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{% extends "layout_multi_sections.html" %} {% block page_title %} Sweat {% endblock %} {% block css_register %} {% endblock %} {% block main_nav %} {% include "includes/leedsgirlscan/main_nav.html" %} {% endblock %} {% block sectionOneTop %}
The This Girl Can campaign is all about encouraging women to get back into sport and try something new. We shouldn’t worry about how we look whilst playing sport or working out. This Girl Can work hard, sweat, look red in the face and be proud of it.
Here you will find more fun sporting activities and events that you can take part in around Leeds. Are you still looking for some sporting inspiration or a new activity to try?
How about giving a bootcamp class a go, or trying the Leeds indoor climbing wall?
What are you waiting for? This Girl Can Sweat and have fun at the same time!
Get InvolvedJoining a gym is a great way to exercise and take part in group classes.
There is a selection of gyms in Leeds, each have varying membership costs and joining fees. If you are interested in joining a gym then contact them and ask to look round first so you know exactly what facilities you will have access to. Most gyms have a copy of their class timetable online so you can check what classes you can take part in.
SWith a Bodyline membership you can save money on sporting activities that are taking place at Leeds City Council leisure centres. You can book classes and activities in advance to make sure you will have a place.
When you sign up you also get a free gym induction if you take out the gym based membership option. With discounts and special offers on leisure activities throughout Leeds, the Bodyline membership is a great way of trying a number of different classes for a reasonable price.
If you refer a friend then you get £25 for every friend who takes out a membership! Quick, get together some friends and you can all try out some new sports together and save money at the same time!
View Membership
The UK Outdoor Fitness group provides friendly outdoor and indoor Bootcamp classes. Designed to be fun and enjoyable, there is an undeniable camaraderie when you are taking part in group exercise. The instructors will push you hard and make sure you get the most out of each session.
The people behind UK Outdoor Fitness believe that everybody should and can exercise, they offer sessions suitable for all ages and levels of fitness. The majority of classes are split into beginners, intermediate and advanced so you can choose which group is best for you.
If you are worried that you aren’t fit enough to do a bootcamp, then UK Outdoor Fitness are here to prove you wrong. You won’t be asked to do anything you aren’t capable of, you might just surprise yourself.
The varied sessions include team games, pair work, power walking, jogging and much more!
We want you to help us persuade more women and girls to get active, whatever their age, size, background or ability.
A Leeds Girls Can Ambassador: Goes through an appropriate recruitment process and signs up to Leeds City Council and Sport England’s policies and procedures. The main aim of an Ambassador is to get more women and girls in Leeds physically active. Ambassadors are required to attend monthly meetings which are vital for receiving guidance and insight around This Girl Can, this helps support their role. An Ambassador is not paid for their time, they are not required to lead a group and the roles can vary.
For further information on becoming an ambassador or any of our other roles contact Dani Penney
Apply nowDo you have an event, club or sport you would like to see here? Please get in touch.


Recapture and Revitalize your Talent Brand with our RPI Offering
The trend toward recruitment process outsourcing (RPO) has succeeded in masking truth of the talent acquisition processes by leveraging the inherent weaknesses of traditional talent acquisition metrics. The damage you may be doing to your talent brand and the long-term damage to your cultural sustainment or transformation efforts could be catastrophic.
Cost Savings
Speed
Compliance
Quality
Human Resources vs Procurement vs Operations
Has it reduced Third Party Spend? No!
RPO merely consolidates the same or often greater total spend under the RPO provider, a handful special vendors, and The BIG BOYS when it comes to executive and board member hire.
Has it increased speed of hire? No!
The RPO process merely consolidates the limited sources of inputs (resumes). Therefore, the quality of the inputs hasn’t increased nor has the screen through which the inputs are sorted. Moreover, the hiring process, now being administered by an outsourced supplier, hasn’t changed at all. How could this possibly increase the speed of hire? And is speed really what you are after?
Has it increased compliance or enhanced corporate culture? Maybe?
RPO, in reality, has merely matrixed the responsibility of compliance to the RPO provider. RPO has merely outsourced compliance to an auditable line-item on the invoice remitted from the RPO provider.
Has it increased the quality of your hires?
Here is where the rubber really meets the road. Were your hires poor before the choice to move to RPO? Are they better now? Based on what criteria?
Does the CEO feel acquiring and retaining talent is a problem?
Who is accountable to solve this problem?
Does the solution have a budget fitting the size of the problem?


Use a Service Facade pattern to provide loose coupling between a service requester and functionality that is provided by an application or capability that is not service-enabled. The loose coupling conceals the complexities of the provider and presents a standard web service interface.
A Service Facade pattern also introduces a mediation point for the application of standard functions such as logging.When an enterprise architecture moves towards a service-oriented architecture, it is often necessary to accommodate existing legacy systems that run large parts of the business, but that do not have a service capability. These applications often use functions that must be accessible to newer service-enabled software packages, but that offer access only through adapters, messaging, or other non-service-oriented integration techniques.
Legacy applications do not fit naturally into a service-oriented architecture (SOA) environment and changes to such systems are expensive and require scarce skills. The problem is to find a way of integrating such systems with newer service-oriented packages and applications. In particular, it is necessary to create a bridge between the synchronous HTTP protocol that is generally used with web services, and the messaging protocols that are often used by legacy applications.
This pattern is appropriate when an enterprise wants to present a service interface to requesting clients as part of an SOA environment, but provider applications cannot easily be upgraded to provide service interfaces.
This pattern is used where provider applications provide an XML interface and client applications support calls to web services. The pattern can be extended with transforms to support a service facade to applications with non-XML interfaces over WebSphere MQ.


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--- layout: default title: ICERT'17 - Keynote Speakers meta: The keynote speakers of ICEPR ---
We are very happy to announce the following keynote speakers for the International Conference on Energy Research and Technology (ICERT'17):
Dr. Cigdem Eskicioglu
Dr. Cigdem Eskicioglu is an Associate Professor at the School of Engineering, University of British Columbia (UBC) in Canada. She is also the leader of UBC Bioreactor Technology Group (Website) on UBC’s Okanagan Campus. Her group focuses on advanced anaerobic digester processes for enhanced biogas production, fate of micropollutants in advanced biological treatment processes, side-stream treatment for ammonia removal and odor minimimization.
Topic of Keynote: Emerging Pretreatment Technologies for Enhanced Bioenergy Production from Organic Waste
Keynote AbstractDr. Wen-Feng Lin
Professor Lin has particular expertise in physical chemistry, electrochemistry and electrochemical engineering. The primary themes of his research are related to energy, environment and water. He collaborates internationally and has led a significant number of externally funded projects, ranging from fundamental understanding of electro-catalysis at atomic and molecular levels to applied R&D in energy materials, fuel cells, batteries, and ozone generation from water for water treatment and advanced oxidation technologies; resulting in an output of over 160 publications, 6 patents and contributions to 2 spin-outs.
Topic of Keynote: Direct Alcohol Fuel Cells as an Enabling Clean Energy Technology for Portable Devices and Transportation - From Fundamental to System Engineering
Keynote Abstract


Figure 83: Johann van der Schijff. Back to the Future, 1993.
[ Return to: Part 1 - 3.Artistic Feedback; 3.4.Video Art ]
The fictional ergonomics of this work is such that the rock, carved to fit into a human hand, becomes a spacecraft once it is thrown at the enemy. A surveillance antenna, similar to the one turning on the plane, and a micro-chip embedded in stone, are the guiding devices linking the piece to the conceptual cybernetic system of the other work. This archetypal handweapon is suspended in space by an old fuel line protruding out of the base of the sculpture. [Figure 83]
Figure 84: Eduardo Paolozzi. Japanese War God, 1958.
(Read 1987: 235)
Back to the Future reflects on how human intelligence has led to the invention of increasingly efficient machines for wholesale suicide. The work draws formally on sculptures made by Paolozzi and Brancussi. Paolozzi's bronze sculptures such as Head and Japanese War God [Figure 84] combine found objects to form new relationships between each other, thereby creating archetypes. The base refers, not only visually to the zig-zag motive found in Brancussi's sculpture, but also to his approach of merging the base to become part of the sculpture. [Figure 85]
Figure 85: Constantin Brancusi. Adam and Eve, 1921.
(Shanes 1989: 61)
Figure 86: Johann van der Schijff. Back to the Future, 1993.
Mild-steel; copper; stainless steel;
sandstone; computer components.
H: 2030 mm W: 590 mm L: 600 mm
Figure 88: Johann van der Schijff. Drawing no. 2,
Back to the Future, 1993.
Figure 89: Johann van der Schijff. Drawing no. 3,
Back to the Future, 1993.
Figure 90: Johann van der Schijff. Drawing no. 4,
Back to the Future, 1993.
Figure 91: Johann van der Schijff. Drawing no. 5,
Back to the Future, 1993.


Tsau //Khaeb National Park infrastructure worth N$80 million inaugurated
Various infrastructure worth N$80 million for the Tsau //Khaeb National Park in Lüderitz was inaugurated.
This infrastructure will enable the Ministry of Environment and Tourism to efficiently manage the park and its supporting zones.


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Recent Check-ins are a big help to other drivers when planning their journey. Tell everyone what you thought about this charging location.


Best SEO Agency in Aurangabad, Pune
SEO-Search Engine Optimization
SEO is the best opportunity for small businesses to promote their products or services on search engines like Google & Bing with no charges to be paid to them. What makes it challenging is to get top ranking on search pages.
SEO may target different kinds of searches, including image search,video search, academic search,news search, and industry-specific vertical search engines.
Optimizing a website may involve editing its content, adding content,and modifying HTML and associated coding to both increase its relevance to specific keywords and remove barriers to the indexing activities of search engines like Google ,Yahoo etc.Promoting a site to increase the number of backlinks, or inbound links.
SEO differs from local search engine optimization in that the latter is focused on optimizing a business' online presence so that its web pages will be displayed by search engines when a user enters a local search for its products or services. The former instead is more focused on national or international searches.
SEO means Search engine optimization. It is a set of rules for optimizing your website so that it can achieve higher rankings in search engines’ organic results.
It is a great way to increase the quality of a web site by making it user-friendly, faster and easier to navigate.
SEO can be considered as a complete framework since the whole process has a number of rules (or guidelines), a number of stages and a set of controls.
SEO is not only about search engines but good SEO practices improve the user experience and usability of a web site.Users trust search engines and having a presence in the top positions for the keywords the user is searching, increases the web site’s trust.
SEO is good for the social promotion of your web site. People who find your web site by searching Google or Yahoo are more likely to promote it on social media channels through social media marketing services in Aurangabad.
SEO is important for the smooth running of a big web site. Web sites with more than one author can benefit from SEO in a direct and indirect way.SEO can put you ahead of the competition. If two web sites are selling the same thing, the search engine optimized website is more likely to have more customers and make more sales.


