Privacy Policy
Effective Date: 05/21/2018
What Do You Do With My Information?
At AWeber Systems, Inc. (“AWeber”, “We”, “Us” or “Our”), we respect your privacy and take protecting it seriously. This Privacy Policy covers our collection, use and disclosure of information we collect through our website and service, www.aweber.com owned and operated by AWeber Systems, Inc. It also describes the choices available to you regarding our use of your personal information and how you can access and update this information. The use of information collected through our service shall be limited to the purpose of providing the service for which our Clients have engaged us.
Our Data Processing and Security Terms contains more information about how we process data.
EU - U.S. Privacy Shield and Swiss - U.S. Privacy Shield
AWeber Systems, Inc. participates in and has certified its compliance with the EU - U.S. Privacy Shield Framework and the Swiss - U.S. Privacy Shield Framework. AWeber Systems, Inc. is committed to subjecting all personal data received from European Union (EU) member countries and Switzerland, in reliance on the Privacy Shield Framework, to the Framework's applicable Principles. To learn more about the Privacy Shield Framework, visit the U.S. Department of Commerce's Privacy Shield List. https://www.privacyshield.gov/list
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If you have an unresolved privacy or data use concern that we have not addressed satisfactorily, please contact our U.S.-based third party dispute resolution provider (free of charge) at https://feedback-form.truste.com/watchdog/request.
Under certain conditions, more fully described on the Privacy Shield website https://www.privacyshield.gov/article?id=How-to-Submit-a-Complaint, you may invoke binding arbitration when other dispute resolution procedures have been exhausted.
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Access To Personal Information
Upon request we will provide you with information about whether we hold any of your personal information. If your personal information changes, or if you no longer desire our service, you may correct, update, delete or deactivate it by making the change on our member information page or by contacting us. We will respond to your request within 30 days.
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We use this information to analyze trends, administer our website, track visitors' movements around our website, and gather demographic information about our visitors as a whole. We do link this automatically collected data to other information we collect about our website visitors.
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We partner with a third party ad network to either display advertising on our website or to manage our advertising on other websites. Our ad network partner uses cookies and web beacons to collect non-personal information about your activities on our website, as well as other websites, to provide you targeted advertising based upon your interests.
If you wish to not have this information used for the purpose of serving you targeted ads, you may opt-out by clicking here (or if located in the European Union click here). Please note this does not opt you out of being served advertising. You may continue to receive generic ads.
Testimonials
We post customer testimonials on our website, which may contain personal information. We do obtain our Clients' consent to post their name along with their testimonial via email prior to posting the testimonial on our website. However, if you wish to request the removal of your testimonial, please contact us.
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You can connect your account to social media websites using services like Facebook Connect. These services will authenticate your identity and provide you the option to share certain personal information with us, such as your name and email address, so you can share messages on these websites. Services like Facebook Connect give you the option to post information about your activities on this website to your profile page, allowing you to share with others within your network.
Notification Of Privacy Policy Changes
We may update our privacy policy to reflect changes to our information practices. If we make any material changes, we will notify you or our client by email (sent to the email address specified in your account) or by means of a notice on our website prior to the change becoming effective. We encourage you to periodically review this page for the latest information on our privacy practices.
Links To Other Sites
Our website includes links to other websites whose privacy practices may differ from ours. If you submit personal information to any of those websites, your information is governed by their privacy policies. We encourages you to carefully read the privacy policy of any website you visit. This privacy policy applies only to information collected by our website.
Public Forums
Our website offers publicly accessible blogs or community forums. You should be aware that any information you provide in these areas may be read, collected, and used by others who access them. To request removal of your personal information from our blog or community forum, contact us. In some cases, we may not be able to remove your personal information, in which case we will let you know if we are unable to do so and why.
We also have an engineering blog, which is managed by a third party application that may require you to register to post a comment. We do not have access or control of the information posted to this blog. You will need to contact or login into the third party application if you want to remove the personal information that was posted to the comments section. To learn how the third party application uses your information, please review its privacy policy.
Contact Us
If you have any questions or suggestions regarding our privacy policy, please contact us via email at<EMAIL_ADDRESS>or via postal mail at 1100 Manor Drive Chalfont, PA 18914, USA.


Apache ServiceComb Accept Code Donation From NewCapec Institute
Apache ServiceComb Accept Code Donation From NewCapec Institute
Apache ServiceComb Accept Code Donation From NewCapec Institute
Apache ServiceComb Meetup -Shanghai 2019.09.20 Report (PPT)
Apache ServiceComb Meetup
ServiceComb Pack Integrated Discovery Service Eurek
ServiceComb Pack Integrated Discovery Service Consul
This article describes how to use the ServiceComb Pack 0.4.0 cluster and how it works
Chuanzhiboke and Apache ServiceComb course and technical resources co-construction ceremony ended successfully
Apache ServiceComb Meetup Report (PPT)
OpenSource India Summit: Hands-on Apache ServiceComb(Incubating) MicroServices Workshop
Apache ServiceComb (incubating) Meetup
Apache ServiceComb (incubating) enters new stage
Apache ServiceComb (incubating) Day Report
Real time updated information of Apache ServiceComb (incubating) Day
Changes between v1 and v2 of zipkin and how Java Chassis upgraded to zipkin2
Analysis of service registry and propose a new architecture for it
An introduction Service-Center Management Console
微服务是否是业界期待已久的企业架构解决方案？在对遗留系统进行微服务的改造过程中存在怎样的困难和挑战，应该注意些什么？
How is distributed saga in ServiceComb comparing with other consistency solutions?
The design of distributed saga in ServiceComb
The background of data consistency problem in the scenario of microservice architecture
an introduction how build seckill demo project step by step
an introduction how to build seckill demo project step by step
an introduction of how to build seckill demo project step by step
An introduction of how to do stress test on company demo with jmeter in kubernetes cluster
This article introduces practice of using kubernetes autoscale in the micro-serviced Company demo to achieve on-demand resource control
This blog will introduce how to deploy company based on ServiceComb in the kubernetes cluster, and intercom communication of company
An introduction of how service-center and microservices work together
A introduction of how to set up distributed tracing with ServiceComb and Zipkin
该文档演示：从已有基于spring boot的单体应用利用ServiceComb实现微服务化和云化之路
A step by step guide on how to use ServiceComb to develop a complete project
This article describes how to use the ServiceComb Pack 0.4.0 cluster and how it works
ServiceComb Pack Integrated Discovery Service Consul
ServiceComb Pack Integrated Discovery Service Eurek
ServiceComb Pack Integrated Discovery Service Consul
ServiceComb Pack Integrated Discovery Service Eurek
该文档演示：从已有基于spring boot的单体应用利用ServiceComb实现微服务化和云化之路
Apache ServiceComb Meetup -Shanghai 2019.09.20 Report (PPT)
Apache ServiceComb Meetup
Apache ServiceComb Meetup Report (PPT)
OpenSource India Summit: Hands-on Apache ServiceComb(Incubating) MicroServices Workshop
Apache ServiceComb (incubating) Meetup
Apache ServiceComb (incubating) Day Report
Real time updated information of Apache ServiceComb (incubating) Day
Apache ServiceComb Meetup
微服务是否是业界期待已久的企业架构解决方案？在对遗留系统进行微服务的改造过程中存在怎样的困难和挑战，应该注意些什么？
ServiceComb Pack Integrated Discovery Service Eurek
ServiceComb Pack Integrated Discovery Service Consul
This article describes how to use the ServiceComb Pack 0.4.0 cluster and how it works
This article introduces practice of using kubernetes autoscale in the micro-serviced Company demo to achieve on-demand resource control
Chuanzhiboke and Apache ServiceComb course and technical resources co-construction ceremony ended successfully
A step by step guide on how to use ServiceComb to develop a complete project
This blog will introduce how to deploy company based on ServiceComb in the kubernetes cluster, and intercom communication of company
A introduction of how to set up distributed tracing with ServiceComb and Zipkin
An introduction of how to do stress test on company demo with jmeter in kubernetes cluster
This article introduces practice of using kubernetes autoscale in the micro-serviced Company demo to achieve on-demand resource control
This blog will introduce how to deploy company based on ServiceComb in the kubernetes cluster, and intercom communication of company
Apache ServiceComb Accept Code Donation From NewCapec Institute
Apache ServiceComb Meetup -Shanghai 2019.09.20 Report (PPT)
Chuanzhiboke and Apache ServiceComb course and technical resources co-construction ceremony ended successfully
Apache ServiceComb Meetup Report (PPT)
OpenSource India Summit: Hands-on Apache ServiceComb(Incubating) MicroServices Workshop
Apache ServiceComb (incubating) Meetup
Apache ServiceComb (incubating) enters new stage
Apache ServiceComb (incubating) Day Report
Real time updated information of Apache ServiceComb (incubating) Day
An introduction of how to do stress test on company demo with jmeter in kubernetes cluster
Analysis of service registry and propose a new architecture for it
Apache ServiceComb (incubating) enters new stage
Apache ServiceComb (incubating) Day Report
How is distributed saga in ServiceComb comparing with other consistency solutions?
The design of distributed saga in ServiceComb
The background of data consistency problem in the scenario of microservice architecture
an introduction how build seckill demo project step by step
an introduction how to build seckill demo project step by step
an introduction of how to build seckill demo project step by step
An introduction Service-Center Management Console
An introduction of how service-center and microservices work together
Apache ServiceComb (incubating) enters new stage
Changes between v1 and v2 of zipkin and how Java Chassis upgraded to zipkin2
A introduction of how to set up distributed tracing with ServiceComb and Zipkin


