6.1 - Open source License
This document is open source, which means that it can be updated by the community. The motivation to release this as open source is so that it is continuously improved by the community. This will ensure that the standards for safety, quality and performance of what is expected in machine learning systems will keep increasing, whilst being kept in check on a realistic level by both suppliers and companies.
0.6.2 - Contributing.md
The Institute for Ethical AI & Machine Learning’s AI-RFX committee is in charge of the contributing community for all of the templates under the AI-RFX Procurement Framework. Anyone who would like to contribute, add suggestions, or provide example and practical uses of this template, please contact us through the website, or send us an email via<EMAIL_ADDRESS>0.6.3 - License
This document is registered under this MIT License (raw file), which means that anyone can re-use, modify or enhance this document as long as credit is given to The Institute for Ethical AI & Machine Learning. It also includes an “as is” disclaimer. Please read the license before using this template.
Machine Learning Maturity Model
1 - Practical benchmarks
This Machine Learning Maturity Model assessment criteria is directly aligned with the Responsible Machine Learning Principle #6 - Practical accuracy.
Explanation
-
Having the right benchmark metrics is one of the most important points to consider during the evaluation of machine learning solutions. Relevant benchmarks that are considered in this section include accuracy, time, time-to-accuracy, and computational resources.
-
The criteria of what makes good benchmarks can vary significantly depending on the task complexity, dataset size, etc. However the objective of this criteria is to assess that suppliers are able to follow best practices in data science, and make sure these are aligned with the use-case requirements.
Requirements
-
Suppliers must be able to demonstrate best practices in software development, data science and industry-specific knowledge when presenting benchmarks. These benchmarks include:
-
Time - Supplier must provide estimated processing times
-
Accuracy - Supplier must provide metrics beyond accuracy as relevant
-
Time-to-accuracy - Supplier must provide information on the estimate time and resources it takes to train new models to a reasonable accuracy
-
Computational resources - Supplier must provide insight on computational resources required for efficient use of their system
-
|
# |
Question |
Red flags |
|
1.1 |
Does the supplier have a process and/or infrastructure to make available statistical metrics beyond accuracy? |
|
|
1.2 |
Does the supplier have a process to ensure their machine learning evaluation metrics (i.e. cost functions & benchmarks) are aligned to the objective of the use-case? |
|
|
1.3 |
Does the supplier have a process to validate the way they evaluate predictions as correct or incorrect? |
|
|
1.4 |
Does the supplier use reasonable statistical methods when comparing performance of different models? |
|
|
1.5 |
Does the supplier provide comprehensible information on the time performance of their solution? |
|
|
1.6 |
Does the supplier provide comprehensible estimates on time and resources required to develop a model from scratch to a reasonable accuracy? |
|
|
1.7 |
Does the supplier provide minimum and recommended system requirements? |
|
|
1.8 |
Does the supplier provide comprehensible documentation around their benchmarks? |
|
|
1.9 |
Does the supplier ensure staff in the benchmark processes have the right exp.? |
|
2 - Explainability by justification
This Machine Learning Maturity Model assessment criteria is directly aligned with the Responsible Machine Learning Principle #3 - Explainability by justification.
Explanation
-
When domain experts are asked how they came to a specific conclusion, they don’t answer by pointing to the neurons that fired in their brains. Instead domain experts provide a “justifiable” explanation of how they came to that conclusion.
-
Similarly, with a machine learning model the objective is not to demand an explanation for every single weight in the algorithm. Instead, we look for a justifiable level of reasoning on the end-to-end process around and within the algorithm.
-
The level of scrutiny for an explanation to be “justifiable” will most certainly vary depending on the critical nature of the use-case, as well as the level of feedback that can be analysed by humans.
Requirements
-
This criteria is heavily dependent on Criteria 1 - Practical benchmarks, as suppliers have the right processes and capabilities around their accuracy metrics.
-
Suppliers must make a reasonable case about how their solution (or solution + human) will be able to provide at least the same level (or higher) of justification when making a final decision on an instance of data analysis as a domain expert would.
-
In order for suppliers to propose at least the same level of justification, they must also provide the current level of justification as a benchmark, from a quantitative perspective.
|
# |
Question |
Red flags |
|
2.1 |
Does the supplier provide audit trails to assess the data that went through the models? |
|
|
2.2 |
Does the supplier have a process and/or infrastructure to explain input/feature importance? |
|
|
2.3 |
Does the supplier provide capabilities to explain how input/features affect results? |
|
|
2.4 |
Does the supplier have the process and/or infrastructure to use model explainability techniques when developing deep learning / more complex models? |
|
|
2.5 |
Does the supplier have process and/or infrastructure to work with domain experts to abstract their knowledge into models? |
|
|
2.6 |
Does the supplier provide comprehensible information around their explainability processes? |
|
|
2.7 |
Does the supplier ensure the staff involved in the explainability processes have the right experience? |
|
3 - Data and model assessment processes
This Machine Learning Maturity Model assessment criteria is directly aligned with the Responsible Machine Learning Principle #2: Bias evaluation.
Explanation
-
Any any non-trivial decisions (defined as having more than 1 option) always carry an inherent bias without exception
-
Hence the objective is not to remove bias from a machine learning completely. Instead, the objective is to ensure that the "desired bias" is aligned with our accuracy/objectives, and "undesired bias" is identified and mitigated.
-
To be more specific, bias in machine learning boils down to the error between development and production. As a result of this, all machine learning models start to “degrade” as soon as they are put in production. The reasons for this include:
-
Unseen data is not representative to the data used in development
-
Temporal data changes as time goes on (e.g. inflation affects price)
-
Human-generated data changes as people and projects change
-
-
Bias in machine learning is a challenge that can be tackled by ensuring there are processes in place to identify, document and mitigate bias
Requirements
-
This assessment criteria is heavily dependent on Criteria 1 - Explainability by justification, and Criteria 2 - Practical benchmarks being in place.