Matheus Oliveira
lMatheuslOliveiral
An instructor focused on helping pople start programming for web


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why use higher-order functions?
• javascript
An argument for increased higher-order function usage to improve readability and debugging.
More concise code
Consider the following examples in English:
I went to the city.
I drove to the city.
Note that went can be used in more cases than drove. However, drove more accurately describes how I got to the city. Drove is the more concise version of went.
Now consider a similar example in javascript:
var array = [1, 2, 3];
for(var i = 0; i < array.length; i++) {
//code
} // -> ???
array.some(function(element) {
//code
}); // -> true/false
Similarly, the for loop is more versatile and can perform the same operation as the some() function below it, but its versatility is also a drawback because we don’t know what it will do until we study the code within its curly brackets. The some() function is much more descriptive as it will always iterate over the array and return true or false depending on if any of the array’s elements pass a truth test.
The takeaway here is that higher-order functions are much clearer than their low-level equivalents.
Standard inputs and outputs
Higher-order functions wrap low-level code within an appropriately named function with the goal of standardizing the inputs and outputs.
For example:
var array = [1, 2, 3];
var callback = function(element) {
return element + 1;
}
array.map(callback); // -> [2, 3, 4]
The higher-order function map takes an array and a callback function as input and performs the callback on every element of the array. Map then returns a new array with the results of the operation.
The important thing to note in this example is that map will always return an array and that array will always have the same number of elements as the array passed into it. The input and output for map is standardized to these constraints so that no surprises come out of it.
Conclusion
Higher-order functions offer some great readability benefits at only a slight cost to the call stack and run time. The main difficulty with them involves learing what each one does. Much like expanding your vocabulary in English, expanding your higher order function vocabulary in Javascript takes time, but the benefits are well worth it.
External resources


Tests for compute service
Bases: nova.test.TestCase
Bases: nova.tests.compute.test_compute.BaseTestCase
This is for unit coverage of aggregate-related methods defined in nova.compute.api.
Ensure we can add a host to an aggregate.
Ensure we can add multiple hosts to an aggregate.
Ensure ComputeHostNotFound is raised when adding invalid host.
Ensure InvalidAggregateAction is raised when zones don’t match.
Ensure InvalidAggregateAction is raised with wrong avail_zone.
Ensure we can delete an aggregate.
Ensure InvalidAggregateAction is raised when non empty aggregate.
Ensure we can remove a host from an aggregate.
Ensure ComputeHostNotFound is raised when removing invalid host.
Ensure metadata can be updated
Bases: nova.tests.compute.test_compute.BaseTestCase
Ensure instance can be soft rebooted
Can’t backup an instance which is already being backed up.
Can’t backup an instance which is already being backed up.
Make sure create associates security groups
Verify that an instance cannot be created without a display_name.
Make sure image properties are copied into system metadata.
Test an instance type with just enough ram and disk space
Test an instance type with ok much user data.
Test an instance type with too much user data.
Test an instance type with malformed user data.
Test an instance type with no min_ram or min_disk
Test an instance type with too little disk space
Test an instance type with too little memory
Make sure destroying disassociates security groups
Make sure destroying security groups disassociates instances
Ensure instance can be deleted after a soft delete
Test get instance
Test searching instances by image
Test searching instances by image
Test searching instances by metadata
Test searching by multiple options at once
Test searching instances by name (display_name)
Test searching instances by state
Get an instances latest fault
Test get instance with an integer id
Ensure instance can be hard rebooted while soft rebooting
Ensure instance hostname is set during creation.
Ensure we can write a file to an instance
Test the instance architecture
Test the instance_name template
Test if the architecture is unknown.
Ensure instance can be paused
Ensure instance can be hard rebooted
Ensure instance can be soft rebooted
Verify building an instance has a reservation_id that matches return value from create
Verify building 2 instances at once results in a reservation_id being returned equal to reservation id set in both instances
Ensure invalid flavors raise
Ensure invalid flavors raise
Ensure instance can be restored from a soft delete
Ensure instance can be resumed (if suspended)
Ensure instance can have its admin password set
Ensure a snapshot of an instance can be created
Can’t snapshot an instance which is already being snapshotted.
Ensure a snapshots min_ram and min_disk are correct.
A snapshot of an instance should have min_ram and min_disk set to that of the instances original image unless that image had a disk format of vhd.
Ensure a snapshots min_ram and min_disk are correct.
A snapshot of a non-shrinkable VHD should have min_ram and min_disk set to that of the original instances flavor.
Ensure a snapshots min_ram and min_disk are correct.
Do not show an attribute that the orig img did not have.
Ensure a snapshots min_ram and min_disk are correct.
A snapshots min_ram and min_disk should be set to default if an instances original image cannot be found.
Ensure instance can’t be soft rebooted while rebooting
Ensure instance can be suspended
Ensure instance can be unpaused
Make sure we can a vnc console for an instance.
Bases: nova.tests.compute.test_compute.BaseTestCase
This is for unit coverage of aggregate-related methods defined in nova.compute.manager.
Bases: nova.tests.compute.test_compute.BaseTestCase
Bases: nova.tests.compute.test_compute.BaseTestCase
Bases: nova.tests.compute.test_compute.BaseTestCase
Tests for re-scheduling exception handling logic
Test with no rescheduling and an additional exception occurs clearing the original build error’s exception context.
Spawn fails and re-scheduling is disabled.
Spawn fails and re-scheduling is enabled. Original exception should not be re-raised.
Bases: nova.tests.compute.test_compute.BaseTestCase
Tests related to re-scheduling build requests
no filter_properties will disable re-scheduling
no request spec will also disable re-scheduling
no retry info will also disable re-scheduling
Bases: nova.tests.compute.test_compute.BaseTestCase
Make sure that the instance can be terminated in ERROR state
Confirm check_can_live_migrate_destination works on positive path
Confirm check_can_live_migrate_destination works on positive path
Confirm check_can_live_migrate_destination works on positive path
Confirm check_can_live_migrate_source works on positive path
Make sure we can get console output from instance
Make sure we can get console output from instance
Make sure create associates a config drive.
Default of memory limit=None is unlimited
Make sure create associates a config drive.
Test passing of oversubscribed cpu policy from the scheduler.
Test passing of oversubscribed disk policy from the scheduler.
Test passing of oversubscribed disk policy from the scheduler, but with insufficient disk.
Test passing of oversubscribed ram policy from the scheduler.
Test passing of oversubscribed ram policy from the scheduler, but with insufficient memory.
Make sure we can get diagnostics for an instance.
check the persistence of the ERROR(scheduling) state
Contrived test to ensure finish_resize doesn’t raise anything
Make sure we don’t leave the instance in RESIZE on error
Ensure notifications on instance migrate/resize
Ensure that the flavor is reverted to the original on revert
Ensure we can write a file to an instance
Ensure we can inject network info
Test that instance is set to error state when exception is raised
Test that we handle InstanceTerminationFailure which is propagated up from the underlying driver.
Raise useful error if console type is an unrecognised string
Confirm exception when pre_live_migration fails.
Confirm live_migration() works as expected correctly.
ensure locked instance cannot be changed
Raise useful error is console type is None
When a spawn fails the network must be deallocated
Make sure we can a vnc console for an instance.
Ensure instance can be paused and unpaused
Confirm post_live_migration() works as expected correctly.
Ensure instance can be powered off
Ensure instance can be powered on
Confirm raising exception if instance doesn’t have fixed_ip.
Confirm setup_compute_volume is called when volume is mounted.
Ensure prep_resize raise a migration error
Ensure instance can be hard rebooted
Ensure instance can be soft rebooted
Ensure instance can be rebuilt
Ensure notifications on instance migrate/resize
Ensure instance can be rebuilt
Ensure instance can be rescued and unrescued
Ensure we can reset networking on an instance
Ensure instance can be migrated/resized
Ensure instance status set to Error on resize error
Ensure vm_state is ERROR when error occurs
Ensure notifications on instance migrate/resize
Ensure instance fails to migrate when source and destination are the same host
Ensure failure when running an instance that already exists
block device mapping failure test.
Make sure that when there is a block device mapping problem, the instance goes to ERROR state, keeping the task state
spawn failure test.
Make sure that when there is a spawning problem, the instance goes to ERROR state, keeping the task state
Ensure run instance generates appropriate usage notification
Detect when a vm is terminated behind the scenes
Make sure it is possible to run and terminate instance
Make sure timestamps are set for launched and destroyed
Make sure it is possible to run and terminate instance with volume attached
Ensure instance can have its admin password set
Test setting password while instance is rebuilding.
Ensure expected exception is raised if set_admin_password not authorized.
Ensure expected exception is raised if set_admin_password not implemented by driver.
Ensure instance can be snapshotted
Ensure task_state is set to None if snapshot fails
Ensure instance can be started
ensure that task_state is reverted after a failed operation
Ensure instance can be stopped
ensure instance can be suspended and resumed
Ensure vm_state is ERROR when suspend error occurs
Ensure that a failure in terminate_instance does not result in the task state being reverted from DELETING (see LP 1046236).
Ensure terminate_instance generates correct usage notification
Make sure we can a vnc console for an instance.
Bases: nova.tests.compute.test_compute.BaseTestCase
Some instance-types are marked ‘disabled’ which means that they will not show up in customer-facing listings. We do, however, want those instance-types to be availble for emergency migrations and for rebuilding of existing instances.
One legitimate use of the ‘disabled’ field would be when phasing out a particular instance-type. We still want customers to be able to use an instance that of the old type, and we want Ops to be able perform migrations against it, but we don’t want customers building new slices with ths phased-out instance-type.
We don’t want to require a customers instance-type to change when ops is migrating a failed server.
A rebuild or a restore should only change the ‘image’, not the ‘instance_type’. Therefore, should be allowed even when the slice is on disabled type already.
Bases: object
Bases: nova.tests.compute.test_compute.BaseTestCase
Bases: exceptions.Exception


. TITANIC A film review by Christopher Null Copyright 1997 Christopher Null
So here I am, newly engaged, and the subject of honeymoons comes up.  How about Alaska? This is the best idea we've come up with so far... and then we go to see TITANIC.
Well, I can't think of anything that would change my mind faster than the sight of 1500 ice-covered dead bodies, bobbing up and down in the ocean, after the sinking of a luxury liner. Let's jump right on the boat, huh?
And maybe that speaks to the power of the film. When the ship is launched on its maiden voyage, you can feel the splendor in the theater. When it bangs against the iceberg and starts to sink, you can feel the death in the air. When all that's left is ocean and darkness, you shiver in your seat with the dying.
The point I'm getting at is that this is a movie about *atmosphere*. The Titanic itself is the star--the only star--and the only reason to leave your house. Leonardo DiCaprio as a third-class drifter? Forget it. Kate Winslet, one of my favorite actresses around, as a first-class dreamer who falls for the lug? She's fine, but sounds like she belongs in the Valley. Billy Zane as her betrothed? Puh-leeze. These parts could have been played by anybody and then forgotten. The movie would remain the same.
Don't even think about waiting for video or cable with this film. You'll hate it. I also think it's safe to say you can arrive 90 minutes late and still see the good stuff (the rest is Cameron's sappy love story that is both exceedingly phony and wholly unoriginal, not to mention the scenes from "the present.") Then again, lots of sobs in the audience attest that the film is both an effects extravaganza *and* a chick flick (and a dessert topping, I guess).
Whatever it's pigeon-holed as, you're going to love the movie, even if it is 3 1/2 hours long. But I've rambled too much. I've got a honeymoon to plan.

