Event Location:
Engine 4 Coworking Space
Complejo Onofre Carballeira Esq. PR-2 con Int. PR-5
Bayamón, PR, Puerto Rico
Abstract:
Alguna vez ha tenido que recuperar una base de datos SQL Server, para restaurar una tabla? Le han solicitado archivos de backups encriptados para cumplir con estándares de seguridad? Los respaldos de SQL Server utilizan al máximo los recursos de hardware disponible? Durante esta sesión veremos cómo recuperar un objeto de la base de datos sin tener que recuperar la BD completa, vea cómo podemos recuperar registros modificados directamente de los archivos de log y conozca los nuevos algoritmos para realizar backups de SQL Server hasta con un 90% de compresión.
Speaker(s): German Ordonez,
Abstract:
Contar con un “Disaster Recovery”, es una de las preocupaciones de muchos CIO, debido a la cantidad de diferentes sistemas, la complejidad de los mismos, el costo en dinero y operaciones de mantener un sitio alterno de recuperación, y no se hable de las pruebas, entre otras cosas más, hace que este sea un objetivo difícil de alcanzar. En esta sesión cubriremos las bases de “Site Recovery”, un muy poderoso servicio de Microsoft Azure, que nos permitirá implementar un “Disater Recovery”, para la mayoría de nuestras plataformas sean Windows o Linux, Hyper-V o VMWare.
Speaker(s): Isaias Martinez,
Abstract:
Microsoft is streamlining its expert-level technical certifications. The new Microsoft Certified Solutions Associate (MCSA) and Microsoft Certified Solutions Expert (MCSE) Data Management and Analytics Certification paths is aligning to industry-recognized areas of competence while providing flexibility to showcase your specific skills on Microsoft products and services.
Speaker(s): Kahlil Mendez,
Abstract:
Conozca como las soluciones de Quest permiten monitorear un ambiente de bases de datos SQL Server, identificar problemas de espacio, identificar bloqueos en tiempo real, hacer diagnóstico histórico del comportamiento de la base de datos, generar alertas o notificaciones sobre los problemas en la base de datos, vea como rápidamente se puede hacer afinamiento a consultas SQL problemáticas, durante la sesión presentaremos los tips para identificar problemas en la base de datos y encontrar rápidamente las soluciones.
Speaker(s): German Ordonez,
Abstract:
Any data warehousing project has to include a data loading strategy during the development and operational phases. As the industry is moving to more modern and scalable alternatives in the cloud, it is important to understand what are our options and best practices to design the best strategy for our scenario. In this session we will go through some Polybase basics and various Azure SQL Data Warehouse loading best practices.
Speaker(s): Guillermo Caicedo Santiago,
Abstract:
The era of Windows only SQL Server is over. In this session, we will explore the capabilities that brings SQL Server 2017 on a Linux environment and its integration with the rest of the ecosystem.
Speaker(s): Jose L. Rivera,
Abstract:
Microsoft, Google and even Apple are pushing the power of machine learning as the future of technology. This is the greatest moment of starting to make your application intelligent, predictive and using the power of machine learning. In this talk you will be able to use the power of Azure and combine it with Xamarin.
Speaker(s): Juan Martí,
Abstract:
(In Spanish) Are you wonder how to know if your servers are up to date in their antivirus/antimalware? In this session I’ll explain and demonstrate how to create a custom solution for a server antivirus/antimalware definitions update scanning status report. The technologies used on the solution are: SQL Server (Database Reporting Services), Microsoft Endpoint Protection 2012, PowerShell Scripts (.ps1), Command files (.cmd), Windows Scheduled Tasks, and Active Directory Group Policy.
Speaker(s): Jorge Sanchez,
Abstract:
Power BI when combined with Office 365 and Azure opens up many new enterprise wide and self service scenarios. Learn how you can leverage other tools, which integrate natively with Power BI, to enable actionable, collaborative and process driven BI.
Speaker(s): Ramon Ponte,
Abstract:
In this session, attendees will learn basic data exploration using R, and will be able to run an entire prediction case using Linear Regression, to predict quality of wine based on a real life case, but also stressing the importance for Data Scientists in the interpretation of the results.
Speaker(s): Nestor Figueroa,
Abstract:
ggplot2 is the most popular R package for data visualization. This incredibly versatile and powerful package allows you to create complex visualizations with simple code. In this session, we will cover the basics of ggplot2 and show you how to extend its capabilities with other packages. We will also provide some tips to consider when creating graphs.
Speaker(s): Orlando Mezquita,
Abstract:
With the evolution of SQL Server R Services into Machine Learning Services in SQL Server 2017 expanding scalable R in-database analytics by adding scalable Python machine learning providing enterprise-scale data science within SQL Server’s database engine. In this session, we will demonstrate how to create, deploy and consume machine learning models within SQL Server 2017.
Speaker(s): Jose L. Rivera,
This is a list of speakers from the XML Guidebook records. The details and URLs were valid at the time of the event.
LinkedIn: http://www.linkedin.com/in/jorgesanchezpr
Mr. Jorge S#225;nchez brings +27 years of experience in the IT industry, currently working for Rock Solid Technologies as IT Manager. Previously, Mr. Sanchez worked 8 years in Microsoft Puerto Rico as SQL Server Regional Specialist for Caribbean and Central America with direct focus in Puerto Rico, Costa Rica, Dominican Republic, and Trinidad. Before that, he worked 14 years as Senior Consultant, Developer, and SQL Server Certified Instructor within the Microsoft Partners Ecosystem.
LinkedIn: http://www.linkedin.com/profile/view?id=189422534
Contact: http://sqlconqueror.com
Jose L. Rivera is a seasoned data professional, speaker, homebrewer, trainer (former MCT), Data Platform MVP and community leader based in Puerto Rico working as Senior Financial Analyst for MCS. He shares his knowledge and experiences throughout his community work as Chapter Leader for the PASS Global Spanish VC (facebook.com/SpanishPASSVC), Vice-President of the Puerto Rico PASS (prpass.org), Mentor of GDG Puerto Rico (facebook.com/gdgpuertorico) and LATAM Regional Mentor at PASS. Jose L is a frequent speaker in SQL Saturday events throughout US and Latin America. He loves data, analytics, teaching, learning, having fun with his family, craft beer (a lot) and everything in between.
Strategic Solutions Consultant – Latinoamerica y Caribe Ingeniero de Sistemas de la Universidad Nacional de Colombia, Especialista en Gerencia de Proyectos de la Universidad del Rosario, con mas de 14 anos de experiencia en Administracion de bases de datos Oracle. Consultor de las soluciones para bases de datos en desarrollo, administracion y alta disponibilidad dentro de Quest, gestor en la region de Latinoamerica de las iniciativas de mejoramiento para la familia de productos de TOAD en Oracle y SQL Server
LinkedIn: https://www.linkedin.com/in/interactivetechnologygroup/
Contact: https://www.itg.online/
For the last 6 years, Kahlil Mendez has developed and managed “Interactive Technology Group”, a Microsoft Learning Partner in San Juan, Puerto Rico with an entrepreneurial and dynamic vision for IT professional development and Learning as a Service Solutions. His current objective is to improve the technical occupational skills challenges through innovative training services for a positive effect in the IT labor marketplace.
LinkedIn: https://pr.linkedin.com/in/juan-marti-31a64564
Contact: http://polybits.io
Passionate Software Developer in mobile and web technologies. Leader of the Google Developers Group Puerto Rico Chapter and collaborator of various other developers communities in the US and Puerto Rico.
LinkedIn: http://pr.linkedin.com/in/isaiasrma
Contact: http://www.isaiasmartinez.com
Awarded as MVP C# for 7 consecutive years. Over 20 years of extensive experience in management, design and implementation of information technology projects aimed to automate processes to minimize time, save resources and improve operational efficiency. This to enhance the vision and mission of the organizations. The experience has been gathered in projects performed in various countries such as: Venezuela, Bolivia, Panama, Colombia, and Puerto Rico. In the last 6 years, I have specialized as Cloud Architect, leading the implementation of Microsoft Azure Solutions in Puerto Rico. My certifications include Microsoft MCP, MCSD, MCSE, MCT, MCDBA.#160;
Strategic Solutions Consultant – Latinoamerica y Caribe Ingeniero de Sistemas de la Universidad Nacional de Colombia, Especialista en Gerencia de Proyectos de la Universidad del Rosario, con mas de 14 anos de experiencia en Administracion de bases de datos Oracle. Consultor de las soluciones para bases de datos en desarrollo, administracion y alta disponibilidad dentro de Quest, gestor en la region de Latinoamerica de las iniciativas de mejoramiento para la familia de productos de TOAD en Oracle y SQL Server
LinkedIn: http://www.linkedin.com/profile/view?id=189422534
Contact: http://sqlconqueror.com
Jose L. Rivera is a seasoned data professional, speaker, homebrewer, trainer (former MCT), Data Platform MVP and community leader based in Puerto Rico working as Senior Financial Analyst for MCS. He shares his knowledge and experiences throughout his community work as Chapter Leader for the PASS Global Spanish VC (facebook.com/SpanishPASSVC), Vice-President of the Puerto Rico PASS (prpass.org), Mentor of GDG Puerto Rico (facebook.com/gdgpuertorico) and LATAM Regional Mentor at PASS. Jose L is a frequent speaker in SQL Saturday events throughout US and Latin America. He loves data, analytics, teaching, learning, having fun with his family, craft beer (a lot) and everything in between.
N/A
LinkedIn: https://www.linkedin.com/in/nestor-figueroa-326a33/
Over 20 years of experience in management consulting to companies in Puerto Rico and Fortune 500 corporations in industries such as Healthcare, Insurance, Education, and Government. In 1995, he started his consulting career with international firm PricewaterhouseCoopers in New York. At PwC, Nestor led large implementations of Integrated Analytics and Data Warehousing. In 2002, Mr. Figueroa co-founded Nagnoi. Today, Nagnoi is a leading healthcare Big Data analytics software company with customers in PR and the US. Nestor holds a Bachelor’s Degree in Industrial Engineering from Penn State University and MBA from the Massachusetts Institute of Technology (MIT) Sloan School of Management.
LinkedIn: https://www.linkedin.com/in/orlandomezquita/
Contact: http://www.MasterDataAnalysis.com
Orlando is principal consultant and trainer at Master Data Analysis. He is a passionate analytics professional who shares his knowledge through MasterDataAnalysis.com. Orlando provides analytics training and consulting services with Power BI, Excel, R, and Minitab.
LinkedIn: http://www.linkedin.com/in/gcaicedo/
Contact: http://www.sqltitan.com
Guillermo Caicedo es un profesional de bases de datos y tecnolog#237;a trabajando como Consultor en Nagnoi, LLC. Ha ayudado a numerosas empresas a obtener conocimiento sobre sus datos por desarrollar e implementar soluciones de inteligencia empresarial en sus organizaciones. El Sr. Caicedo tambi#233;n tiene una amplia experiencia desarrollando aplicaciones web y m#243;viles. Guillermo es un Data Platform MVP y l#237;der del Cap#237;tulo PASS para Puerto Rico (PRPASS).
The following is a list of sponsors that helped fund the event.


