This page displays measures for the network Semantic Network [Organization x Assets].
Network images have nodes colored by group and sized by betweenness centrality.
Organization-Level Measures
These measures were run on the specified network, and produced a vector of values, one value for each Organization node.
Input network: Semantic Network [Organization x Assets]
Measure Min Mean Max Std.Dev Centrality, Hub 0 0.062 0.518 0.111 Centrality, Hub [Unscaled] 0 0.044 0.366 0.078 Centrality, Out-Degree 0 0.014 0.116 0.025 Centrality, Out-Degree [Unscaled] 0 33.207 276 59.221 Assets-Level Measures
These measures were run on the specified network, and produced a vector of values, one value for each Assets node.
Input network: Semantic Network [Organization x Assets]
Measure Min Mean Max Std.Dev Centrality, Authority 0 0.023 0.209 0.056 Centrality, Authority [Unscaled] 0 0.017 0.147 0.039 Centrality, In-Degree 0 0.014 0.128 0.033 Centrality, In-Degree [Unscaled] 0 35.667 326 85.387 Overall Top-Ranked Organization
This chart shows the Organization nodes that are top-ranked overall using the node-level measures listed below. The value shown for a node is the average of its measure values, each of which is first converted into a z-score. The final scores are then normalized to be between zero and one.
There are 29 nodes in the nodeset.
Overall Score Measure List
Centrality, Hub [Semantic Network [Organization x Assets]]
Centrality, Out-Degree [Semantic Network [Organization x Assets]]
Centrality, Hub
A node is hub-central to the extent that its out-links are to nodes that have many in-links. Individuals or organizations that act as hubs are sending information to a wide range of others each of whom has many others reporting to them. Technically, an agent is hub-central if its out-links are to agents that have many other agents sending links to them. The scientific name of this measure is hub centrality and it is calculated on agent by agent matrices.
If the node of interest has a higher than normal value (greater than 1 standard deviation(s) above the mean) the row is colored red. The row is green if the node is within 1 standard deviation of the mean. Finally, the row is colored blue if the node has a lower than normal value (less than one standard deviation(s) below the mean).
Input network(s): Semantic Network [Organization x Assets]
Rank Organization Value Unscaled 1 organization 0.518 0.366 2 role 0.282 0.199 3 task 0.170 0.120 4 capability 0.154 0.109 5 goal 0.152 0.108 6 planning 0.113 0.080 7 exposure 0.110 0.078 8 authority 0.087 0.061 9 thinking 0.057 0.041 10 ownership 0.057 0.041 11 division 0.047 0.033 12 preference 0.037 0.026 13 skill 0.016 0.011 14 government_organization 0.002 0.002 15 __data 0 0 16 _enron1 0 0 17 _news 0 0 18 china 0 0 19 governmental 0 0 20 international 0 0 21 iranian 0 0 22 israeli 0 0 23 0 0 24 member 0 0 25 military 0 0 26 national 0 0 27 passenger 0 0 28 user 0 0 29 work 0 0
Value statistics Min: 0 Mean: 0.062 Lower quartile: 0 Max: 0.518 Std.dev: 0.111 Median: 0 Upper quartile: 0.098
Unscaled value statistics Min: 0 Mean: 0.044 Lower quartile: 0 Max: 0.366 Std.dev: 0.078 Median: 0 Upper quartile: 0.070 Centrality, Out-Degree
For any node, e.g. an individual or a resource, the out-links are the connections that the node of interest has to other nodes. For example, imagine an agent by knowledge network where the number of out-links an agent would have is the number of pieces of knowledge it is connected to. The scientific name of this measure is out-degree and it can be calculated on any network. Individuals or organizations who are high in out-degree for resources have more resources or are associated with more types of resources than are others.
If the node of interest has a higher than normal value (greater than 1 standard deviation(s) above the mean) the row is colored red. The row is green if the node is within 1 standard deviation of the mean. Finally, the row is colored blue if the node has a lower than normal value (less than one standard deviation(s) below the mean).
Input network(s): Semantic Network [Organization x Assets]
Rank Organization Value Unscaled 1 organization 0.116 276 2 role 0.066 157 3 task 0.038 90 4 goal 0.033 79 5 capability 0.032 77 6 planning 0.028 66 7 authority 0.022 53 8 exposure 0.021 49 9 ownership 0.013 31 10 thinking 0.013 31 11 division 0.011 26 12 preference 0.008 19 13 skill 0.003 8 14 government_organization 4.209e-04 1 15 __data 0 0 16 _enron1 0 0 17 _news 0 0 18 china 0 0 19 governmental 0 0 20 international 0 0 21 iranian 0 0 22 israeli 0 0 23 0 0 24 member 0 0 25 military 0 0 26 national 0 0 27 passenger 0 0 28 user 0 0 29 work 0 0
Value statistics Min: 0 Mean: 0.014 Lower quartile: 0 Max: 0.116 Std.dev: 0.025 Median: 0 Upper quartile: 0.021
Unscaled value statistics Min: 0 Mean: 33.207 Lower quartile: 0 Max: 276 Std.dev: 59.221 Median: 0 Upper quartile: 51 Overall Top-Ranked Assets
This chart shows the Assets nodes that are top-ranked overall using the node-level measures listed below. The value shown for a node is the average of its measure values, each of which is first converted into a z-score. The final scores are then normalized to be between zero and one.
There are 27 nodes in the nodeset.
Overall Score Measure List
Centrality, Authority [Semantic Network [Organization x Assets]]
Centrality, In-Degree [Semantic Network [Organization x Assets]]
Centrality, Authority
A node is authority-central to the extent that its in-links are from nodes that have many out-links. Individuals or organizations that act as authorities are receiving information from a wide range of others each of whom sends information to a large number of others.
If the node of interest has a higher than normal value (greater than 1 standard deviation(s) above the mean) the row is colored red. The row is green if the node is within 1 standard deviation of the mean. Finally, the row is colored blue if the node has a lower than normal value (less than one standard deviation(s) below the mean).
Input network(s): Semantic Network [Organization x Assets]
Rank Assets Value Unscaled 1 gain 0.209 0.147 2 loss 0.183 0.129 3 policy 0.123 0.087 4 source 0.069 0.049 5 resource 0.043 0.030 6 rewards 0.004 0.003 7 air 0 0 8 airline 0 0 9 airport 0 0 10 baggage 0 0 11 communication_link 0 0 12 domain_generation_algorithms 0 0 13 facilities 0 0 14 firewalls 0 0 15 house 0 0 16 hypervisor 0 0 17 label 0 0 18 labels 0 0 19 list 0 0 20 news 0 0 21 product 0 0 22 remote_access_tools 0 0 23 report 0 0 24 rundll32 0 0 25 space 0 0 26 time 0 0 27 toc 0 0
Value statistics Min: 0 Mean: 0.023 Lower quartile: 0 Max: 0.209 Std.dev: 0.056 Median: 0 Upper quartile: 0
Unscaled value statistics Min: 0 Mean: 0.017 Lower quartile: 0 Max: 0.147 Std.dev: 0.039 Median: 0 Upper quartile: 0 Centrality, In-Degree
The number of links directed into a node normalized by the maximum number of such links. This measure is also called Column Degree Centrality because it is computed by taking the sum of the column values in the input network.
If the node of interest has a higher than normal value (greater than 1 standard deviation(s) above the mean) the row is colored red. The row is green if the node is within 1 standard deviation of the mean. Finally, the row is colored blue if the node has a lower than normal value (less than one standard deviation(s) below the mean).
Input network(s): Semantic Network [Organization x Assets]
Rank Assets Value Unscaled 1 gain 0.128 326 2 loss 0.108 275 3 policy 0.071 182 4 source 0.042 108 5 resource 0.026 67 6 rewards 0.002 5 7 air 0 0 8 airline 0 0 9 airport 0 0 10 baggage 0 0 11 communication_link 0 0 12 domain_generation_algorithms 0 0 13 facilities 0 0 14 firewalls 0 0 15 house 0 0 16 hypervisor 0 0 17 label 0 0 18 labels 0 0 19 list 0 0 20 news 0 0 21 product 0 0 22 remote_access_tools 0 0 23 report 0 0 24 rundll32 0 0 25 space 0 0 26 time 0 0 27 toc 0 0
Value statistics Min: 0 Mean: 0.014 Lower quartile: 0 Max: 0.128 Std.dev: 0.033 Median: 0 Upper quartile: 0
Unscaled value statistics Min: 0 Mean: 35.667 Lower quartile: 0 Max: 326 Std.dev: 85.387 Median: 0 Upper quartile: 0


About Our Build Team
This project was developed by students at Lambda School.


"The doers are the major thinkers. The people that really create the things that change this industry are both thinkers and doers in one person."Steve Jobs
About Me
Education
2014-2018
Maasai Mara University-Pursued an undergraduate program in Bachelor of Science(botany).
2010-2013
Attended Githiga High School and attained a mean grade of B+
2001-2009
tttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttt Attended Simmoga Valley Academy for my primary education and achieved a score of 389 out of 500.
Job Experience
Internship Program
I worked as an intern at Muhoroni Sugar Company in kisumu for a period of three months between August and October in the year 2018.
Hobbies and Skills
- Listening to Music
- Reading short stories
- Coding
My Projects
Some of the projects I have worked on include:
Favourite Creature
In this project, I described a creature called the Phoenix. This was a mythical creature that had many origins and was believed to have magical powers. This creature was widely known for its rebirth from death as a new Phoenix was born out of the ashes of a dead Phoenix. To visit the web page with more details click here.
E-commerce landing page
I created a simple landing page for an online store as a side project. The site can be viewed from here.


Your new package is there! You can edit the package properties in the package manager. Use the edit button in the upper right corner of the package details panel to open the package editor.
Here is what you should do:
Of course, the most interesting thing is how to add, edit and develop libraries within your library package. When the package was initialized, a "libraries" folder with a single sample library has been created. In the package details you will find the local path to your package and the library layout file under "Installation". You can use any versioning system to manage your files there.
Once you have finished your package, don't forget to specify the package version so users of you package will be informed of updates. Finally you can publish the package files to a place of your choice and submit the download URL to the Salt Mine server.