The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


firebase-hosting-html-css-prettier
Firebase Hosting with HTML, CSS, Prettier, Docker sample code.
GitHub


Submissions History
| Date | Referrer | Form | Recipient | IP | Sent Email Text |
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| Date | Referrer | Form | Recipient | IP | Sent Email Text |
|
|
{{referrer}} | {{form_name}} | {{to_email}} | {{ip}} | {{#user_message}} {{.}} {{/user_message}} |


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Clicks from Deepawali 2014


{% from "components/card.html" import card %} {% extends "dashboard/base.html" %} {% block title %}Mod Dashboard — Manage OZs{% endblock %} {% block pagetitle %}{{ game }} — Moderator Actions{% endblock %} {% block pagesubtitle %}Manage OZs{% endblock %} {% block subcontent %}
| Name | Locked OZ? | |
|---|---|---|
| {{ oz }} | {{ oz.user.email }} | ✔ |
| {{ p }} | {{ p.user.email }} |
This will randomly select the given number of OZs from anyone in the OZ pool. Those chosen will be listed with the locked OZs above, but replace any non-locked OZs. For example: if there are 4 locked OZs and you select 5 random OZs, hitting "shuffle" will give you 9 OZs.
Note: Locked OZs can be removed by the Necro going in to their player settings and manually removing their "zombie" function. BUT legacy players can force their role to be zombies, so don't remove a locked OZ once you set them unless you're certain they didn't set it themselves!
Also, because of this function, legacy players may in fact deduce that they are selected as OZs if you lock in the OZ settings before the game. It is therefore advisable to only set your OZs right before the game starts
There are currently {{ total_players }} players in this game.
{% endcall %} {% call card("Set OZs") %}This will set the currently listed OZs to all be locked OZs. Any OZ that is not set as locked at the game start will not start as a zombie.
{% endcall %}


Bacon ipsum dolor amet kevin prosciutto short ribs picanha biltong, jowl t-bone pork chop tail tongue venison kielbasa short loin flank. Filet mignon jowl chuck kielbasa pig, shank drumstick sausage meatloaf alcatra. Meatball buffalo beef, turducken fatback corned beef beef ribs jerky tongue ball tip spare ribs doner pork belly ham.
Capicola ham salami beef tri-tip pork swine leberkas strip steak biltong meatloaf bresaola t-bone. Pancetta beef ribs rump biltong boudin meatball buffalo. Jerky pastrami jowl pork loin, meatball fatback strip steak buffalo frankfurter turducken filet mignon tri-tip doner boudin chicken.
Turducken picanha cow landjaeger capicola, shoulder turkey pastrami pork belly sausage ball tip shankle bresaola shank frankfurter. Pastrami kevin meatball tri-tip meatloaf shoulder chuck.
Bacon ipsum dolor amet kevin prosciutto short ribs picanha biltong, jowl t-bone pork chop tail tongue venison kielbasa short loin flank. Filet mignon jowl chuck kielbasa pig, shank drumstick sausage meatloaf alcatra. Meatball buffalo beef, turducken fatback corned beef beef ribs jerky tongue ball tip spare ribs doner pork belly ham.
Capicola ham salami beef tri-tip pork swine leberkas strip steak biltong meatloaf bresaola t-bone. Pancetta beef ribs rump biltong boudin meatball buffalo. Jerky pastrami jowl pork loin, meatball fatback strip steak buffalo frankfurter turducken filet mignon tri-tip doner boudin chicken.
Turducken picanha cow landjaeger capicola, shoulder turkey pastrami pork belly sausage ball tip shankle bresaola shank frankfurter. Pastrami kevin meatball tri-tip meatloaf shoulder chuck.


You can also add images to each slide. Also just place them within a folder and give the name of that folder as option to your Slidevs.


user_close()
spio_t_error_code user_close( void **puser_data, spio_t_bits close_options );
This is the prototype for one of the methods of user defined streams. It is used when SICStus wants to close one or both directions of a user defined stream.
SP_create_stream()
.
On successful return, if the stream has been closed and any resources
freed, then *puser_data
should be set to NULL
.
If user_close
fails, it can still set *puser_data
to
NULL
to signify that the stream is no longer usable.
SPIO_DEVICE_CLOSE_OPTION_READ
SPIO_DEVICE_CLOSE_OPTION_WRITE
SPIO_DEVICE_CLOSE_OPTION_FORCE
user_close
returned an error.
Note that a bidirectional stream should only close the directions
specified by the close_options
. Also note that
user_close
for a bidirectional stream may be called several
times and that the same direction flag, e.g.
SPIO_DEVICE_CLOSE_OPTION_READ
may be specified more than once,
even if that direction has already been closed successfully.
Once a call to user_close
has set *puser_data
to
NULL
, none of the device methods will be called
again. Note that a *puser_data
may be set to NULL
even
when a failure code is returned. This is useful if the failure is
unrecoverable.
There is no option to specify non-blocking close, it is expected that
user_close
will finish quickly. To make this more likely,
user_flush_output
is called before non-forcibly closing an
output stream.
On success, return SPIO_S_NOERR
or some other success code and
set *puser_data
if and only if the user data and any other
resources have been freed.
On failure, return a SPIO error code. Error codes with special meaning
for user_close
:
SPIO_E_END_OF_FILE
Other error codes may also be returned.
Should close one or all directions depending on the
close_options
. If all directions have been closed, the user
data should be deallocated and *puser_data
set to
NULL
.
cpg-ref-SP_create_stream. Defining a New Stream.


Vacation Site
Vacation site was created during the bootstrap introduction phase of the class. My partner, Marc, and I used html, css, and bootstrap functions.
Animal Shelter SiteAnimal site was light expression of gathered knowledge during the second part of the CSS section of the intro phase of the class. My partner, Justin, and I used html and css commands.
Interior Design SiteInterior design site was developed to practice css. My partner, Justin, and I utilized html and css functions.
Refurbished SocksRefurbished site was developed to practice css. My partners, Justin and Liz, and I utilized html and css functions.
Band SiteBando site was developed to practice css. My partners, Justin and Liz, and I utilized html and css functions.
Resume SiteResume site was developed to practice css. My partners, Justin and Liz, and I utilized html and css functions.
Pets sitePets site was developed to practice html and later with css. My partner, Reese, and I initially developed a similar site with HTML while I adjusted some properties with other partners, Justin and Liz, in advanced HTML and CSS commands.
Cat SiteCat blog was developed to practice html. The site is similar to one I created with my partner, Reese. The example is a copy of one created by Justin. My partner, Justin, and I utilized html and css functions to adjust this site after its creation.
Boring Lecture SiteBoring site was developed to practice css. My partner, Justin, and I utilized html and css functions to create this rushed example of perfection.
CupcakeCupcake site was developed to practice css and html. My partner, Justin, and I created this masterpiece.
Resort SiteCupcake site was developed to practice html and some css. My partner, Marina, and I utilized enjoyed creating this masterpiece with a huge assistance to Marina's template design created before class.
Hello World SiteHello World was created by my partner, Reese, and I to practice the basics of HTML. My partner, Marina, and I modified this in another assignment for HTML.
My First WebpageMy first webpage was a basic HTML site I created with my partner, Reese.
Good-bye SiteGood-bye site was created by my partner, Reese, and I to practice html. This is where it all started.


DID Agents have confirmed that Armada TACO Agents recieved a leak of the problem: "today that The Sky Drifters announced at the most recent meeting that they will be holding a referendum to determine their membership of the Free Skies Coalition. More news is coming in as we speak, and we will keep you updated by editing this article if necessary."
leaked by FeaturedEpic (reported by jamesm2w)