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ONNX Version Converter¶
ONNX provides a library for converting ONNX models between different opset versions. The primary motivation is to improve backwards compatibility of ONNX models without having to strengthen the spec for ONNX backends. This allows backend developers to offer support for a particular opset version and for users to write or export models to a particular opset version but run in an environment with a different opset version. Implementation wise, the library leverages the in-memory representation that is much more convenient to manipulate than the raw protobuf structs, and converters to and from the protobuf format which were developed for the ONNX Optimizer.
You may be interested in invoking the provided op-specific adapters, or in implementing new ones (or both). Default adapters only work in the default domain, but can be generalized to work cross-domain or utilizing new conversion methods, dependent on the nature of relevant breaking changes.
Invoking The Version Converter¶
The version converter may be invoked either via C++ or Python.
The Python API is described, with example, here.
The C++ API consists of a single function
ModelProto ConvertVersion(
const ModelProto& mp_in,
const OpSetID& initial_version,
const OpSetID& target_version);
which accepts an input ModelProto
, the initial opset version of the model,
and the target opset verison, and which returns a new ModelProto
which
is the result of apply all relevant adapters between initial_version and
target_version. For a list of available passes, see
convert.h.
Implementing Adapters¶
You can implement a new adapter by subclassing Adapter
, and registering
your new adapter with VersionConverter::registerAdapter()
. Adapters operate
on an in-memory graph representation defined in ir.h.
There are a number of examples in the adapters
directory. Please ensure that all adapters convert from opset version i to i + 1
or i - 1, i.e. from Version 6 to Version 5 or vice versa, even if the 2 versions
being converted between are Version 1 and Version 6.
If your adapter applies in the default domain, please consider adding it to the core ONNX repository


Help:
MylearnMate 1.0 Beta is a freeware. Use, share and spread with the joy of learning by touch.
In case of suggestion or feedback, please write to us at<EMAIL_ADDRESS>Note: Presently, there is no support provided for this tool.


Our service is completely free of cost.
We work for humanity. We ensure to respond to your call
always.
Open for you, always. Our doors are never closed.
We tie up with Hospitals, Rehab centres and Safe-Shelter camps. At rehab centres, we try to ensure the availability (on demand) of medical counsellors to attend to you.
Your data is safe with us. You chose what to provide us with.
We use a Graph Database at the backend to create a scalable yet concise social graph. This ensures that we are able to provide the necessary assistance in the speediest way possible.
Find the lost, and shorten the time required to find them.
We don't do anything with your data unless you permit us. We make sure it is kept confidential.
We recommend you to store all your data (populate the database) before calamity strikes, so you can make use of our services as and when needed.
Help us populate the database by adding in your data (such as name, role, organization) and relations with other people (currently restricted to 15 per person). Relations could be any one among the type friend, neighbour, colleague, relative or owner. For pets, the owner node will take accountability.
After a calamity strikes, no matter where you are, your connectivity on our remote graph remains unaffected. In case you are found somewhere unconcious and not in a state to give your identity, a quick retina scan/birthmark details can be obtained, and the nearest neighbouring nodes be alerted of the same. You can also claim the presence of someone, or initiate a search for a lost enitity.
— Lost&Found team
Bangalore, Karnataka, India
lostandfound@gmail.com


A Concordion extension introduces additional functionality to Concordion. Extensions are able to:
extension


 You can use it to create an account when you have EOS on an exchange.
Make an EOS token transfer with the following data:
Recipient: accountcreatIf you transfer less than the required minimum amount or any other error occurs, the transferred funds will be automatically refunded by the smart contract.
If you transfer more than required, the remaining balance will be forwarded to your newly created account.
 In the following example, we are using Binance.
When you withdraw your EOS to the accountcreat smart contract, it will perform the following steps in order:
It's open source on github. If you want to use this code or need help modifying it to your needs, contact me at hello@eos-account-creator.com.