'Science is simply common sense at its best'
~Huxley, Thomas H.
'Science is nothing but perception.'
~Plato
Do you observe what you see around you?
Do you see the science behind the things you see?
Come this January and enjoy one of the most fascinating and interesting quizzes you will have ever encountered.
You need not be a geek or a freak, just common sense is what we seek.


Go through the files in this repository in the following order:
micro.c
myprintf.c
esoterica.c
pointer_lesson.c
rap.c
double_pointer_lesson.c
review.c
Work through any exercises presented those files and get them to compile. If you don’t understand some function that is used in any of these files, definitely look them up!


Siyamak Abasnezhad
Full stack Web Developer
About Me
"There is no end to education. It is not that you read a book, pass an examination, and finish with education. The whole of life, from the moment you are born to the moment you die, is a process of learning."
Jiddu Krishnamurti.
Programming Skills
- HTML5
- CSS3 & SCSS
- JavaScript
- jQuery
- Vue
- Python
- Django
- Laravel
- PHP
- MySQL
lorim ipsum
The numbers in the table specify the first browser version that fully supports the property..lorim ipsum
The numbers in the table specify the first browser version that fully supports the property..lorim ipsum
The numbers in the table specify the first browser version that fully supports the property..Work Example
Bouquet
The Chainsmokers
2015
Roses
The Chainsmokers
2017
Water
The Chainsmokers
2015


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extremely low
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gRaphaël bar chart integration example
The following demo shows an example of data visualisation using gRaphaël's bar
charts.
- Choosing any of the Aggregate buttons changes the currently used column
- Editing data in the grid automatically refreshes the chart with updated values.
Aggregate:


Evening/Overnight Patrol Report
Patrol:
MOD:
Date:
Time In/Time Out:
Special Event:
Yes
No
If yes, specify:
The Inn Grounds
OK
Needs Attention
Time Last Checked:
Marina Entrance Points
OK
Needs Attention
Time Last Checked:
Parking Lots
OK
Needs Attention
Time Last Checked:
Bathrooms
OK
Needs Attention
Time Last Checked:
Outdoor Cafe/Deck
OK
Needs Attention
Time Last Checked:
Docks
OK
Needs Attention
Time Last Checked:
Pool
OK
Needs Attention
Time Last Checked:
Unusual Incidents/Open or Unlocked Doors:
notes/comments
Time Noted:
Maintenance Issues/Needs
notes/comments
Time Noted:
Damaged/Vandalized Property:
notes/comments
Time Noted:
Guests' Comments/Suggestions
notes/comments
Time Noted:
Lost & Found Items:
notes/comments
Time Noted:
SUBMIT


Note: running this test interactively may activate compositor prioritization during loading, which may skew the results.


In this post I describe what happens when a slave's Foreign Key setup is different from that of the master. I'm in particular interested in a setup where the slave has a subset of the master's foreign keys, or no foreign keys at all. I wish to observe whether integrity holds.
Making the changes
Which foreign keys do we have and how do we drop them? If you want to do this by hand, well, good luck! Fortunately, common_schema provides with quite a few handy views and routines to assist us. Consider viewing the existing foreign keys on sakila:
master> SELECT create_statement FROM common_schema.sql_foreign_keys WHERE TABLE_SCHEMA='sakila'; +-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ | create_statement | +-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ | ALTER TABLE `sakila`.`address` ADD CONSTRAINT `fk_address_city` FOREIGN KEY (`city_id`) REFERENCES `sakila`.`city` (`city_id`) ON DELETE RESTRICT ON UPDATE CASCADE | | ALTER TABLE `sakila`.`city` ADD CONSTRAINT `fk_city_country` FOREIGN KEY (`country_id`) REFERENCES `sakila`.`country` (`country_id`) ON DELETE RESTRICT ON UPDATE CASCADE | | ALTER TABLE `sakila`.`customer` ADD CONSTRAINT `fk_customer_address` FOREIGN KEY (`address_id`) REFERENCES `sakila`.`address` (`address_id`) ON DELETE RESTRICT ON UPDATE CASCADE | | ALTER TABLE `sakila`.`customer` ADD CONSTRAINT `fk_customer_store` FOREIGN KEY (`store_id`) REFERENCES `sakila`.`store` (`store_id`) ON DELETE RESTRICT ON UPDATE CASCADE | | ALTER TABLE `sakila`.`film` ADD CONSTRAINT `fk_film_language` FOREIGN KEY (`language_id`) REFERENCES `sakila`.`language` (`language_id`) ON DELETE RESTRICT ON UPDATE CASCADE | | ALTER TABLE `sakila`.`film` ADD CONSTRAINT `fk_film_language_original` FOREIGN KEY (`original_language_id`) REFERENCES `sakila`.`language` (`language_id`) ON DELETE RESTRICT ON UPDATE CASCADE | | ALTER TABLE `sakila`.`film_actor` ADD CONSTRAINT `fk_film_actor_actor` FOREIGN KEY (`actor_id`) REFERENCES `sakila`.`actor` (`actor_id`) ON DELETE RESTRICT ON UPDATE CASCADE | | ALTER TABLE `sakila`.`film_actor` ADD CONSTRAINT `fk_film_actor_film` FOREIGN KEY (`film_id`) REFERENCES `sakila`.`film` (`film_id`) ON DELETE RESTRICT ON UPDATE CASCADE | | ALTER TABLE `sakila`.`film_category` ADD CONSTRAINT `fk_film_category_category` FOREIGN KEY (`category_id`) REFERENCES `sakila`.`category` (`category_id`) ON DELETE RESTRICT ON UPDATE CASCADE | | ALTER TABLE `sakila`.`film_category` ADD CONSTRAINT `fk_film_category_film` FOREIGN KEY (`film_id`) REFERENCES `sakila`.`film` (`film_id`) ON DELETE RESTRICT ON UPDATE CASCADE | | ALTER TABLE `sakila`.`inventory` ADD CONSTRAINT `fk_inventory_film` FOREIGN KEY (`film_id`) REFERENCES `sakila`.`film` (`film_id`) ON DELETE RESTRICT ON UPDATE CASCADE | | ALTER TABLE `sakila`.`inventory` ADD CONSTRAINT `fk_inventory_store` FOREIGN KEY (`store_id`) REFERENCES `sakila`.`store` (`store_id`) ON DELETE RESTRICT ON UPDATE CASCADE | | ALTER TABLE `sakila`.`payment` ADD CONSTRAINT `fk_payment_customer` FOREIGN KEY (`customer_id`) REFERENCES `sakila`.`customer` (`customer_id`) ON DELETE RESTRICT ON UPDATE CASCADE | | ALTER TABLE `sakila`.`payment` ADD CONSTRAINT `fk_payment_rental` FOREIGN KEY (`rental_id`) REFERENCES `sakila`.`rental` (`rental_id`) ON DELETE SET NULL ON UPDATE CASCADE | | ALTER TABLE `sakila`.`payment` ADD CONSTRAINT `fk_payment_staff` FOREIGN KEY (`staff_id`) REFERENCES `sakila`.`staff` (`staff_id`) ON DELETE RESTRICT ON UPDATE CASCADE | | ALTER TABLE `sakila`.`rental` ADD CONSTRAINT `fk_rental_customer` FOREIGN KEY (`customer_id`) REFERENCES `sakila`.`customer` (`customer_id`) ON DELETE RESTRICT ON UPDATE CASCADE | | ALTER TABLE `sakila`.`rental` ADD CONSTRAINT `fk_rental_inventory` FOREIGN KEY (`inventory_id`) REFERENCES `sakila`.`inventory` (`inventory_id`) ON DELETE RESTRICT ON UPDATE CASCADE | | ALTER TABLE `sakila`.`rental` ADD CONSTRAINT `fk_rental_staff` FOREIGN KEY (`staff_id`) REFERENCES `sakila`.`staff` (`staff_id`) ON DELETE RESTRICT ON UPDATE CASCADE | | ALTER TABLE `sakila`.`staff` ADD CONSTRAINT `fk_staff_address` FOREIGN KEY (`address_id`) REFERENCES `sakila`.`address` (`address_id`) ON DELETE RESTRICT ON UPDATE CASCADE | | ALTER TABLE `sakila`.`staff` ADD CONSTRAINT `fk_staff_store` FOREIGN KEY (`store_id`) REFERENCES `sakila`.`store` (`store_id`) ON DELETE RESTRICT ON UPDATE CASCADE | | ALTER TABLE `sakila`.`store` ADD CONSTRAINT `fk_store_address` FOREIGN KEY (`address_id`) REFERENCES `sakila`.`address` (`address_id`) ON DELETE RESTRICT ON UPDATE CASCADE | | ALTER TABLE `sakila`.`store` ADD CONSTRAINT `fk_store_staff` FOREIGN KEY (`manager_staff_id`) REFERENCES `sakila`.`staff` (`staff_id`) ON DELETE RESTRICT ON UPDATE CASCADE | +-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
Most of the foreign key constraints use RESTRICT for DELETE (meaning you are not allowed to delete a parent row when children exist), and CASCADE for UPDATE (meaning changes to parent will propagate to children). This is good, since I want to test behavior of both RESTRICT and CASCADE.
OK, we wish to remove these constraints from the slave. To see what we are going to do, consider:
slave1> select drop_statement from common_schema.sql_foreign_keys where table_schema='sakila'; +-----------------------------------------------------------------------------------+ | drop_statement | +-----------------------------------------------------------------------------------+ | ALTER TABLE `sakila`.`address` DROP FOREIGN KEY `fk_address_city` | | ALTER TABLE `sakila`.`city` DROP FOREIGN KEY `fk_city_country` | | ALTER TABLE `sakila`.`customer` DROP FOREIGN KEY `fk_customer_address` | | ALTER TABLE `sakila`.`customer` DROP FOREIGN KEY `fk_customer_store` | | ALTER TABLE `sakila`.`film` DROP FOREIGN KEY `fk_film_language` | | ALTER TABLE `sakila`.`film` DROP FOREIGN KEY `fk_film_language_original` | | ALTER TABLE `sakila`.`film_actor` DROP FOREIGN KEY `fk_film_actor_actor` | | ALTER TABLE `sakila`.`film_actor` DROP FOREIGN KEY `fk_film_actor_film` | | ALTER TABLE `sakila`.`film_category` DROP FOREIGN KEY `fk_film_category_category` | | ALTER TABLE `sakila`.`film_category` DROP FOREIGN KEY `fk_film_category_film` | | ALTER TABLE `sakila`.`inventory` DROP FOREIGN KEY `fk_inventory_film` | | ALTER TABLE `sakila`.`inventory` DROP FOREIGN KEY `fk_inventory_store` | | ALTER TABLE `sakila`.`payment` DROP FOREIGN KEY `fk_payment_customer` | | ALTER TABLE `sakila`.`payment` DROP FOREIGN KEY `fk_payment_rental` | | ALTER TABLE `sakila`.`payment` DROP FOREIGN KEY `fk_payment_staff` | | ALTER TABLE `sakila`.`rental` DROP FOREIGN KEY `fk_rental_customer` | | ALTER TABLE `sakila`.`rental` DROP FOREIGN KEY `fk_rental_inventory` | | ALTER TABLE `sakila`.`rental` DROP FOREIGN KEY `fk_rental_staff` | | ALTER TABLE `sakila`.`staff` DROP FOREIGN KEY `fk_staff_address` | | ALTER TABLE `sakila`.`staff` DROP FOREIGN KEY `fk_staff_store` | | ALTER TABLE `sakila`.`store` DROP FOREIGN KEY `fk_store_address` | | ALTER TABLE `sakila`.`store` DROP FOREIGN KEY `fk_store_staff` | +-----------------------------------------------------------------------------------+
To actually make the DROP, we use common_schema's eval():
slave1> call common_schema.eval("select drop_statement from common_schema.sql_foreign_keys where table_schema='sakila'");
eval() is a handy routine which invokes statements generated by the given query.
This concludes the setup part.
Tests will include:
- Attempting to delete a parent row
- Attempting to add an invalid child row
- Attempting to update parent row
I was thinking there would be a difference between the two binary log file formats: STATEMENT and ROW. But the tests I produced showed no difference.
Tests
Attempting to delete parent row:
master> delete from actor where actor_id=1; ERROR 1451 (23000): Cannot delete or update a parent row: a foreign key constraint fails (`sakila`.`film_actor`, CONSTRAINT `fk_film_actor_actor` FOREIGN KEY (`actor_id`) REFERENCES `actor` (`actor_id`) ON UPDATE CASCADE) slave1> select * from actor where actor_id=1; +----------+------------+-----------+---------------------+ | actor_id | first_name | last_name | last_update | +----------+------------+-----------+---------------------+ | 1 | PENELOPE | GUINESS | 2006-02-15 04:34:33 | +----------+------------+-----------+---------------------+
Good: the master refused the DELETE, and no DELETE occurred on slave. Integrity is intact.
Attempting to add an invalid child row:
master> insert into film_actor (actor_id, film_id, last_update) values (9999, 1, NOW()); ERROR 1452 (23000): Cannot add or update a child row: a foreign key constraint fails (`sakila`.`film_actor`, CONSTRAINT `fk_film_actor_actor` FOREIGN KEY (`actor_id`) REFERENCES `actor` (`actor_id`) ON UPDATE CASCADE) slave> select * from film_actor where actor_id=9999; Empty set (0.00 sec)
Integrity is still intact.
Attempting to update parent row: there is nothing invalid about this operation. I'm wondering whether changes are CASCADEd on slave as well as on master:
master> update actor set actor_id=999 where actor_id=199; master> select count(*) from film_actor where actor_id=999; +----------+ | count(*) | +----------+ | 15 | +----------+
The 999 value wasn't there before on the master, so this verifies the CASCADE works on master. As for slave:
slave> select count(*) from actor where actor_id=999; +----------+ | count(*) | +----------+ | 1 | +----------+ slave> select count(*) from film_actor where actor_id=999; +----------+ | count(*) | +----------+ | 0 | +----------+
Bummer! The actor's row was updated, but cascading did not work on slave.
This is actually documented. However, the documentation only relates to the issue of slave tables being MyISAM. The problem occurs even when the slave tables are InnoDB, and have no foreign key constraints.
Conclusion
My personal interest in the scenario is due to something I'm working on, I'll elaborate on a future post. People sometime hope to get rid of foreign keys, and might wonder whether replication performance would boost having constraints removed on slaves.
When slave does not enforce foreign keys, you cannot rely on integrity with cascading constraints. An ugly patch might be to use triggers so as to simulate their behavior. Performance wise this is very bad.
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Lee likes to tell funny, silly tales. He tells tales about his dreams. Lee had a silly dream of a queen. Nineteen green beans got stuck deep in the mud. A queen sees the green beans and tries to free them from the mud. But the hill is too steep, and she cannot get to them. The queen tells a bee to get the beans. But the bee will not speak. "Please, bee. I am too weak" weeps the queen. The bee tells three fleas to help. The queen, bee, and fleas get the beans. They eat the beans at a feast. The beans are a yummy terat!
in-context