-
Suppliers must be able to demonstrate processes and infrastructure they have to identify undesired bias through best practices in data science as well as awareness of domain-specific considerations
The Institute for Ethical AI & Machine Learning is working with the IEEE p7003 working group to develop the p7003 Algorithmic Bias Considerations standard that will facilitate this assessment criteria once it is released as suppliers that obtain this certification will verify that they have the relevant process towards data and model assessment.
|
# |
Question |
Red flags |
|
3.1 |
Does the supplier have a process to assess representability of datasets? |
|
|
3.2 |
Does the supplier have a process to identify and document undesired biases during the development of their models? |
|
|
3.3 |
Does the supplier have capabilities to track performance metrics in production to identify and mitigate new bias? |
|
|
3.4 |
Does the supplier provide comprehensible information around their data and model evaluation processes? |
|
|
3.5 |
Does the supplier demonstrate the team they have allocated has the right expertise to perform the data and model assessment efficiently? |
|
4 - Infrastructure for reproducible operations
This Machine Learning Maturity Model assessment criteria is directly aligned with the Responsible Machine Learning Principle #4 - Reproducible operations.
Explanation
-
Similar to production software, machine learning requires infrastructure to ensure reliable and robust service offerings
-
Different to traditional software however, machine learning introduces complexities beyond the code, such as versioning and orchestration of models
-
This requirements demand the suppliers to be conscious of this, and ensure their infrastructure is able to cope with these challenges
Requirements
-
Suppliers must also be able to demonstrate their capabilities to version, roll-back, diagnose and/or deploy models to production
-
Suppliers must have the processes and/or infrastructure to be able to separate the development of new models (i.e. new capabilities) from the serving in production of the models
-
Suppliers must demonstrate the ability to scale their services as required by the use-case.
|
# |
Question |
Red flags |
|
4.1 |
Does the supplier have process and/or infrastructure to version models? |
|
|
4.2 |
Does the supplier have process/infrastructure to re-train previous version of models? |
|
|
4.3 |
Does the supplier have a clear separation for their workflows around development and production of models? |
|
|
4.4 |
Does the supplier have a QA/BETA process within their machine learning lifecycle? |
|
|
4.5 |
Does the supplier have a reasonable process to deploy and revert back models in production? |
|
|
4.6 |
Does the supplier have the infrastructure to reproduce and diagnose errors observed in production efficiently? |
|
|
4.7 |
Does the supplier have the capabilities to scale their computation horizontally? |
|
|
4.8 |
Does the supplier have the capabilities to scale their computation vertically? |
|
|
4.9 |
Does the supplier have the capabilities to provision the relevant resources required by for the computation of the models across their infrastructure? |
|
|
4.10 |
Does the supplier have a stable release cycle and method to provide updates to machine learning infrastructure? |
|
|
4.11 |
Does the supplier provide the required functionality to extend the features provided? |
|
|
4.12 |
Does the supplier provide comprehensible documentation on the infrastructure around their machine learning? |
|
|
4.13 |
Does the supplier demonstrate the team they have allocated for the design, development and delivery of the machine learning infrastructure have the right expertise? |
|
5 - Privacy enforcing infrastructure
This Machine Learning Maturity Model assessment criteria is directly aligned with the Responsible Machine Learning Principle #7 - Trust beyond the user.
Explanation
-
Both suppliers and companies have a responsibility to protect the user’s privacy when their data is collected and stored into the solution.
-
Different to the B2C world where there might be a clear user, in B2B solutions there may be a large number of different user types that interact with the solution (directly and indirectly), increasing the complexity of data protection.
-
Whilst section “8 - Security risks processes” focuses on addressing data security risks from “external” threats, this section focuses on mitigating privacy violations that can arise from “internal” parties (whether intentional or unintentional)
Requirements
-
Suppliers must demonstrate awareness and capability to protect privacy of users in the platform across multiple levels.
-
The right communication channels should be established to ensure that all the stakeholders from the supplier are capable of separating and protecting personally identifiable information where reasonable.
-
When anonymization techniques are used, suppliers must demonstrate the use of reasonable techniques that can mitigate re-identification attacks.
|
# |
Question |
Red flags |
|
5.1 |
Does the supplier have the process and/or infrastructure to restrict access to user data? |
|
|
5.2 |
Does the supplier have processes and/or infrastructure in place to ensure data is anonymised where reasonable? |
|
|
5.3 |
Does the supplier have capabilities to ensure privacy protection on the data that is used on models? |
|
|
5.4 |
Does the supplier have the internal capabilities to ensure compliance with the relevant regulations? |
|
|
5.5 |
Does the supplier have the capability to ensure privacy protection on the metadata contained within their models? |
|
|
5.6 |
Does the supplier have processes in place that ensure user privacy measures are in place based on the consent that has been given? |
|
|
5.7 |
Does the supplier provide comprehensible documentation on the infrastructure around their machine learning? |
|
|
5.8 |
Does the supplier demonstrate the team they have allocated has the right expertise to ensure the required level of privacy is provided to the users in the system? |
|
6 - Operational process design
This Machine Learning Maturity Model assessment criteria is directly aligned with the Responsible Machine Learning Principle #1: Human augmentation.
Explanation
-
This criteria focuses on the processes required for the responsible operation of the solution. It assesses whether the proposed process takes into consideration the fail-safe steps in place to mitigate the impact of errors / incorrect predictions.
-
The operational process involves the full end-to-end steps that are performed around the machine learning system, including human intervention or analysis at any relevant step of the process
Requirements
-
This criteria is heavily dependent on Criteria 3 - Data and model assessment process, as suppliers have the plan in place in order for the operational/business/digital transformation to take place.
-
The main objective of this criteria is to ensure suppliers show explicitly they have considered the impact of incorrect predictions or errors, and are able to mitigate some of these through the operational steps introduced beyond the technology.
|
# |
Question |
Red flags |
|
6.1 |
Does the supplier have a process to assess the need for a human-in-the-loop design process based on the impact of incorrect predictions? |
|
|
6.2 |
Does the supplier have a process to assess whether a human-in-the-loop review process is or isn’t necessary? |
|
|
6.3 |
Does the supplier have a process to assess whether a temporary human-in-the-loop process is necessary? |
|
|
6.4 |
Does the supplier have a process to assess whether a scheduled human review is or isn’t necessary after the deployment of a model? |
|
|
6.5 |
Is the supplier able to ensure the right domain experts are involved in the human in the loop review process? |
|
|
6.6 |
Does the supplier demonstrate the team they have allocated has the right expertise to ensure safety, quality and performance in the design of the operational process? |
|
7 - Change management capabilities
This Machine Learning Maturity Model assessment criteria is directly aligned with the Responsible Machine Learning Principle #5 - Displacement strategy.