Z-index changes stacking order and creates a stacking context.
z-index applies only to objects that have the position property set to fixed, absolute, or relative, aka "positioned elements".
On positioned elements, can use top/left/right/bottom to nudge element to corners, and z-index.
position: fixed;
position: absolute;
position: relative;
Stacking context is a group of elements with a common parent that move forward or backward together (atomically) in the stacking order.
Stacking context is a subset of DOM.
See the Pen CSS(webkit) filters by Rıza Selçuk Saydam (@rss) on CodePen.
See the Pen Opacity : CSS-Tricks Almanac by Dennis Gaebel (@grayghostvisuals) on CodePen.
See the Pen CSS 3D Transform - Card Flip by Adrian Parr (@adrianparr) on CodePen.
See the Pen pIgBf by Louis Lazaris (@impressivewebs) on CodePen.
See the Pen GLbca by Hugo Giraudel (@HugoGiraudel) on CodePen.


Specman the only Blitzbok in Dubai Dream Team
by Herman Mostert - Sport24Cape Town - Springbok Sevens speedster Rosko Specman is the only Blitzbok named in the Dream Team for the Dubai Sevens.
The Blitzboks won the tournament after beating New Zealand 15-0 in the final but it was only Specman who cracked the nod for the Dream Team announced by organisers.
Specman, who was also Player of the Final, made the Dream Team alongside two New Zealanders - Tone NG Shiu and Scott Curry, Australia's Lachie Anderson, England's Dan Bibby, Ireland's Jordan Conroy and Samoa's Tomasio Alosio.
Specman, who scored four tries in the tournament, said it was a memorable performance by the Blitzboks.
"It was such a massive honour to play with these guys again. It is such a great feeling to know that when you run onto the field with this band of brothers, nothing will be left out there.
"It is such a privilege to be able to express your God-given talent with a team like this.  I love this game."
The Blitzboks now return home for the Cape Town Sevens this weekend (December 13-15) where they will contest a tricky pool alongside Fiji, USA and Japan.
Register your interest for the British & Irish Lions tickets in South Africa 2021


Material Design
UI Standards for Web and App designers
Material is a design system created by Google to help teams build
high-quality digital experiences for Android, iOS, Flutter, and
the web projects.
Material Components are interactive building blocks for creating a
user interface, and include a built-in states system to
communicate focus, selection, activation, error, hover, press,
drag, and disabled states. Component libraries are available for
Android, iOS, Flutter, and the web.


Everything you need for a trip to the gym will fit inside this surprisingly spacious Products Name Here. Stock it with a water bottle, change of clothes, pair of shoes, and even a few beauty products.


@(message: String) @main(message,"Login"){
Knoldus strongly believes in strongly sharing knowledge. We conduct knowledge sessions every week. Enthusiasts outside Knoldus who would like to contribute or attend are most welcome. drop a mail on knoldus.com and enjoy the Scala sessions with generous snacks and drinks.


|
Step 1 |
Under Record Options in the dropdown menu below, select the type of record you wish to create (dataset vs. non-dataset). more details Dataset records adhere to the ISO 19115 standard. Non-dataset records (e.g., tools, visualization products, models, scripts, etc.) adhere to the Dublin Core standard. |
|
Step 2 |
(Optional) Load Defaults. more details MILES users: If you are creating a record related to the MILES project, select MILES from the Load Defaults dropdown. Certain fields in the form will prepopulate with MILES default values. Please add to these fields as appropriate: for example, you will likely want to add place and thematic keywords related to your specific dataset. The bounding coordinates provided here are for the entire state of Idaho; modify to fit the location of your dataset as appropriate. Other users: If you are using NKN to store your data, select NKN as Data Manager from the Load Defaults dropdown. Please provide the first author's name and university affiliation toward the end of the Use Restrictions text where it currently reads "(Name, University Affiliation)". |
|
Step 3 |
Under Basic Information (in the form), enter the Title of your record. Then, under the Record Options dropdown, select Save this record as a draft. NOTE: You MUST save the record as a draft in order to add/upload files to the record. |
|
Step 4 |
Continue to fill out the remaining form fields, and click the Add Files button to attach documents to your record. When finished, select Save this record as a draft under the Record Options dropdown. |
|
Step 5 |
To PUBLISH your record, you must complete ALL form fields (unless labeled as optional). Under the Record Options dropdown, select Publish record to NKN's portal. You will see a message stating that your Metadata Record was Submitted. more details Once submitted, we will review and publish your record. If you make changes to a record you have already submitted, you will need to re-publish. |
|
Tips |
Use the My Records dropdown to see a list of your existing records, the date they were last modified and published, and to load a record into the webform for editing or to delete the record. To view your metadata record as an xml document, use the View XML as... dropdown and select your preferred format (ISO, Dublin Core, or ESRI-ISO). |