|
JavaTM 2 Platform Standard Ed. 5.0 |
|||||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | |||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |
java.lang.Object javax.crypto.KeyAgreementSpi
public abstract class KeyAgreementSpi
This class defines the Service Provider Interface (SPI)
for the KeyAgreement
class.
All the abstract methods in this class must be implemented by each
cryptographic service provider who wishes to supply the implementation
of a particular key agreement algorithm.
The keys involved in establishing a shared secret are created by one
of the
key generators (KeyPairGenerator
or
KeyGenerator
), a KeyFactory
, or as a result from
an intermediate phase of the key agreement protocol
(@see #engineDoPhase(java.security.Key, boolean) engineDoPhase).
For each of the correspondents in the key exchange,
engineDoPhase
needs to be called. For example, if the key exchange is with one other
party, engineDoPhase
needs to be called once, with the
lastPhase
flag set to true
.
If the key exchange is
with two other parties, engineDoPhase
needs to be called twice,
the first time setting the lastPhase
flag to
false
, and the second time setting it to true
.
There may be any number of parties involved in a key exchange.
KeyGenerator
,
SecretKey
| Constructor Summary | |
|---|---|
KeyAgreementSpi()
|
| Method Summary | |
|---|---|
protected abstract Key |
engineDoPhase(Key key,
boolean lastPhase)
Executes the next phase of this key agreement with the given key that was received from one of the other parties involved in this key agreement. |
protected abstract byte[] |
engineGenerateSecret()
Generates the shared secret and returns it in a new buffer. |
protected abstract int |
engineGenerateSecret(byte[] sharedSecret,
int offset)
Generates the shared secret, and places it into the buffer sharedSecret , beginning at offset inclusive. |
protected abstract SecretKey |
engineGenerateSecret(String algorithm)
Creates the shared secret and returns it as a secret key object of the requested algorithm type. |
protected abstract void |
engineInit(Key key,
AlgorithmParameterSpec params,
SecureRandom random)
Initializes this key agreement with the given key, set of algorithm parameters, and source of randomness. |
protected abstract void |
engineInit(Key key,
SecureRandom random)
Initializes this key agreement with the given key and source of randomness. |
| Methods inherited from class java.lang.Object |
|---|
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
| Constructor Detail |
|---|
public KeyAgreementSpi()
| Method Detail |
|---|
protected abstract void engineInit(Key key, SecureRandom random) throws InvalidKeyException
If the key agreement algorithm requires random bytes, it gets them
from the given source of randomness, random
.
However, if the underlying
algorithm implementation does not require any random bytes,
random
is ignored.
key
- the party's private information. For example, in the case
of the Diffie-Hellman key agreement, this would be the party's own
Diffie-Hellman private key.random
- the source of randomness
InvalidKeyException
- if the given key is
inappropriate for this key agreement, e.g., is of the wrong type or
has an incompatible algorithm type.protected abstract void engineInit(Key key, AlgorithmParameterSpec params, SecureRandom random) throws InvalidKeyException, InvalidAlgorithmParameterException
key
- the party's private information. For example, in the case
of the Diffie-Hellman key agreement, this would be the party's own
Diffie-Hellman private key.params
- the key agreement parametersrandom
- the source of randomness
InvalidKeyException
- if the given key is
inappropriate for this key agreement, e.g., is of the wrong type or
has an incompatible algorithm type.
InvalidAlgorithmParameterException
- if the given parameters
are inappropriate for this key agreement.protected abstract Key engineDoPhase(Key key, boolean lastPhase) throws InvalidKeyException, IllegalStateException
key
- the key for this phase. For example, in the case of
Diffie-Hellman between 2 parties, this would be the other party's
Diffie-Hellman public key.lastPhase
- flag which indicates whether or not this is the last
phase of this key agreement.
InvalidKeyException
- if the given key is inappropriate for
this phase.
IllegalStateException
- if this key agreement has not been
initialized.protected abstract byte[] engineGenerateSecret() throws IllegalStateException
This method resets this KeyAgreementSpi
object,
so that it
can be reused for further key agreements. Unless this key agreement is
reinitialized with one of the engineInit
methods, the same
private information and algorithm parameters will be used for
subsequent key agreements.
IllegalStateException
- if this key agreement has not been
completed yetprotected abstract int engineGenerateSecret(byte[] sharedSecret, int offset) throws IllegalStateException, ShortBufferException
sharedSecret
, beginning at offset
inclusive.
If the sharedSecret
buffer is too small to hold the
result, a ShortBufferException
is thrown.
In this case, this call should be repeated with a larger output buffer.
This method resets this KeyAgreementSpi
object,
so that it
can be reused for further key agreements. Unless this key agreement is
reinitialized with one of the engineInit
methods, the same
private information and algorithm parameters will be used for
subsequent key agreements.
sharedSecret
- the buffer for the shared secretoffset
- the offset in sharedSecret
where the
shared secret will be stored
sharedSecret
IllegalStateException
- if this key agreement has not been
completed yet
ShortBufferException
- if the given output buffer is too small
to hold the secretprotected abstract SecretKey engineGenerateSecret(String algorithm) throws IllegalStateException, NoSuchAlgorithmException, InvalidKeyException
This method resets this KeyAgreementSpi
object,
so that it
can be reused for further key agreements. Unless this key agreement is
reinitialized with one of the engineInit
methods, the same
private information and algorithm parameters will be used for
subsequent key agreements.
algorithm
- the requested secret key algorithm
IllegalStateException
- if this key agreement has not been
completed yet
NoSuchAlgorithmException
- if the requested secret key
algorithm is not available
InvalidKeyException
- if the shared secret key material cannot
be used to generate a secret key of the requested algorithm type (e.g.,
the key material is too short)
|
JavaTM 2 Platform Standard Ed. 5.0 |
|||||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | |||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |
Copyright © 2004, 2010 Oracle and/or its affiliates. All rights reserved. Use is subject to license terms. Also see the documentation redistribution policy.


2014-12-25
Before starting to read this: Phil has written an updated version of this guide, using FLTK 1.3.4 and Visual Studio 2017. You should probably head over there.
Download FLTK from [here][ftlk]: get
fltk-1.3.3-source.tar.gz
and decompress it, using for
example 7-Zip. You should end up with
a directory fltk-1.3.3
; I have put mine in a subfolder of
my Documents
folder:
Now open the project file fltk.sln
in
fltk-1.3.3/ide/VisualC2010
. If asked, say you’re okay with
updating the project files.
Make sure you have selected Debug as your solution configuration.
In the context menu of the demo project, select Set as StartUp Project.
Now build the solution (Build – Build Solution or Ctrl + Shift + B). If successful, you should be able to run the demo project (Debug – Start Debugging or F5).
Change the solution configuration to Release and build again. Check if you can run the demo project (F5).
Now we have to copy the generated library files to the right
directory so Visual Studio finds them. In my installation, the libraries
and headers are in
C:\Program Files (x86)\Microsoft Visual Studio 12.0\VC
(just VC
from now on). If you have the right directory,
there should be a bunch of subdirectories:
Now copy (don’t move) the following from you fltk-1.3.3
folder:
The complete FL
directory to
VC\include
All .lib
files from fltk-1.3.3\lib
(except README.lib
) to VC\lib
. There should be
14 files: seven pairs of two files each, one of which has an added “d”
(for “debug”) at the end of the file name.
fluid.exe
and fluidd.exe
from
fltk-1.3.3\fluid
to VC\bin
To set up a FLTK project, select File – New – Project and choose Win32 Project:
In the wizard under Application Settings, check Empty project:
Create a source file with Project – Add New Item:
Enter this code:
#include<FL/Fl.H>
#include<FL/Fl_Box.H>
#include<FL/Fl_Window.H>
int main()
{
(200,200,"Window title");
Fl_Window window(0,0,200,200,"Hey, I mean, Hello, World!");
Fl_Box box.show();
windowreturn Fl::run();
}
Now open Project – Properties and make sure you
have selected Debug in Configuration in the top left
corner. Under Linker – Input, edit Additional
Dependencies and add fltkd.lib
,
wsock32.lib
, comctl32.lib
,
fltkjpegd.lib
and fltkimagesd.lib
, each on
their own line:
Because this is the Debug configuration, we would like to
have a visible console window. To get that, go to Linker –
System and set /SUBSYSTEM:CONSOLE
for
SubSystem:
Now build and run the project. You should get a console window and the GUI window:
Now go back to Project – Properties and select
Release as the configuration. Go to Linker –
Input and edit the Additional Dependencies just like
before, but this time, add fltk.lib
,
wsock32.lib
, comctl32.lib
,
fltkjpeg.lib
and fltkimages.lib
—notice the
missing d
at the end of the names:
Make sure that under Linker – System, the option
/SUBSYSTEM:WINDOWS
is selected so we don’t get a console
window. Now build and run to see if only the GUI window shows up.
That’s how you set up any project using FLTK!
A lot of people struggling with FLTK are using it in the first place because they’re working through Bjarne Stroustrup’s book “Programming—Principles and Practice Using C++”, which uses FLTK in its Chapters 12 to 16 for GUI examples. Stroustrup provides a small interface library to be used in all the exercises. Here is how to set up a project using this interface library!
Create a new Win32 project. I’ve called mine
stroustrup_ppp
:
Make sure it’s empty by selecting Empty project in Application Settings in the Application Wizard.
Now set all the project properties just like above so Visual Studio properly links to the FLTK library files.
For the example, we’re going to do the drill of Chapter 12. Select Project – Add New Item to add a new file:
If you haven’t already, download the standard
library access header and the GUI support
code from the website and put them
in the same directory as chapter12_drill.cpp
. Ignore all
the matrix files and Gui.h
—download GUI.h
.
Don’t download simple_window.cpp
as everything is already
defined in simple_window.h
. The directory should look like
this afterwards:
Now add all the files to the project using Project – Add Existing Item:
This is actually overkill as we’re not going to use
GUI.h
and GUI.cpp
for a while, but you won’t
have to add anything else when you eventually use them.
For some reason, in Point.h
, the constructors are
commented out. Uncomment everything in the struct.
Now edit chapter12_drill.cpp
and enter this code:
#include "Graph.h"
#include "Simple_window.h"
int main()
{
using namespace Graph_lib;
(150,150);
Point tl(tl,600,400,"My window");
Simple_window win.wait_for_button();
win}
If you try to build your solution now, you get a slew of errors. Here is how to get rid of them:
In std_lib_facilities.h
, comment out the complete
vector
part:
This should get rid of errors related to vector
and
initializer lists; you lose range checking, though.
Next, because list
initialization inside member initializer list or non-static data member
initializer is not implemented in VS 2013, comment out the
constructor of Lines
in graph.h
(line 238).
For the same reason, replace the constructor of Text
:
Next, in GUI.cpp
, add Graph_lib::
in front
of Window&
in the definition of
Button::attach
(line 8), In_box::attach
(line
30) and Out_box::attach
(line 49). In
Graph.cpp
, in the definition of can_open
,
replace return ff;
with return bool(ff);
(line
319).
Finally, in GUI.cpp
, remove the constructor
Menu::Menu
as it is already defined in
GUI.h
.
Now you should be able to build and run the project!


Two dogs found running loose on Tobin Bridge rescued by State Police
by Manoella MacedoBOSTON - The Tobin Bridge has gone to the dogs!
On Thursday afternoon, at around 3:30 p.m., a State Police trooper responded to a call for two dogs running loose on the Tobin Bridge.
Two drivers had pulled over and kept the dogs from being hit by cars until police arrived.
Troopers then safely put the two pups in the back of a cruiser. Chelsea Animal Control officers transported the dogs to an animal hospital.
It is unknown who the dogs belong to and how they ended up on the bridge by themselves.