Explanation
-
When performing any large-scale IT projects it is important to have the right change management plans in place. With AI & machine learning systems it is no different.
-
Large-scale machine learning systems are often introduced to create a new data analysis capability and/or automate an existing process. This results in people that will need to be re-trained to use the systems in the right way, as well as ensuring that the gains in efficiency are distributed by allocating the now available time accordingly.
Requirements
-
This criteria is heavily dependent on Criteria 6 - Operational Process Design, as suppliers have the plan in place in order for the operational/business/digital transformation to take place.
-
For larger system rollouts, suppliers must be able to demonstrate the right processes and infrastructure to deal with the impact of their proposed automation, in regards to the stakeholders that are being partially or fully automated.
-
Suppliers must also be able to show they have the processes and infrastructure to perform training and handover for relevant stakeholders that will be operating the solution
|
# |
Question |
Red flags |
|
7.1 |
Does the supplier provide a comprehensible plan to roll out the solution internally? |
|
|
7.2 |
Does the supplier have capability to carry out the business change plan proposed? |
|
|
7.3 |
Does the supplier have the capability to provide training for stakeholders that will operate the solution? |
|
|
7.4 |
Does the supplier provide a comprehensible plan for handover? |
|
|
7.5 |
Does the supplier have a process and/or capability to support the transition of stakeholders that are automated? |
|
|
5.7 |
Does the supplier provide comprehensible documentation and material on change management plans and training material? |
|
|
5.8 |
Does the supplier demonstrate the team they have allocated has the right expertise to perform the change management delivery, trainings and handover? |
|
8 - Security risk processes
This Machine Learning Maturity Model assessment criteria is directly aligned with the Responsible Machine Learning Principle #8 - Data risk awareness.
Explanation
-
This section focuses on the “external” threats & risks around the data contained in the systems proposed by the solution.
-
This section doesn’t only require suppliers to have the right technical safeguards, but also to have the right education for relevant users that will interact with potentially complex systems
-
This section should be complementary to the standard security questions that are often provided through questionnaires in tender processes.
Requirements
-
Suppliers must demonstrate awareness around data and system security, including around their machine learning models, as well as the infrastructure around it.
-
Suppliers should provide an overview of how their systems as well as processes are secured.
-
Suppliers should demonstrate they have the right processes in place around stakeholders that operate the solution
-
Suppliers should demonstrate they have the right processes in place to educate the stakeholders that operate the solution
|
# |
Question |
Red flags |
|
8.1 |
Does the supplier have processes to ensure only privileged users have access |
|
|
8.2 |
Does the supplier ensure all machine learning model data is encrypted at transport? |
|
|
8.3 |
Does the supplier ensure all machine learning model data is encrypted at rest? |
|
|
8.4 |
Does the supplier have processes and/or infrastructure in place to ensure that all the encryption keys and relevant passwords are not shared or repeated across deployments? |
|
|
8.5 |
Does the supplier have processes and/or infrastructure to assess the level of protection require based on exposure? |
|
|
8.6 |
Does the supplier have a process to ensure confidential information is not exposed through logs or other mediums of metrics? |
|
|
8.7 |
Does the supplier have processes and/or infrastructure to ensure system access is restricted to privileged users as required? |
|
|
8.8 |
Does the supplier demonstrate the team they have allocated has the right expertise to fortify their machine learning infrastructure against external threats? |
|


This conference proceeding is a collection of the papers accepted by the CENet 2017 − the 7th International Conference on Computer Engineering and Networks held on Shanghai from 22−23 July, 2017.
This proceeding contains the five parts: Part I focuses on Machine learning (21 papers); Part II Wireless communication (21 papers); Part III Information theory (21 papers), Part IV Cloud science (14 papers) and Part V Data analysis (21 papers).
Each part can be used as an excellent reference by industry practitioners, university faculty, and undergraduate as well as graduate students who need to build a knowledge base of the most current advances and state-of-practice in the topics covered by this conference proceedings. This will enable them to produce, maintain, and manage systems with high levels of trustworthiness and complexity
Thanks go to the authors for their hard work and dedication as well as the reviewers for ensuring the selection of only the highest quality papers; their efforts made this proceedings possible.
| Sessions |
|---|
| Session I - Machine Learning |
| Session II - Wireless communication |
| Session III -Information Theory |
| Session IV - Cloud Science |
| Session V - Date Analysis |
| Session I - Machine Learning |
| Classification of EEG Signal by STFT-CNN Framework: Identification of Right-/left-hand Motor Imagination in BCI Systems PoS(CENet2017)001 pdf |
| Analyze EEG Signals with Convolutional Neural Network Based on Power Spectrum Feature Selection PoS(CENet2017)002 pdf |
| The Moving Objects Tracking Algorithm in Soccer Robot System Based on Extended Kalman Filter PoS(CENet2017)003 pdf |
| A Study on Improved Cockroach Swarm Optimization Algorithm PoS(CENet2017)004 pdf |
| The Classification of Hyperspectral Images Based on Band-Grouping and Convolutional Neural Network PoS(CENet2017)005 pdf |
| A Fuzzy Density Peak Optimization Initial Centers Selection for K-medoids Clustering Algorithm PoS(CENet2017)006 pdf |
| Research on Clearance of Aerial Remote Sensing Images Based on Image Fusion PoS(CENet2017)007 pdf |