### Plan for today
1. Setup
2. Presentation
3. Solving exercises
4. Summary
### Link for presentation
### https://bartosz822.github.io/slick-cats-workshop-presentation
### Clone repository:
### https://github.com/bartosz822/slick-cats-workshop
## Setup
Recommended IDE
### Setup for IntelliJ
*Recommended IntelliJ version 2018.1 or higher*
* clone repository
* open project as an sbt project and wait for sbt to download all dependencies
* make sure everything compiles
* click with left click on exercises package and select 'Run all ScalaTests...' (they should all fail)
* follow comments and implements all workshop methods
*If you have any problems don't hestitate to ask for our help :)*
Whorkshop db schema
### Example data inserts
### data.TestData
## Why slick?
* Efficiency with Pre-Optimization
* Type Safety
* Productive, Composable Model for Building Queries
* Better separation of businness logic from db
* Free and open source
# Slick
##### Scala Language-Integrated Connection Kit
>Slick is a modern database query and access library for Scala. It allows you to work with stored data almost as if you were using Scala collections while at the same time giving you full control over when a database access happens and which data is transferred. You can write your database queries in Scala instead of SQL, thus profiting from the static checking, compile-time safety and compositionality of Scala. Slick features an extensible query compiler which can generate code for different backends.
Slick Basics
> Importing apropriate profile for db. A profile’s api object contains all commonly needed imports and other parts of Slick such as database handling.
```scala
import slick.jdbc.H2Profile.api._
```
> Slick’s API is fully asynchronous and runs database calls in a separate thread pool. For running user code in composition of DBIOAction and Future values, apropriate ExecutionContext needs to be imported.
```scala
import scala.concurrent.ExecutionContext.Implicits.global
```
### Case Class definition
```scala
case class Cat(
id: Option[Long],
name: String,
breedId: Long,
sex: String,
age: Int
)
```
### Table definition
```scala
class Cats(tag: Tag) extends IdTable[Cat](tag, "cats") {
def id = column[Long]("id", O.PrimaryKey, O.AutoInc)
def name = column[String]("name")
def breedId = column[Long]("breed_id")
def sex = column[String]("sex")
def age = column[Int]("age")
def breedFk = foreignKey("breed_fk", breedId, Breeds.query)(_.id)
def * = (id.?, name, breedId, sex, age) <> (Cat.tupled, Cat.unapply)
}
object Cats {
lazy val query = TableQuery[Cats]
}
```
### Action creating table in db
```scala
Cats.query.schema.create
```
### Adding to table
```scala
Cats.query ++ Cat(id = None, name = "Bella", breedId = 2, sex = "F", age = 3)
```
### Accesing table
```scala
Cats.query.forEach(println)
Cats.query.filer(_.age < 5)
Cats.query.map(_.name).forEach(println)
```
### Joins
Monadic
```scala
for {
cat <- Cats.query
breed <- Breeds.quey if breed.id === cat.breedId
} yield {
(cat, breed)
}
```
Applicative
```scala
Cats.query
.join(Breeds.Query)
.on(_.breedId === _.id)
```
### Running an action
```scala
catsDb = {
url = "jdbc:h2:mem:cats"
driver = org.h2.Driver
connectionPool = disabled
keepAliveConnection = true
}
```
```scala
val db = Database.forConfig("catsDb")
val action = Cats.query.forEach(println)
val futureResult = db.run(action)
```
## Main concept
### Scala queries >> actions >> running actions on db and getting results
## Queries
### No standard Scala Types but its representations (Rep)
```scala
Rep[T]
Query -> Rep[Seq[T]]
TableQuery
```
### Similiar operations as Scala Collections like flatMap, filter, take, groupBy etc.
## Actions
* Action is something that you can execute on db
* Actions are created when calling .result on a Query, or .create, or += and similiar and have type DBIO
* Actions can be composed with andThen, flatMap, DBIO.seq or
transactionally
* Parts of action do not run concurrently
## Action compositon
```scala
for {
bella <- catsRepository.findByName("Bella")
breed <- breedsRepository.findExistingById(bella.breedId)
} yield {
breed.id
}
```
### Queries and actions are just descriptions of operation
### Operation on database is performed when action is executed on database service.
```scala
db.run(action)
```
## To sum up Slick gives us
* Table
* Query
* DBIO (action)
* Future (with result)
Slick + cats
# Cats
# Typeclass
### The problem
```scala
case class Cat(name: String, breedName: String)
def present = ??? //this one needs to be generic
val skipper = Cat("Skipper", "Maine Coon")
present(skipper)
// = "This is Skipper, Maine Coon"
```
### OOP
```scala
trait Showable {
def show: String
}
case class Cat(name: String, breedName: String) extends Showable {
def show: String = name + ", " + breedName
}
def present(showable: Showable) = "This is " + showable.show
val skipper = Cat("Skipper", "Maine Coon")
present(skipper)
// = "This is Skipper, Maine Coon"
```
### Typeclass
```scala
case class Cat(name: String, breedName: String)
trait Show[A] {
def show(a: A): String
}
val showCat = new Show[Cat] {
def show(cat: Cat): String = cat.name + ", " + cat.breedName
}
def present[A](a: A, s: Show[A]) = "This is " + s.show(a)
val skipper = Cat("Skipper", "Maine Coon")
present(skipper, showCat)
// = "This is Skipper, Maine Coon"
```
### Typeclass resolution in Scala
```scala
def present[A](a: A, s: Show[A]) = "This is " + s.show(a)
present(skipper, showCat)
//we have to pass showCat instance every time
```
### Implicits to the rescue
```scala
implicit val showCat = new Show[Cat] {
def show(cat: Cat): String = cat.name + ", " + cat.breedName
}
def present[A](a: A)(implicit s: Show[A]) = "This is " + s.show(a)
//compiler will automatically search for Show[A] by its type
present(skipper)
//we said that we know how to show cat a few lines above, we don't have to repeat it
```
### Cat, instances and logic - separated
```scala
package instances
object CatInstances {
implicit val showCat = new Show[Cat] {
def show(cat: Cat): String = cat.name + ", " + cat.breedName
}
}
```
```scala
package services
object Presenter {
def present[A](a: A)(implicit s: Show[A]) = "This is " + s.show(a)
}
```
```scala
import model.Cat
import instances.CatInstances.showCat
import services.Presenter.present
present(skipper)
```
### Postfix operators
```scala
implicit class ShowOps[A](a: A) {
def show(implicit s: Show[A]) = s.show(a)
}
```
```scala
val skipper = Cat("Skipper", "Maine Coon")
skipper.show
```
### Monad
```scala
def pure[A](a: A): M[A]
//Option
def pure[A](a: A) = Some(a)
//List
def pure[A](a: A) = List(a)
//DBIO
def pure[A](a: A) = DBIO.successful(a)
```
```scala
def flatMap[A, B](fa: M[A])(f: (A) => M[B]): M[B] //bind in Haskell world
//in standard cases implemented as
fa.flatMap(f)
```
If something is a monad, it's also Applicative and Functor
### Option T
###### Convenience data type for M[Option[A]]
```scala
def tryFindCat(id: Long): OptionT[DBIO, Cat]
def tryFindBreedForCat(cat: Cat): OptionT[DBIO, Breed]
def getFavoriteFood(cat: Cat): Food
val cat: OptionT[DBIO, Cat] = tryFindCat(id)
val breed: OptionT[DBIO, Breed] = cat.flatMap(tryFindBreedForCat)
val favoriteFood: OptionT[DBIO, Food] = cat.map(getFavoriteFood)
```
### Either T
###### Convenience data type for M[Either[A, B]]
```scala
def tryFindCat(id: Long): EitherT[DBIO, Error, Cat]
def tryFindBreedForCat(cat: Cat): EitherT[DBIO, Error, Breed]
def getFavoriteFood(cat: Cat): Food
val cat: EitherT[DBIO, Error, Cat] = tryFindCat(id)
val breed: EitherT[DBIO, Error, Breed] = cat.flatMap(tryFindBreedForCat)
val favoriteFood: EitherT[DBIO, Error, Food] = cat.map(getFavoriteFood)
```
### Wrapping
```scala
def tryFindCatBefore(id: Long): DBIO[Option[Cat]]
def tryFindCatAfter(id: Long): OptionT[DBIO, Cat] = OptionT(tryFindCatBefore(id))
```
### Semigroup
###### Adding things up
```scala
def combine(x: A, y: A): A
```
```scala
List(1, 2, 3).combine(List(4, 5)) //List(1, 2, 3, 4, 5)
1.combine(2) // 3
```
### Semigroup
###### Adding wrappers if contained elements can be added
```scala
def combine(fx: DBIO[A], fy: DBIO[A])(implicit s: Semigroup[A]): DBIO[A] = {
fx.zip(fy).map { case (x, y) => x.combine(y) }
}
```
```scala
def fetchPersian: DBIO[List[Cat]]
def fetchMaineCoon: DBIO[List[Cat]]
fetchPersian.combine(fetchMaineCoon) //DBIO[List[Cat]]
```
### Validated vs. Either
```scala
valid[List[String], Int](1) combine
valid[List[String], Int](2) combine
valid[List[String], Int](3)
//Valid(6)
```
```scala
Right[List[String], Int](1) combine
Right[List[String], Int](2) combine
Right[List[String], Int](3)
//Right(6)
```
### Validated vs. Either
```scala
invalid[List[String], Int](List("err1")) combine
invalid[List[String], Int](List("err2")) combine
valid[List[String], Int](3)
//Invalid(List("err1", "err2"))
```
```scala
Left[List[String], Int](List("err1")) combine
Left[List[String], Int](List("err2")) combine
Right[List[String], Int](3)
//Left(List("err1"))
```
# Drawbacks
# Let's Code!
![](imgs/traffic.png)
# Extras
### Unicorn
```scala
/** Id class for type-safe joins and queries. */
case class UserId(id: Long) extends AnyVal with BaseId[Long]
case class UserRow(
id: Option[UserId],
email: String,
name: String
) extends WithId[Long, UserId]
/** Table definition for users. */
class Users(tag: Tag) extends IdTable[UserId, UserRow](tag, "USERS") {
def email = column[String]("EMAIL")
def name = column[String]("LAST_NAME")
override def * = (id.?, email, name) <> (UserRow.tupled, UserRow.unapply)
}
```
https://github.com/VirtusLab/unicorn


career
Objectives
Education
Experience
Work
Experience
Kamanashis Biswas
Kumlai
Rampal
Bagerhat
E-mail<EMAIL_ADDRESS>Skype: Kamanashis Biswas
Phone: +8801915545595
I am basically a techonology learner and problem solver,who continiously excel himself
to solve real human problems utlizing his technical ability.Empowering myself to be expert
in building web application.Utilize my analytical skills to develop IT
and business strategies to increase productivity.
I am basically a techonology learner and problem solver,who continiously excel himself
to solve real human problems utlizing his technical ability.Empowering myself to be expert
in building web application.Utilize my analytical skills to develop IT
and business strategies to increase productivity.
B.Sc in Computer Science and Engineering
North Western University, Khulna
Higher Seccondry School Certificate(Exam-2017)
Govt. Rampal College.
Seccondry School Certificate(Exam-2015)
Baintala Union Seccondary School.
1 Year.
Apsis Solution LTD.
Software Engineer