Fields leads Arkansas St. over Colorado St. 80-78
FORT COLLINS, Colo. (AP) - Caleb Fields had 22 points off the bench as Arkansas State narrowly beat Colorado State 80-78 on Wednesday night.
Marquis Eaton had 18 points for Arkansas State (5-1), which earned its fourth consecutive win. Jerry Johnson Jr. added 15 points. Christian Willis had 13 points and six rebounds for the road team.
Nico Carvacho had 15 points and five boards for the Rams (3-2). David Roddy added 14 points and three blocks. Isaiah Stevens had 11 points, eight assists and six rebounds.
Arkansas State takes on Stephen F. Austin at home next Saturday. Colorado State faces New Mexico State on Monday.
For more AP college basketball coverage: https://apnews.com/Collegebasketball and http://twitter.com/AP_Top25
This was generated by Automated Insights, http://www.automatedinsights.com/ap, using data from STATS LLC, https://www.stats.com
Sign up for Daily Newsletters
Copyright © 2019 The Washington Times, LLC.
Please read our comment policy before commenting.


| ID | Requirement | Priority | State | Contributors |
|---|---|---|---|---|
| 1 | View all the gym classes available through a chatbot | MUST | Complete | Matineh Akhlaghinia |
| 2 | Book a gym class through a chatbot | MUST | Complete | Matineh Akhlaghinia |
| 3 | Cancel a booking through a chatbot | MUST | Complete | Matineh Akhlaghinia |
| 4 | Give feedback for a certain class through a chatbot | COULD | Incomplete | N/A |
| 5 | View all the gym classes available through a digital assistant | MUST | Complete | Romain Dumon |
| 6 | Book a gym class through a digital assistant | MUST | Complete | Romain Dumon |
| 7 | Cancel a booking through a digital assistant | MUST | Complete | Romain Dumon |
| 8 | Give feedback for a certain class through a digital assistant | COULD | Incomplete | N/A |
| 9 | Send reminders to the users | COULD | Incomplete | N/A |
| 10 | The bot should be accessible easily | MUST | Complete | Matineh Akhlaghinia, Sachchit Prasad |
| 11 | To be able to talk to the bot in different ways and get the desired response | MUST | Complete | Matineh Akhlaghinia, Sachchit Prasad |
| 12 | The bot responses should be short and comprehensive enough for any age | MUST | Complete | Matineh Akhlaghinia, Sachchit Prasad |
| 13 | Make the booking process easier, more accessible | MUST | Complete | Matineh Akhlaghinia, Sachchit Prasad, Romain Dumon |
| 14 | Book the gym class when the bot has all the required information, date, time and class type | MUST | Complete | Matineh Akhlaghinia |
| 15 | Secure authentication | MUST | Complete | Matineh Akhlaghinia, Sachchit Prasad, Romain Dumon |
| 16 | Confirmation with the user before booking the class | MUST | Complete | Matineh Akhlaghinia |
| 17 | Booking the right class | MUST | Complete | Matineh Akhlaghinia |
| 18 | For the bot to be published on different platforms like Skype, Slack and Facebook Messenger | COULD | Partially Complete | Matineh Akhlaghinia, Sachchit Prasad, Romain Dumon |
| 19 | Correct responses and not getting stuck in a loop | MUST | Complete | Matineh Akhlaghinia, Sachchit Prasad |
| 20 | (From NHS perspective) Reduce non-attendance | SHOULD | Complete | Matineh Akhlaghinia, Sachchit Prasad, Romain Dumon |
| ID | Work Package | Category | Contributors |
|---|---|---|---|
| 1 | Biweekly Report 1 | Report Writing | Sachchit Prasad |
| 2 | Biweekly Report 2 | Report Writing | Sachchit Prasad |
| 3 | Biweekly Report 3 | Report Writing | Sachchit Prasad, Matineh Akhlaghinia |
| 4 | Biweekly Report 4 | Report Writing | Sachchit Prasad, Matineh Akhlaghinia |
| 5 | Biweekly Report 5 | Report Writing | Sachchit Prasad, Matineh Akhlaghinia |
| 6 | Client Meeting 1 | Client Liason | Sachchit Prasad, Matineh Akhlaghinia |
| 7 | Client Meeting 2 | Client Liason | Matineh Akhlaghinia |
| 8 | Client Meeting 3 | Client Liason | Matineh Akhlaghinia |
| 9 | Alexa Skill Dev | Technology Dev | Romain Dumon |
| 10 | Chat Bot Dev | Technology Dev | Sachchit Prasad, Matineh Akhlaghinia |
| 11 | API Dev | Technology Dev | Matineh Akhlaghinia |
| 12 | Website | Web Dev | Sachchit Prasad, Matineh Akhlaghinia |
| 13 | Video | Other | Sachchit Prasad, Matineh Akhlaghinia, Romain Dumon |
| Area | Critical Evaluation Description |
|---|---|
| Architecture design | Within our architecure, for authenticating Nuffield customers, we needed to redirect them to Team 31 dashboard and use the facebook login authentication system. For us to be able to recognize the users, we associated each user session with a user authentication token. So whenever the user logs in through facebook auth, Team 31 system, inserts the authentication token to our database and in our system, during a time interval of 15s we make requests to our database to see if the token is there or not. If it was there that means the user is logged in and will continue the booking process otherwise it will send a timeout session to the user. This implementation works fine but it's very inefficient in terms of the number of calls we make to the database and in terms of the security. |
| UI and UX | Since the final products were a chatbot and a digital assistant - this limited a lot of the options in terms of what we could focus on in order to improve the UI and UX.
As a result we looked at what areas could be improved and really focused on making these the best they could be.
For the UI we had decided to use cards for the bots - this made the design much more visually appealing and made navigating the app more interesting yet also easier. The cards also allowed us to add functionalities such as booking in a much more convenient manner. For the UX we spent a lot of time designing the user journey through the app and trying to get a user from the beginning of their conversation to the end of the conversation as quickly as possible. At the same time looked at how to make the transitions between reply and response as intuitive as possible so that a user would not be surprised or confused by a response to their query. |
| Functionality | The bot and digital assistant both provide a high level of functionality as desired - they allow the users to book, view and cancel classes (and this can be easily expanded into other areas). They are both robust in the type and varied response of replies given back to the user. |
| Stability | LUIS had recently changed for the MS Bot Framework which resulted in many changes having to be made to the natural language processing - however all parts of the project (including both the bot and digital assistant) function as epxected under reasonable queries and can stil deal with questions it would not expect. |
| Efficiency | The aim was to take an intent from a user and then then get them to their desired action in as few steps as possible. This would be measured by number of messages and the number of utterances that would have to be made. As a lot of time has been spent reviewing user journeys - it has meant that as of now, the bot and digital assistant are both very good at handling queries and resolving them as fast as possible. |
| Compatibility | The MS Bot platform allows the bots to be deployed over a range of bot platforms such as Facebook Messenger, Slack, Skype etc. This means it is very highly adaptable to whichever platform the client chooses to deploy the bot on. |
| Maintainability | The main core of the app is contained within LUIS and Azure - anytime a change is wanted to be made and rolled out across all platforms, this can be changed in the respective MS portals and the changes will be automatically deployed across the various platforms. For more features to be added to the bot the Node code can be edited and tested - this makes it very easy to maintain the bot. The digital assistant is also centralised in Amazon's Developer site and as a result changes (e.g. a new feature) are very easy to implement. |
| Evaluation of Testing |
We had conducted two forms of test cases for this project, user testing and unit and integration testing. User testing - as our bot would be used with a very wide audience, which could range from young teens to people in their old age, it was vital that the application would work for all demographics and would be as usable for one particular audience as well as another. For this we had made sure that the user would test all aspects of the app, from booking, viewing and cancelling a class to being able to go back and forth within an app with ease. These test had confirmed that our initial flow design and application designs had been fruitful as every user was able to use the application with ease. To keep this testing fair, we made sure that the demographic range of our users matches that of the end user audience. Unit and Integration testing - For our unit and integration testing of the bot intents, we used microsoft bot emulator and we spend a lot of time for QA testing while we could have automated the process to generate test cases and get the results. It would have saved us a lot of time as well as pointing out subtle things that we might have missed during testing |
| Project management | The project management was done well by the team leaders - information was always relayed to all members of the team and everyone had their own tasks to complete during each sprint. If help was required for any team members then they would be assisted accordingly. Furthermore, any new developments with the client or major changes would be communicated effectively. All the work was centralised in one repository meaning that all members had instant access and could make their changes when they wished and could share files as they completed them and other team members could make sure progress was all up-to-date. |
There are various features which can be added to both the bot and digital assistant in order to improve the experience - these would mainly come from extra features that were not within the timeframe of this project.


Dear Friends,
We are a group of young adults from around the world who have come together to create educational opportunities to the young children of Laos. We collectively support and guide children to pursue their dreams and discover their potentials to lift themselves out of their cycle of poverty. We offer hopes and opportunities to under-privileged children of Laos by supporting them on their educational path, allowing them the opportunity to see the larger world.
We kindly ask for your support to help us provide opportunities for children in Laos especially the ones who are in the rural areas, who are not privileged to attend school or educational curriculum.
I am the the founder of Dreams Givers. I was born in Laos and grew up in an environment where good education was hard to come by. I was given the chance to study abroad when I became older. I founded this organization so I can provide that oppurtunity to future generations.
Hello! My name is Vivi. "Education is the most powerful weapon which can use to change the world." That's why I joined Dreams Givers, to make a difference to light up the hope.
My name is Mathilde. I believe in education and I believe that if we help these childern who cannot afford it we will make a change in this world. I believe in the famous quote: Open schools, you will close jails - Victor Hugo.
My name is Jens. I've found that no matter what system one is taugh in, school unifies young people in ways nothing else can achieve. This is why I joined Dreams Givers to be an exmple to inspire others to create a future all of us can look forward to.
My name is Kong, I am studying to become an English teacher at Souphanouvong University. I am volunteer my time to Dreams Givers because I grew up in similar conditions to those who I educate at Dreams Givers.
My name is Vajkub. Her and I grew up together learning at the same school. His vision and passion has inspired me to volunteer my time to Dreams Givers.
My name is Yen. I decided to join Dreams Givers because I grew up in similar situations to those who we support. I always believe that dreams givers is extremely amazing organization that will support underprivileged children in Laos to be able to attend school and have brighter future.
My name is Yeng.