| RELOCATE: A Container Based Moving Target Defense Approach PoS(CENet2017)008 pdf |
| A New Fuzzy Neural Network Model and Its Application on Network Operating Situation Awareness PoS(CENet2017)009 pdf |
| Vectoring Gauss Mixture Model Mean Parameters in Speaker Verification PoS(CENet2017)010 pdf |
| Improved Locally Linear Embedding by Using Adaptive Neighborhood Selection Techniques PoS(CENet2017)011 pdf |
| Improved Ant Colony Optimization in Express Distribution Routing PoS(CENet2017)012 pdf |
| Vision-Based Object Fuzzy Recognition in Plain Industrial Environment by Contour Matching PoS(CENet2017)013 pdf |
| Prediction Model of Network Security Situation based on Elman Neural Network PoS(CENet2017)014 pdf |
| Region-based Pre-selection of Overlapping Area for the SIFT Algorithm PoS(CENet2017)015 pdf |
| Research on Mobile Robot SLAM Based on Laser Range Finder PoS(CENet2017)016 pdf |
| Fast Face Recognition Based on 2D Fractional Fourier Transform PoS(CENet2017)017 pdf |
| Privacy Preserving SVM with Different Kernel Functions for Multi-Classification Datasets PoS(CENet2017)018 pdf |
| The Particle Swarm Optimization Algorithm of Three- state Reliability Redundancy Allocation Problem PoS(CENet2017)019 pdf |
| Object Tracking Using On-line Distance Metric Learning PoS(CENet2017)020 pdf |
| Video Age Estimation with Multiple Stacked CNN Models PoS(CENet2017)021 pdf |
| Session II - Wireless communication |
| A Method to Improve the Performance for Storing Massive Small Files in Hadoop PoS(CENet2017)022 pdf |
| A Downlink Interference Suppression Scheme for Ultra Dense Networks PoS(CENet2017)023 pdf |
| Research on Socket Communication System Based on Zigbee Wireless Sensor Network PoS(CENet2017)024 pdf |
| Lightweight Security Trust Model for Wireless Sensor Network with Mobile Sink PoS(CENet2017)025 pdf |
| Research into Radar Net Anti-interference Strategy Based on Improved Genetic Algorithm PoS(CENet2017)026 pdf |
| A Vehicular Ad Hoc Networks Clustering Algorithm based on Position-Competition PoS(CENet2017)027 pdf |
| Research on Influence Factors of Quantitative Perception of Internet Word of Mouth Based on Online Reviews PoS(CENet2017)028 pdf |
| Finding Full Coverage Agent Group (FCAG) of Information Diffusion in Online Social Networks PoS(CENet2017)029 pdf |
| A Routing Algorithm via Constructing the Weighted Minimum Connected Dominating Set in Mobile Adhoc Network PoS(CENet2017)030 pdf |
| Research on Network Coding Algorithm in Two-way Relay Hybrid System of Wireless/Power Line PoS(CENet2017)031 pdf |
| Multi-Radio Multi-Channel Protocol for Emergency Wireless Mesh Network PoS(CENet2017)032 pdf |
| Application of Improved RFID Anti - Collision Algorithm in Library Management PoS(CENet2017)033 pdf |
| Guard Time Settings in TDMA Family MAC Protocol for Underwater Sensor Networks PoS(CENet2017)034 pdf |
| A Load Balancing Strategy Based on Multiple Metrics in Spatial Image File Access Process in Hadoop PoS(CENet2017)035 pdf |
| A Proportional Fair Resource Allocation Algorithm for Hybrid Hierarchical Backhaul Networks PoS(CENet2017)036 pdf |
| Design and Development of Vehicle Collision-Avoidance System Based on UWB Wireless Sensor Networks PoS(CENet2017)037 pdf |
| Clustering Opportunistic Ant-based Routing Protocol for Wireless Sensor Networks PoS(CENet2017)038 pdf |
| Research on Node Deployment in Structural Health Monitoring Based on Reliability PoS(CENet2017)039 pdf |
| Missing Data Reconstruction Using Adaptively Updated Dictionary in Wireless Sensor Networks PoS(CENet2017)040 pdf |
| Research on safety mobile terminal and judging credibility method based on call tracking PoS(CENet2017)041 pdf |
| A Survey of Side-Channel Attack and Security Assessment for Cryptographic Equipment PoS(CENet2017)042 pdf |
| Session III -Information Theory |
| Damage Prediction Model of High Pressure Water Jet Impinging Coal Rock Based on PSO-BPNN PoS(CENet2017)043 pdf |
| Research on Power Attack Comprehensive Experiment Platform Based on SAKURA - GHardware Circuit PoS(CENet2017)044 pdf |
| Leakage Point Locating Algorithm Based on Generalized Mutual Correlation in Underground Water Pipeline PoS(CENet2017)045 pdf |
| FPGA Implementation of AES Algorithm Resistant Power Analysis attacks PoS(CENet2017)046 pdf |
| A Linux-based Dynamic Partial Reconfiguration System Applied on Xilinx Zynq PoS(CENet2017)047 pdf |
| Performance of 3D Printing in Construction by Using Computer Control Technology PoS(CENet2017)048 pdf |
| Study Logistics Architecture for Grain Container Multimodal Transport Based on Multi-Agent PoS(CENet2017)049 pdf |
| An Intelligent Method for Solving Flexible Job Shop Scheduling Problem PoS(CENet2017)050 pdf |
| Green Suppliers Evaluation by Fuzzy Clustering and TOPSIS Suppliers PoS(CENet2017)051 pdf |
| A Real-time Reputation Evaluation Model for Stimulating in MANETs PoS(CENet2017)052 pdf |
| A Three-layer Hybrid Bounding Volume Hierarchy for Collision Detection PoS(CENet2017)053 pdf |
| A Multi-thread UI Framework Without Graphics Driver Of Mouse On VxWorks System PoS(CENet2017)054 pdf |
| Research on An Electronic Map Retrieval Algorithm Based on Big Data PoS(CENet2017)055 pdf |
| A New Method for Constructing TV User Profiles Based on Micro-blog PoS(CENet2017)056 pdf |
| A Hybrid Neural Network for Sentence Classification PoS(CENet2017)057 pdf |
| A Real-time Performance Monitoring Framework Based on ACC Model in Collaboration Business Process Environment PoS(CENet2017)058 pdf |
| The Research of C4ISR System Design And Modeling Method Based on Model PoS(CENet2017)059 pdf |
| Mobile Robot Moving Target Detection and Tracking System PoS(CENet2017)060 pdf |
| Frequent sequence mining from massive access log for user’s behaviour investigation PoS(CENet2017)061 pdf |
| Constrained Multi-objective Optimization Algorithm Based on ε Adaptive Weighted Constraint Violation PoS(CENet2017)062 pdf |
| The SNM Algorithm Based on a Variety of Edit Distance and Variable Window PoS(CENet2017)063 pdf |
| Session IV - Cloud Science |
| 3D Visualization of Point Clouds Using HTML5 and WebGL PoS(CENet2017)064 pdf |
| Construction and Model of Index System under Information Condition PoS(CENet2017)065 pdf |
| Research Based on Cloudy Data Store Circumscriptive Real-time Demographic System PoS(CENet2017)066 pdf |