Regularly building bootable SD archives based on latest debootstrap packages and compiling most recent kernel revision.
ARM Debian 9 stretch system for raspberry pi 2
ARM Debian 9 stretch system for raspberry pi 3
ARM64 Debian 9 stretch system for raspberry pi 3
ARM Kali Linux rolling system for raspberrypi 3


Release 201342 is online and you must login directly to a store/cluster.
Why are we doing this?
Every account now has a unique secure domain for improved performance and reliability.
Just enter any domain associated with your account to be immediately transported to the new admin site. Thanks!


{% extends 'base.html' %} {% block content %}
Your account has been activated successfully
{% endif %} {% if activation_expired %}Your activation key has expired. Click here to obtain a new activation link.
{% endif %} {% if already_active %}Your account is already active.
{% endif %} {% if new_link %}We have send you a new link. Please check your inbox.
{% endif %}


\documentclass[12pt]{article}
\usepackage{amsmath}
\title{$<$texlive-element$>$}
\author{Compile TeX{} directly in your browser!}
\date{\today}
\begin{document}
\maketitle
\textit{pdftex.js} is a compiler for the \TeX{}
typesetting program created using Mozilla's Emscripten
Compiler. It offers programmable desktop
publishing features and extensive facilities for
automating most aspects of typesetting and desktop
publishing, including numbering and cross-referencing,
tables and figures, page layout, bibliographies, and
much more. It supports \LaTeX{} which was originally written
in 1984 by Leslie Lamport and has become the dominant method for
using \TeX;
% This is a comment, not shown in final output.
% The following shows typesetting power of LaTeX:
\begin{align}
E_0 &= mc^2 \\
E &= \frac{mc^2}{\sqrt{1-\frac{v^2}{c^2}}}
\end{align}
\end{document}


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About
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Services
We provide following services.
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3 August 2010 - The Ministry of Health of Indonesia has announced a new case of human infection of H5N1 avian influenza. A 34-year-old female from Tangerang District, Banten Province developed symptoms on 2 July, was hospitalized on 4 July and died on 7 July. Laboratory tests were positive for H5N1 virus infection. Investigations into the source of her infection are ongoing.
Of the 168 cases confirmed to date in Indonesia, 139 have been fatal.


An elegant way for musicians to organize their instrument collection.
I come from a family of musicians. My father and uncle grew up playing guitar at all hours of the day, much to the chagrin of my grandparents. My mother and sister play the piano. Myself, I can play guitar, saxophone, drums, bass, ukulele, and am teaching myself piano. So, yeah, of course I'd develop at least one app about music.
This was my first foray into an actual web app. I utilized the Sintatra framework to create a basic MVC CRUD app. A user can catalogue their instrument collection, adding new instruments with Name and Description properties. There is also basic authentication built into the app via the
BCrypt
gem, so a user can securely sign up and log in with ease.


The application is implemented in Deal.java and Card.java. Both classes are extracted from a SUN tutorial.
Here and with the Credit Card Validator Demo you see that the values of URL query parameters are passed to the
declared onLoad
-method.
You must pass parameters in the URL query, try CardDemo.html?numHands=3&cardsPerHand=5.
Otherwise there will be a NullPointerException
.


Gen (r) Asim Bajwa to head CPEC Authority
by News DeskThe federal government has decided to appoint former Inter-Services Public Relations (ISPR) chief Lt Gen (r) Asim Saleem Bajwa as the head of the China-Pakistan Economic Corridor Authority (CPECA) for a period of four years.
A summary has been sent to the federal cabinet for approval, which will be accepted in the next meeting.
President Dr Arif Alvi had promulgated an ordinance for the establishment of the CPECA in Oct.
The authority is aimed at accelerating the pace of CPEC-related activities, will find new drives of growth, unlock the potential of the interlinked production network and global value chains through regional and global connectivity.
In Aug, Prime Minister Imran Khan had announced that the government was forming a ‘China-Pakistan Economic Corridor Authority (CPECA)’ to ensure timely completion of corridor’s projects.
Presiding over a meeting at the Prime Minister Office, PM Imran had said: “To ensure uninterrupted progress on CPEC projects, CPEC Authority is being formed.”
He said the CEPCA would help ensure coordination among the departments concerned. “The completion of CPEC will not only benefit Pakistan and China but also the entire region,” the prime minister had said.
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Mitigator
public protocol Mitigator
A Mitigator
recieves errors that happen. Mitigate means ‘make (something bad) less severe’.
So do that or rethrow what you cannot handle.
-
See
DefaultMitigator
for an example implementation of this function.Declaration
Swift
func mitigate(thrower: ()throws -> ()) throws
Parameters
thrower
A function used as a wrapper around throwing functions in
Air
,ResponseController
andTransformController
.


Our Ingredients
We only use the freshest and 100% organic ingredients, locally sourced from small farmers. If we can't get an ingredient fresh, we don't make that flavor till we get it fresh.
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In Papa's Ice Cream Shop, we do not have rules when inventing new flavors. All we strive for is to have the smoothest, creamiest, tastiest ice cream your mouth will ever experience! Trust us, we have been doing this for over two decades.
We only use the freshest and 100% organic ingredients, locally sourced from small farmers. If we can't get an ingredient fresh, we don't make that flavor till we get it fresh.