Insider
Insider is a testing utility framework which sets an HTTP client-sever communication channel between your app and testing environments like Appium, Calabash, Frank, etc. Some real use cases where Insider can be useful as part of the automation testing process:
- Set a particular state for the app during the test scenario;
- Simulate push notifications;
- Simulate app invocation using custom schemes / universal links;
- Simulate back-end responses;
- Manage files / directories in application sandbox;
- Collect metrics from the app during test execution (CPU, memory, etc.);
- etc.
Insider runs an HTTP server inside the application and listens for commands. By default Insider runs on http://localhost:8080
. A command represents a simple HTTP request: http://localhost:8080/<command>
Features
| Built-in Features | Commands | HTTP Method | |
|---|---|---|---|
| 💡 | Invoke a method on a registered delegate with given parameters; | /invoke |
POST |
| 📎 | Invoke a method on a registered delegate with given parameters and wait for response; | /invokeForResponse |
POST |
| 📢 | Send local notifications through NSNotificationCenter with given parameters; | /notification |
POST |
| 📱 | Get device system state information (CPU, memory, IP address, etc); | /systemInfo |
GET |
| :floppy_disk: | Manage files / directories in application sandbox (Documents, Library, tmp); | /documents/<command> /library/<command> /tmp/<command> |
See the table below |
Supported commands for file managing feature:
| File Managing Commands | HTTP Method |
|---|---|
List items: /<directory>/list |
GET |
Download items: /<directory>/download |
GET |
Upload items: /<directory>/upload |
POST |
Move items: /<directory>/move |
POST |
Delete items: /<directory>/delete |
POST |
Create folder: /<directory>/create |
POST |
In the scripts
directory can be found some sample ruby scripts which show the built-in features in action.
Check out the API reference for more information.
Installation
Manual installation
In order to include the Insider library into your project, you need to build a dynamic framework from provided source code and include it into your project, or inlcude the entire Insider library as sub-project by copying it to your project directory or include as Git submodule.
Carthage
If you are using Carthage, you can always use it to build the library within your workspace by adding the line below to your Cartfile
.
github "alexmx/Insider"
CocoaPods
If you are using CocoaPods, you can as well use it to integrate the library by adding the following lines to your Podfile
.
platform :ios, '8.0'
use_frameworks!
target 'YourAppTarget' do
pod "Insider", :git => 'git@github.com:alexmx/Insider.git'
end
Usage
Use case #1: Simulate Push Notifications
import Insider
class AppDelegate: UIResponder, UIApplicationDelegate {
func application(_ application: UIApplication, didFinishLaunchingWithOptions launchOptions: [UIApplicationLaunchOptionsKey: Any]?) -> Bool {
Insider.shared.start(withDelegate: self)
return true
}
func application(_ application: UIApplication, didReceiveRemoteNotification userInfo: [AnyHashable: Any]) {
// Process push notification
}
}
extension AppDelegate: InsiderDelegate {
func insider(_ insider: Insider, invokeMethodWithParams params: JSONDictionary?) {
// Simulate push notification
application(UIApplication.shared, didReceiveRemoteNotification: params!)
}
}
In order to test this example run InsiderUseCases
application target, after go to scripts
directory and run invoke_method.rb
script.
Use case #2: Simulate app invocation using a custom scheme
import Insider
class AppDelegate: UIResponder, UIApplicationDelegate {
func application(_ application: UIApplication, didFinishLaunchingWithOptions launchOptions: [UIApplicationLaunchOptionsKey: Any]?) -> Bool {
Insider.shared.start(withDelegate: self)
return true
}
func application(_ application: UIApplication, handleOpen url: URL) -> Bool {
// Process custom scheme invocation
return true
}
}
extension AppDelegate: InsiderDelegate {
func insider(_ insider: Insider, invokeMethodForResponseWithParams params: JSONDictionary?) -> JSONDictionary? {
// Simulate app invokation using a custom scheme
let url = URL(string: "insiderDemo://hello/params")
let response = application(UIApplication.shared, handleOpen: url!)
return ["response" as NSObject : response as AnyObject]
}
}
In order to test this example run InsiderUseCases
application target, after go to scripts
directory and run invoke_method_with_response.rb
script.
Use case #3: Get application system information during test execution
import Insider
class AppDelegate: UIResponder, UIApplicationDelegate {
func application(_ application: UIApplication, didFinishLaunchingWithOptions launchOptions: [UIApplicationLaunchOptionsKey: Any]?) -> Bool {
Insider.shared.start()
return true
}
}
As it is a built-in feature there is no need to set a delegate for Insider in this case. In order to test this example run InsiderDemo
application target, after go to scripts
directory and run system_info.rb
script.
Use case #4: Add files to Documents folder in application sandbox.
import Insider
class AppDelegate: UIResponder, UIApplicationDelegate {
func application(_ application: UIApplication, didFinishLaunchingWithOptions launchOptions: [UIApplicationLaunchOptionsKey: Any]?) -> Bool {
Insider.shared.start()
return true
}
}
As it is a built-in feature there is no need to set a delegate for Insider in this case.
There are 3 directories supported in application sandbox:
* Documents: http://localhost:8080/documents
* Library: http://localhost:8080/library
* tmp: http://localhost:8080/tmp
You can create new folders. Upload, download, move, remove files / folders from application sandbox.
In order to test this example run InsiderDemo
application target, and open http://localhost:8080/documents, http://localhost:8080/library or http://localhost:8080/tmp url in your browser. You will see the files which are in your application sandbox.
If you need to use the sandbox files managing API in your automation scripts please check File Managing Commands section above.
Credits
Insider uses these amazing libaries under the hood: * GCDWebServer * iOS-System-Services
License
This project is licensed under the terms of the MIT license. See the LICENSE file.


{include file="public/heads" /} {include file="public/left" /} {include file="public/nav" /}
訂單流程
用戶下單
運輸中
清關中
抵台待取
訂單查詢
*訂單號/手機號/姓名必須填寫準確才能査詢
立即査詢
{include file="public/statistical_code" /}