This practical codelab is the third installment of the Flipp Android Engineer Onboarding course. Other codelabs that are part of the course are as follows:
In this codelab you'll learn how to use the Room Persistence Library in the Flipp app. The Room Persistence Library provides an abstraction layer over SQLite to allow for more robust database access while harnessing the full power of SQLite.
The library helps you create a cache of your app's data on a device that's running your app. This cache, which serves as your app's single source of truth, allows users to view a consistent copy of key information within your app, regardless of whether users have an internet connection.
There are 3 major components in Room:
Database
: Contains the database holder and serves as the main access point for the underlying connection to your app's persisted, relational data. The class that's annotated with @Database
should satisfy the following conditions:
RoomDatabase
.@Dao
.At runtime, you can acquire an instance of Database by calling Room.databaseBuilder()
or Room.inMemoryDatabaseBuilder()
.
Entity
: Represents a table within the database.
DAO
: Contains the methods used for accessing the database.
The app uses the Room database to get the data access objects, or DAOs, associated with that database. The app then uses each DAO
to get entities from the database and save any changes to those entities back to the database. Finally, the app uses an entity to get and set values that correspond to table columns within the database.
ViewModel
, LiveData
and Repository
.Store Info
feature and implement a local cache using the Room persistence library to cache store information locally.Entity
that represents StoreInfo
objects.StoreInfoRepository
class.The starter code for this codelab can be found here.
Create a class StoreInfoEntity
in the package com.wishabi.flipp.app.storeinfo.data
and add the required Room annotations.
@Entity(tableName = "store_info")
data class StoreInfoEntity(
@PrimaryKey var storeName: String,
var storeAddress: String,
var storeContactNumber: String,
var storeWebsite: String,
var storeRating: Float,
var storeNumReviews: Int)
Let's see what these annotations do.
Each @Entity
class represents a SQLite table. Annotate your class declaration to indicate that it's an entity. You can specify the name of the table if you want it to be different from the name of the class. PrimaryKey
Every entity needs a primary key. To keep things simple, store name acts as its own primary key. ColumnInfo(name = "storeName")
Specifies the name of the column in the table if you want it to be different from the name of the member variable.
Every property that's stored in the database needs to have public visibility, which is the Kotlin default.
Next, Create a data class StoreHoursEntity
in the package com.wishabi.flipp.app.storeinfo.data
and add the following code to store the store hours data.
@Entity(tableName = "store_hours")
data class StoreHoursEntity(
@PrimaryKey(autoGenerate = true) val id: Int,
val dayIndex: Int,
val storeHours: String,
val storeName: String
)
The autoGenerate
flag on the primary key annotation is used to generate unique keys. The StoreInfoEntity
and the StoreHoursEntity
tables are connected by the storeName
property.
Finally, In order to query the StoreInfoEntity
along with its StoreHours
, you must first model the one-to-many relationship between the two entities. To do this, create a new data class StoreInfoWithHours
in the package com.wishabi.flipp.app.storeinfo.data
where each instance holds an instance of the parent entity and a list of all corresponding child entity instances. Add the @Relation annotation to the instance of the child entity, with parentColumn set to the name of the primary key column of the parent entity and entityColumn set to the name of the column of the child entity that references the parent entity's primary key.
data class StoreInfoWithHours(
@Embedded val storeInfo: StoreInfoEntity,
@Relation(
parentColumn = "storeName",
entityColumn = "storeName"
)
val storeHours: List<StoreHoursEntity>
)
In the DAO (data access object), you specify SQL queries and associate them with method calls. The compiler checks the SQL and generates queries from convenience annotations for common queries, such as @Insert. Room uses the DAO to create a clean API for your code.
The DAO must be an interface or abstract class.
By default, all queries must be executed on a separate thread.
Room has coroutines support, allowing your queries to be annotated with the suspend modifier and then called from a coroutine or from another suspension function.
First, let's write a DAO that provides a method to insert store info. Create a new Kotlin class file called StoreInfoDao
in the package com.wishabi.flipp.app.storeinfo.data
and copy paste the following code and fix the imports as neessary to make it compile.
@Dao
interface StoreInfoDao {
@Insert(onConflict = OnConflictStrategy.REPLACE)
suspend fun insert(storeInfo: StoreInfoEntity)
}
Let's walk through it:
StoreInfoDao
is an interface; DAOs must either be interfaces or abstract classes.@Dao
annotation identifies it as a DAO class for Room.suspend fun insert(storeInfo: StoreInfoEntity)
: Declares a suspend function to insert StoreInfo.@Insert
annotation is a speical DAO method annotation where you don't have to provide any SQL!Next, Let's write a DAO that provides a method to insert store hours. Create a new Kotlin class file called StoreHoursDao
in the package com.wishabi.flipp.app.storeinfo.data
and copy paste the following code and fix the imports as necessary to make it compile.
@Dao
interface StoreHoursDao {
@Insert(onConflict = OnConflictStrategy.REPLACE)
suspend fun insert(storeHours: StoreHoursEntity)
}
Finally, let's add a method that returns an instance of the data class that pairs the parent entity (StoreInfo) and the child entity (StoreHours). This method requires Room to run two queries, so add the @Transaction
annotation to this method to ensure that the whole operation is performed atomically. Open the class StoreInfoDao
and add the following method:
@Transaction
@Query("SELECT * FROM store_info where storeName = :storeName")
suspend fun getStoreInfoWithHours(storeName: String): StoreInfoWithHours
SQLiteOpenHelper
.LiveData
, the queries are automatically run asynchronously on a background thread.Open class AppDatabase
which is an abstract class that extends RoomDatabase
. First, add the new two Entity
classes to the entities
array.
entities = {SearchMerchantsItem.class, Merchant.class, Flyer.class, TrendingSearch.class, LastVisitedMerchant.class, StoreInfo.class, StoreHours.class},
Next, add two abstract methods to fetch the two new DAO
s you created, in the AppDatabase
class.
public abstract StoreInfoDao storeInfoDao();
public abstract StoreHoursDao storeHoursDao();
As part of the first codelab in this series, you implemented a Repository
which abstarcts out the data sources. The repository is not part of the Architecture components libraries, but it is a suggested best practice for code separation and architecture.
Open the file StoreInfoRepository
and modify the function fetchStoreInfo()
to now work with the database along with the api call. For the sake of the codelab and to limit the complexity, you are going to implement the follwing requirements:
StoreInfo
over the network using our existing retrofit service. (same as before)StoreInfo
object into StoreInfoEntity
and store it to the database using the StoreInfoDao
.StoreHoursEntity
objects from the list of store hours you receive from the API, and insert the list into database using the StoreHoursDao
.StoreInfoWithHours
using the StoreInfoDao
.StoreInfo
to StoreInfoWithHours
.Here is the complete code for the method:
suspend fun fetchStoreInfo(postalCode: String, storeName: String): StoreInfoWithHours {
val storeInfoApiService = ServiceManager.getService(RetrofitProvider::class.java)
.getRetrofitStoreInfoService()
val storeInfo = storeInfoApiService.getStoreInfo()
// Hack for the purposes of the codelab
storeInfo.storeName = storeName
// Store locally
val appDatabase = ServiceManager.getService(FlippRepository::class.java).database
appDatabase.storeInfoDao().insert(storeInfo.getEntity())
appDatabase.storeHoursDao().insert(storeInfo.getStoreHours())
return appDatabase.storeInfoDao().getStoreInfoWithHours(storeName)
}
However, to get the code to compile, you would need to add two additional functions to the StoreInfo
.
getEntity()
: This would convert a StoreInfo
object to a StoreInfoEntity
object.getStoreHours()
: This would convert the list of store hours in StoreInfo
to a list of StoreHoursEntity
objects.To avoid the StoreInfo
class from knowing abour Room entities, add these methods as extension functions
.
Next, open the file Extensions.kt
and add the following two methods.
fun StoreInfo.getStoreHours(): List<StoreHoursEntity> {
val hoursList = mutableListOf<StoreHoursEntity>()
this.storeHours.forEachIndexed { index, hours ->
val storeHour = StoreHoursEntity(id = 0, dayIndex = index, storeHours = hours, storeName = storeName)
hoursList.add(storeHour)
}
return hoursList
}
fun StoreInfo.getEntity() = StoreInfoEntity(storeName, storeAddress, storeContactNumber, storeWebsite, storeRating, storeNumReviews)
Now that your repository is ready, you need to update the StoreInfoActivityViewModel
. Currently, the viewmodel is accepting a StoreInfo
object from the repository and updating its LiveData
fields.
Modify the fetchStoreInfo()
method to accept an object of StoreInfoWithHours
from the repository and update its LiveData
fields accordingly.
Here is the complete code for the method:
fun fetchStoreInfo(postalCode: String?, storeName: String?) {
if (postalCode.isNullOrEmpty() || storeName.isNullOrEmpty()) {
_storeInfo.postValue(Error("Invalid postal code or store name"))
return
}
if (_storeInfo.value is Success
&& this.postalCode.equals(postalCode, ignoreCase = true)
&& this.storeName.equals(storeName, ignoreCase = true)) {
return
}
loadingLiveData.postValue(true)
this.postalCode = postalCode
this.storeName = storeName
viewModelScope.launch {
try {
val storeInfoWithHours = repository.fetchStoreInfo(postalCode, storeName)
val storeInfo = storeInfoWithHours.storeInfo
val storeHours = storeInfoWithHours.storeHours
storeNameLiveData.postValue(storeInfo.storeName)
storeAddressLiveData.postValue(storeInfo.storeAddress)
sundayStoreHoursLiveData.postValue(storeHours[0].storeHours)
mondayStoreHoursLiveData.postValue(storeHours[1].storeHours)
tuesdayStoreHoursLiveData.postValue(storeHours[2].storeHours)
wednesdayStoreHoursLiveData.postValue(storeHours[3].storeHours)
thursdayStoreHoursLiveData.postValue(storeHours[4].storeHours)
fridayStoreHoursLiveData.postValue(storeHours[5].storeHours)
saturdayStoreHoursLiveData.postValue(storeHours[6].storeHours)
storeWebsiteLiveData.postValue(storeInfo.storeWebsite)
storeContactNumberLiveData.postValue(storeInfo.storeContactNumber)
storeRatingLiveData.postValue(storeInfo.storeRating)
storeNumReviewsLiveData.postValue(storeInfo.storeNumReviews)
loadingLiveData.postValue(false)
} catch (e: Exception) {
_storeInfo.postValue(Error("There was an error fetching store information"))
loadingLiveData.postValue(false)
}
}
}
Compile and run the app. Now you should see the following screen after following the below steps:
View Store Info
menu option.You should now see a page similar to the below screenshot.
The final solution for this codelab can be found here.