Adaptive Sensing
An essential trade off within all mobile sensing apps is between (a) data density (high sampling rates) and continuity (no data gaps) and (b) battery drain. It is easy to configure Sensus to optimize either of these objectives in isolation, but striking a balance between them poses significant challenges. For example, one might wish to sample high-density accelerometry when the device is likely to be used in particular ways (e.g., while walking). Similarly, one might wish to sample light and temperature data, but only in particular geographic locations. No single, static sensing configuration would satisfy such objectives while also having minimal impact on the battery. Such use cases require dynamic sensing configurations that adapt to usage contexts.
Adaptive Control
The figure below depicts concepts that drive adaptive sensing within Sensus.
The following concepts are indicated:
Sensing agent: An entity that controls sensing parameters (e.g., sampling rates and continuity) on the basis of observed state (i.e., Datum objects).
Opportunistic observation: Sensing agents can be configured to observe state information actively or opportunistically. Opportunistic observation captures any Datum object generated by Sensus during normal operation. In this mode, the app will not take any extra actions to observe data. This has the effect of minimizing battery drain at the expense of weaker state estimates and sensing control.
Opportunistic control: In response to opportunistically observed data, the sensing agent may decide to control sensing in a particular way (e.g., by enabling continuous sensing or increasing sampling rates). Such decisions are only possible upon the arrival of opportunistic data.
Action interval: In contrast with opportunistic sensing, sensing agents can be configured to actively seek out data. This has the effect of strengthening state estimates and sensing control at the expense of increased battery drain. The action interval indicates how frequently Sensus should actively observe data for state estimation.
Active observation duration: Once the action interval elapses, Sensus will begin to actively observe each Datum generated by the app. This parameter governs how long active observation should continue before checking the control criterion.
Control criterion: Regardless of whether Sensus is observing opportunistically or actively, the control criterion defines state estimates that trigger sensing control. For example, a criterion might specify that sensing control should occur when the average acceleration magnitude exceeds a critical threshold. This is demonstrated in the example sensing agent. This agent also demonstrates a control criterion based on proximity of the phone to a surface (e.g., face) and user activities (e.g., walking).
Control completion check interval: Once sensing control is invoked, the agent will periodically recheck the control criterion to determine whether it is still met. If the criterion is not met, then sensing control ends, the sensing agent transitions sensing settings as needed (e.g., reducing sampling rates), and the sensing agent returns to its idle state. If the criterion is still met, then sensing control continues unabated until the next completion check occurs. This parameter governs how long the agent should wait between each completion check.
Sensus supports two mechanisms for incorporating sensing agents into a study. The first involves writing software (in C#) to define the sensing agent. The second involves defining the agent in a JSON-based specification language. The following sections provide more information about these two alternatives.
Software-Defined Sensing Agents
Android
On Android, Sensus supports a plug-in architecture for software-defined sensing agents. This architecture is intended to support research into adaptive sensing by providing a simple interface through which researchers can deploy agents that implement specific adaptation approaches. Follow the steps below to implement and deploy an Android sensing agent.
- Create a new Android Class Library project in Visual Studio. In Visual Studio for Mac, the following image shows the correct selection:
Add a NuGet reference to the Sensus package.
Add a new class that inherits from SensingAgent. Be sure to provide a parameterless constructor for your class, as this constructor will be called at run time to create your agent. Your class will be required to override a few methods related to sensing control. These methods are where your sensing agent should execute its control policy. The SensingAgent class provides a set of predefined control criterion functions here. You can call these diretly from your code or write your own functions to suit your adaptation requirements.
Build the library project, and upload the resulting .dll to a web-accessible URL. A convenient solution is to upload the .dll to a Dropbox directory and copy the sharing URL for the .dll file.
Generate a QR code that points to your .dll (e.g., using QR Code Generator). The content of the QR code must be exactly as shown below:
sensing-agent:URL
where URL is the web-accessible URL that points to your .dll file. If you are using Dropbox, then the QR code content will be similar to the one shown below (note the
dl=1
ending of the URL, and note that the following URL is only an example -- it is not actually valid):sensing-agent:https://www.dropbox.com/s/dlaksdjfasfasdf/SensingAgent.dll?dl=1
In your Protocol settings, tap "Set Agent" and scan your QR code. Sensus will fetch your .dll file and extract any agent definitions contained therein. Select your desired agent.
Continue with configuration and distribution of your protocol. When run, your protocol will invoke the selected sensing agent according to the state diagram shown above.
See the following implementation for an example:
- Acceleration (code here): A sensing agent that samples continuously if the device is moving or near a surface (e.g., face).
iOS
In contrast with Android, iOS does not allow apps to load code (e.g., from the above .dll assembly) at run time. Thus, software-defined adaptive sensing agents are more limited on iOS compared with Android. Here are the options:
The app comes with one example sensing agent (code here); however, this is simply for demonstration and is unlikely to work well in practice. Nonetheless, you can select this agent when configuring the Protocol.
You can redeploy Sensus as your own app, to which you can add your own agent implementations.
You can implement your own agent implementations following the instructions above for Android and email our team<EMAIL_ADDRESS>to include your implementation in a future release of the iOS app.
Adaptive Sensing Policy Language (ASPL) Defined Sensing Agents
In addition to the software-defined adaptive sensing agents described above, Sensus supports the definition of sensing agents in a general-purpose adaptive sensing policy language (ASPL). ASPL specifies both the control criteria as well as the control actions depicted in the above state diagram. The example ASPL policy file demonstrates the ASPL format. The elements of the format are described in the documentation for SensingAgent and AsplSensingAgent. If more than 1 AsplStatement is provided to the AsplSensingAgent, then the first one whose criterion is satisfied by the observed data will be used for sensing control.
In the example ASPL policy file, you will see many places where property types and property names are specified. In
general, each AsplElement will specify a property type that is the fully-qualified
name of a Datum sub-type. The fully-qualified type name is the concatenation of the type's namespace
and name. For example, the AccelerometerDatum type has namespace Sensus.Probes.Movement
and name AccelerometerDatum
. The fully-qualified type name is thus Sensus.Probes.Movement.AccelerometerDatum
.
You can find the namespace for each type in the API documentation (e.g., see
here). The API documentation page for each type also lists
the properties available for that type (e.g.,
here).
So, for example, if you wish to specify the X
property within the AsplElement, then the property type should be
Sensus.Probes.Movement.AccelerometerDatum
and the property name should be X
.
The other places where property types and property names appear in ASPL are the BeginControlSettings and EndControlSettings. These types and names refer to the Probe properties that should be changed to begin and end sensing control. For example, you may wish to begin control by enabling the AccelerometerProbe with a sampling rate of 50 Hz. This involves two settings: (1) enabling the probe, and (2) setting the sampling rate. Each of these is specified within a ProtocolSetting in the ASPL JSON.
Softare- Versus ASPL-Defined Sensing Agents
There are pros and cons of software- and ASPL-defined sensing agents:
Software-Defined
- Pros: Sophistication of control criteria. Actions are not limited to the logical structure of ASPL. Any criterion that can be implemented in C# would be feasible.
- Cons: Low-level programming is required. Third-party deployment of iOS agents is complicated by iOS's prohibition of run-time code loading (see above). Changing the agent definition (whether on Android or iOS) involves modifying code and, for iOS, redeploying the application.
ASPL-Defined
- Pros: Agent definitions use the relatively simple ASPL syntax. Agent definitions can be loaded at run-time into both Android and iOS without the need for code changes or app redeployment.
- Cons: ASPL has limited logical expressiveness.
Distributing Sensing Agent Policies
Regardless of whether a software- or ASPL-defined sensing agent is used, a policy must be provided to the agent, specifying the agent's control parameters. This can be done in two ways:
Set within protocol: In the protocol settings, tap "Set Agent Policy", then select your JSON policy file. This works well to set the initial policy used by the sensing agent; however, this is not a very effective means of providing updated sensing policies to users during an ongoing study. Updating the policy would require the study administrator to edit the protocol and distribute the new protocol to all users (e.g., via email), who would then need to manually update their protocols.
Send via push notification: Request a push notification update with the
type
set to SensingAgentPolicy andcontent
set to the policy you wish to provide. Sensus will parse thecontent
into a JSON object and pass the resulting object to your agent via <xref:Sensus.Adaptation.SensingAgent.ProtectedSetPolicyAsync>. This is an effective option for updating the sensing agent's policy during ongoing studies, as users will not need to do anything in order to receive the updated policies.
Testing and Debugging
Regardless of whether your sensing agent targets Android or iOS, there are a few ways to test and debug it:
Monitor the agent state: Within your Protocol settings, tap "View Agent State" to see a real-time display of your agent's state. You will see it cycle through the state diagram shown above.
Write to the log file: See the code for the example agents above. You will see calls that write to the log file. Use similar calls in your code to write information about the behavior of your agent to the log. Run your agent for a while in the app and then share the log file with yourself from within the app. Note that the size of the log file is limited, so you might not be able to view the entire log history of your agent.
Flash notifications on the screen: On Android, you can flash notifications on the screen as shown in the example code. These messages will appear for a short duration.
Run your agent in the debugger: By far the most useful approach is to configure a development system and run Sensus in the debugger with your sensing agent. You will need to add your agent code to the Sensus app projects in order to step through it in the debugger.
Known Limitations and Future Improvements
Initiation of continuous sensing from the background on iOS: iOS places significant constraints on Sensus's ability to operate in the background. This impacts all sensing agents' ability to initiate continuous sensing from the background. A sensing agent will be able to update its state estimtes from the background upon receipt of a push notification; however, there is no known way to initiate continuous background operation while in the background state. As a result, if the agent's state estimate indicates that continuous sensing control is warranted, then this control will not be initiated until the next time the app is brought to the foreground by the user. It would be useful to add the option of notifying the user from the background when continuous sensing is requested by the sensing agent.
Termination of sensing control: Currently, sensing control (whether opportunistic or active) can only be terminated after the control completion check interval elapses. This is probably too coarse, and it would be helpful to complement this check with checks run after a certain number of data readings have been observed while in a control state.
Data Streams
In addition to directly impacting the data streams that are collected (e.g., via sampling rates and enabling/disabling probes), the use of a sensing agent will cause additional data to be written to the local data store. These include:
Datum-level tagging: Each Datum collected by the app will be tagged with a description of the sensing agent's state at the time when the Datum was collected. This is achieved by setting SensingAgentStateDescription to the value of StateDescription.
Sensing agent lifecycle: Each time the sensing agent transitions from one state to another, a SensingAgentStateDatum will be written to the local data store to record the transition. This will be done regardless of whether any other Datum readings are collected.