Math package¶
Submodules¶
Math.Statistics module¶
This module contains statistics calculation function.
Addition, subtraction, multiplication, and division of lists, as well as basic statistical calculation functions such as variance, standard deviation, and autocorrelation coefficient.
Functions¶
- chack_list_all_num(listA) -> bool
Determine the content of the list as one numerical data.
- plus(listA, listB) -> list
The two lists are added element by element.
- sub(listA, listB) -> list
Subtract two lists element by element.
- mult(listA, listB) -> list
Multiply two lists element by element.
- div(listA, listB) -> list
Divide two lists element by element.
- log(listA) -> list:
Multiply all the numerical elements in the list by the natural logarithm.
- round_list(listA, ndigits: int) -> list:
Take all the values in the list to a few digits after the decimal point.
- mean(listA) -> float
Calculate arithmetic mean of numerical data in a list.
- variance(listA) -> float
Calculate the variance of all elements in a list.
- standard_deviation(listA) -> float
Calculate the standard deviation of all elements in a list.
- covariance(listA, listB) -> float
Calculate the covariance of two lists of one-dimensional data.
- coefficient_of_variation(listA) -> float
Calculate the coefficient of variation of all elements in a list.
- correlation_coefficient(listA, listB) -> float
Calculate the linear correlation coefficient of two lists of one-dimensional data.
- coefficient_of_determination(listA, listB) -> float
Calculate the coefficient of determination of two lists of one-dimensional data.
- mean_square_error(target_list, predict_list) -> float
The regression index used to calculate the forecast error. Calculate the mean square error of two lists of one-dimensional data.
- mean_absolute_error(target_list, predict_list) -> float:
The regression index used to calculate the forecast error. Calculate the mean absolute error(MAE) of two lists of one-dimensional data.
- root_mean_squard_error(target_list, predict_list) -> float:
The regression index used to calculate the forecast error. Calculate the root mean square error(RMSE) of two lists of one-dimensional data.
- normalized_mean_squard_error(target_list, predict_list) -> float:
The regression index used to calculate the forecast error. Calculate the normalized mean square error(NRMSE) of two lists of one-dimensional data.
- ACF(listA, listB) -> float
Calculate the autocorrelation coefficients of two lists of one-dimensional data.
Notes
Pay attention to the input data type of the function.
Use the function “list_to_dataframe” or “dataframe_to_list” of the module “TimeSeriesAnalysis.ProcessData” to convert the data type.
-
Math.Statistics.
ACF
(listA, listB) → float¶ Calculate the autocorrelation coefficients of two lists of one-dimensional data.
Autocorrelation coefficients: The autocorrelation as a function of the lag. Algorithm: Calculate the autocorrelation coefficients according to the formula.
- Parameters
listA (list ,ndarray, pandas.Series and pandas.DataFrame. One-dimensional numerical list.) –
listB (list ,ndarray, pandas.Series and pandas.DataFrame. One-dimensional numerical list.) –
- Returns
- Return type
float. The autocorrelation coefficients of two lists of one-dimensional data.
Error
ValueError: The two lists must be the same length.Solution: Make sure that the number of numerical data in the two lists is the same (the same length).ValueError: List content must be one-dimensional numerical data: listA=”,chack_A, “; listB=”, chack_B.Solution: ‘True’, one-dimensional numerical list. For example,[1,5,8,6,3]. ‘False’, two-dimensional lists, strings, and one-dimensional non-numerical lists.For example, [[3, 6], [7, 2, 9, 5]] , ‘5’ , [3, 6, 7, ‘2’, 9, 5] . If’chack_A’ is’True’, it means that ‘listA’ does not need to be changed; if it is’False’, the input ‘listA’ is changed to a one-dimensional list of numerical data.ListB has the same judgment and processing method as listA.TypeError: object of type ‘NoneType’ has no len()Solution: ‘listA’ or ‘listB’ is None. Check that the input list is a one-dimensional list, for example, [1,5,8,6,3].References
Autocorrelation coefficients wiki: https://en.wikipedia.org/wiki/Autocorrelation
Autocorrelation Function: https://www.real-statistics.com/time-series-analysis/stochastic-processes/autocorrelation-function/
-
Math.Statistics.
chack_list_all_num
(listA) → bool¶ Determine the content of the list as one numerical data.
This function is to check the input list, the content is a one-dimensional numerical list. If the input is not a one-dimensional list, or the content of the one-dimensional list is not all numerical data, it will be returned ‘False’, otherwise it will return ‘True’.
- Parameters
listA (list ,ndarray, pandas.Series and pandas.DataFrame. One-dimensional numerical list.) –
- Returns
- Return type
bool(True or False) ‘True’, one-dimensional numerical list. For example, [1,5,8,6,3] ‘False’, two-dimensional lists, strings, and one-dimensional non-numerical lists.For example, [[3, 6], [7, 2, 9, 5]] , ‘5’ , [3, 6, 7, ‘2’, 9, 5]
Error
ValueError: The list must be one-dimensional numerical data, and there is at least one numerical data in it.Solution: The entered’listA’ is an empty list. Check that the input list is a one-dimensional list, for example, [1,5,8,6,3].TypeError: object of type ‘NoneType’ has no len()Solution: Please do not enter ‘None’.Check that the input list is a one-dimensional list, for example, [1,5,8,6,3].
-
Math.Statistics.
coefficient_of_determination
(train_target, target_list, predict_list) → float¶ Calculate the coefficient of determination of two lists of one-dimensional data.
Coefficient of determination: Explainable variation / total variation. A measure of how much proportional dependent variable variation can be explained by regression lines and independent variables. The closer to 1, the variation explained by this regression line is close to 100%. Algorithm: Square after calculating the linear correlation coefficient.
- Parameters
target_list (list ,ndarray, pandas.Series and pandas.DataFrame. One-dimensional numerical list.) –
predict_list (list ,ndarray, pandas.Series and pandas.DataFrame. One-dimensional numerical list.) –
- Returns
- Return type
float. The coefficient of determination of two lists of one-dimensional data.
Error
ValueError: The two lists must be the same length.Solution: Make sure that the number of numerical data in the two lists is the same (the same length).ValueError: List content must be one-dimensional numerical data: train_target=”,chack_train_target, “; target_list=”, chack_target_list, “; predict_list=”, chack_predict_list.Solution: ‘True’, one-dimensional numerical list. For example,[1,5,8,6,3]. ‘False’, two-dimensional lists, strings, and one-dimensional non-numerical lists.For example, [[3, 6], [7, 2, 9, 5]] , ‘5’ , [3, 6, 7, ‘2’, 9, 5] . If’chack_train_target’ is’True’, it means that ‘train_target’ does not need to be changed; if it is’False’, the input ‘train_target’ is changed to a one-dimensional list of numerical data. ‘target_list’ and ‘predict_list’ have the same judgment and processing method as ‘train_target’.TypeError: object of type ‘NoneType’ has no len()Solution: ‘listA’ or ‘listB’ is None. Check that the input list is a one-dimensional list, for example, [1,5,8,6,3].References
Coefficient of determination: https://en.wikipedia.org/wiki/Coefficient_of_determination
-
Math.Statistics.
coefficient_of_variation
(listA) → float¶ Calculate the coefficient of variation of all elements in a list.
Coefficient of variation: Can compare the standard deviation between different unit variables. Algorithm: First calculate the mean and standard deviation of the list, and finally divide the standard deviation by the mean and multiply by 100.
- Parameters
listA (list ,ndarray, pandas.Series and pandas.DataFrame. One-dimensional numerical list.) –
- Returns
- Return type
float. The coefficient of variation of all elements in a list.
Error
ValueError: The list must be one-dimensional numerical data, and there is at least one numerical data in it.Solution: The entered’listA’ is an empty list. Please change to a one-dimensional list of numerical data.ValueError: The list must be one-dimensional numerical data.Solution: ‘listA’ contains a string or is two-dimensional list. Check that the input list is a one-dimensional list, for example, [1,5,8,6,3].TypeError: object of type ‘NoneType’ has no len()Solution: ‘listA’ is None. Check that the input list is a one-dimensional list, for example, [1,5,8,6,3].References
Coefficient of variation: https://en.wikipedia.org/wiki/Coefficient_of_variation
-
Math.Statistics.
correlation_coefficient
(listA, listB) → float¶ Calculate the linear correlation coefficient of two lists of one-dimensional data.
Correlation coefficient: The strength of linear correlation between two variables. Algorithm: Calculate the linear correlation coefficient according to the formula.
- Parameters
listA (list ,ndarray, pandas.Series and pandas.DataFrame. One-dimensional numerical list.) –
listB (list ,ndarray, pandas.Series and pandas.DataFrame. One-dimensional numerical list.) –
- Returns
- Return type
float. The linear correlation coefficient of two lists of one-dimensional data.
Error
ValueError: The two lists must be the same length.Solution: Make sure that the number of numerical data in the two lists is the same (the same length).ValueError: List content must be one-dimensional numerical data: listA=”,chack_A, “; listB=”, chack_B.Solution: ‘True’, one-dimensional numerical list. For example,[1,5,8,6,3]. ‘False’, two-dimensional lists, strings, and one-dimensional non-numerical lists.For example, [[3, 6], [7, 2, 9, 5]] , ‘5’ , [3, 6, 7, ‘2’, 9, 5] . If’chack_A’ is’True’, it means that ‘listA’ does not need to be changed; if it is’False’, the input ‘listA’ is changed to a one-dimensional list of numerical data.ListB has the same judgment and processing method as listA.TypeError: object of type ‘NoneType’ has no len()Solution: ‘listA’ or ‘listB’ is None. Check that the input list is a one-dimensional list, for example, [1,5,8,6,3].References
Correlation coefficient: https://www.statisticshowto.com/probability-and-statistics/correlation-coefficient-formula/
-
Math.Statistics.
covariance
(listA, listB) → float¶ Calculate the covariance of two lists of one-dimensional data.
Covariance: After calculating the respective averages of the two lists, subtract the averages one by one and multiply them to form a value. Algorithm: After the entire list is calculated, all the values are added up, and finally divided by the length of the list minus one.
- Parameters
listA (list ,ndarray, pandas.Series and pandas.DataFrame. One-dimensional numerical list.) –
listB (list ,ndarray, pandas.Series and pandas.DataFrame. One-dimensional numerical list.) –
- Returns
- Return type
float. The covariance of two lists of one-dimensional data.
Error
ValueError: The two lists must be the same length.Solution: Make sure that the number of numerical data in the two lists is the same (the same length).ValueError: List content must be one-dimensional numerical data: listA=”,chack_A, “; listB=”, chack_B.Solution: ‘True’, one-dimensional numerical list. For example,[1,5,8,6,3]. ‘False’, two-dimensional lists, strings, and one-dimensional non-numerical lists.For example, [[3, 6], [7, 2, 9, 5]] , ‘5’ , [3, 6, 7, ‘2’, 9, 5] . If’chack_A’ is’True’, it means that ‘listA’ does not need to be changed; if it is’False’, the input ‘listA’ is changed to a one-dimensional list of numerical data.ListB has the same judgment and processing method as listA.TypeError: object of type ‘NoneType’ has no len()Solution: ‘listA’ or ‘listB’ is None. Check that the input list is a one-dimensional list, for example, [1,5,8,6,3].References
Covariance: https://en.wikipedia.org/wiki/Covariance
-
Math.Statistics.
div
(listA, listB) → list¶ Divide two lists element by element.
Division: Divide the elements of two lists pair by pair, and return a list of results.
- Parameters
listA (list ,ndarray, pandas.Series and pandas.DataFrame. One-dimensional numerical list.) –
listB (list ,ndarray, pandas.Series and pandas.DataFrame. One-dimensional numerical list.) –
- Returns
- Return type
list. Result of division.
Error
ValueError: The two lists must be the same length.Solution: Make sure that the number of numerical data in the two lists is the same (the same length).ValueError: List content must be one-dimensional numerical data: listA=”,chack_A, “; listB=”, chack_B.Solution: ‘True’, one-dimensional numerical list. For example,[1,5,8,6,3]. ‘False’, two-dimensional lists, strings, and one-dimensional non-numerical lists.For example, [[3, 6], [7, 2, 9, 5]] , ‘5’ , [3, 6, 7, ‘2’, 9, 5] . If’chack_A’ is’True’, it means that ‘listA’ does not need to be changed; if it is’False’, the input ‘listA’ is changed to a one-dimensional list of numerical data.ListB has the same judgment and processing method as listA.TypeError: object of type ‘NoneType’ has no len()Solution: ‘listA’ or ‘listB’ is None. Check that the input list is a one-dimensional list, for example, [1,5,8,6,3].
-
Math.Statistics.
log
(listA) → list¶ Multiply all the numerical elements in the list by the natural logarithm.
- Parameters
listA (list ,ndarray, pandas.Series and pandas.DataFrame. One-dimensional numerical list.) –
- Returns
- Return type
list. Result of natural logarithm list.
Error
ValueError: List content must be one-dimensional numerical data: listA=”,chack_A”.Solution: ‘True’, one-dimensional numerical list. For example,[1,5,8,6,3]. ‘False’, two-dimensional lists, strings, and one-dimensional non-numerical lists.For example, [[3, 6], [7, 2, 9, 5]] , ‘5’ , [3, 6, 7, ‘2’, 9, 5] . If’chack_A’ is’True’, it means that ‘listA’ does not need to be changed; if it is’False’, the input ‘listA’ is changed to a one-dimensional list of numerical data.TypeError: object of type ‘NoneType’ has no len()Solution: ‘listA’ or ‘listB’ is None. Check that the input list is a one-dimensional list, for example, [1,5,8,6,3].References