Rolls-Royce Cullinan Black Badge revealed
British marque's 'high-sided vehicle' gets a power bump and dark-themed makeover. It's coming to Australia, priced from $760,000 before on-road costs.
by James Wong- shares
Rolls-Royce has revealed the stealthy and more powerful Cullinan Black Badge, becoming the first model on the marque's 'Architecture of Luxury' platform to get the Black Badge treatment.
According to the high-end carmaker, the Cullinan Black Badge has had its drivetrain and chassis "re-engineered for enhanced dynamic performance", including increased outputs from the engine.
Under the bonnet of the Black Badge, the company's long-running 6.75-litre twin-turbo V12 has been boosted by 29PS (21kW) to 600PS (441kW), and up 50Nm to a massive 900Nm.
While the company hasn't confirmed if those increased outputs translate into an improvement in acceleration, it does say "[the] Black Badge Cullinan benefits from a bespoke transmission and throttle treatment that creates a sense of urgency without ever undermining its Rolls-Royce peerage".
"The drive-train, ZF 8-speed gear box and both front and rear steered axles work collaboratively to adjust the levels of engagement depending on throttle and steering inputs," Rolls-Royce continues.
"Changes to suspension components and settings add to the suite of dynamic technologies that ensure an appropriate balance between dynamism and refinement. The result is an extremely well-mannered tourer at low speeds and a vivid driver’s device when pressed."
Visually, there's plenty of black chrome and darkened accents throughout the exterior, a new available set of 22-inch forged alloy wheels, and coloured brake calipers for the uprated brake system.
This particular example shown in the press images gets bright yellow highlights inside and out, too, featuring on the exterior coach lines while Forge Yellow leather adorns part of the cabin – including the rear 'Viewing Suite' chairs that pop out of the luggage area.
In keeping with the more sporting vibe, the Cullinan Black Badge also picks up glossy Technical Carbon veneer for the interior, which features on the instrument panel, door ornamentation, and the rear tray tables.
Adorning the middle seat of the second row is the infinity symbol, also known as the lemniscate, to reflect the Black Badge's "unrelenting pursuit of power".
"Black Badge reflects the desires of a distinct group of Rolls-Royce clients: men and women who take risks, break rules and build success on their own terms. Indeed, before we launched Black Badge in 2016 the idea of creating a product that would satisfy this subversive cohort – that is highly dynamic and wilfully rebellious in aesthetic – caused a great deal of internal debate," said Torsten Müller-Ötvös, CEO for Rolls-Royce Motor Cars.
"However, once the marque’s designers, engineers and craftspeople began pursuing this dramatic alter ego, it became clear that these motor cars could not only exist comfortably beneath this revered and historic brand but they would define a new space within the super-luxury market."
While the company anticipates most Black Badge customers will specify their vehicle in signature black, the Cullinan will be offered with Rolls-Royce's usual dizzying array of interior and exterior paint and trim options.
Australia
Rolls-Royce's local division has confirmed the Cullinan Black Badge will be available in Australia, priced from $760,000 before on-road costs.
As always, the company notes "each Black Badge Cullinan is bespoke, actual pricing is dependent on customer specification".
Click the images for the full gallery
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Rolls-Royce Cullinan Black Badge revealed


src/services/ticket.service.ts
TicketService class for registration of new mirrors
constructor(service: SmartMirrorService)
|
|
Defined in src/services/ticket.service.ts:24
|
|
Creates the AuthService
Parameters :
|
| Public get |
get(id: void)
|
|
Defined in src/services/ticket.service.ts:38
|
|
Gets a registration ticket for a new mirror with given id
Parameters :
Returns :
Observable<>
|
| Public register |
register(number: void)
|
|
Defined in src/services/ticket.service.ts:47
|
|
Registers a new mirror with the given ticket number for the authenticated user
Parameters :
Returns :
Observable<>
|
| Private api |
api: |
Default value : /tickets
|
|
Defined in src/services/ticket.service.ts:24
|
|
relative url to the tickets api |
|
|
import {Injectable} from '@angular/core';
import {Observable} from 'rxjs';
import {SmartMirrorService} from "./smartmirror.service";
import {Mirror} from "../models/mirror";
import {Ticket} from "../models/ticket";
import 'rxjs/add/operator/catch';
import 'rxjs/add/operator/map';
/**
* TicketService class for registration of new mirrors
*/
@Injectable()
export class TicketService {
/**
* relative url to the tickets api
* @type {string}
*/
private api = '/tickets';
/**
* Creates the AuthService
* @param service Injected internal http service for interacting with SmartMirror Api
*/
constructor(private service: SmartMirrorService) {
}
/**
* Gets a registration ticket for a new mirror with given id
* @param id The id of the mirror to create a new registration ticket
* @return {Observable<R>} ticket for registration
*/
public get(id:string): Observable<Ticket> {
return this.service.get(this.api + '/' + id).map(res => <Ticket>res.json());
}
/**
* Registers a new mirror with the given ticket number for the authenticated user
* @param number The ticket number of the mirror to register with
* @return {Observable<R>} the created mirror with default settings
*/
public register(number: string): Observable<Mirror> {
return this.service.post(this.api + '/' + number, {}).map(res => <Mirror>res.json());
}
}


Only the lead ministry can view this entry unless it is shared with others (select in bottom field). To transfer or change the lead ministry (ie. EMLI to ENV), please contact the Corp Cal team.


wavesurfer.js will automatically
fallback to HTML5 Media if Web Audio is not
supported. However, you can choose to use audio
element manually. Simply set the backend
option to "MediaElement"
.
var wavesurfer = Object.create(WaveSurfer);
wavesurfer.init({
container: document.querySelector('#wave'),
backend: 'MediaElement'
});
wavesurfer.load('/static/media/demo.mp3');
Press this button to see the same demo with pre-decoded peaks:


Telecom Italia to expand data center business under Google deal
by Elvira PollinaMILAN (Reuters) - Telecom Italia (TIM) plans to spin off its data center operations and look for an investor ahead of a potential market listing, it said on Friday, after announcing a partnership with Google to develop its cloud business.
The move is part of a plan by Chief Executive Luigi Gubitosi aimed at reviving the former monopoly, an underperforming business saddled with 24 billion euros ($26.45 billion) of debt.
TIM, which owns 22 data centers across Italy and has its own cloud business, will invest in new top-tier data facilities to host Google Cloud, Gubitosi told analysts during a call, adding the Italian cloud market is expanding by 20% a year.
Gubitosi said the new company, which has a core profit target of 400 million euros by 2024, will look for one or more infrastructure funds to become minority partners to finance its expansion.
Later, TIM, which would keep control of the new entity, could consider listing a stake of it.
Gubitosi has set cutting TIM’s mountain of debt and extracting value from its network assets as the company’s main objectives.
An easing of tensions between the group’s top investors, French conglomerate Vivendi and U.S. activist fund Elliott, which took control of the board last year, could help the CEO deliver on promises.
TIM’s shares were up 1.8% at 1641 GMT.