| Cloud Computing Strategy Analysis in Small and Medium-Sized Enterprises PoS(CENet2017)067 pdf |
| Trust Model Based on Role and Attribute in Cloud Environment PoS(CENet2017)068 pdf |
| Research of Knowledge Mapping Construction Method Based on Scientific Research Results PoS(CENet2017)069 pdf |
| A Novel Trust Management Framework for Mobile Cloud Computing Environment PoS(CENet2017)070 pdf |
| The Impact of Dynamic Retrieval Emissivity on the Microwave Surface-sensitive Channels over Land PoS(CENet2017)071 pdf |
| Design and Implementation of Camp us Keeping Fit Application Based on IOS and Bmob Cloud PoS(CENet2017)072 pdf |
| Parallel Routing Method in Churn Tolerated Resource Discovery PoS(CENet2017)073 pdf |
| Design and Key Calculation of a Big Data Platform for Transmission Channels Monitoring PoS(CENet2017)074 pdf |
| Analysis of College Students' Spirit Index Based on Structural Equation Model PoS(CENet2017)075 pdf |
| Formal Design and Verification for A Typical Security Gateway PoS(CENet2017)076 pdf |
| Resource Authentication Management Model Based on CA and DHT in Internet of Things PoS(CENet2017)077 pdf |
| Session V - Date Analysis |
| Adaptive short-time fractional Fourier transform used in time-frequency analysis PoS(CENet2017)078 pdf |
| Designing A Unified Architecture Graphics Processing Unit PoS(CENet2017)079 pdf |
| Parallelism of MRT Lattice Boltzmann Method based on Multi-GPUs PoS(CENet2017)080 pdf |
| Visual Detection of Stream Cipher Sequences on ZUC Algorithm PoS(CENet2017)081 pdf |
| Integrated Flight and Propulsion Distributed Control System Simulation Platform Design PoS(CENet2017)082 pdf |
| Analysis on Total Factor Productivity of Chinese Information Service Industry and Influencing Factors PoS(CENet2017)083 pdf |
| A Kind of Seabed Seismic Data Acquisition Cell for Acoustic Measurement PoS(CENet2017)084 pdf |
| An Unified Payment Method of Charging Piles Based on Blockchain PoS(CENet2017)085 pdf |
| A New Method for Consistency Correction of Judgment Matrix in AHP PoS(CENet2017)086 pdf |
| Coal Mine Lead-acid Battery Intelligent Charger PoS(CENet2017)087 pdf |
| A Research on Interior Location of Improved Monte Carlo Algorithm Based on RSSI PoS(CENet2017)088 pdf |
| A Base Sleep Mechanism for Minimizing Energy Cost PoS(CENet2017)089 pdf |
| File Version Based Continuous Data Protection On Distributed Object PoS(CENet2017)090 pdf |
| A Recommendation System Based on the Education Big Data for E-learning PoS(CENet2017)091 pdf |
| Query Optimization Ways of DB2 to Improve Database Performance PoS(CENet2017)092 pdf |
| Discrete curvature and torsion-based parameterization scheme for data points PoS(CENet2017)093 pdf |
| Interval T-S fuzzy modeling based on minimizing 1-norm on approximation error PoS(CENet2017)094 pdf |
| A Prediction Algorithm for Airfare Based on Time Series PoS(CENet2017)095 pdf |
| Detecting Anomalous User Behavior in Database PoS(CENet2017)096 pdf |
| A Novel Strategy to Improve the Performance of Speed And the Currentloopsfor Servo Control PoS(CENet2017)097 pdf |
| Framework of Frequently Trajectory Extraction from AIS Data PoS(CENet2017)098 pdf |


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Download Pigments by Sorin Covor


SCTableViewController
@interface SCTableViewController
: UITableViewController <UISplitViewControllerDelegate,
UIPopoverControllerDelegate>
This class functions as a means to simplify development with SCTableViewModel.
SCTableViewController conveniently provides several ready made navigation bar types based on SCNavigationBarType, provided that it is a subview of a navigation controller. SCTableViewController also defines placeholders for a tableView and a tableViewModel that the user can allocate and assign. If a tableViewModel is defined, SCTableViewController also connects its doneButton (if present) to tableViewModel’s commitButton automatically.
In addition, SCTableViewController fully manages memory warnings and makes sure the assigned table view is released once a memory warning occurs and reloaded once the view controller is loaded once more.
Finally, SCTableViewController provides several useful actions (SCTableViewControllerActions) that notify the delegate object of events like the view appearing or disappearing.
-
Contains a valid SCTableViewModel that is associated with tableView and ready to use. If this model is replaced by a custom one, the class will automatically take care of associating it with tableView.
Declaration
Objective-C
@property (nonatomic, strong) SCTableViewModel *tableViewModel;
-
The type of the navigation bar.
Declaration
Objective-C
@property (nonatomic) SCNavigationBarType navigationBarType;
-
The navigation bar’s Add button. Only contains a value if the button exists on the bar.
Declaration
Objective-C
@property (nonatomic, readonly) UIBarButtonItem *addButton;
-
The editButtonItem of ‘SCTableViewController’‘s superclass.
Declaration
Objective-C
@property (nonatomic, readonly) UIBarButtonItem *editButton;
-
The navigation bar’s Cancel button. Only contains a value if the button exists on the bar.
Declaration
Objective-C
@property (nonatomic, readonly) UIBarButtonItem *cancelButton;
-
Set to TRUE to allow the cancel button to appear when entering editing mode. Default: TRUE. @note: Only applicable if navigationBarType == SCNavigationBarTypeEditRight.
Declaration
Objective-C
@property (nonatomic) BOOL allowEditingModeCancelButton;
-
The navigation bar’s Done button. Only contains a value if the button exists on the bar.
Declaration
Objective-C
@property (nonatomic, readonly) UIBarButtonItem *doneButton;
-
When set to YES, the view controller will automatically disable all navigation bar buttons until its view fully appears. This setting is inhereted by all automatically generated detail view controllers. Default: NO.
Declaration
Objective-C
@property (nonatomic) BOOL autoDisableNavigationButtonsUntilViewAppears;
-
Undocumented
Declaration
Objective-C
@property (nonatomic, strong) UIPopoverController *popoverController
-
The set of view controller action blocks.
Declaration
Objective-C
@property (nonatomic, readonly) SCTableViewControllerActions *actions;
-
The current state of the view controller.