Class AbstractImpl
- FileRouter\Router\AbstractImpl implements FileRouter\Router
Abstract
Namespace: FileRouter\Router
License: See LICENSE file for more information
Author: Pierre Klink<EMAIL_ADDRESS>Located at FileRouter/Router/AbstractImpl.php
Namespace: FileRouter\Router
License: See LICENSE file for more information
Author: Pierre Klink<EMAIL_ADDRESS>Located at FileRouter/Router/AbstractImpl.php
public
|
#
__construct(
Create instance and set source path and allowed file extension. |
protected
|
|
public
string
|
|
public
|
|
public
|
|
public
|
handleRoute()
|
protected
string
|
$fileExtension | 'php' |
#
Allowed file extension of the routing files. Only files with this extension are relevant for routing |
protected
|
$sourcePath |
|
#
Source path for routing. Only files in this path are relevant for routing. |


--- layout: default --- {% include home-header.html %}
Some special information about Disqus and what it does. It functions and beneficial features.


Introduction
This article is focused on providing clear, simple, actionable guidance for providing Java Bean Validation security functionality in your applications.
Bean validation (JSR303 aka Bean Validation 1.0 /JSR349 aka Bean Validation 1.1) is one of the most common ways to perform input validation in Java. It is an application layer agnostic validation spec which provides the developer with the means to define a set of validation constraints on a domain model and then perform validation of those constraints through out the various application tiers.
One advantage of this approach is that the validation constraints and the corresponding validators are only written once, thus reducing duplication of effort and ensuring uniformity:
Typical Validation:
Bean Validation:


A reader of Indian Autos Blog managed to capture additional video footage of a convoy of Corvette ZR1 prototypes testing in traffic. We clearly see one hardtop with composite body panels and that absolutely gigantic rear wing, following behind what appears to be a stock Z06. A convertible is third in line, with a midsize wing out back. The fourth Corvette on the highway appears to be another ZR1 convertible, this time with what may or may not be a third wing design – camouflage makes it difficult to tell.
Thanks to CorvetteBlogger and photographer David Wesel, we now know that Chevrolet is testing not just a coupe, but also a convertible body for the ZR1.
What was most significant about the lunar voyage was not that man set foot on the Moon but that they set eye on the earth.
This is a somewhat surprising development, considering the ZR1's position as a pure performance machine – convertibles add weight and lose some stiffness – as well as the original ZR1's hardtop-only body, plus we've only ever seen C7 ZR1 coupes undergoing tests.
(Then again, Chevy added a Z06 convertible this time around, so it's fully willing to sell you lots of power with whatever kind of roof you want.) In addition to the convertible revelation, we now see that two types of wings are being tested. One sits a bit lower and is reasonably subtle, while the other flies high and proclaims the car's track-readiness to the world thanks to more surface area and large endplates.
Mitsubishi Montero
sport utility vehicle manufactured by Mitsubishi. It was named after Leopardus pajeros, the Pampas cat, which is native to the Patagonia plateau region of southern Argentina.
But pajero in Spanish is also a common term of abuse, usually translated as "wanker" or "tosser", thus perhaps why alternative names have been used for many markets. It is known as the Mitsubishi Montero (meaning "mountain hunter"
in the Americas (except Brazil and Jamaica), and Spain, while in the United Kingdom it is known as the Mitsubishi Shogun.
Thanks to their success, the Pajero, Montero and Shogun names were also applied to other, mechanically unrelated models, such as the Pajero Mini kei car, the Pajero Junior and Pajero iO/Pinin mini SUVs, and the Mitsubishi Pajero/Montero/Shogun Sport. Main rivals are the Land Rover Discovery, Toyota Land Cruiser Prado SWB/LWB and Nissan Patrol Y61.
Nissan GT-R
The Nissan GT-R is a handbuilt 2-door 2+2 high performance vehicle produced by Nissan unveiled in 2007.[3][4][5] It is the successor to the Nissan Skyline GT-R although no longer part of the Skyline range itself.
Similar to the later generations of the Skyline GT-Rs, the Nissan GT-R is four-wheel drive with a twin-turbo 6 cylinder engine and has the signature four round tail lights. However, the GT-R is an entirely new model sharing little with its Skyline siblings and is a complete redesign from previous Skylines rather than an incremental evolution; the four-wheel-steering HICAS system has been removed and the former straight-6 RB26DETT engine has been replaced with a new V6 VR38DETT.
Because of the GT-R's heritage, the chassis code for the all-new version has been called CBA-R35, or 'R35' for short (where CBA is the prefix for emission standard), carrying on the naming trend from previous Skyline GT-R generations. The GT-R has also retained its Skyline predecessor's nickname Godzilla, given to it by the Australian motoring publication Wheels in its July 1989 edition.
Special thanks toCars. Photographs by Net car show.


Latest
C8 Blog - Connecting and Empathy
Developing Empathy through understanding and connecting with others
C7 Blog - Motivation, Self and Resilience
Understanding our motives, and finding sustained motivation
C6 Blog - Self Regulation: Triggers, Choices and Responses
Beyond self-control... and goodbye to reactions
T5 Project - Pete's Javasript Minesweeper Challenge
my first real Javascript interactive game!
Play MinesweeperT4 Project - Pete's first Javascript event Dots Challenge
and my first Javascript interactive game!
Play Dot CounterRecent Stuff
T4 Blog - Learning JavaScript Basics
Basic language elements, arrays, objects, looping and ternary operators
C3 Blog - Musings on Process and Meditation
Just do it and trust the process... and the breath of life
T3 Blog - Design to web process
Learning Skeleton and rebuilding the blog structure.
Getting the semantic structure right and leveraging a basic CSS framework
Come have a look at my wireframes..