AESEncoder()
语法
Result = AESEncoder(*Input, *Output, Size, *Key, Bits, *InitializationVector [, Mode])概要
Encodes the specified input buffer using the AES algorithm into the output buffer.
参数
*Input The input buffer with the plain data. *Output The output buffer which will receive the encoded data. It has to be different than the input buffer. Size The amount of bytes to encode. It has to be at least 16 bytes. To encode something smaller, padding has to be added before the encoding. *Key A buffer containing the key for encoding. Its size depends of the 'Bits' parameter: l6 bytes for 128-bit encryption, 24 bytes for 192 bit and 32 bytes for 256-bit. Bits The size of the key used by the ciphering. Valid values are 128, 192 and 256. *InitializationVector The InitializationVector is a random data block, used to initialize the ciphering to avoid breach in decoding (only needed when using the #PB_Cipher_CBC mode). The initialization vector is always 16 bytes long. 模式 （可选） This can be one of the following value: #PB_Cipher_CBC: Default mode of encoding (Cipher Block Chaining). Needs an '*InitializationVector'. Recommended as more secure than ECB mode. #PB_Cipher_ECB: Alternative mode (Electronic CodeBook). It doesn't use a random value or chaining (each block is ciphered independently) making it less strong the CBC.
返回值
Returns nonzero if the encoding was successful, zero otherwise.
备注
AES is an industry class cipher algorithm and is good balanced between speed and security. Here is the Wikipedia introduction about AES: 'In cryptography, the Advanced Encryption Standard (AES) is an encryption standard adopted by the U.S. government. The standard comprises three block ciphers, AES-128, AES-192 and AES-256, adopted from a larger collection originally published as Rijndael. Each AES cipher has a 128-bit block size, with key sizes of 128, 192 and 256-bit, respectively. The AES ciphers have been analyzed extensively and are now used worldwide.'
PureBasic uses a RFC compliant implementation of AES. More information can be found in the RFC 3602: http://www.ietf.org/rfc/rfc3602.txt.
Example: CBC
; Should be compiled in ascii mode ; String$ = "Hello this is a test for AES" *CipheredString = AllocateMemory(Len(String$)+1) ; Space for the string and its *DecipheredString = AllocateMemory(Len(String$)+1) ; null terminating character (ASCII mode) If AESEncoder(@String$, *CipheredString, Len(String$), ?Key, 128, ?InitializationVector) Debug "Ciphered: "+PeekS(*CipheredString) AESDecoder(*CipheredString, *DecipheredString, Len(String$), ?Key, 128, ?InitializationVector) Debug "Deciphered: "+PeekS(*DecipheredString) EndIf DataSection Key: Data.b $06, $a9, $21, $40, $36, $b8, $a1, $5b, $51, $2e, $03, $d5, $34, $12, $00, $06 InitializationVector: Data.b $3d, $af, $ba, $42, $9d, $9e, $b4, $30, $b4, $22, $da, $80, $2c, $9f, $ac, $41 EndDataSection
Example: ECB
; Should be compiled in ascii mode ; String$ = "Hello this is a test for AES" *CipheredString = AllocateMemory(Len(String$)+1) ; Space for the string and its *DecipheredString = AllocateMemory(Len(String$)+1) ; null terminating character (ASCII mode) If AESEncoder(@String$, *CipheredString, Len(String$), ?Key, 128, 0, #PB_Cipher_ECB) Debug "Ciphered: "+PeekS(*CipheredString) AESDecoder(*CipheredString, *DecipheredString, Len(String$), ?Key, 128, 0, #PB_Cipher_ECB) Debug "Deciphered: "+PeekS(*DecipheredString) EndIf DataSection Key: Data.b $06, $a9, $21, $40, $36, $b8, $a1, $5b, $51, $2e, $03, $d5, $34, $12, $00, $06 EndDataSection
参阅
AESDecoder(), StartAESCipher()
已支持操作系统
所有


In this stage, you need to add a new feature to the project — pick one name from the dictionary at random; this person's share will be paid by others. Make it a lucky day for somebody!
Make sure you give your users a choice whether they want to use this feature or not. Don't turn it on by default.
After picking a random name, print it so that everyone knows who is the lucky one.
In this stage your program should perform the following steps together with the steps from the previous stages:
"No one is joining for the party"
is expected as an output;Yes
), choose a name from the dictionary keys at random and print the following: {Name} is the lucky one!
;No one is going to be lucky
.Do not print the output of the previous stage (see examples).
The greater-than symbol followed by a space (>
) represents the user input. Note that it's not part of the input.
Example 1: The feature is used
Enter the number of friends joining (including you):
> 5
Enter the name of every friend (including you), each on a new line:
> Marc
> Jem
> Monica
> Anna
> Jason
Enter the total bill value:
> 100
Do you want to use the "Who is lucky?" feature? Write Yes/No:
> Yes
Jem is the lucky one!
Example 2: The feature is skipped
Enter the number of friends joining (including you):
> 5
Enter the name of every friend (including you), each on a new line:
> Marc
> Jem
> Monica
> Anna
> Jason
Enter the total bill value:
> 100
Do you want to use the "Who is lucky?" feature? Write Yes/No:
> No
No one is going to be lucky
Example 3: Invalid input
Enter the number of friends joining (including you):
> 0
No one is joining for the party


Thanks for taking an interest in my profile and CV. I'm an IT professional with over 9 years of experience in IT Infrastructure.
I recently graduated from a Full Stack Web Development Bootcamp conducted by the University of Sydney. This was an in person immersive bootcamp conducted on campus. We were challenged with several assignments and projects and built a very strong foundation in programming.
During the 6 month course I built a strong foundation on technologies like HTML, CSS, Javascript, JQUERY, React, Node, SQL and MongoDB. I've also taught myself React Hooks, Python, Django with DRF, React-Native, PHP with OOPS and Laravel.
I look forward to using my skills for creating and developing awesome applications.
I've also completed certifications in AWS - Solutions Architect Associate and AWS Developer.
Though I may not have the necessary commerical expereince required, I can compensate for and bring to you my passion and excitment for web development, continuous learning, hardwork and always willing to go the extra mile.
I can be reached on<PHONE_NUMBER> for a further discussion.
Please scroll below to have a look at some of the projects I've created and deployed to the cloud.
CollectIT is my latest full stack IT Asset Management application (MERN stack ) and currently in it's final stages of development. This application will be open sourced once complete. More details about the apps features and technologies can be found on the github link's README.
Book Attic is my latest full stack web appliction side project and is a Book listing online application created using React with Hooks and Bootstrap in the frontend and Django with DRF along with SQLite in the backend. The front-end application is deployed to NOW here and the backend to HEROKU and is currently live. The idea behind the project was to demonstrate understanding and knowledge of another backend language Django along with DRF with a React Frontend.
The idea behind this project was to create a platform where users can exchange or sell items ranging from simple goods to cars etc. This is a full stack application that uses JQUERY in the frontend and nodejs along with a SQL ORM in the backend. It uses JWT web tokens for authencation and bcrypt for hashing passwords. The application is deployed to Heroku here
Goalkeeper is a full stack application built with PHP and Laravel along with React in the frontend that allows users to set their goals for the day and keep a track of them. Users can register, login, create, update and delete goals. The main goal behind the application is to demonstrate working knowledge of PHP with the Laravel framework with CRUD operations and React in the frontend. The application is deployed to Heroku here
This project is an online travel agency that allows users to get flight and ticketing information to book flight tickets. It uses JQuery in the front-end and consumes 3 APIs . A demo of the application can be seen in the README of the applications github page Here.
Apart from the three main projects we tasked with completing assignments every week and created multiple small applications using the technologies we learned. I've add links to some of the applications below:
A simple CRUD application created using JQUERY in the frontend and firebase in the backend.
A simple trivia game created using JQUERY in the frontend.
A simple RPG game created using JQUERY in the frontend.
A simple full stack MERN application retrieving books information for users
Apart from being a tech enthusiast, my passions include reading, long drives, travel, adventure, gaming, history, watching documentaries.


You've probably use a learning algorithm dozens of times a day without knowing it. Every time you use a web search engine like Google or Bing to search the internet, one of the reasons that works so well is because a learning algorithm, one implemented by Google or Microsoft, has learned how to rank web pages. Every time you use Facebook or Apple's photo typing application and it recognizes your friends' photos, that's also machine learning. Every time you read your email and your spam filter saves you from having to wade through tons of spam email, that's also a learning algorithm. For me one of the reasons I'm excited is the AI dream of someday building machines as intelligent as you or me. We're a long way away from that goal, but many AI researchers believe that the best way to go towards that goal is through learning algorithms that try to mimic how the human brain learns.
WHY MACHINE LEARNING WAS DEVELOPED?
machine learning was developed as a new capability for computers to automate process and give better suggestions than any human could by providing implicit information to the computer and today it touches many segments of industry and basic science.
Learning algorithms are also widely used for self- customizing programs. Every time you go to Amazon or Netflix or iTunes Genius, and it recommends the movies or products and music to you, that's a learning algorithm. If you think about it they have million users; there is no way to write a million different programs for your million users. The only way to have software give these customized recommendations is to become learn by itself to customize itself to your preferences. Finally learning algorithms are being used today to understand human learning and to understand the brain.
DEFINITION
Arthur Samuel -
He defined machine learning as the field of study that gives computers the ability to learn without being explicitly learned.
Tom Mitchell -
So Tom defines machine learning by saying that a well-posed learning problem is defined as follows. He says, a computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E. I actually think he came out with this definition just to make it rhyme.
Snippet
"A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E."
Suppose your email program watches which emails you do or do not mark as spam, and based on that learns how to better filter spam. What is the task T in this setting?
● Classify emails as spam or not spam.
● Watching you label emails as spam or not spam.
● The number (or fraction) of emails correctly classified as spam/not spam.
● None of the above, this is not a machine learning algorithm.
BIFURCATION IN ML
There are several different types of learning algorithms. The main two types are what we call supervised learning and unsupervised learning. I'll define what these terms mean more in the next couple sessions. It turns out that in supervised learning, the idea is we're going to teach the computer how to do something. Whereas in unsupervised learning, we're going to let it learn by itself. Don't worry if these two terms don't make sense yet. In the upcoming sessions, I'm going to say exactly what these two types of learning are also we have two types of approaches in Supervised Learning called Classification and Regression .
Now we can work on a Classification based DATASET.
Like how we have a HELLO WORLD program in every programming language we have
HELLO WORLD dataset in DATA SCIENCE.
That is called as IRIS dataset.
IRIS is a flower. Based on its different varieties of flower we can classify it and then we can identify which breed of flower it belongs to of IRIS familiy.