Declaration
Objective-C
@property (nonatomic, readonly) SCViewControllerState state;
-
Property is TRUE if the view controller have been dismissed due to the user tapping the Cancel button. This property is useful if you do not with to subclass this view controller. See also SCTableViewControllerDelegate to get notified when the view controller is dismissed.
Declaration
Objective-C
@property (nonatomic, readonly) BOOL cancelButtonTapped;
-
Property is TRUE if the view controller have been dismissed due to the user tapping the Done button. This property is useful if you do not with to subclass this view controller. See also SCTableViewControllerDelegate to get notified when the view controller is dismissed.
Declaration
Objective-C
@property (nonatomic, readonly) BOOL doneButtonTapped;
-
Method returns the Interface Builder data definition with the given name.
Declaration
Objective-C
- (SCDataDefinition *)dataDefinitionWithIBName:(NSString *)ibName;
Parameters
ibName
The name of the data definition. This name is typically found in IB’s document outline pane.
Return Value
The data definition.
-
This method should be implemented by SCTableViewController subclasses that wish to provide data source object to an SCObjectSection that has been created in Interface Builder.
Note
There is no need to implement this method for sections with data definitions of type SCUserDefaultsDefinition or SCiCloudKeyValueDefinition.
Declaration
Objective-C
- (NSObject *)objectForSection:(SCObjectSection *)objectSection atIndex:(NSUInteger)index;
Parameters
objectSection
The object section that needs the data source object.
index
The object section index.
Return Value
An object compatible with objectSection’s data definition.
-
This method should be implemented by SCTableViewController subclasses that wish to provide data source objects to an SCArrayOfObjectsSection that has been created in Interface Builder. The method is typically called by the STV framework for sections with data definitions of type SCClassDefinition or SCDictionaryDefinition.
Note
There is no need to implement this method for sections with data definitions of type SCEntityDefinition or SCWebServiceDefinition.
Declaration
Objective-C
- (NSMutableArray *)objectsForSection:(SCArrayOfObjectsSection *)objectsSection atIndex:(NSUInteger)index;
Parameters
objectsSection
The objects section that needs the data source objects.
index
The objects section index.
Return Value
A mutable array of objects compatible with objectsSection’s data definition.
-
The object that acts as the delegate of ‘SCTableViewController’. The object must adopt the SCTableViewControllerDelegate protocol.
Declaration
Objective-C
@property (nonatomic, weak) id delegate;
-
///////////////////////////////////////////////////////////////////////////////////////
///////////////////////////////////////////////////////////////////////////////////////
Declaration
Objective-C
@property (nonatomic, retain, readonly) NSArray *_STV_ibDataDefinitions;
-
Undocumented
Declaration
Objective-C
@property (nonatomic, readonly) BOOL ibEmbedded
-
Returns TRUE if the view controller currently has been given focus by its master model.
Declaration
Objective-C
@property (nonatomic, readonly) BOOL hasFocus;
-
Undocumented
Declaration
Objective-C
@property (nonatomic, readonly) BOOL staticContentAddedToModel
-
Undocumented
Declaration
Objective-C
- (void)invalidateStaticContent;
-
Method used internally to add all static content added in IB to the table view model.
Declaration
Objective-C
- (void)addStaticContentToModel;
-
Method should be overridden by subclasses to perform any required initialization.
Warning
Subclasses must call [super performInitialization] from within the method’s implementation.Declaration
Objective-C
- (void)performInitialization;
-
Method gets called when the Cancel button is tapped. If what you want is to get notified when the Cancel button gets tapped without subclassing ‘SCTableViewController’, consider using SCTableViewControllerDelegate.
Declaration
Objective-C
- (void)cancelButtonAction;
-
Method gets called when the Done button is tapped. If what you want is to get notified when the Cancel button gets tapped without subclassing ‘SCTableViewController’, consider using SCTableViewControllerDelegate.
Declaration
Objective-C
- (void)doneButtonAction;
-
Method gets called when the Edit button is tapped.
Declaration
Objective-C
- (void)editButtonAction;
-
Method gets called when the Cancel button is tapped while the table view is in editing mode.
Declaration
Objective-C
- (void)editingModeCancelButtonAction;
-
Dismisses the view controller with the specified values for cancel and done.
Declaration
Objective-C
- (void)dismissWithCancelValue:(BOOL)cancelValue doneValue:(BOOL)doneValue;
-
Called by master model to have the view controller gain focus.
Declaration
Objective-C
- (void)gainFocus;
-
Called by master model to have the view controller lose focus.
Declaration
Objective-C
- (void)loseFocus;


‘Fabulous’: Sky Sports pundit raves about Liverpool FC star
Sky Sports pundit Danny Mills heaps praise on Liverpool FC star Jordan Henderson
by The Sport Review staffDanny Mills has hailed Liverpool FC captain Jordan Henderson as a “fabulous player”.
The England international has played a key role in Liverpool FC’s title challenge so far this season.
Henderson scored Liverpool FC’s equaliser in a 2-1 victory over Tottenham Hotspur at Anfield earlier this month.
The Merseyside outfit are sitting at the top of the Premier League table and eight points clear of Leicester City and Chelsea FC.
The Liverpool FC skipper helped England to qualify for Euro 2020 on Friday night after their 6-0 victory over Montenegro.
Henderson was used in a holding role in the Three Lions side despite being used in a more advanced position at Liverpool FC.
Former Manchester City defender Mills spoke in glowing terms about Henderson’s versatility in midfield.
“The age he’s at now, the experience that he has, the authority that he has in the middle there to play that holding role – he can still get forward, of course he can,” Mills told talkSPORT.
“If things aren’t quite going well, he can direct the traffic.
“There are better creative players ahead of him. That’s the point. Jordan Henderson is a fabulous player.
“If you’re going to play one holding midfield player out and out – it’s not quite his position that he plays at Liverpool – but he would be, I think, the best player to play in that role.”
Liverpool FC have never won the Premier League title despite finishing in second place on four different occasions.
Henderson has finished as a Premier League runner up on two different occasions under Brendan Rodgers and Jurgen Klopp.
The 29-year-old lifted the Champions League trophy last season thanks to 2-0 win against Tottenham Hotspur in Madrid.
Liverpool FC singed Henderson in a £20m deal from Sunderland under Kenny Dalglish in 2011.
Henderson has scored one goal and has made one assist in 11 games in the Premier League.