Copyright Bruce Wilcox<EMAIL_ADDRESS>brilligunderstanding.com
Revision 6/20/2022 cs12.2
The CS engine is a collection of capabilities which requires data to exploit. On startup the system loads cs_init and cs_initmore, dictionary, LIVEDATA, and TOPIC data.
Two init files define bot variables, limits, hookups to outside, and other things. Run-command parameters are processed first, then cs_init.txt, then cs_initmore.txt. If any parameter is redefined by a later collection, that last one wins. cs_init.txt is used for parameters that are machine-dependent and vary based on where cs is being started. cs_initmore.txt is used for globally applicable parameters.
For each language, CS reads in DICT/… data. Initially it loads the .txt forms of data and then writes out .bin forms (dict.bin and facts.bin) for faster loading on restarts. When only 1 language is requested, the bin forms go in the folder for that language. If multipe languages are requested, the bin files go in the top level DICT folder. Japanese language has no cs-visible dictionary. Where multiple languages have the same spelled word, each gets its own entry with appropriate language bits to differentiate.
A sample english dictionary entry is this:
march ( meanings=12 glosses=11 NOUN_ABSTRACT VERB_PRESENT
VERB_INFINITIVE NOUN VERB NOUN_SINGULAR COMMON4
KINDERGARTEN posdefault:NOUN VERB_DIRECTOBJECT VERB_NOOBJECT )
march~1nz (^progress~2) a steady advance
parade~5v
demonstrate~4vz march in protest
The word, then in parens various flags (properies, systemflags, internalbits) along with a count of how many meanings it has and how many glosses it has. There may be multiple parts of speech for this word in the flags. In the subsequent lines each meaning is given. A meaning identifies its part of speech and links (usually) to a synonym. The synonym (eg demonstrate~4) is which entry of the demonstrate word has the same meaning. n and v here refer to noun and verb postags. the z indicates that OUR definition here is considered the master definition for the synonym set (synset). Words often are more specific instances of general words, like dog is a canine is a mammal. Those ISA links will be facts linking the master of the more specific to the more general. This allows us to mark appropriately higher words when we see a lower one.  Progress~2 is the entry “onward_motion~1nz (^motion~1) the act of moving forward” which in turn uplinks to motion~1. So when we see the word march (as a noun) we will mark it and progress and motion.
A sample spanish dictionary entry is much simpler. It does not come from Wordnet (which has structure and glosses and synsets). It comes from Treetagger’s simple data.
imprimibles ( ADJECTIVE ADJECTIVE_NORMAL ) lemma=imprimible
impuestos ( VERB_PAST_PARTICIPLE NOUN VERB NOUN_SINGULAR NOUN_PLURAL ) lemma= impuesto|imponer NC VLadj
For english we don’t have conjugated forms in the dictionary, we compute their lemma on the fly. For spanish the dictionary lists all forms of the word and provides the name of the lemma. Words that represent multiple parts of speech can require different lemmas, so the lemma data indicates what the lemma is for each part of speech. Treetagger lists parts of speech tags differently from english (often more complex). The flags inside parens are mappings to english pos. The lemma data keeps the actual tags from treetagger. The top level of Dict has spanish tag documentation file that explains what the spanish tag means and a file spanish tags that maps the spanish tag to a list of cs tags. their english equivalent where known. Eg,
NC Common nouns (mesas, mesa, libro, ordenador)
NC NOUN NOUN_SINGULAR NOUN_PLURAL
Note that NC spanish says nothing about singular or plural. Treetagger doesn’t care. We have our own code to detect plurality, gender, and pronoun referencing.
There is no binary form for this data. It includes global data and language specific data. Global data includes the names of top-level internet domains, definitions of predefined query types, and system- controlled punctuation behavior. Language specific data includes the rules for pos-tagging (english only), spelling substitutions that come with cs (as opposed to replace: ones listed in your scripts), explicit canonicals, handling for currency, numbers, months.
This folder contains subfolders BUILD0 and BUILD1. :build 0 makes the content of BUILD0 and :build botname makes the content of BUILD1. These are loaded in order. The text files are read, then rewritten out as .bin files for faster loading in the future. Files are canon, describe, dict, fact, keywords, macros, map, patternwords, private, script.
Canon are lemma (canonicals) explicitly defined in script using canon: .
does-1 do-1
The above says that the word does has a lemma of do in the first language in the language= list (english).
### Describe and Map Describe and map are documentation/debug files, not relevant to running code.
### Dict Dict is supplemental words with optional flags as found in
concept definitions. eg.
+ authoring VERB_PRESENT_PARTICIPLE VERB ADJECTIVE_PARTICIPLE NOUN_GERUND GRADE3_4
Fact includes global variables and their value, then facts created.
$awe_talks=ja-+0
#`end variables
( ~normalcreature component ( head count 1 x00100000 ) x00100020 )
( 323i_Touring instance BMW x00100001 256 )
In the above, the value of $awe_talks is set to a json array. Then there is a complex fact. Subject and verb are simple but object is itself a fact. Each fact has fact flags. When a fact needs to designate it is visible to only a specific bot, it may have a tailing bot id number (like 256 above).
Keywords are the list of concepts and implied concepts (topic) along with their keywords and flags on the concept. A T in front of the ~ indicates this is a topic, not just a concept.
~musicalbums NODUPLICATE ( ~musicalbums_ja_jp ~musicalbums_en_us )
~creditcardlist NODUPLICATE ( 'Discover`1048576-1
Visa`1048576-1 MasterCard`1048576-1 Visa`1-1 MasterCard`1-1 )
Some concepts allow repeated words and some don’t. If the words are simple, they are delimited by space in front and back (see ~musicalbums). Complex words use a backtick at the end followed by the additional complexity of possibly what bot id is allowed to see the membership and possibly what language the word is from. Note above has two copies of Visa and MasterCard, for 2 different bots. And note Discover has a quote in front of it.
Macros are the functions defined in script.
^setcategory O 1 0 D( ^category $_specialty $_allowpivot $_category )
^if 00G( $holder.n) `
It has function name, kind of function (O = outputmacro), bot id, flags on the arguments, number of arguments, list in parens of argument names as well as all local variable names, followed by compiled code.
Patternwords is a list of words to be marked with the PATTERNWORD flag. They are words not known in dictionary which arise in patterns and for which we don’t want spellcheck to find some dictionary word to replace it.
private is a list of word pairs from replace: , with original and replacement words.
Script are the TOPICS definitions. The first line tells how many topics, when this was compiled for what build and with what version of cs.
00081 May31'22-16:41:25-1654011685478 xander 12.1
Successive pairs of lines are topic data.
First line of pair is name, flag bits, checksum, number of top level rules, number of gambits, byte size and what file it is defined in.
Second line of pair is a text string of all the botnames allowed to see this topic, and then its compiled rules. Each rule ends in backtick. Followed by the next rule. The first 3 digits of a rule are the jump code that translates to an offset where the next rule begins. A zero offset will happen at the very end of all rules
TOPIC: ~services_en_us 0x3 99311429 18 0 2406 services_en_US.top
" all " 00y u: ( ) ^check_lang ( en_US ) `00B u: ( [ vine ] )
^fail ( TOPIC ) `000