TWINS A film review by Randy Parker Copyright 1996 Randy Parker
RATING: **1/2 (out of ****)
(Review written in 1988)
TWINS, directed by Ivan Reitman and starring Arnold Schwarzenegger and Danny DeVito, is the epitome of high-concept, disposable entertainment.
In this check-your-brain-in-at-the-door movie, Schwarzenegger and DeVito are fraternal twins, the result of a genetic engineering experiment gone awry. Schwarzenegger as Julius is the twin who received all the "good" genes.  DeVito as Vincent is the twin who received the "leftover" genes. Raised in an orphanage in L.A., he is a good-for-nothing car thief and general hoodlum. Separated at birth, the two brothers finally meet for the first time after Julius leaves his island-home to track down Vincent in L.A.
Given the brilliant casting and talent involved, TWINS has very little ambition. Although the movie is consistently entertaining, it never really rises above the level of high-concept formula. TWINS is far too willing to milk easy laughs from the striking dissimilarity between the twins and far too reluctant to develop its characters. The strength and heart of TWINS is the relationship between the two brothers, who grow to love and respect one another. Unfortunately, the film's action-oriented subplots distract from the strong chemistry that develops between Julius and Vincent.
The most pleasant surprise in TWINS is Schwarzenegger's knack for comedy. His performance as a fish out of water unfamiliar with American culture is convincing and endearing. His social ineptitude and inexperience produce the movie's funniest moments.
DeVito, on the other hand, is exactly what you'd expect. He plays the standard loud-mouth, smart-alec character that we've come to know and hate. This incarnation of the character is not much different from his past roles; he's as obnoxious, immature, and irritating as ever. Kelly Preston also deserves mention; she gives TWINS a healthy dose of pulse-raising sizzle as the sexy and stunning beauty who takes interest in Julius.
Fortunately, despite all its predictable and uninspired humor, TWINS delivers a lot more fun and entertainment than you might expect.
--- Randy Parker<EMAIL_ADDRESS>http://www.shoestring.org
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


Social Media Following Over Time
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All functions
|
|
SER_posterior_e_loglik_rss()
|
posterior expected loglikelihood for a single effect regression |
|
check_projection()
|
Check whether b in space spanned by the non-zero eigenvectors of A |
|
check_semi_pd()
|
Check whether A is positive semidefinite |
|
coef(<susie>)
|
extract regression coefficients from susie fit |
|
compute_MXt()
|
Computes M%*%t(standardized.X) using sparse multiplication trick |
|
compute_Xty()
|
Computes t(standardized.X)%*%y using sparse multiplication trick |
|
compute_ss()
|
Compute the needed summary statistics for `susie_ss` from the 'raw' data |
|
estimate_residual_variance()
|
Estimate residual variance |
|
estimate_residual_variance_ss()
|
Estimate residual variance for summary statistics |
|
estimate_residual_variance_z()
|
Estimate residual variance for summary statistics |
|
loglik()
|
The log likelihood function for SER model (based on summary data betahat, shat2), as a function of prior variance V |
|
predict(<susie>)
|
predict future observations or extract coefficients from susie fit |
|
set_R_attributes()
|
sets the attributes for the R matrix |
|
set_X_attributes()
|
sets three attributes for matrix X |
|
single_effect_regression()
|
Bayesian single-effect linear regression of Y on X |
|
single_effect_regression_rss()
|
Bayesian single-effect linear regression of Y on X. |
|
single_effect_regression_ss()
|
Bayesian single-effect linear regression of y on X. |
|
summary(<susie>)
|
summarize susie fit |
|
susie()
|
SUm of Single Effects (SuSiE) Regression |
|
susie_auto()
|
An attempt to automate reliable running of susie even on hard problems |
|
susie_bhat()
|
Summary statistics version of SuSiE on betahat, the corresponding standard error, and correlation (or covariance) matrix |
|
susie_get_cs()
|
Extract credible sets from SuSiE fit |
|
susie_get_lfsr()
|
Local false sign rate (lfsr) for susie credible sets |
|
susie_get_objective()
|
Get evidence lower bound (ELBO) from fitted SuSiE model |
|
susie_get_pip()
|
Compute posterior inclusion probability (PIP) for all variables |
|
susie_get_posterior_mean()
|
Get posterior mean for coefficients from fitted SuSiE model |
|
susie_get_posterior_sd()
|
Get posterior standard deviation for coefficients from fitted SuSiE model |
|
susie_get_prior_variance()
|
Get updated prior variance |
|
susie_get_residual_variance()
|
Get updated residual variance |
|
susie_init_coef()
|
initialize a susie object using regression coefficients |
|
susie_plot()
|
Plot per variable summary in SuSiE CSs |
|
susie_plot_iteration()
|
Diagnostic plot for SuSiE iterations |
|
susie_rss()
|
Bayesian sum of single-effect (susie) linear regression using z scores |
|
susie_slim()
|
Slim the result of fitted SuSiE model |
|
susie_ss()
|
Bayesian sum of single-effect (susie) linear regression using summary stat |
|
susie_trendfilter()
|
perform trend filtering using SuSiE |
|
update_each_effect()
|
update each effect once |
|
update_each_effect_rss()
|
update each effect once |
|
update_each_effect_ss()
|
update each effect once |


Dmitriy Uvin
Student • Backend Developer • Sportsmen
Résumé
― I do not compete with others, the only competition that challenges me is myself. Trying to become better with each day. I do sports, read books, upgrading mindset, and want to make my dreams come true!
“Be yourself; everyone else is already taken.” – Oscar Wilde
Timeline
-
September 2019 - till now
Studen in KPI, FICT-
I thought, that KPI is the most technological university in Ukraine. But when i started learning, i was upset a little. not so technological, but it's good maybe, now i expect only fro my skills and character. Let's do it!
- Making some friends
- Realising that i need to do something to become good and needed for people man.
- Discovering a lot of new things, mostly very interesting.
- Started to learn PHP and his frameworks the more harder, to create a company, in the future.
-
-
Summer 2019
Becoming a man-
- Trying to be not lazy.
- Searching for work, like, loader, waiter and so...
- Working as loader, load -> unload, and so on for a month. (Very valuable expeirence)
- Working like Frontend-Backend-Devops developer, i think that
- Entering to KPI, the best tech... Ha-Ha... It will be later:D
-
-
September, 2016 - May 19
My Lyceum-
- Good study
- First love... And second a little bit later.
- First fight, first quarelling, teenage problems, you know...
- Growing up mentally, physically
- Different friends and a lot of familiar people.
- At all good.
-
-
September, 2008 - May 2016
My first School-
- Good study for 4 years, then gettings worst.
- A lot of friends
- Sometimes a lot of fun, sometime a lot of sadness
- Fun at all
-
-
The best childhood
Of course, with sweet, toys, late evenings with friends... And a lot of fun and sometimes not so much fun:)
- First years of my life.
-
4th January 2002
Born in Zhytomyr, Ukraine
Languages
Skills
Personality
Like 😃
Dislike 😞
DESIRES 🥺
Education
2018 - nowadays
My baggage-
- Udemy
- YouTube, big amount of different playlists
- Books
- DIfferent free resources, Codecademy, HTMLAcademy, Hexlet etc.


Mississippi just began selling its first-ever state lottery tickets
Proceeds go to education, infrastructure
by Lauren M. Johnson, CNN(CNN) - For the first time, Mississippi residents can now play their own state lottery.
Instead of going to Louisiana, Tennessee or Arkansas, residents can purchase lottery tickets right in their own backyard. Tickets went on sale at 5 a.m. Monday after lawmakers passed a bill last year authorizing a state lottery.
State Rep. Alyce G. Clarke, a lottery advocate for whom the bill is named, made the first ceremonial purchase, the Mississippi Lottery Corporation said in a news release.
"This is not only a monumental day for the MLC, but it is also an incredible day for Mississippi," said Dr. Mike McGrevey, chairman of the MLC Board.
More than 1,200 approved retailers -- including convenience stores, grocery stores, casinos and even barber shops -- jumped on board to get their chance at selling a winning ticket. They all went through a screening process that took about 30 days.
"Today would not have been possible without our retailers," said MLC President Tom Shaheen. "Some are so excited they are hosting their own giveaways and promotions."
The bill was signed into law on September 1, 2018, and after more than a year of preparation, residents are ready to get scratching. Proceeds will go towards education and infrastructure across the state.
While scratch-off tickets are now available, Powerball and Mega Millions tickets will not be sold until January 2020.
Only five states remain without a lottery: Alabama, Nevada, Alaska, Hawaii, and Utah.


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How And Why To Make Side Projects Work At A Digital Agency
It’s no secret that the digital industry is booming. From exciting startups to global brands, companies are reaching out to digital agencies, responding to the new possibilities available. However, the industry is fast becoming overcrowded, heaving with agencies offering similar services — on the surface, at least.
Producing creative, fresh projects is the key to standing out. Unique side projects are the best place to innovate, but balancing commercially and creatively lucrative work is tricky. So, this article looks at how to make side projects work and why they’re worthwhile, drawing on lessons learned from our development of the UX Companion app.
Why Integrate Side Projects?
Being creative within the constraints of client briefs, budgets and timelines is the norm for most agencies. However, investing in research and development (R&D) as a true, creative outlet is a powerful addition. In these side projects alone, your team members can pool their expertise to create and shape their own vision — a powerful way to develop motivation, interdisciplinary skills and close relationships.
Building R&D into the identity and culture of an agency can also lead to new client work. These projects act as a road map, showing clients exciting new technologies and ideas that will differentiate you from competitors. One of our earliest projects turned our website into a brochure, optimized for the first iPad’s touch interactions. By demonstrating the final product, we went on to win a project to create a similar product for a new client.
How To Make Side Projects Work
At Cyber-Duck, we’re still working on achieving that perfect balance between commerce and creativity. But we have fresh inspiration on how it’s done from having worked on UX Companion. The app gained a popular following in early October, as one of the first native apps to offer a full glossary of user experience (UX) terms and theory — but the development process was definitely a learning process.
Ensure The Project Is Worth It
Commercializing side projects alongside client work isn’t easy. Even if such projects are intended to generate additional revenue streams, they are not directly related to your core business. Those with a more qualitative aim, such as promoting expertise or technological experimentation, are even harder to justify.
A significant shift in mindset is required to support either type of side project — weighing the longer-term, incremental benefits against committing what would otherwise be immediately billable time. Many agencies do this with a time-bound model of 80% client time versus 20% R&D time, inspired by Google’s successes with Gmail and Google Reader — which they have since (tellingly) phased out. I’d instead recommend the following guidelines.
Conclusion
Given the crowded, competitive nature of the digital space, investing in creative side projects is a powerful way to stand out, experiment and feed innovation back into client work.
Many of the tips covered in this article share some common ground — if you manage your side projects with as much professionalism as you manage client projects, then you’re likely to succeed. Achieving the perfect balance with client work isn’t easy, but we’re confident that following these lessons will be great for our next project, and hopefully they’ll help you on your way.