I am interested in pursuing tiny habits most of all out of the suggested reading. I find that I often want to intiate a habit but struggle commit to it fully. Tiny habits seems like an interesting method to cement a habit before pursuing it fully. I will try timeboxing on my next few assignments as I can see potential it has in improving productivity and will also help to set aside time for me to study. So far I have tried tiny habits in one instances - by doing two minutes of running at the end of my gym workouts. So far it has been successful and I will try to continue and increase the running time until I am able to do a proper warmdown after each gym session. I am interested in trying the pomodoro technique properly after hearing about it a lot during school but never having tried it in practice. Meditation is less appealing to me but I will try it over the next week or so in 2 minute sections as suggested in the "power of meditation" article and see if I see any changes. I also found the flow article very interesting in particular the section on hyperfocus. I look forward to trying some new methods to improve my productivity.


[Index] [1] [2] [3] [4] [5] [6] [7] [8]
HDFView is a Java-based tool for browsing and editing HDF4 and HDF5 files. HDFView allows users to browse through any HDF4 and HDF5 file, starting with a tree view of all top-level objects in an HDF file's hierarchy. HDFView allows a user to descend through the hierarchy and navigate among the file's data objects. The content of a data object is loaded only when the object is selected, providing interactive and efficient access to HDF4 and HDF5 files. HDFView editing features allow a user to create, delete, and modify the value of HDF objects and attributes.
The HDFView graphical user interface (GUI) is simple and easy-to-use. First, HDFView was implemented by using the JavaTM 2 Platform, which is machine-independent. The GUI components have the same look-and-feel for all machines. Second, HDFView uses conventional folders and icons to display groups and datasets in a tree structure. Users can easily expand or collapse folders to navigate the hierarchical structure of an HDF file. Third, HDFView shows data content as text (table or plain text) or as an image.
General information about HDF (HDF4 and HDF5) is available at http://www.hdfgroup.org/.
Information about the HDFView tool and related products is available from the HDF-Java Home page.
[Index] [1] [2] [3] [4] [5] [6] [7] [8]


String
constructors and methods like toString()
or substring()
that can be replaced with a simpler expression.
For example, calls to these methods can be safely removed in code
like "string".substring(0)
, "string".toString()
, or
new StringBuilder().toString().substring(1,3)
.
Example:
System.out.println(new String("message"));
After the quick-fix is applied:
System.out.println("message");
Note that the quick-fix removes the redundant constructor call, and this may affect String
referential equality.
If you need to preserve it, even though it is considered bad practice, suppress the warning or use the inspection setting to ignore
redundant String
constructor calls.
Use the Do not report String constructor calls option below to not report code like the example above.
This will avoid changing the outcome of String comparisons with ==
or !=
after applying
the quick-fix in code that uses new String()
calls to guarantee a different object identity.
New in 2018.1


Kmeans Model Selection in 1D
Uses netlab since haven't had time to switch over to pmtk
% This file is from pmtk3.googlecode.com clear all setSeed(0); mix = gmm(1, 3, 'spherical'); mix.centres = [-1 0 1]'; mix.covars = [0.1 0.1 0.1]; %e = 1e-2; e2 = e/2; %mix.priors = [0.5-e2, 0.5-e2, e]; bins = -2:0.1:2; n = 1000; ndx = 1:n; Xtrain = gmmsamp(mix, n); figure; hist(Xtrain, bins); title('Xtrain') printPmtkFigure kmeansModelSel1dTrain Xtest = gmmsamp(mix, n); %figure %hist(Xtest, bins); %title('Xtest') %Ks = [1:9]; %Ks = [1 2 3 4 5 10 15 20 25]; Ks = [2 3 4 5 6 10 15]; %pemp = normalize(hist(Xtrain, bins)); for i=1:length(Ks) K = Ks(i); mu = kmeansFit(Xtrain, K)'; Xhat = kmeansDecode(kmeansEncode(Xtest, mu'), mu'); mse(i) = mean(sum((Xhat - Xtest).^2,2)); mus{i} = mu; end figure; for i=1:6 mu = mus{i}; K = Ks(i); subplot(2,3,i); %bar(bins,pemp); hold on for k=1:K %h=line([mu(k) mu(k)], [0 0.1*max(pemp)]); h=line([mu(k) mu(k)], [0 1]); set(h, 'color', 'r', 'linewidth', 3); hold on end title(sprintf('K=%d, mse=%5.4f', K, mse(i))) end printPmtkFigure kmeansModelSel1dKmeans figure; plot(Ks, mse, 'o-', 'linewidth', 2); title('MSE on test vs K for K-means') printPmtkFigure kmeansModelSel1dMse finebins = -2:0.001:2; options = foptions; for i=1:length(Ks) K = Ks(i); mix = gmm(1, K, 'spherical'); mix = gmmem(mix, Xtrain, options); nll(i) = -sum(log(gmmprob(mix, Xtest))) models{i} = mix; end figure; for i=1:6 mix = models{i}; subplot(2,3,i); K = Ks(i); pmodel = gmmprob(mix, finebins(:)); plot(finebins, pmodel, '-', 'linewidth', 2); title(sprintf('K=%d, nll=%5.4f', K, nll(i))) end printPmtkFigure kmeansModelSel1dGmm figure; plot(Ks, nll, 'o-', 'linewidth', 2) title('NLL on test set vs K for GMM') printPmtkFigure kmeansModelSel1dNLL
nll = 1.2448e+03 Maximum number of iterations has been exceeded nll = 1.0e+03 * 1.2448 1.1990 Maximum number of iterations has been exceeded nll = 1.0e+03 * 1.2448 1.1990 1.1970 Maximum number of iterations has been exceeded nll = 1.0e+03 * 1.2448 1.1990 1.1970 1.2026 Maximum number of iterations has been exceeded nll = 1.0e+03 * Columns 1 through 4 1.2448 1.1990 1.1970 1.2026 Column 5 1.1996 Maximum number of iterations has been exceeded nll = 1.0e+03 * Columns 1 through 4 1.2448 1.1990 1.1970 1.2026 Columns 5 through 6 1.1996 1.2033 Maximum number of iterations has been exceeded nll = 1.0e+03 * Columns 1 through 4 1.2448 1.1990 1.1970 1.2026 Columns 5 through 7 1.1996 1.2033 1.2053


R.E.M. and the Decemberists playing in the hostel dining room, making me feel at home.