"""
Gradient boosting is general-purpose algorithm proposed by Friedman [GB]_.
It is one of the most efficient machine learning algorithms used for classification, regression and ranking.
The key idea of algorithm is iterative minimization of target **loss** function
by training each time one more estimator to the sequence. In this implementation decision trees are taken as such estimators.
**hep_ml** provides non-standard loss functions for gradient boosting.
There are for instance, loss functions to fight with correlation or loss functions for ranking.
See :class:`hep_ml.losses` for details.
See also libraries: XGBoost, sklearn.ensemble.GradientBoostingClassifier
.. [GB] J.H. Friedman 'Greedy function approximation: A gradient boosting machine.', 2001.
"""
from __future__ import print_function, division, absolute_import
import copy
import numpy
from sklearn.base import BaseEstimator, ClassifierMixin, RegressorMixin
from sklearn.utils.random import check_random_state
from .commonutils import score_to_proba, check_xyw
from .tree import SklearnClusteringTree
from .losses import AbstractLossFunction, AdaLossFunction, \
KnnFlatnessLossFunction, BinFlatnessLossFunction, \
KnnAdaLossFunction, LogLossFunction, RankBoostLossFunction
__author__ = 'Alex Rogozhnikov'
__all__ = ['UGradientBoostingClassifier', 'UGradientBoostingRegressor']
class UGradientBoostingBase(BaseEstimator):
""" Base class for gradient boosting estimators """
def __init__(self, loss=None,
n_estimators=100,
learning_rate=0.1,
subsample=1.,
min_samples_split=2,
min_samples_leaf=1,
max_features=None,
max_leaf_nodes=None,
max_depth=3,
splitter='best',
update_tree=True,
train_features=None,
random_state=None):
"""
`max_depth`, `max_leaf_nodes`, `min_samples_leaf`, `min_samples_split`, `max_features` are parameters
of regression tree, which is used as base estimator.
:param loss: any descendant of AbstractLossFunction, those are very various.
See :class:`hep_ml.losses` for available losses.
:type loss: AbstractLossFunction
:param int n_estimators: number of trained trees.
:param float subsample: fraction of data to use on each stage
:param float learning_rate: size of step.
:param bool update_tree: True by default. If False, 'improvement' step after fitting tree will be skipped.
:param train_features: features used by tree.
Note that algorithm may require also variables used by loss function, but not listed here.
"""
self.loss = loss
self.n_estimators = n_estimators
self.learning_rate = learning_rate
self.subsample = subsample
self.min_samples_split = min_samples_split
self.min_samples_leaf = min_samples_leaf
self.max_features = max_features
self.max_leaf_nodes = max_leaf_nodes
self.max_depth = max_depth
self.update_tree = update_tree
self.train_features = train_features
self.random_state = random_state
self.splitter = splitter
self.classes_ = [0, 1]
def _check_params(self):
"""Checking parameters of classifier set in __init__"""
assert isinstance(self.loss, AbstractLossFunction), \
'LossFunction should be derived from AbstractLossFunction'
assert self.n_estimators > 0, 'n_estimators should be positive'
assert 0 < self.subsample <= 1., 'subsample should be in (0, 1]'
self.random_state = check_random_state(self.random_state)
def _estimate_tree(self, tree, leaf_values, X):
"""taking indices of leaves and return the corresponding value for each event"""
leaves = tree.transform(X)
return leaf_values[leaves]
def fit(self, X, y, sample_weight=None):
self._check_params()
self.estimators = []
self.scores = []
n_samples = len(X)
n_inbag = int(self.subsample * len(X))
# preparing for loss function
X, y, sample_weight = check_xyw(X, y, sample_weight=sample_weight)
assert isinstance(self.loss, AbstractLossFunction), 'loss function should be derived from AbstractLossFunction'
self.loss = copy.deepcopy(self.loss)
self.loss.fit(X, y, sample_weight=sample_weight)
# preparing for fitting in trees, setting appropriate DTYPE
X = self._get_train_features(X)
X = SklearnClusteringTree.prepare_data(X)
self.n_features = X.shape[1]
y_pred = numpy.zeros(len(X), dtype=float)
self.initial_step = self.loss.compute_optimal_step(y_pred=y_pred)
y_pred += self.initial_step
for stage in range(self.n_estimators):
# tree creation
tree = SklearnClusteringTree(
criterion='mse',
splitter=self.splitter,
max_depth=self.max_depth,
min_samples_split=self.min_samples_split,
min_samples_leaf=self.min_samples_leaf,
max_features=self.max_features,
random_state=self.random_state,
max_leaf_nodes=self.max_leaf_nodes)
# tree learning
residual, weights = self.loss.prepare_tree_params(y_pred)
train_indices = self.random_state.choice(n_samples, size=n_inbag, replace=False)
tree.fit(X[train_indices], residual[train_indices],
sample_weight=weights[train_indices], check_input=False)
# update tree leaves
leaf_values = tree.get_leaf_values()
if self.update_tree:
terminal_regions = tree.transform(X)
leaf_values = self.loss.prepare_new_leaves_values(terminal_regions, leaf_values=leaf_values,
y_pred=y_pred)
y_pred += self.learning_rate * self._estimate_tree(tree, leaf_values=leaf_values, X=X)
self.estimators.append([tree, leaf_values])
self.scores.append(self.loss(y_pred))
return self
def _get_train_features(self, X):
if self.train_features is None:
return X
else:
assert all(f in X.columns for f in self.train_features), [X.columns, self.train_features]
return X.loc[:, self.train_features]
def staged_decision_function(self, X):
"""Raw output, sum of trees' predictions after each iteration.
:param X: data
:return: sequence of numpy.array of shape [n_samples]
"""
X = SklearnClusteringTree.prepare_data(self._get_train_features(X))
y_pred = numpy.zeros(len(X)) + self.initial_step
for tree, leaf_values in self.estimators:
y_pred += self.learning_rate * self._estimate_tree(tree, leaf_values=leaf_values, X=X)
yield y_pred
def decision_function(self, X):
"""Raw output, sum of trees' predictions
:param X: data
:return: numpy.array of shape [n_samples]
"""
result = None
for score in self.staged_decision_function(X):
result = score
return result
@property
def feature_importances_(self):
"""Returns feature importances for all features used in training.
The order corresponds to the order in `self.train_features`
:return: numpy.array of shape [n_train_features]
"""
import warnings
warnings.warn('feature_importances_ of gb returns importances corresponding to used columns ')
total_sum = sum(tree.feature_importances_ for tree, values in self.estimators)
return total_sum / len(self.estimators)
[docs]class UGradientBoostingClassifier(UGradientBoostingBase, ClassifierMixin):
"""This version of gradient boosting supports only two-class classification and only special losses
derived from AbstractLossFunction."""
[docs] def fit(self, X, y, sample_weight=None):
"""Train formula.
Only two-class binary classification is supported with labels 0 and 1.
:param X: dataset of shape [n_samples, n_features]
:param y: labels, array-like of shape [n_samples]
:param sample_weight: array-like of shape [n_samples] or None
:return: self
"""
X, y, sample_weight = check_xyw(X, y, sample_weight=sample_weight, classification=True)
return UGradientBoostingBase.fit(self, X, y, sample_weight=sample_weight)
[docs] def staged_predict_proba(self, X):
"""Predicted probabilities for each event
:param X: data
:return: sequence of numpy.array of shape [n_samples, n_classes]
"""
for score in self.staged_decision_function(X):
yield score_to_proba(score)
[docs] def predict_proba(self, X):
"""Predicted probabilities for each event
:param X: pandas.DataFrame with all train_features
:return: numpy.array of shape [n_samples, n_classes]
"""
return score_to_proba(self.decision_function(X))
[docs] def predict(self, X):
"""Predicted classes for each event
:param X: pandas.DataFrame with all train_features
:return: numpy.array of shape [n_samples] with predicted classes.
"""
return numpy.argmax(self.predict_proba(X), axis=1)
[docs]class UGradientBoostingRegressor(UGradientBoostingBase, RegressorMixin):
"""Gradient Boosted regressor. Approximates target by sum of predictions of several trees."""
[docs] def fit(self, X, y, sample_weight=None):
"""Fit estimator.
:param X: dataset of shape [n_samples, n_features]
:param y: target values, array-like of shape [n_samples]
:param sample_weight: array-like of shape [n_samples] or None
:return: self
"""
return UGradientBoostingBase.fit(self, X, y, sample_weight=sample_weight)
[docs] def staged_predict(self, X):
"""Return predictions after each new tree
:param X: data
:return: sequence of numpy.array of shape [n_samples]
"""
for score in self.staged_decision_function(X):
yield score
[docs] def predict(self, X):
"""Predict values for new samples
:param X: pandas.DataFrame with all train_features
:return: numpy.array of shape [n_samples]
"""
return self.decision_function(X)