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Infant-controlled-timing Class
These docs have moved here.Reference for DEVELOPERS of new frames only! Mixin to allow parent to control frame progression by holding down a key when the child is not looking. Enables "infant-controlled" study designs via parent live-coding of infant looking behavior. To add this behavior to an existing frame:
- Call
this.startParentControl()
at the point you want looking time to start being measured. The parent will only be able to end the trial using the endTrialKey during the parent control period. Lookaway time will only accumulate and lookaways will only be able to end the trial during the parent control period. However, lookaways will be recorded as events throughout the frame. If the child is already looking away before the parent control period begins, lookaway time will begin accumulating only after the first look. - Call
this.endParentControl()
at the point you want the parent not to be able to control frame progression anymore. For instance, if hiding stimuli and stopping recording after completing the frame, or if pausing the frame. - If you need to be able to pause and re-start the frame, call
this.endParentControl()
when pausing andthis.startParentControl()
when re-starting. Note that startParentControl will start a "fresh" looking time measurement: looking and lookaway time will both be 0, the child will not be assumed to have already looked, etc. - Define a function
onLookawayCriterion
that says what should happen when the parent presses the endTrialKey or the child reaches the lookaway criterion. E.g., this might typically move to the next frame. If needed you can accessthis.get('trialEndReason')
(either 'parentEnded' or 'lookaway') to distinguish between these two possibilities. - If it is possible for the frame to end by other mechanisms than the trialEndKey or reaching the lookaway criterion,
then when that happens: set the
trialEndReason
accordingly (e.g.,this.set('trialEndReason', 'ceiling')
) and callthis.setTrialEndTime()
to calculate a total looking time based on the - So that your frame will capture information about looking time and the reason the trial ended, and make that
available to the researcher, add the following to your frame's
meta.data.properties
:totalLookingTime: { type: 'number' }, trialEndReason: { type: 'string' }
Item Index
Data collected
Methods
endParentControl
()
End period of parent control of trial progression, for instance because trial is paused. Looks to/away will still be logged as events but trial will not end based on parent input.
onSessionRecordingStarted
()
Hook called when session recording is started automatically. Override to do frame-specific actions at this point (e.g., beginning a test trial).
startParentControl
()
Begin period of parent control of trial progression. After calling startParentControl(), we wait for the first infant look to the screen (this may be immediate because infant is already looking. After that, whenever infant look-away time reaches criterion (either due to long enough continuous lookaway, or due to cumulative time looking away) we will call onLookawayCriterion().
If startParentControl is called multiple times, each time it's called it starts a "fresh" interval of parent control - i.e. no stored lookaway time, and no assumption the child has previously looked.
Properties
endTrialKey
String
Key parent should press to manually move on to next trial. This allows you to have parents control the study by giving instructions like "press q when the child looks away for at least a few seconds" instead of "hold down w whenever the child isn't looking." Use an empty string, '', to not allow this function for this trial. You can look up the names of keys at https://keycode.info. Default is 'q'.
Default: 'q'
lookawayKey
String
Key parent should press to indicate the child is looking away. If a key is provided, then the trial will end if the child looks away looks long enough per the lookawayType and lookawayThreshold. You can also use 'mouse' to indicate that mouse down/up should be used in place of key down/up events. Use an empty string, '', to not record any lookaways for this trial. You can look up the names of keys at https://keycode.info. Default is 'w'.
Default: 'w'
lookawayThreshold
String
Lookaway threshold in seconds. How long does the child need to look away before the trial ends? Depending on the lookawayType, this will refer either to the total amount of time the child has looked away since their first look to the screen, or to the length of a single continuous lookaway.
Default: 2
lookawayTone
String
Type of audio to play during parent-coded lookaways - 'tone' (A 220), 'noise' (pink noise), or 'none'. These tones are available at https://www.mit.edu/~kimscott/placeholderstimuli/ if you want to use them in instructions.
Default: 'noise'
lookawayType
String
Type of lookaway criterion. Must be either 'total' (to count total lookaway time) or 'continuous' (to count only continuous lookaway time). Whichever criterion type is used, only lookaways after the first look to the screen are considered.
Default: 'total'
Data keys collected
These are the fields that will be captured by this frame and sent back to the Lookit server. Each of these fields will correspond to one row of the CSV frame data for a given response - the row will havekey
set to the data key name, and value
set to the value for this response.
Equivalently, this data will be available in the exp_data
field of the response JSON data.
totalLookingTime
Number
Total looking time during this frame, in seconds. Looking time is calculated as the total time spent looking between: (1) The start of the parent control period, or the first look during that period if the child is not looking initially and (2) The end of the trial due to the parent pushing the end trial key, the child reaching the lookaway criterion, or the frame being completed without either of these happening (e.g., a video is played N times or an image is shown for N seconds). All time spent looking away, per parent coding, is excluded, regardless of the duration of the lookaway.
This value will be null if the trial is not completed by any of the above mechanisms, for instance because the parent ends the study early during this frame.
trialEndReason
String
What caused the trial to end: 'lookaway' (the child reached the lookaway threshold), 'parentEnded' (the parent pressed the endTrialKey), or 'ceiling' (the frame ended without either of those happening).
This value will be null if the trial is not completed by any of the above mechanisms, for instance because the parent ends the study early during this frame.
Events
lookawayEnd
When parent records a lookaway ending. This will NOT be triggered at the start of this frame if the parent is not holding down the lookawayKey, only when the key is actually released. Lookaways are recorded regardless of whether the parent control period has started.
lookawayEndedTrial
When trial ends due to lookaway criterion being reached.
lookawayStart
When parent records a lookaway starting. This will be triggered at the start of this frame if the parent is already holding down the lookawayKey, and otherwise only when the key is newly pressed down. Lookaways are recorded regardless of whether the parent control period has started.
parentControlPeriodEnd
When interval of parent control of trial ends - i.e., lookaways cannot lead to ending trial, parent cannot press key to end trial.
parentControlPeriodStart
When interval of parent control of trial begins - i.e., lookaways begin counting up to threshold. Lookaway events are recorded throughout, but do not count towards ending trial until parent control period begins.
parentEndedTrial
When trial ends due to parent pressing key to end trial


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A&P show to feature 'spectacular' line-up
by Richard DavisonOrganisers are expecting increased entries - and visitors to match - at the West Otago A&P Show on Saturday.
Society president Robert Kane said the annual celebration of town and country in Tapanui was entering its 106th year ''in good heart''.
''We've avoided some of the clashes with other events that have occurred in previous years, so we've got boosted entries in some sections, and are expecting a bumper crowd in bright sunshine, if I've prayed to the right weather gods.''
A ''spectacular'' programme of entertainment, stalls and traditional A&P events had been lined up by the organising committee.
Included were a demonstration car rescue by the Tapanui Volunteer Fire Brigade, a remote-controlled helicopter display, dog trials, shearing, wood-chopping and pet displays.
Entertainment would be provided by magician Jonathan Usher, the Otago Museum, fairground rides and several local musicians and dance troupes.
''The A&P committee always comes up with new and exciting events, as well as the traditional and ever-popular activities.''
In common with many other A&P shows this summer, cattle would be making a ''welcome'' return to West Otago, following a hiatus because of M. bovis.
''All the cattle this year will have pre-entered from properties with clear M. bovis history.''
There would be gaps between each exhibitor in the stalls.
''The animals will not be making nose-to-nose contact in the showroom or stalls.
''We're also disinfecting and washing everything down between exhibitors, ensuring full biosecurity measures are in place.''


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Major Projects
Teamtask: API, website, and iOS app
Senior Project
- Complete platform designed to make task assignment easy for organization leaders, and to encourage compliance among organization members
- API spec written in Swagger as a declarative source of truth for what resources and methods should look like
- API requests proxied by AWS API Gateway to AWS Lambda functions written in Node with Vandium secure layer
- Data stored in normalized form on MySQL, hosted by AWS RDS
- User registration and authentication provided by AWS Cognito and its JavaScript SDK
- Web app written in ReactJS with MobX state management and Bootstrap aesthetics and hosted using AWS S3 static sites
- Mobile client written in pure Swift with appropriate SDKs
Major technologies and tools: ReactJS with mobx, Node.js with Vandium, npm, Bootstrap/reactstrap, AWS Lambda, AWS Cognito, AWS API Gateway, AWS S3 static site hosting, AWS Route53, AWS CloudFront, MySQL, Swagger
April–June 2018
Searching for Similar Programs, data collection and machine learning
Data Science Capstone Project
- Together with two teammates, collaborated with the Kestrel Institute to apply machine learning methods to a project
- Goal: measure similarity of two compiled Java programs without decompiling — do they solve the same problem?
- Created a bot which ran on three servers to scrape almost 100,000 user solutions from coding challnge sitess
- Developed a bash-script pipeline to compile Java code safely using Docker containers, and handed off tarballs to teammate for neural network training
- Wrote a paper describing the data set and the LTSM Siamese neural network structure we used; it will be submitted to conferences next year
Major technologies and tools: Python3.6, Jupyter notebooks, BeautifulSoup, Docker, bash, Keras.io
Jan–June 2018
Consieuten, a compiler for C-like syntax
Programming Languages Final Project
- Developed a compiler in pure Python implementing a C-like language with if statements, while loops, and structs, translating them into IA-32 assembly
- Conversion to A-Normal Form and lambda lifting are essential steps to streamlining assembly code generation
- Optimizations will include constant propagation, dead code elimination, and register allocation
Major technologies and tools: Python3.6, nasm, gdb, Puppet & Vagrant, git
Apr–Jun 2018
Scala, Spark, and Seinfeld, a playful data science project
Distributed Computing Final Project
- Used Scala with Apache Spark to analyze every line of dialogue ever spoken in Seinfeld
- Also pulled information from IMDb public datasets
- Performed rudimentary sentiment analysis and investigated the relationship between user IMDb rating and episode sentiment
- Created visualization in R using ggplot2
- Assembled findings into a slide deck
Major technologies and tools: Scala, Apache Spark, R with ggplot2
Mar 2018


|
PKD Rig
Set of Pipeline and Rigging Tools
|
Class to export and import single XML weight maps from a geometry. More...
Public Member Functions | |
| def | __init__ (self) |
| def | export_weights (self) |
| Export out the weights. More... | |
| def | import_weights (self) |
| Import the previously exported weights. More... | |
| def | save_data (self) |
| Save out the deformer information to a test xml file. More... | |
| def | load_data (self) |
| Load the data information from the test xml file. More... | |
| def | datapath (self) |
| File path to the xml file which contains data relevant to the deformer. More... | |
Public Attributes | |
| target | |
| The current geometry that is being processed. More... | |
| import_data | |
| The deformer data that is read from the data xml file. More... | |
| export_data | |
| The deformer data that is written to the data xml file. More... | |
| data | |
| Data property which is exported to the single data file. More... | |
Properties | |
| folder = property(_get_folder_, _set_folder_) | |
| User defined folder where the weight and data file are saved. More... | |
| deformer = property(_get_deformer_, _set_deformer_) | |
| Which type of deformer is processed. More... | |
| target_deformer = property(_get_target_defomer_, _set_target_defomer_) | |
| The target maya deformer on which the export and import commands will be processed. More... | |
| file = property(_get_file_, _set_file_) | |
| The file name which contains the individual weights'''. More... | |
Class to export and import single XML weight maps from a geometry.
This class is used when there normaly just one iteration of deformer in the rig eg skinCluster, muscle , cloth etc
@remarks
Definition at line 74 of file libWeights.py.
| def PKD_Tools.libWeights.Weights.__init__ | ( | self | ) |
Definition at line 90 of file libWeights.py.
| def PKD_Tools.libWeights.Weights.datapath | ( | self | ) |
File path to the xml file which contains data relevant to the deformer.
This property is used in testing scenarios along with load_data /save_data method.
Definition at line 220 of file libWeights.py.
| def PKD_Tools.libWeights.Weights.export_weights | ( | self | ) |
Export out the weights.
Does error checks before exporting. This method may be written in the subclasses By default we use the maya deformerWeights command to export the weight
Definition at line 102 of file libWeights.py.
| def PKD_Tools.libWeights.Weights.import_weights | ( | self | ) |
Import the previously exported weights.
Attempt to recreate the deformer first in case none are found Use the default maya command as much as possible.
Definition at line 128 of file libWeights.py.
| def PKD_Tools.libWeights.Weights.load_data | ( | self | ) |
Load the data information from the test xml file.
Definition at line 205 of file libWeights.py.
| def PKD_Tools.libWeights.Weights.save_data | ( | self | ) |
Save out the deformer information to a test xml file.
Definition at line 200 of file libWeights.py.
| PKD_Tools.libWeights.Weights.data |
Data property which is exported to the single data file.
The deformer is rebuilt from the information that is exported eg joints used in skinning, cluster pivot point. This property will be customised in further subclasses as per their requirement.
Definition at line 206 of file libWeights.py.
| PKD_Tools.libWeights.Weights.export_data |
The deformer data that is written to the data xml file.
This content should be same as import_data
Definition at line 97 of file libWeights.py.
| PKD_Tools.libWeights.Weights.import_data |
The deformer data that is read from the data xml file.
This content should be same as export_data
Definition at line 96 of file libWeights.py.
| PKD_Tools.libWeights.Weights.target |
The current geometry that is being processed.
Definition at line 91 of file libWeights.py.
|
static |
Which type of deformer is processed.
This must be a valid maya node type eg 'skinCluster', 'blendShape' etc
Definition at line 246 of file libWeights.py.
|
static |
The file name which contains the individual weights'''.
Definition at line 257 of file libWeights.py.
|
static |
User defined folder where the weight and data file are saved.
Definition at line 240 of file libWeights.py.
|
static |
The target maya deformer on which the export and import commands will be processed.
Definition at line 251 of file libWeights.py.