7/26/2018, 1:02:05 AM
Favs: 3
Retweets: 0
link--- layout: tweet ---
R.E.M. and the Decemberists playing in the hostel dining room, making me feel at home.
7/26/2018, 1:02:05 AM
Favs: 3
Retweets: 0
link


Header
This is my fist sentence.
This is another sentence.
Here goes a third sentence.
Ofcourse this is the fourth.


{% extends "_base.html" %} {% block main %} {% with "Create a post" as form_action_name %} {% include "blog/forms/post.html" %} {% endwith %} {% endblock %}


About Metapackages
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001 /* 002 * Licensed to the Apache Software Foundation (ASF) under one or more 003 * contributor license agreements. See the NOTICE file distributed with 004 * this work for additional information regarding copyright ownership. 005 * The ASF licenses this file to You under the Apache License, Version 2.0 006 * (the "License"); you may not use this file except in compliance with 007 * the License. You may obtain a copy of the License at 008 * 009 * http://www.apache.org/licenses/LICENSE-2.0 010 * 011 * Unless required by applicable law or agreed to in writing, software 012 * distributed under the License is distributed on an "AS IS" BASIS, 013 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 014 * See the License for the specific language governing permissions and 015 * limitations under the License. 016 */ 017 package org.apache.commons.math.analysis.interpolation; 018 019 import org.apache.commons.math.exception.DimensionMismatchException; 020 import org.apache.commons.math.exception.util.LocalizedFormats; 021 import org.apache.commons.math.exception.NumberIsTooSmallException; 022 import org.apache.commons.math.analysis.polynomials.PolynomialFunction; 023 import org.apache.commons.math.analysis.polynomials.PolynomialSplineFunction; 024 import org.apache.commons.math.util.MathUtils; 025 026 /** 027 * Computes a natural (also known as "free", "unclamped") cubic spline interpolation for the data set. 028 *
 029 * The {@link #interpolate(double[], double[])} method returns a {@link PolynomialSplineFunction} 030 * consisting of n cubic polynomials, defined over the subintervals determined by the x values, 031 * x[0] < x[i] ... < x[n]. The x values are referred to as "knot points."
 032 *
 033 * The value of the PolynomialSplineFunction at a point x that is greater than or equal to the smallest 034 * knot point and strictly less than the largest knot point is computed by finding the subinterval to which 035 * x belongs and computing the value of the corresponding polynomial at x - x[i]  where 036 * i is the index of the subinterval. See {@link PolynomialSplineFunction} for more details. 037 *
 038 *
 039 * The interpolating polynomials satisfy:
 040 *
The value of the PolynomialSplineFunction at each of the input x values equals the 041 * corresponding y value.
 042 *
Adjacent polynomials are equal through two derivatives at the knot points (i.e., adjacent polynomials 043 * "match up" at the knot points, as do their first and second derivatives).
 044 *

 045 *
 046 * The cubic spline interpolation algorithm implemented is as described in R.L. Burden, J.D. Faires, 047 * Numerical Analysis, 4th Ed., 1989, PWS-Kent, ISBN 0-53491-585-X, pp 126-131. 048 *
 049 * 050 * @version $Revision: 983921 $ $Date: 2010-08-10 12:46:06 +0200 (mar. 10 ao??t 2010) $ 051 * 052 */ 053 public class SplineInterpolator implements UnivariateRealInterpolator { 054 055 /** 056 * Computes an interpolating function for the data set. 057 * @param x the arguments for the interpolation points 058 * @param y the values for the interpolation points 059 * @return a function which interpolates the data set 060 * @throws DimensionMismatchException if {@code x} and {@code y} 061 * have different sizes. 062 * @throws org.apache.commons.math.exception.NonMonotonousSequenceException 063 * if {@code x} is not sorted in strict increasing order. 064 * @throws NumberIsTooSmallException if the size of {@code x} is smaller 065 * than 3. 066 */ 067 public PolynomialSplineFunction interpolate(double x[], double y[]) { 068 if (x.length != y.length) { 069 throw new DimensionMismatchException(x.length, y.length); 070 } 071 072 if (x.length < 3) { 073 throw new NumberIsTooSmallException(LocalizedFormats.NUMBER_OF_POINTS, 074 x.length, 3, true); 075 } 076 077 // Number of intervals. The number of data points is n + 1. 078 int n = x.length - 1; 079 080 MathUtils.checkOrder(x); 081 082 // Differences between knot points 083 double h[] = new double[n]; 084 for (int i = 0; i < n; i++) { 085 h[i] = x[i + 1] - x[i]; 086 } 087 088 double mu[] = new double[n]; 089 double z[] = new double[n + 1]; 090 mu[0] = 0d; 091 z[0] = 0d; 092 double g = 0; 093 for (int i = 1; i < n; i++) { 094 g = 2d * (x[i+1] - x[i - 1]) - h[i - 1] * mu[i -1]; 095 mu[i] = h[i] / g; 096 z[i] = (3d * (y[i + 1] * h[i - 1] - y[i] * (x[i + 1] - x[i - 1])+ y[i - 1] * h[i]) / 097 (h[i - 1] * h[i]) - h[i - 1] * z[i - 1]) / g; 098 } 099 100 // cubic spline coefficients -- b is linear, c quadratic, d is cubic (original y's are constants) 101 double b[] = new double[n]; 102 double c[] = new double[n + 1]; 103 double d[] = new double[n]; 104 105 z[n] = 0d; 106 c[n] = 0d; 107 108 for (int j = n -1; j >=0; j--) { 109 c[j] = z[j] - mu[j] * c[j + 1]; 110 b[j] = (y[j + 1] - y[j]) / h[j] - h[j] * (c[j + 1] + 2d * c[j]) / 3d; 111 d[j] = (c[j + 1] - c[j]) / (3d * h[j]); 112 } 113 114 PolynomialFunction polynomials[] = new PolynomialFunction[n]; 115 double coefficients[] = new double[4]; 116 for (int i = 0; i < n; i++) { 117 coefficients[0] = y[i]; 118 coefficients[1] = b[i]; 119 coefficients[2] = c[i]; 120 coefficients[3] = d[i]; 121 polynomials[i] = new PolynomialFunction(coefficients); 122 } 123 124 return new PolynomialSplineFunction(x, polynomials); 125 } 126 127 }