NKDInterleavedTwoOfFiveBarcode Class Reference
| Inherits from | NKDBarcode : NSObject |
|---|---|
| Declared in | NKDInterleavedTwoOfFiveBarcode.h NKDInterleavedTwoOfFiveBarcode.m |
Overview
@class NKDInterleavedTwoOfFiveBarcode
Interleaved 2 of 5 uses Wide and Narrow bars interleaved (digits 1 and 2 occupy the same space, one is bars, the other by the spaces. Therefore, we won’t be using encodeChar - we’ll override the barcode method to use a class-specific method called getBarForDigit:forBarNumber
Because of the interleaving, there must be an even number of characters; With the check-digit, odd
length values become even and even-length become odd. Odd numbers (even when check-digit used)
are zero-padded to make an even-length value.
– barcode
Overridden to implement interleaving of encoding
- (NSString *)barcode
Return Value
String of 0s and 1s that represent the encoded data
Discussion
@method barcode
Most other barcodes implemented in this framework use a distinct set of bars and gaps to represent a single character. The interleaved 2 of 5 barcode encodes two characters in each distinct grouping - one using the bar widths and one using the spaces. Therefore, we have to encode two characters at a time when the superclass behavior is to encode individual characters.
Declared In
NKDInterleavedTwoOfFiveBarcode.h
– _getBarForDigit:forBarNumber:
Tells if the (bar/gap) in a particular position for a particular character should be wide or narrow.
- (BOOL)_getBarForDigit:(char)inChar forBarNumber:(int)theBar
Discussion
@method _getBarForDigit:forBarNumber:
This is a private method used by the overridden barcode method.
Declared In
NKDInterleavedTwoOfFiveBarcode.h
– initiator
Overriden method specifies Interleaved Two of Five start character
- (NSString *)initiator
Return Value
“1010” representing narrow bar, narrow gap, narrow bar, narrow gap
Discussion
@method initiator
Declared In
NKDInterleavedTwoOfFiveBarcode.h
– terminator
Overriden method specifies Interleaved Two of Five stop character
- (NSString *)terminator
Return Value
“11101” representing medium bar, narrow gap, narrow bar narrow gap. The two narrow bars allows the code to be read in reverse; “1010” could also be used, but would result in a unidirectional barcode
Discussion
@method terminator
Declared In
NKDInterleavedTwoOfFiveBarcode.h
– generateChecksum
Generates simple 10-modulus(10) check digit
- (void)generateChecksum
Discussion
@method generateChecksum
The check digit for interleaved 2 of 5 is a simple modulus 10 calculation - in other words, the check digit is what number needs to be added to the sum of the other digits to make a number evenly divisible by 10.
Declared In
NKDInterleavedTwoOfFiveBarcode.h
– isContentValid
Enforces numeric-only content for this barcode.
- (BOOL)isContentValid
Return Value
YES if the provided string value has only numeric values, NO otherwise
Discussion
@method isContentValid
Declared In
NKDInterleavedTwoOfFiveBarcode.h


The Simpsons: Disney+ Will Add Original Uncropped Episodes - But Not Yet
by Sam StoneWhile The Simpsons is available to stream in its entirety on Disney+, viewers have noticed that the episodes have been cropped to fit the streaming platform's aspect ratio, resulting in vital sight gags and other details being trimmed out of frame. Fortunately, this is poised to change next year.
Due to fan outcry, Disney has announced it will restore earlier seasons to its original aspect ratio of 4:3 sometime in early 2020. In a written statement, Disney explained that the decision to crop the first seasons to the current aspect ratio of 16:9 was to match the visual consistency of more recent episodes that switched to the more modern, high definition presentation. A sample of the current cropping and its effects are below.
Click the button below to start this article in quick view.
Following Disney's acquisition of Fox earlier this year, Disney announced plans to include several notable Fox properties on to Disney+ at launch, including all 30 seasons of The Simpsons and National Graphic programming.
Prior to the acquisition, Fox renewed the popular animated series for its 31st and 32nd seasons, as it continues to be the longest-running scripted television series of all time, with 713 episodes currently produced and counting.
The Simpsons was created by Matt Groening and developed by James L. Brooks, Groening and Sam Simon. Brooks, Groening and Al Jean are executive producers of the series from Gracie Films Production in association with 20th Century Fox Television. The series airs Sunday nights at 8 p.m. on Fox.
(via Variety)
https://schema.orgBreadcrumbListListItem1Homehttps://www.cbr.com/ListItem2TVhttps://www.cbr.com/category/tv/ListItem3TV Newshttps://www.cbr.com/category/tv/news-tv/ListItem4The Simpsons: Disney+ Will Add Original Uncropped Episodes - But Not Yethttps://www.cbr.com/simpsons-disney-uncropped-episodes/


Sketchnotes
If you want to learn more about making sketchnotes, Veronica's talk about How and Why to Start Sketchnoting will get you started.
UX Week 2012 | Veronica Erb | How and Why to Start Sketchnoting from Adaptive Path on Vimeo.
Latest Sketchnotes
Check out all of my sketchnotes on Flickr.
Resources
If video isn't your thing, or you're looking for more from some other great folks, Veronica highly recommends the resources below.
An Article
Sketchnotes 101 by Craighton Berman
This is the article that held my hand through my first sketchnote experiments. It’s got everything you need if you want to get started quickly without watching a video or reading a book. And, I highly recommend starting quickly—don’t give yourself time to worry about whether you’re doing it “right.”
Books
The Sketchnote Handbook by Mike Rhode
This fantastic how-to book covers sketchnoting in a beautifully crafted, hand-illustrated book. Though sketching while notetaking has been around for ages, Mike coined the term “sketchnotes” and can be credited with really getting the sketchnote movement going.
The Graphic Facilitator’s Guide by Brandy Agerbeck
Graphic facilitation is the bigger, more interactive cousin of sketchnoting, and there’s lots to learn from this technique. Brandy is an awesome graphic facilitator (and fellow Grinnell College alumna) who makes lots of great videos about her work. If you’re moved enough, you might even use sketchnoting as a way to transition to graphic facilitation rockstar.
Another Video
Doodlers, unite! by Sunni Brown
In this TED talk, Sunni argues for the validity of the doodle. The research and stories she tells support sketchnoting just as well as they support the doodle.
Sketchnotes
Gather ideas for sketchnoting by checking out other great sketchnotes. Searching for the sketchnotes tag on Flickr is a good place to start.


Slackmojize
Easily resize images for Slack Emojis
Perfect, your emoji is ready to upload to slack.
Download this image
and then go to your
Slack Group's emoji settings
to upload.
Create another emoji?
Try again?
Creating your emoji, one moment please
Drop your image here or
click to select
Select the output size:
32px
64px
128px
Make your emoji


{% extends "withoutnav_template.html" %} {% from "govuk_frontend_jinja/components/button/macro.html" import govukButton %} {% block per_page_title %} Resend email link {% endblock %} {% block maincolumn_content %}
Emails sometimes take a few minutes to arrive. If you do not receive an email link, Notify can send you a new one.
{{ govukButton({ "element": "a", "text": "Resend email link", "href": url_for('main.resend_email_link', next=redirect_url) }) }}
You’ll need to:


Miscellaneous IOSPs.
GTOPO Topograpic data
NLDN Lightning
NMC legacy (pre-BUFR) obs data
USPLN Lightning


Use this tool to ensure your data is ready to send the EA with correctly formatted EA ID and site name.
What's an EA ID?
Your EA unique identifier. It's usually your permit number.
Use this tool to ensure your data is ready to send the EA with correctly formatted EA ID and site name.
Your EA unique identifier. It's usually your permit number.


--- layout: post title: Novice Racing & Free Sailing tags: [] status: publish type: post published: true meta: {} ---
Club members are again invited to come and sail on 6th August at 1pm.
These sessions are aimed at any member new or old, whether boat owner or not, to come and try your sailing skills in a non-competitive environment. We will provide safety and organise short fun races if required.Come and test that new boat out, sail one of the club boats or crew for somebody else.
For more information or to let me know you can come or if you are interested but can’t come this month contact Ava at novice-sailing@sheffieldviking.org.uk


{% extends "layout.html" %} {% block content %}
Click on the following link to view a .pdf version of my resume: Justin Glover resume
Check back soon for an html version.


1116 Orchard Street
Tacoma, Washington
By appointment only
(317) 585-8468
It is recommended that all patients coming into services complete an intake so that we have an idea about what we will be working on and to see if I am the best fit for services in regards to your mental health condition.
All patients must be 18 years or older.
At this time I do not see families or couples.
At this time I do not accept insurance. Therapy can be expensive, but it is worth the investment, Here are
a couple options:
Out-of-network benefits: Contact your insurance company and ask how much they'll reimburse for therapy provided
by an out-of-network LICSW-A (licenses clinical social worker associate) After you pay me directly, I will
provide you with a receipt that you can submit to your insurance company for reimbursement,
Private pay- This means that you would be required to pay upfront. I will request that you have a card on
file so that I can omit the hassle of collecting payment after our session is completed. I will automatically
charge your card upon completion of the session.