People Power
As John Heilemann in the last episode of “The True Story of the Internet” says, “By now we all know the web
has changed the world, but this is the story of how the world is changing the web.” It was termed as Web 2.0
time when the World Wide Web sites emphasized on user generated content and social networking. Towards the
end of the episode, the Founder of Craigslist says: “The web is not just about making money, it’s about
setting communication free.”
The Internet has changed the society and new age entrepreneurs are shaping the world by making the web
interactive. The communication was uprising all around us. New generation companies like YouTube, MySpace
and Facebook, turned the web into a two-way participatory and democratic medium controlled by no-one and
shaped by everyone which is known as the Web 2.0.
The Web 2.0(also known as Participative (or Participatory) and Social Web) refers to websites that emphasize
user-generated content, ease of use, participatory culture and interoperability (i.e., compatible with other
products, systems, and devices) for end users. A Web 2.0 website allows users to interact and collaborate
with each other through social media dialogue as creators of user-generated content in a virtual community.
This contrasts the first generation of Web 1.0-era websites where people were limited to viewing content
in a passive manner. Examples of Web 2.0 features include social networking sites or social media sites
(e.g., Facebook), blogs, wikis, folksonomies ("tagging" keywords on websites and links), video sharing sites
(e.g., YouTube), image sharing sites (e.g., Flickr), hosted services, Web applications ("apps"),
collaborative consumption platforms, and mashup applications.
The term Web 2.0 did not resurface until 2002. Kinsley and Eric focus on the concepts currently associated
with the term where, as Scott Dietzen puts it, "the Web becomes a universal, standards-based integration
platform". In 2004, the term began to popularize when O'Reilly Media and MediaLive hosted the first Web 2.0
conference. In their opening remarks, John Battelle and Tim O'Reilly outlined their definition of the "Web
as Platform", where software applications are built upon the Web as opposed to upon the desktop. The unique
aspect of this migration, they argued, is that "customers are building your business for you". They argued
that the activities of users generating content (in the form of ideas, text, videos, or pictures) could be
"harnessed" to create value. O'Reilly and Battelle contrasted Web 2.0 with what they called "Web 1.0".
They associated this term with the business models of Netscape and the Encyclopedia Britannica Online.
For example, Netscape framed "the web as platform" in terms of the old software paradigm: their flagship
product was the web browser, a desktop application, and their strategy was to use their dominance in the
browser market to establish a market for high-priced server products. Control over standards for
displaying content and applications in the browser would, in theory, give Netscape the kind of market
power enjoyed by Microsoft in the PC market. Much like the "horseless carriage" framed the automobile
as an extension of the familiar, Netscape promoted a "webtop" to replace the desktop, and planned to
populate that webtop with information updates and applets pushed to the webtop by information providers
who would purchase Netscape servers.
In short, Netscape focused on creating software, releasing updates and bug fixes, and distributing it to
the end users. O'Reilly contrasted this with Google, a company that did not, at the time, focus on
producing end-user software, but instead on providing a service based on data, such as the links that
Web page authors make between sites. Google exploits this user-generated content to offer Web searches
based on reputation through its "PageRank" algorithm. Unlike software, which undergoes scheduled
releases, such services are constantly updated, a process called "the perpetual beta". A similar
difference can be seen between the Encyclopædia Britannica Online and Wikipedia – while the Britannica
relies upon experts to write articles and release them periodically in publications, Wikipedia relies on
trust in (sometimes anonymous) community members to constantly write and edit content. Wikipedia editors
are not required to have educational credentials, such as degrees, in the subjects in which they are
editing. Wikipedia is not based on subject-matter expertise, but rather on an adaptation of the open
source software adage "given enough eyeballs, all bugs are shallow". This maxim is stating that if
enough users are able to look at a software product's code (or a website), then these users will be able
to fix any "bugs" or other problems. The Wikipedia volunteer editor community produces, edits, and
updates articles constantly. O'Reilly's Web 2.0 conferences have been held every year since 2004,
attracting entrepreneurs, representatives from large companies, tech experts and technology reporters.
Characteristics
Instead of merely reading a Web 2.0 site, a user is invited to contribute to the site's content by
commenting on published articles, or creating a user account or profile on the site, which may enable
increased participation. By increasing emphasis on these already-extant capabilities, they encourage users
to rely more on their browser for user interface, application software ("apps") and file storage facilities.
This has been called "network as platform" computing. Major features of Web 2.0 include social networking
websites, self-publishing platforms (e.g., WordPress' easy-to-use blog and website creation tools), "tagging
" (which enables users to label websites, videos or photos in some fashion), "like" buttons (which enable a
user to indicate that they are pleased by online content), and social bookmarking.
Users can provide the data and exercise some control over what they share on a Web 2.0 site. These sites may
have an "architecture of participation" that encourages users to add value to the application as they use it
. Users can add value in many ways, such as uploading their own content on blogs, consumer-evaluation
platforms (e.g. Amazon and eBay), news websites (e.g. responding in the comment section), social networking
services, media-sharing websites (e.g. YouTube and Instagram) and collaborative-writing projects. Some
scholars argue that cloud computing is an example of Web 2.0 because it is simply an implication of
computing on the Internet.


{% if books %}
{% include 'book_details.html' with book=book details=book.details admin=request.user.is_staff only %}
{{ book.comments }}


WikiLeaks<EMAIL_ADDRESS>leaked emails, 2009 mail archive
This message is part of a particular mailbox provided at WikiLeaks
and it should be discussed here.
[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]
My Pic
- To<EMAIL_ADDRESS>- Subject: My Pic
- From: ss<EMAIL_ADDRESS>- Date: Tue, 21 Aug 2007 13:13:25 -0700 (PDT)
- Domainkey-signature: a=rsa-sha1; q=dns; c=nofws; s=s1024; d=yahoo.com; h=X-YMail-OSG:Received:Date:From:Subject:To:MIME-Version:Content-Type:Content-Transfer-Encoding:Message-ID; b=6lD/cG+HIqOzlQ1W9NGmujBUCmxZHK/UxP45/fI8tfC+PwQTKs5Pg9j77amTpyjsOA92J0brB0gsO7skSURsc9JRzPvjrsQvfEAo3NCDLC2ZJAbsFVJTPcEhvw69rgsUKMJudWoWuSTkmjVnFoQEB5oYzi+D387nDzxxNtNPK0g=;
Here ya go, I think it's a good one, lol
Building a website is a piece of cake.
Yahoo! Small Business gives you all the tools to get online.
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Return Entire Message
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 Well, you can! The actress recently shared a glimpse of her daily workout routine chart!
In Bollywood, Katrina Kaif is synonymous with hotness.  Well, to maintain the same, Kat has to undergo some intense workouts and a lot of sacrifices, because being fit ain’t an easy task. Recently, we got our hands on the Tiger Zinda Hai actress’ daily gym routine and guys; it’s giving us some highly inferior complexities!
Taking to her Instagram account, Katrina Kaif shared a glimpse of her workout chart in the gym. The list includes some intense weight training and cardio exercises. The circuit 1 on Katrina Kaif's gym routine includes Kb reverse lunge, jump squats, kb double pulse walking lunge, box jumps, weighted sumo squats with heels lifted and kb deadlifts. While the second circuit includes Kb squat and press, drop push-up, supine weighted bent-knee jackknife, X mountain climber, beer can lateral flye and plank jacks with theraloop above the knee. And two sets of all these on a regular basis, sigh. (ALSO READ - Katrina Kaif To Collaborate With Ace Boxer Floyd Mayweather? Her Sultry Boxing Video Hints At It)
Check out the picture below:
As they say, you win some, you lose some!
On the work front, Katrina Kaif was last seen in Ali Abbas Zafar’s Bharat alongside Salman Khan. Up next, she has Rohit Shetty’s cop-drama Sooryavanshi co-starring Akshay Kumar. Recently, Katrina also launched her dream makeup line ‘Kay by Katrina’ and it has opened to a good response. At present, the actress is busy promoting that and adding some interesting beauty products to the same.
image source: instagram/katrinakaif


http to ts3server Redirect
Redirecting To
If you were not automaticaly redirected, Click Here to try again.
Info also in table below if you would rather enter it manually.
| Server Address | |
| Server Password | |
If you do not have TeamSpeak 3 installed, Download Here


Pets
A pet (or companion animal) is an animal kept primarily for a person's company or protection, as opposed to working animals, sport animals, livestock, and laboratory animals, which are kept primarily for performance, agricultural value, or research. The most popular pets are noted for their attractive appearances and their loyal or playful personalities.
Pets commonly provide their owners (or guardians) physical and emotional benefits. Walking a dog can supply both the human and pet with exercise, fresh air, and social interaction. Pets can give companionship to elderly adults who do not have adequate social interaction with other people. There is a medically approved class of therapy animals, mostly dogs, that are brought to visit confined humans. Pet therapy utilizes trained animals and handlers to achieve specific physical, social, cognitive, and emotional goals with patients.