LKCompilerDelegate
@protocol LKCompilerDelegate<NSObject>
/**
* Indicates that the specified compiler has encountered a recoverable problem.
* The delegate should return YES if the compiler should attempt to continue.
* Extra information about this warning may be found in the info dictionary.
* All warnings must contain a human readable description identified by the key
* kLKHumanReadableDesciption, specific warnings may contain other information.
* See the LKCompilerErrors.h file for more information.
*/
- (BOOL)compiler: (LKCompiler*)aCompiler
generatedWarning: (NSString*)aWarning
details: (NSDictionary*)info;
/**
* Indicates that the specified compiler has encountered an unrecoverable
* problem. Extra information about this error may be found in the info
* dictionary. All errors must contain a human readable description
* identified by the key kLKHumanReadableDescription, specific errors may
* contain other information. See the LKCompilerErrors.h file for more
* information.
*
* In normal circumstances, errors are unrecoverable. A delegate may perform
* some manipulation on the AST to eliminate the error, however. In this case,
* the delegate should return YES, causing the semantic analysis to be retried.
*/
- (BOOL)compiler: (LKCompiler*)aCompiler
generatedError: (NSString*)anError
details: (NSDictionary*)info;
@end
Undocumented
-
Indicates that the specified compiler has encountered a recoverable problem. The delegate should return YES if the compiler should attempt to continue. Extra information about this warning may be found in the info dictionary. All warnings must contain a human readable description identified by the key kLKHumanReadableDesciption, specific warnings may contain other information. See the LKCompilerErrors.h file for more information.
Declaration
Objective-C
- (BOOL)compiler:(LKCompiler *)aCompiler generatedWarning:(NSString *)aWarning details:(NSDictionary *)info;
Swift
func compiler(_ aCompiler: LKCompiler!, generatedWarning aWarning: String!, details info: [AnyHashable : Any]!) -> Bool
-
Indicates that the specified compiler has encountered an unrecoverable problem. Extra information about this error may be found in the info dictionary. All errors must contain a human readable description identified by the key kLKHumanReadableDescription, specific errors may contain other information. See the LKCompilerErrors.h file for more information.
In normal circumstances, errors are unrecoverable. A delegate may perform some manipulation on the AST to eliminate the error, however. In this case, the delegate should return YES, causing the semantic analysis to be retried.
Declaration
Objective-C
- (BOOL)compiler:(LKCompiler *)aCompiler generatedError:(NSString *)anError details:(NSDictionary *)info;
Swift
func compiler(_ aCompiler: LKCompiler!, generatedError anError: String!, details info: [AnyHashable : Any]!) -> Bool


Explore Dialog
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Drag and drop to change the order of the widget's inputs.
You can also pick an input and move it to the parameters or vice-versa.
Reorganize widgets inputs and parameters to best suit your workflow needs.
Inputs
{% for p in inputs %}
({{p.short_name}}) {{p.name}}
{% endfor %}

Parameters
{% for p in parameters %}
({{p.short_name}}) {{p.name}}
{% endfor %}
Drag and drop to change the order of the widget's outputs. Mainly used for resolving entangled connections on a workflow.

Outputs
{% for p in outputs %}
({{p.short_name}}) {{p.name}}
{% endfor %}
You can also measure the time needed to execute this widget and have the elapsed time be an output of the widget itself. Tick the checkbox and you're good to go:


{* Copyright 2011-2013 Here's A Hand Limited Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. *} {extends file="page.htm"} {block name="content"}
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| Drive: | mechanum | platform | |||
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| Match | Alliance | push | auto | landfill | player station | ruins stack | coopertition | gets a step can | max can on top | max can in stack | number of cans | litter in can | max tote | number of totes | number of stacks | vote | comment |


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AMAIZING PEOPLE ARE MAKING AMAIZING DESIGNS IN A FUN ENVIRONMENT
When you work with us, you are part of a team of committed collaborators. All aspects of our software development process are integrated,interactive and agile.
Our approach unifies design,development and product management to create exceptional products. We start each project by understanding your bussiness goals,the user's needs and product requirements. Then we help translate them into effective and elegant solutions.
Before we sign a contract or while a line of code our team will spend the neccesary time needed to understand your product vision
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Based on your requirement, we can provide accomplete offering or just the pieces you need.
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Through your project, our designer create and implement visual design and workflows, solicit user feedback and work with you to make sure what gets built is what is needed.
All engineers are fluent int the latest enterprise, mobile and web developmet technologies
They collaborate with your team to write, and improve code on a daily basis, using proven practices such as test-driven development and programming
Planning and development is iteratve.Because we are constantly coding and testing, the product we build are always ready to go live.
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Reports any calls to JUnit's
assertEquals()
method with arguments of type array. Arrays should be checked with one of the
assertArrayEquals()
methods.
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July
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PLANTS
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longipes
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Whistler, June 24 2015
David Burns @AutomatedTester
Manager, Automation and Tools (A-Team)
| Tree Closed for a Test or Bustage | Count |
|---|---|
| Failing Test - No Try run done | 7 |
| Try breadth of run not sufficient | 3 |
| Bustage - No Try Run | 5 |
| Bustage - Try breadth of run not sufficient | 3 |
| Perf Regression | 1 |
* One month of closure reason


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--- layout: page title: About header-img: "img/about-bg.png" ---
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RFC 4180 Compliant Client-Side CSV File Handler
This page demonstrates the usage of CSV file handler. It loads a CSV file from local hard drive and converts it to a CSVFile object in the browser.
Column separator:
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Quote string:
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Skip first N rows:
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Enumerations
The following enumerations are available globally.
-
This is an enum that is used to indicate the state of the instance that conforms to the RVS_BTDriver_State_Machine protocol.
See moreDeclaration
Swift
internal enum RVS_BTDriver_State_Machine_StateEnum
-
These PIDs show the current state of the vehicle.
See moreDeclaration
Swift
internal enum RVS_BTDriver_OBD_Command_Service_01_PIDs : String, CaseIterable
-
This is the same as the first set of PIDs, except with freeze-frame data.
See moreDeclaration
Swift
internal enum RVS_BTDriver_OBD_Command_Service_02_PIDs : String, CaseIterable
-
These PIDs ask to return stored trouble codes.
See moreDeclaration
Swift
internal enum RVS_BTDriver_OBD_Command_Service_03_PIDs : String, CaseIterable
-
This is a “one-shot” PID to clear test results, and reset the “check engine” light.
See moreDeclaration
Swift
internal enum RVS_BTDriver_OBD_Command_Service_04_PIDs : String, CaseIterable
-
These are for test results that include non-CAN oxygen sensor test reults.
See moreDeclaration
Swift
internal enum RVS_BTDriver_OBD_Command_Service_05_PIDs : String, CaseIterable
-
These are for test results that include CAN oxygen sensor tests.
See moreDeclaration
Swift
internal enum RVS_BTDriver_OBD_Command_Service_06_PIDs : String, CaseIterable
-
These PIDs are for pending diagnostic codes.
See moreDeclaration
Swift
internal enum RVS_BTDriver_OBD_Command_Service_07_PIDs : String, CaseIterable
-
These PIDs are for On-Board Component control.
See moreDeclaration
Swift
internal enum RVS_BTDriver_OBD_Command_Service_08_PIDs : String, CaseIterable
-
These are Vehicle information PIDs
See moreDeclaration
Swift
internal enum RVS_BTDriver_OBD_Command_Service_09_PIDs : String, CaseIterable
-
These are permanent disgnostic test PIDs
See moreDeclaration
Swift
internal enum RVS_BTDriver_OBD_Command_Service_0A_PIDs : String, CaseIterable
-
These are the “wrapper” enum cases for the various OBD PIds.
See moreDeclaration
Swift
internal enum RVS_BTDriver_OBD_Command_Services
-
This enum is used to define OBD Commands As Strings.
See moreDeclaration
Swift
internal enum RVS_BTDriver_OBD_ELM327_AT_Command : String
-
This is a wrapper for further exploration of the OBD type.
See moreDeclaration
Swift
public enum RVS_BTDriver_Vendor_OBD_Types : Equatable
-
This enum is used to help identify what the device is.
See moreDeclaration
Swift
public enum RVS_BTDriver_DeviceType : Equatable
-
This enum is used to select which vendors to include.
See moreDeclaration
Swift
public enum RVS_BTDriver_VendorTypes : Equatable
-
This enum is used to cast a property value into its proper data type.
See moreDeclaration
Swift
public enum RVS_BTDriver_PropertyProtocol_Type_Enum : Equatable