Arbok is a powerful abuser of paralysis spread and can punish conventional paralysis absorbers very hard. Through defense drops, paralysis, and partial trapping, Arbok is a specialist at locking down slow teams and picking them apart. However, Arbok tends to struggle against healthy offensive teams so it requires significant paralysis support against them. In exchange for this required support however, Arbok can be a very powerful mid to end-game presence.
- Acid
- Earthquake
- Glare
- Wrap
Glare should be used whenever possible to start spreading paralysis. Later on, Acid could be used to drop the foe's defense and open up more Wrap damage. Earthquake hits Wrap resists like GolDon. The combination of Acid with Wrap or Earthquake can assist in breaking down Arbok checks, so carefully consider which move is the best in the given situation.
Mega Drain can hit GolDon slightly harder and heal a lot of health, in exchange for losing Earthquake's synergy with Acid. Rock Slide can hit flying types and especially Aerodactyl, which resists the rest of Arbok's kit.
Aerodactyl: Resists everything except the rare Rock Slide Arbok. Needs to be wary of Glare, however.
Rock types: Typically have the bulk to handle Earthquake coverage, but need to be wary of Acid defense drops and paralysis.
Faster psychic types: Pokemon like Starmie and Alakazam will threaten big damage or paralysis against Arbok. However they need to be wary of Glare or being paralyzed by other teammates.
Paralysis: A paralyzed Arbok means that it will greatly struggle to do its job of outspeeding and trapping.
Author: Kurai
Publish Date: 24th July 2020


Manual menu, linking to id
Sometimes manually linking the menu is needed. Simplemenu can do that too.
Manually linking can be done through id
and data-name
.
These slides explain the use of id
.
Load the Simplemenu script and make a reference to it in the Reveal plugin options.


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Payment Amount: {{ transaction.amount }}


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Multilingual records in Jurism
Multilingual records in Jurism
${toc}
🔗 Introduction
Jurism is able to store records with multilingual variants (translations or transliterations) of names and the major text fields. This post explains how to configure a multilingual environment in Jurism and edit records with language variants.
🔗 Setup
When Jurism is first installed, it is not “aware” of variant languages
or scripts. To configure for multilingual operation, visit
Preferences
→ Languages
, and type a language name in the input
box that says Add a language
. This will open a pulldown menu as
shown below. (Note that, due to a small bug, you currently need to
click on a menu entry to select it, pressing Enter does not work.)
To add transliterations for a language, click on the (+)
symbol next
to it. There are several options for sub-variants. For most script conversions,
select script and scroll to find the appropriate script.
Romanization is a special case. Because a script conversion to the roman alphabet is not a simple one-to-one mapping for most languages, the rule set should be indicated. Mainstream rules for romanization are set by a project of the American Library Association and the U.S. Library of Congress known as “ALALC-97.” This is listed in the Variant menu:
To give your language variants more friendly names, click on the code for each in the leftmost column, and enter a descriptive name for it, and press Enter.
🔗 Item language
While it is not required, Jurism will work most smoothly with multilingual content
if the Language field is set to the two-character ISO code of the primary language
of the item. Let us take a Japanese entry as an example, for which the ISO code is ja
:
🔗 Headline field language settings
We will refer to fields that show before variants are added as “headline fields.” In our example, the headline Title field value is 吾輩はねこである. To add variants to this field, place the cursor over the field label and right-click. This shows the available variant language options. Note that transliteration options show only for the headline field’s own language, which defaults to those appropriate to the language set in the Language field.
Selecting an option under the Add Variant heading adds a field pre-populated with the headline content. Press Backspace to clear the field and enter the appropriate content. Then press enter to close the field and save the item.
🔗 Conclusion
The sections above cover the basic operations for setting up multilingual support in Jurism and manipulating multilingual records. If you have questions, feel free to post queries to the Jurism support list.


Welcome to the website of my photography. Here you will find pictures of around Canada. All the pictures are taken by using the camera model Nikon D5100.
(The picture was taken from pexels. Click the picture to go to the website.)


# Unicode and You!
## An Introduction to **Unicode** in **Python**
Eddie Antonio Santos
Special thanks to Jessica Malik!
# What even *is* Unicode?
Isn't Unicode all those çɦåяäcṱǝrŝ that aren't in ASCII?
No! It's all those characters including ASCII.
Isn't Unicode a character encoding?
No! It supports many character encodings!
Isn't Unicode that emoji 💩 company?
Sort of! They are a consortium that standardizes text, characters, and
emoji!
Unicode is frustrating
Sometimes—But knowing is half the battle!
What is Unicode?
- Standard for representing text in computers
- Maps a code point to every character.
Ever.
- Database of properties for each character
# How do I use Unicode in Python?
How to use Unicode in Python 3
```python
"Hello, World!"
```
```python
"Dzień dobry!"
```
```python
"ᑖᓂᓯ"
```
```python
"Hello, 🌎!"
```
How to use Unicode in Python 2*
```python
u"Hello, World!"
```
```python
u"Dzień dobry!"
```
```python
u"ᑖᓂᓯ"
```
```python
u"Hello, 🌎!"
```
*you must specify the source file's coding
at the top of the file
## In Python 2:
`"Dzień dobry!" != u"Dzień dobry!"`
Recommendation
Use Python 3
What is a character?
letter, digit, punctuation, symbol
space, formatting, control character
How are characters represented in Unicode?
A number called a Code point
- A = U+0041
- Ω = U+03A9
- 語 = U+8A9E
- 𐎄 = U+10384
1,114,112 total; 137,374 (12.33%) used
(As of version 11.0)
How do I get code points in Python?
ord()
What other properties do characters have?
name: unicodedata.name()
general category: unicodedata.category()
Typing Unicode characters in Python
Directly!
By hex code: "\uXXXX"
or "\U000XXXXX"
By name: "\N{NAME}"
Unicode outside of Python
Character Encoding
code points ⬌ bytes
character != byte
Explosion of character encodings!
US-ASCII
Latin-1 == ISO 8859-1 ⊆ Windows Code Page 1252
ISO 8859-2
Windows Code Page 1251
Macintosh Western == MacRoman
Shift-JIS (several)
EBCDIC (several)
...
GB 2312
Big5
A letter written by Madame Marie Curie
Recommendation
Use UTF-8 character encoding
UTF-8 Supports ALL Unicode characters
Backwards compatible with ASCII
Recommendation
Always explicitly specify the character encoding
```python
# -*- coding: UTF-8 -*-
```
```python
open("filename", "w", encoding="UTF-8")
```
```python
socket.write("¿Qué haremos mañana?".encode("UTF-8"))
```
```html
<meta charset="UTF-8">
```
```http
Content-Type: application/json; charset=utf-8
```
Recap
- Unicode characters are code points (numbers)
- Character encodings convert Unicode and bytes
- Recommendation: Use Python 3
- Recommendation: Use UTF-8
- Recommendation: Explicitly specify character encoding
ASK ME QUESTIONS
About Unicode, Python, and the intersection thereof!
# Extra links!
[Unicode! And how ES6 can help!](http://www.eddieantonio.ca/unicode-es6/)


Hello! I'm currently a Computer Science student at the University of Manitoba who loves learning
about new software technologies and design. Ever since I was exposed to web development in elementary
school, my passion for software have grown tremendously throughout the years.
As a hobby, I like to read, draw, and give back to the community by volunteering at local events and
being one of the leaders of a student group called .devClub. In the
future, I hope to create some projects that shows people the beauty behind design.
A booking application that allows users to book for flights, car rentals, and hotels


Looks like your anti-virus program removed an executable we need to manage your game library.
Sadly, it's common for anti-virus programs to quarantine legitimate programs like the Client that require access to your file system.
There should be a way to mark the Client as trusted in your anti-virus program to restore functionality of the Client.