MY TODO LIST
My Activity
Add Task
My Tasks
Congratualtions. You have no pending task at the moment.
COmpleted Tasks


Copyright 2012-2017 Hauser Olsson GmbH.
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.
CrNiCKL (pronounced "chronicle") is a database for time series written in Java and running on top of SQL and NoSQL systems. The base system cannot run by itself, it requires a driver. Currently, drivers are available for JDBC and MongoDB.
The distribution consists of three archives
crnickl-x.y.z.jar
crnickl-x.y.z-javadoc.jar
crnickl-x.y.z-sources.jar
with compiled classes, HTML documentation, and sources. The version number
x.y.z
follows the maven convention. The distribution also includes .asc
files with detached cryptographic signatures.
The software is available from the Maven central
repository. To use version x.y.z
, insert the following dependency into your
pom.xml
file:
<dependency>
<groupId>ch.agent</groupId>
<artifactId>crnickl</artifactId>
<version>x.y.z</version>
<scope>compile</scope>
</dependency>
The recommended way is to use git for accessing the source and maven for building. The procedure is easy, as maven takes care of locating and downloading dependencies:
$ git clone https://github.com/jpvetterli/crnickl.git
$ cd crnickl
$ mvn install
This builds and installs the distribution JARs in your local maven
repository. They can also be found in the target
directory.
When building the software by other means, the following dependency must be addressed:
t2-x.y.z.jar
(see Time2 Library) Replace x.y.z
with the actual version number which can be found in the
POM
file included in the binary JAR:
/META-INF/maven/ch.agent/crnickl/pom.xml
Most often CrNiCKL is pulled as a transitive dependency by the actual CrNiCKL database driver chosen for an application. Drivers are available from GitHub and Maven.
The source is available on GitHub.
More information on CrNiCKL is available at the project web site.
Updated: 2017-10-04/jpv


This intention show dialog with default moxy strategies.
After applying one of them replace a class of annotation @StateStrategyType with selected class


Carol Dweck, one of the worlds leading experts in motivation has introduced the concept of Fixed vs. Growth Mindset. What seems so simplistic in theory we wonder why we haven't thought of it prior to her endorsement. She explains in one of many online video that having a Fixed mindset is the belief that we are born with an absolute level of intelligence. Dr. Dweck then introduced the concept of Growth mindset and that intelligence is mostly based on motivation. I believe that once the concepts are introduced that having a growth mindset is a choice.
Fixed Mindset has become the default setting of society today. By positively rewarding and reinforcing success we have told those that fail they are not smart, they are failures, and are destined to a potential of mediocracy.
On the other hand if we reinforce that success is based on attempting with effort. That people succeed even if they fail because thats when learning occurs. Strong effort and being challenged to grow are pinnacles, we will raise a generation of limitless success. Dr. Dweck cites many studies that confirm that having the growth mindset leads to higher knowledge attainment.
Being introduced to the concept of Fixed vs. Growth Mindset is an eye opener and an actual motivator for me. After deciding to change my life and making a career move to become a coder, I new the knowledge attainment would be a challenge. Not being in an educational environment as a student for 14 years was a detractor to my motivation taking on the challenge at The Iron Yard. Using Growth Mindset as a chosen montra brings me back to my childhood and "I think I Can, I think I Can". I KNOW I CAN DO THIS because I have decided to have a Growth Mindset.
Motivation is a powerful tool that can be used in the fight against evil... errrr or just making it through the Iron Yard. I choose to have a Growth Mindset every day to achieve a high level of learning Explain how Growth Mindset is a choice and that I made one by attending the Iron Yard. Fixed vs. Growth what an exciting thing to be a part of in my own way.


Driver shot, Lamborghini stolen in carjacking incident
by News StaffYork Regional Police are investigating after a male was shot during a carjacking incident Friday night.
The call came in at around 8:30 p.m. for a report of a shooting in the Major Mackenzie Drive and Woodbine Avenue area, police said.
Police said the male driver of the vehicle was approached by three suspects and was shot in the leg.
The suspects then took off in a red Lamborghini that has the licence plate number 8EIGHT8.
The shooting victim suffered non-life-threatening injuries and is being treated in the hospital.
Investigators said that if you see this vehicle, do not approach it or the suspects. Instead, call the police right away.


Contact name
City
Country
Is Promoted
Maria
Berlin
Germany
Ana
Mexico
Mexico
Antonio
Mexico
Mexico
Thomas
London
UK


Survey: German business confidence rises in November
by The Associated PressBERLIN (AP) — A closely watched survey is showing that German business confidence has increased slightly.
The Ifo institute said Monday that its monthly confidence index edged up to 95.0 points in November from 94.7 in October.
The survey is based on responses from some 9,000 firms in Germany, Europe’s biggest economy.
Their assessment of their current situation rose to 97.9 points from 97.8, while their expectations for the coming six months increased to 92.1 points from 91.6.
Analysts had widely predicted that Germany’s economy entered a technical recession in the third quarter, but figures released earlier this month instead showed quarterly growth of 0.1%.
Still, businesses remain cautious in light of the U.S.-China trade war and uncertainty related to Britain’s departure from the European Union.
The Associated Press


Wonderful new coffee shop in our area! Gorgeous clean decor with a mouthwatering cake display. We went for breakfast which was really good - no grease and everything was freshly cooked for us. After that we decided to share a slice of their triple choc cake which was as delicious as it looked. Their prices are incredibly reasonable; the service very good and the vibe was great. My only tiny complaint was that it was incredibly noisy inside (acoustics not great). But then the place was jam packed with people having fun.
Read MoreHave you tried one of our cooked breakfasts yet? if not you need to do yourself a favour and give us a visit. If you fancy something lighter we have lots of homemade cakes and bread plus 12 different teas aswell as fair-trade coffee..there is something for everybody.
 If you don't like something in your life drink coffee to help you feel better. All romantics meet the same fate some day. Drunk and cytical and boring someone in some darl cafe
Read More


Every site tells a story
Glamping accommodations, in most cases, take advantage of the surrounding elements, creating the best space for eco - friendly travel and sustainable tourism.
Go
Glamping accommodations, in most cases, take advantage of the surrounding elements, creating the best space for eco - friendly travel and sustainable tourism.
Go
Step into a tall canvas tent with room to spread out
Glamping brings you to places that hotels and resorts can’t
Glamping itself is a low impact green activity


Choose A Wallet
You can choose either a mobile wallet and a dekstop wallet where you can download
Mobile Wallets
We highly recommend trust wallet as it is very simple to set up and comes with and in a crypto exchange.
Desktop Wallets
We recommend using the wallet that is built by us!
The CaffeeWallet!
CaffeeWallet
Download CaffeeWallet here!


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The top-level Contacts module, used for accessing and modifying the system contacts address book.
See examples for more information.
Adds the specified callback as an event listener for the named event.
Arguments
| name | [String] | Name of the event. |
| callback | [Callback<Object>] | Callback function to invoke when the event is fired. |
Applies the properties to the proxy.
Arguments
| props | [Dictionary] | A dictionary of properties to apply. |
Creates and returns an instance of Titanium.Contacts.Group
.
Arguments
| parameters | [Dictionary<Titanium.Contacts.Group>] | Properties to set on a new object, including any in Titanium.Contacts.Group except those marked as non-creation or read-only. |
Creates and returns an instance of Titanium.Contacts.Person
, and commits all pending
changes to the underlying contacts database.
Arguments
| parameters | [Dictionary<Titanium.Contacts.Person>] | Properties to set on a new object, including any in Titanium.Contacts.Person except those marked as non-creation or read-only. |
Fires a synthesized event to any registered listeners.
Arguments
| name | [String] | Name of the event. |
| event | [Dictionary] | A dictionary of keys and values to add to the Titanium.Event object sent to the listeners. |
Gets all people, unless a limit is specified.
Arguments
| limit | [Number] | Maximum number of people. Android only. |
Gets the group with the specified identifier.
Arguments
| id | [Number] | Group identifier. |
Gets people with a firstName
, middleName
or lastName
field, or a combination
of these fields, that match the specified name.
Arguments
| name | [String] | Name to match. |
Gets the person with the specified identifier.
Arguments
| id | [Number] | Contact identifier. |
Removes the specified callback as an event listener for the named event.
Arguments
| name | [String] | Name of the event. |
| callback | [Callback<Object>] | Callback function to remove. Must be the same function passed to addEventListener. |
Removes a group from the address book.
Arguments
| group | [Titanium.Contacts.Group] | Contact group. |
Removes a contact from the address book.
Arguments
| person | [Titanium.Contacts.Person] | Contact. |
Commits all pending changes to the underlying contacts database.
Arguments
| contacts | [Array<Titanium.Contacts.Person>] | List of contacts to save. Used on Android only. |


Disney Plus issues trigger warning for ‘outdated cultural depict
by Jessica ChasmarDisney’s new streaming service has added a trigger warning to certain classic movies like “The Jungle Book” and “Lady and the Tramp” to address possible “outdated cultural depictions” that could offend viewers.
Disney Plus, which launched Tuesday amid a host of technical issues, issued a disclaimer on some decades-old movies that reads, “This program is presented as originally created. It may contain outdated cultural depictions.”
The warning appears in the movie descriptions for “Dumbo,” “The Jungle Book” and “Lady and the Tramp,” among others that have faced criticism for depicting racial stereotypes.
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One 1946 film titled, “Song of the South,” isn’t featured at all on the streaming service, presumably due to its controversial portrayal of black Americans.
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# This file is dual licensed under the terms of the Apache License, Version # 2.0, and the BSD License. See the LICENSE file in the root of this repository # for complete details. from __future__ import absolute_import, division, print_function import re _canonicalize_regex = re.compile(r"[-_.]+") def canonicalize_name(name): # This is taken from PEP 503. return _canonicalize_regex.sub("-", name).lower()


You accept the hand, and the first thing you notice is how horribly cold it is.  Darkness envelops you, and hours later you wake up in the valley. The maze has disappeared. You appear uninjured, but inside you feel that something is horribly wrong. You stagger to your feet and wonder what terrible fate awaits you.