indicating the probability that \( x \) is a real training example rather than a fake sample the generator has generated. The simplest way to formulate the learning process in a generative adversarial network is a zero-sum game, in which a function $$ \begin{equation} v(\theta^{(g)}, \theta^{(d)}) \label{_auto3} \end{equation} $$
determines the reward for the discriminator, while the generator gets the conjugate reward $$ \begin{equation} -v(\theta^{(g)}, \theta^{(d)}) \label{_auto4} \end{equation} $$
During learning both of the networks maximize their own reward function, so that the generator gets better and better at tricking the discriminator, while the discriminator gets better and better at telling the difference between the fake and real data. The generator and discriminator alternate on which one trains at one time (i.e. for one epoch). In other words, we keep the generator constant and train the discriminator, then we keep the discriminator constant to train the generator and repeat. It is this back and forth dynamic which lets GANs tackle otherwise intractable generative problems. As the generator improves with training, the discriminator's performance gets worse because it cannot easily tell the difference between real and fake. If the generator ends up succeeding perfectly, the the discriminator will do no better than random guessing i.e. 50\%. This progression in the training poses a problem for the convergence criteria for GANs. The discriminator feedback gets less meaningful over time, if we continue training after this point then the generator is effectively training on junk data which can undo the learning up to that point. Therefore, we stop training when the discriminator starts outputting \( 1/2 \) everywhere. At convergence we have $$ \begin{equation} g^* = \underset{g}{\mathrm{argmin}}\hspace{2pt} \underset{d}{\mathrm{max}}v(\theta^{(g)}, \theta^{(d)}) \label{_auto5} \end{equation} $$ The default choice for \( v \) is $$ \begin{equation} v(\theta^{(g)}, \theta^{(d)}) = \mathbb{E}_{x\sim p_\mathrm{data}}\log d(x) + \mathbb{E}_{x\sim p_\mathrm{model}} \log (1 - d(x)) \label{_auto6} \end{equation} $$ The main motivation for the design of GANs is that the learning process requires neither approximate inference (variational autoencoders for example) nor approximation of a partition function. In the case where $$ \begin{equation} \underset{d}{\mathrm{max}}v(\theta^{(g)}, \theta^{(d)}) \label{_auto7} \end{equation} $$ is convex in $\theta^{(g)} then the procedure is guaranteed to converge and is asymptotically consistent ( Seth Lloyd on QuGANs ). This is in general not the case and it is possible to get situations where the training process never converges because the generator and discriminator chase one another around in the parameter space indefinitely. A much deeper discussion on the currently open research problem of GAN convergence is available here. To anyone interested in learning more about GANs it is a highly recommended read. Direct quote: "In this best-performing formulation, the generator aims to increase the log probability that the discriminator makes a mistake, rather than aiming to decrease the log probability that the discriminator makes the correct prediction." Another interesting read
First we import our libraries
import os
import time
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.keras import layers
from tensorflow.keras.utils import plot_model
Next we define our hyperparameters and import our data the usual way
BUFFER_SIZE = 60000
BATCH_SIZE = 256
EPOCHS = 30
data = tf.keras.datasets.mnist.load_data()
(train_images, train_labels), (test_images, test_labels) = data
train_images = np.reshape(train_images, (train_images.shape[0],
28,
28,
1)).astype('float32')
# we normalize between -1 and 1
train_images = (train_images - 127.5) / 127.5
training_dataset = tf.data.Dataset.from_tensor_slices(
train_images).shuffle(BUFFER_SIZE).batch(BATCH_SIZE)
Let's have a quick look
plt.imshow(train_images[0], cmap='Greys')
plt.show()
Now we define our two models. This is where the 'magic' happens. There are a huge amount of possible formulations for both models. A lot of engineering and trial and error can be done here to try to produce better performing models. For more advanced GANs this is by far the step where you can 'make or break' a model.
We start with the generator. As stated in the introductory text the generator \( g \) upsamples from a random sample to the shape of what we want to predict. In our case we are trying to predict MNIST images (\( 28\times 28 \) pixels).
def generator_model():
"""
The generator uses upsampling layers tf.keras.layers.Conv2DTranspose() to
produce an image from a random seed. We start with a Dense layer taking this
random sample as an input and subsequently upsample through multiple
convolutional layers.
"""
# we define our model
model = tf.keras.Sequential()
# adding our input layer. Dense means that every neuron is connected and
# the input shape is the shape of our random noise. The units need to match
# in some sense the upsampling strides to reach our desired output shape.
# we are using 100 random numbers as our seed
model.add(layers.Dense(units=7*7*BATCH_SIZE,
use_bias=False,
input_shape=(100, )))
# we normalize the output form the Dense layer
model.add(layers.BatchNormalization())
# and add an activation function to our 'layer'. LeakyReLU avoids vanishing
# gradient problem
model.add(layers.LeakyReLU())
model.add(layers.Reshape((7, 7, BATCH_SIZE)))
assert model.output_shape == (None, 7, 7, BATCH_SIZE)
# even though we just added four keras layers we think of everything above
# as 'one' layer
# next we add our upscaling convolutional layers
model.add(layers.Conv2DTranspose(filters=128,
kernel_size=(5, 5),
strides=(1, 1),
padding='same',
use_bias=False))
model.add(layers.BatchNormalization())
model.add(layers.LeakyReLU())
assert model.output_shape == (None, 7, 7, 128)
model.add(layers.Conv2DTranspose(filters=64,
kernel_size=(5, 5),
strides=(2, 2),
padding='same',
use_bias=False))
model.add(layers.BatchNormalization())
model.add(layers.LeakyReLU())
assert model.output_shape == (None, 14, 14, 64)
model.add(layers.Conv2DTranspose(filters=1,
kernel_size=(5, 5),
strides=(2, 2),
padding='same',
use_bias=False,
activation='tanh'))
assert model.output_shape == (None, 28, 28, 1)
return model
And there we have our 'simple' generator model. Now we move on to defining our discriminator model \( d \), which is a convolutional neural network based image classifier.
def discriminator_model():
"""
The discriminator is a convolutional neural network based image classifier
"""
# we define our model
model = tf.keras.Sequential()
model.add(layers.Conv2D(filters=64,
kernel_size=(5, 5),
strides=(2, 2),
padding='same',
input_shape=[28, 28, 1]))
model.add(layers.LeakyReLU())
# adding a dropout layer as you do in conv-nets
model.add(layers.Dropout(0.3))
model.add(layers.Conv2D(filters=128,
kernel_size=(5, 5),
strides=(2, 2),
padding='same'))
model.add(layers.LeakyReLU())
# adding a dropout layer as you do in conv-nets
model.add(layers.Dropout(0.3))
model.add(layers.Flatten())
model.add(layers.Dense(1))
return model
Let us take a look at our models. Note: double click images for bigger view.
generator = generator_model()
plot_model(generator, show_shapes=True, rankdir='LR')
discriminator = discriminator_model()
plot_model(discriminator, show_shapes=True, rankdir='LR')
Next we need a few helper objects we will use in training
cross_entropy = tf.keras.losses.BinaryCrossentropy(from_logits=True)
generator_optimizer = tf.keras.optimizers.Adam(1e-4)
discriminator_optimizer = tf.keras.optimizers.Adam(1e-4)
The first object, cross_entropy is our loss function and the two others are our optimizers. Notice we use the same learning rate for both \( g \) and \( d \). This is because they need to improve their accuracy at approximately equal speeds to get convergence (not necessarily exactly equal). Now we define our loss functions
def generator_loss(fake_output):
loss = cross_entropy(tf.ones_like(fake_output), fake_output)
return loss
def discriminator_loss(real_output, fake_output):
real_loss = cross_entropy(tf.ones_like(real_output), real_output)
fake_loss = cross_entropy(tf.zeros_liks(fake_output), fake_output)
total_loss = real_loss + fake_loss
return total_loss
Next we define a kind of seed to help us compare the learning process over multiple training epochs.
noise_dimension = 100
n_examples_to_generate = 16
seed_images = tf.random.normal([n_examples_to_generate, noise_dimension])
Now we have everything we need to define our training step, which we will apply for every step in our training loop. Notice the @tf.function flag signifying that the function is tensorflow 'compiled'. Removing this flag doubles the computation time.
@tf.function
def train_step(images):
noise = tf.random.normal([BATCH_SIZE, noise_dimension])
with tf.GradientTape() as gen_tape, tf.GradientTape() as disc_tape:
generated_images = generator(noise, training=True)
real_output = discriminator(images, training=True)
fake_output = discriminator(generated_images, training=True)
gen_loss = generator_loss(fake_output)
disc_loss = discriminator_loss(real_output, fake_output)
gradients_of_generator = gen_tape.gradient(gen_loss,
generator.trainable_variables)
gradients_of_discriminator = disc_tape.gradient(disc_loss,
discriminator.trainable_variables)
generator_optimizer.apply_gradients(zip(gradients_of_generator,
generator.trainable_variables))
discriminator_optimizer.apply_gradients(zip(gradients_of_discriminator,
discriminator.trainable_variables))
return gen_loss, disc_loss
Next we define a helper function to produce an output over our training epochs to see the predictive progression of our generator model. Note: I am including this code here, but comment it out in the training loop.
def generate_and_save_images(model, epoch, test_input):
# we're making inferences here
predictions = model(test_input, training=False)
fig = plt.figure(figsize=(4, 4))
for i in range(predictions.shape[0]):
plt.subplot(4, 4, i+1)
plt.imshow(predictions[i, :, :, 0] * 127.5 + 127.5, cmap='gray')
plt.axis('off')
plt.savefig(f'./images_from_seed_images/image_at_epoch_{str(epoch).zfill(3)}.png')
plt.close()
#plt.show()
Setting up checkpoints to periodically save our model during training so that everything is not lost even if the program were to somehow terminate while training.
# Setting up checkpoints to save model during training
checkpoint_dir = './training_checkpoints'
checkpoint_prefix = os.path.join(checkpoint_dir, 'ckpt')
checkpoint = tf.train.Checkpoint(generator_optimizer=generator_optimizer,
discriminator_optimizer=discriminator_optimizer,
generator=generator,
discriminator=discriminator)
Now we define our training loop
def train(dataset, epochs):
generator_loss_list = []
discriminator_loss_list = []
for epoch in range(epochs):
start = time.time()
for image_batch in dataset:
gen_loss, disc_loss = train_step(image_batch)
generator_loss_list.append(gen_loss.numpy())
discriminator_loss_list.append(disc_loss.numpy())
#generate_and_save_images(generator, epoch + 1, seed_images)
if (epoch + 1) % 15 == 0:
checkpoint.save(file_prefix=checkpoint_prefix)
print(f'Time for epoch {epoch} is {time.time() - start}')
#generate_and_save_images(generator, epochs, seed_images)
loss_file = './data/lossfile.txt'
with open(loss_file, 'w') as outfile:
outfile.write(str(generator_loss_list))
outfile.write('\n')
outfile.write('\n')
outfile.write(str(discriminator_loss_list))
outfile.write('\n')
outfile.write('\n')
To train simply call this function. Warning: this might take a long time so there is a folder of a pretrained network already included in the repository.
train(train_dataset, EPOCHS)
And here is the result of training our model for 100 epochs
Now to avoid having to train and everything, which will take a while depending on your computer setup we now load in the model which produced the above gif.
checkpoint.restore(tf.train.latest_checkpoint(checkpoint_dir))
restored_generator = checkpoint.generator
restored_discriminator = checkpoint.discriminator
print(restored_generator)
print(restored_discriminator)
So we have successfully loaded in our latest model. Let us now play around a bit and see what kind of things we can learn about this model. Our generator takes an array of 100 numbers. One idea can be to try to systematically change our input. Let us try and see what we get
def generate_latent_points(number=100, scale_means=1, scale_stds=1):
latent_dim = 100
means = scale_means * tf.linspace(-1, 1, num=latent_dim)
stds = scale_stds * tf.linspace(-1, 1, num=latent_dim)
latent_space_value_range = tf.random.normal([number, number],
means,
stds,
dtype=tf.float64)
return latent_space_value_range
def generate_images(latent_points):
# notice we set training to false because we are making inferences
generated_images = restored_generator(latent_space_value_range,
training=False)
return generated_images
def plot_result(generated_images, number):
# obviously this assumes sqrt number is an int
fig, axs = plt.subplots(int(np.sqrt(number)), int(np.sqrt(number)),
figsize=(10, 10))
for i in range(int(np.sqrt(number))):
for j in range(int(np.sqrt(number))):
axs[i, j].imshow(generated_images[i*j], cmap='Greys')
axs[i, j].axis('off')
plt.show()
generated_images = generate_images(generate_latent_points())
plot_result(generated_images, number)
Interesting! We see that the generator generates images that look like MNIST numbers: \( 1, 4, 7, 9 \). Let's try to tweak it a bit more to see if we are able to generate a similar plot where we generate every MNIST number. Let us now try to 'move' a bit around in the latent space. Note: decrease the plot number if these following cells take too long to run on your computer.
plot_number = 225
generated_images = generate_images(generate_latent_points(number=plot_number,
scale_means=5,
scale_stds=1))
plot_result(generated_images, plot_number)
generated_images = generate_images(generate_latent_points(number=plot_number,
scale_means=-5,
scale_stds=1))
plot_result(generated_images, plot_number)
generated_images = generate_images(generate_latent_points(number=plot_number,
scale_means=1,
scale_stds=5))
plot_result(generated_images, plot_number)
Again, we have found something interesting. Moving around using our means takes us from digit to digit, while moving around using our standard deviations seem to increase the number of different digits! In the last image above, we can barely make out every MNIST digit. Let us make on last plot using this information by upping the standard deviation of our Gaussian noises.
plot_number = 400
generated_images = generate_images(generate_latent_points(number=plot_number,
scale_means=1,
scale_stds=10))
A pretty cool result! We see that our generator indeed has learned a distribution which qualitatively looks a whole lot like the MNIST dataset.
So let us start


Gardai arrest two men in relation to the abduction and torture of Kevin Lunney
by Fiona KellyGardai have made two more arrests related to the abduction and torture of Quinn Industrial Holdings executive Kevin Lunney.
This morning, gardai arrested two men, both aged 38, in relation to the abduction on September 17, 2019.
Both men were arrested in the Dublin region for an offence contrary to Section 73 Criminal Justice Act 2006 and are being detained under Section 50 Criminal Justice Act 2007 at Ballymun & Finglas Garda stations.
Gardai are not providing any further information at this time.
Gardai continue to appeal to any person who has any information, 'no matter how insignificant it appears', on this vicious criminal attack or any other criminal activities which have taken place in the Cavan/Fermanagh border area over recent years.
In an interview with BBC Spotlight last month, Mr Lunney recounted how he was slashed with a knife, doused with bleach and branded by his captors.
During the shocking attack the 50-year-old had his leg broken in two places, his fingernails sliced down to the quick and the letters QIH carved on his chest.
Criminals targeting Quinn Industrial Holdings (QIH) executives are believed to have been behind the attack, which lasted for about two and a half hours.
Last week, three people arrested in connection with the investigation were released from garda custody without charge.
The three persons were arrested on Thursday, November 14 in relation to the ongoing investigation.
All three, two men and one woman, were released without charge and a file will be prepared for the Director of Public Prosecutions.


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The significance level alpha of a test for hypothesis H0 gives the probability of incorrectly rejecting hypothesis H0 although it is correct. If the significance level for H0 is set too high, then the test may reject sequences that were actually generated by a random bit generator. If the significance level for a test of H0 is too low, then there is a danger that the test will also accept sequences that were not actually produced by a random bit generator. For these reasons a lot of care should be given to the choice of significance level when defining a test configuration.
Reference: Christian Schiestl, Pseudozufallszahlen in der Kryptographie, in Klagenfurt, 1999.


Display dense vector representation of texts on a 2D plan to better understand embeddings by observing the neighbors of a selected text. It also includes an interactive application to change dynamically the pivot text.
| Version: | 0.0.2 |
| Depends: | R (≥ 3.4) |
| Imports: | methods, RcppAnnoy (≥ 0.0.10), Rtsne, RColorBrewer, dbscan, plotly, assertthat, shiny, shinythemes, shinyjs |
| Suggests: | knitr, testthat, fastrtext, covr |
| Published: | 2018-02-27 |
| Author: | Michaël Benesty [aut, cre, cph] |
| Maintainer: | Michaël Benesty <michael at benesty.fr> |
| BugReports: | https://github.com/pommedeterresautee/projector/issues |
| License: | MIT + file LICENSE |
| URL: | https://github.com/pommedeterresautee/projector |
| NeedsCompilation: | no |
| Materials: | NEWS |
| CRAN checks: | projector results |
| Reference manual: | projector.pdf |
| Package source: | projector_0.0.2.tar.gz |
| Windows binaries: | r-devel: projector_0.0.2.zip, r-release: projector_0.0.2.zip, r-oldrel: projector_0.0.2.zip |
| OS X binaries: | r-release: projector_0.0.2.tgz, r-oldrel: projector_0.0.2.tgz |
| Old sources: | projector archive |
Please use the canonical form https://CRAN.R-project.org/package=projector to link to this page.


One of the more distressing verbal infelicities committed by young persons nowadays is the strewing of their utterances with “innit”, as in “I was like oh my god innit”. My chief objection is that it is almost always ungrammatical. It used to be the case that “innit” was a truncation of “isn't it?”, but if you listen carefully to teen persons, their “innit”s can rarely be construed as such.
The changing fads and fashions of teensprache are a mystery, at least to me, for I have not studied these matters properly. But I think I have stumbled upon the reason why “innit” has become so ubiquitous. Last night, I watched a snippet of BBC4's John Lennon Night, an edition of Top Of The Pops 2 (recorded some years ago, I think) in which Yoko Ono did little introductory pieces to camera for a selection of songs by her bespectacled Liverpudlian husband. The programme began with Yoko saying:
“We didn't think [Give Peace A Chance] would be a big song, but it is, innit?”
Yoko was, of course, using “innit” in a grammatically correct manner, but I am convinced that a generation of young persons heard this when it was originally broadcast and, bowled over by the Beatle relict's verbal flourish, duly began to imitate her on any and every occasion.
I am thus forced to revise my view of “innit” and will henceforth say a little prayer for world peace and a wish for no possessions whenever I overhear it.


Traditionalist rejects national shrine proposal
A cultural anthropologist, Osofo Kofitse Ahadzi, has rejected a proposal to build a national shrine for Ghanaian traditional priests.
He said what they urgently need is a national holiday.
On Wednesday, Minister of Chieftaincy and Religious Affairs Samuel Kofi Dzamesi at a Meet the Press Series in Accra hinted that the government is considering plans to build a national shrine for traditional believers.
Mr Dzamesi said his ministry has been approached by some traditional groupings including Wulomei for the construction of the shrine.
According to him, the construction of a national shrine would not be unrealistic since the Islamic and Christian faiths in the country have a national mosque and national cathedral respectively.
But speaking on the Sunrise morning show on 3FM on Thursday, the cultural anthropologist said the priests do not need a national shrine.
“Our position is that we don’t need a national shrine. Give us our holiday just as you have given the Christians holidays. When you are at the coast, you see that the fishermen don’t go to sea on Tuesdays so if you agree to give us holidays, call us we will tell you the days that we like.”
He said they already have their own shrines just like churches and mosques, so there is no need to put up a national cathedral or national mosque or national shrine.
“How can a settler from Bawku who is a traditionalist come all the way to a national shrine located in Nkoranza or Half Assini or located in Accra? Is it possible?”
He questioned how the minister is going to deal with the various traditional leaders or priests.
“The Wulomei they are in the traditional setting but they are not the totality of traditional priests so if any call is to be made, we must all gather which is not going to be impossible. What are the Wulomei going to do in a shrine?”
According to him, the traditional leaders need maximum three days of holiday to start with.