-
Math.Statistics.
mean
(listA) → float¶ Calculate arithmetic mean of numerical data in a list.
Mean: Add all the numerical data in the list and divide by the length of the list.
- Parameters
listA (list ,ndarray, pandas.Series and pandas.DataFrame. One-dimensional numerical list.) –
- Returns
- Return type
float. Arithmetic mean of numerical data in a list.
Error
ValueError: The list must be one-dimensional numerical data, and there is at least one numerical data in it.Solution: The entered’listA’ is an empty list. Please change to a one-dimensional list of numerical data.ValueError: The list must be one-dimensional numerical data.Solution: ‘listA’ contains a string or is two-dimensional list. Check that the input list is a one-dimensional list, for example, [1,5,8,6,3].TypeError: object of type ‘NoneType’ has no len()Solution: ‘listA’ is None. Check that the input list is a one-dimensional list, for example, [1,5,8,6,3].References
-
Math.Statistics.
mean_absolute_error
(target_list, predict_list) → float¶ Regression indicators for numerical prediction. Calculate the mean absolute error (MAE) of two lists of one-dimensional data.
Mean absolute error can measure the error between actual observations and predicted values. The calculation method is to subtract the two lists, take the absolute value of each number and then add them all, and finally divide by the total number of list elements.
- Parameters
target_list (list ,ndarray, pandas.Series and pandas.DataFrame. One-dimensional numerical list.) –
predict_list (list ,ndarray, pandas.Series and pandas.DataFrame. One-dimensional numerical list.) –
- Returns
- Return type
float. The root mean square error of two lists of one-dimensional data.
Error
ValueError: The two lists must be the same length.Solution: Make sure that the number of numerical data in the two lists is the same (the same length).ValueError: List content must be one-dimensional numerical data: listA=”,chack_A, “; listB=”, chack_B.Solution: ‘True’, one-dimensional numerical list. For example,[1,5,8,6,3]. ‘False’, two-dimensional lists, strings, and one-dimensional non-numerical lists.For example, [[3, 6], [7, 2, 9, 5]] , ‘5’ , [3, 6, 7, ‘2’, 9, 5] . If’chack_A’ is’True’, it means that ‘listA’ does not need to be changed; if it is’False’, the input ‘listA’ is changed to a one-dimensional list of numerical data.ListB has the same judgment and processing method as listA.TypeError: object of type ‘NoneType’ has no len()Solution: ‘listA’ or ‘listB’ is None. Check that the input list is a one-dimensional list, for example, [1,5,8,6,3].References
-
Math.Statistics.
mean_square_error
(target_list, predict_list) → float¶ Regression indicators for numerical prediction. Calculate the mean square error (MSE) of two lists of one-dimensional data.
Mean square error: Measures the mean of the square of the difference between the predicted value and the actual observed value. Algorithm: After subtracting the two lists, the resulting list is multiplied by itself, and finally all the values in the multiplied list are summed, and finally divide by the total number of list elements.
- Parameters
target_list (list ,ndarray, pandas.Series and pandas.DataFrame. One-dimensional numerical list.) –
predict_list (list ,ndarray, pandas.Series and pandas.DataFrame. One-dimensional numerical list.) –
- Returns
- Return type
float. The mean square error of two lists of one-dimensional data.
Error
ValueError: The two lists must be the same length.Solution: Make sure that the number of numerical data in the two lists is the same (the same length).ValueError: List content must be one-dimensional numerical data: listA=”,chack_A, “; listB=”, chack_B.Solution: ‘True’, one-dimensional numerical list. For example,[1,5,8,6,3]. ‘False’, two-dimensional lists, strings, and one-dimensional non-numerical lists.For example, [[3, 6], [7, 2, 9, 5]] , ‘5’ , [3, 6, 7, ‘2’, 9, 5] . If’chack_A’ is’True’, it means that ‘listA’ does not need to be changed; if it is’False’, the input ‘listA’ is changed to a one-dimensional list of numerical data.ListB has the same judgment and processing method as listA.TypeError: object of type ‘NoneType’ has no len()Solution: ‘listA’ or ‘listB’ is None. Check that the input list is a one-dimensional list, for example, [1,5,8,6,3].References
Mean square error: https://en.wikipedia.org/wiki/Mean_squared_error
-
Math.Statistics.
mult
(listA, listB) → list¶ Multiply two lists element by element.
Multiplication: The elements of two lists are multiplied pair by pair to return a list of results.
- Parameters
listA (list ,ndarray, pandas.Series and pandas.DataFrame. One-dimensional numerical list.) –
listB (list ,ndarray, pandas.Series and pandas.DataFrame. One-dimensional numerical list.) –
- Returns
- Return type
list. The result of multiplication.
Error
ValueError: The two lists must be the same length.Solution: Make sure that the number of numerical data in the two lists is the same (the same length).ValueError: List content must be one-dimensional numerical data: listA=”,chack_A, “; listB=”, chack_B.Solution: ‘True’, one-dimensional numerical list. For example,[1,5,8,6,3]. ‘False’, two-dimensional lists, strings, and one-dimensional non-numerical lists.For example, [[3, 6], [7, 2, 9, 5]] , ‘5’ , [3, 6, 7, ‘2’, 9, 5] . If’chack_A’ is’True’, it means that ‘listA’ does not need to be changed; if it is’False’, the input ‘listA’ is changed to a one-dimensional list of numerical data.ListB has the same judgment and processing method as listA.TypeError: object of type ‘NoneType’ has no len()Solution: ‘listA’ or ‘listB’ is None. Check that the input list is a one-dimensional list, for example, [1,5,8,6,3].
-
Math.Statistics.
normalized_mean_squard_error
(target_list, predict_list) → float¶ Regression indicators for numerical prediction. Calculate the normalized mean square error (NRMSE、NRMSD) of two lists of one-dimensional data.
NRMSE is a statistical value, and its method is to normalize RMSE. The normalization method used by this function is that the error between the maximum and minimum is used as the denominator, and RMSE is used as the numerator.
The value is between 0 and 1. The closer the NRMSE is to 0, the smaller the error between the two and the closer the model predicted value to the target value.
- Parameters
target_list (list ,ndarray, pandas.Series and pandas.DataFrame. One-dimensional numerical list.) –
predict_list (list ,ndarray, pandas.Series and pandas.DataFrame. One-dimensional numerical list.) –
- Returns
- Return type
float. The normalized mean square error of two lists of one-dimensional data.
Error
ValueError: The two lists must be the same length.Solution: Make sure that the number of numerical data in the two lists is the same (the same length).ValueError: List content must be one-dimensional numerical data: listA=”,chack_A, “; listB=”, chack_B.Solution: ‘True’, one-dimensional numerical list. For example,[1,5,8,6,3]. ‘False’, two-dimensional lists, strings, and one-dimensional non-numerical lists.For example, [[3, 6], [7, 2, 9, 5]] , ‘5’ , [3, 6, 7, ‘2’, 9, 5] . If’chack_A’ is’True’, it means that ‘listA’ does not need to be changed; if it is’False’, the input ‘listA’ is changed to a one-dimensional list of numerical data.ListB has the same judgment and processing method as listA.TypeError: object of type ‘NoneType’ has no len()Solution: ‘listA’ or ‘listB’ is None. Check that the input list is a one-dimensional list, for example, [1,5,8,6,3].References
Normalized mean square error (NRMSD) wiki: https://en.wikipedia.org/wiki/Root-mean-square_deviation
Normalized mean square error (NRMSE) function: https://www.marinedatascience.co/blog/2019/01/07/normalizing-the-rmse/
-
Math.Statistics.
plus
(listA, listB) → list¶ The two lists are added element by element.
Addition: The two lists are added element by element, and return a list of sums.
- Parameters
listA (list ,ndarray, pandas.Series and pandas.DataFrame. One-dimensional numerical list.) –
listB (list ,ndarray, pandas.Series and pandas.DataFrame. One-dimensional numerical list.) –
- Returns
- Return type
list. The result of adding the two lists.
Error
ValueError: The two lists must be the same length.Solution: Make sure that the number of numerical data in the two lists is the same (the same length).ValueError: List content must be one-dimensional numerical data: listA=”,chack_A, “; listB=”, chack_B.Solution: ‘True’, one-dimensional numerical list. For example,[1,5,8,6,3]. ‘False’, two-dimensional lists, strings, and one-dimensional non-numerical lists.For example, [[3, 6], [7, 2, 9, 5]] , ‘5’ , [3, 6, 7, ‘2’, 9, 5] . If’chack_A’ is’True’, it means that ‘listA’ does not need to be changed; if it is’False’, the input’listA’ is changed to a one-dimensional list of numerical data.ListB has the same judgment and processing method as listA.TypeError: object of type ‘NoneType’ has no len()Solution: ‘listA’ or ‘listB’ is None. Check that the input list is a one-dimensional list, for example, [1,5,8,6,3].
-
Math.Statistics.
root_mean_squard_error
(target_list, predict_list) → float¶ Regression indicators for numerical prediction. Calculate the root mean square error (RMSE) of two lists of one-dimensional data.
Root mean square error can measure the error between actual observations and predicted values. The calculation method is the square root of MSE.
- Parameters
target_list (list ,ndarray, pandas.Series and pandas.DataFrame. One-dimensional numerical list.) –
predict_list (list ,ndarray, pandas.Series and pandas.DataFrame. One-dimensional numerical list.) –
- Returns
- Return type
float. The root mean square error of two lists of one-dimensional data.
Error
ValueError: The two lists must be the same length.Solution: Make sure that the number of numerical data in the two lists is the same (the same length).ValueError: List content must be one-dimensional numerical data: listA=”,chack_A, “; listB=”, chack_B.Solution: ‘True’, one-dimensional numerical list. For example,[1,5,8,6,3]. ‘False’, two-dimensional lists, strings, and one-dimensional non-numerical lists.For example, [[3, 6], [7, 2, 9, 5]] , ‘5’ , [3, 6, 7, ‘2’, 9, 5] . If’chack_A’ is’True’, it means that ‘listA’ does not need to be changed; if it is’False’, the input ‘listA’ is changed to a one-dimensional list of numerical data.ListB has the same judgment and processing method as listA.TypeError: object of type ‘NoneType’ has no len()Solution: ‘listA’ or ‘listB’ is None. Check that the input list is a one-dimensional list, for example, [1,5,8,6,3].References
Root mean square error(RMSE) wiki: https://en.wikipedia.org/wiki/Root-mean-square_deviation
-
Math.Statistics.
round_list
(listA, ndigits: int) → list¶ Take all the values in the list to a few digits after the decimal point.
- Parameters
listA (list ,ndarray, pandas.Series and pandas.DataFrame. One-dimensional numerical list.) –
ndigits (int. A few digits after the decimal point.) –
- Returns
- Return type
list. Return a list of values with a few digits after the decimal point.
Error
ValueError: The list must be one-dimensional numerical data, and there is at least one numerical data in it.Solution: The entered’listA’ is an empty list. Please change to a one-dimensional list of numerical data.ValueError: The list must be one-dimensional numerical data.Solution: ‘listA’ contains a string or is two-dimensional list. Check that the input list is a one-dimensional list, for example, [1,5,8,6,3].TypeError: object of type ‘NoneType’ has no len()Solution: ‘listA’ is None. Check that the input list is a one-dimensional list, for example, [1,5,8,6,3].
-
Math.Statistics.
standard_deviation
(listA) → float¶ Calculate the standard deviation of all elements in a list.
Standard deviation: Square root of variance. All the numerical data in the list are subtracted one by one from the mean value and then squared. Algorithm: After adding them up, divide by the length of the list minus one, and finally take the square root.
- Parameters
listA (list ,ndarray, pandas.Series and pandas.DataFrame. One-dimensional numerical list.) –
- Returns
- Return type
float. The standard deviation of all elements in a list.
Error
ValueError: The list must be one-dimensional numerical data, and there is at least one numerical data in it.Solution: The entered’listA’ is an empty list. Please change to a one-dimensional list of numerical data.ValueError: The list must be one-dimensional numerical data.Solution: ‘listA’ contains a string or is two-dimensional list. Check that the input list is a one-dimensional list, for example, [1,5,8,6,3].TypeError: object of type ‘NoneType’ has no len()Solution: ‘listA’ is None. Check that the input list is a one-dimensional list, for example, [1,5,8,6,3].References
Standard deviation: https://en.wikipedia.org/wiki/Standard_deviation
-
Math.Statistics.
sub
(listA, listB) → list¶ Subtract two lists element by element.
Subtraction: Subtract the elements of two lists pair by pair, and return a list of results.
- Parameters
listA (list ,ndarray, pandas.Series and pandas.DataFrame. One-dimensional numerical list.) –
listB (list ,ndarray, pandas.Series and pandas.DataFrame. One-dimensional numerical list.) –
- Returns
- Return type
list. The result of subtracting the two lists.
Error
ValueError: The two lists must be the same length.Solution: Make sure that the number of numerical data in the two lists is the same (the same length).ValueError: List content must be one-dimensional numerical data: listA=”,chack_A, “; listB=”, chack_B.Solution: ‘True’, one-dimensional numerical list. For example,[1,5,8,6,3]. ‘False’, two-dimensional lists, strings, and one-dimensional non-numerical lists.For example, [[3, 6], [7, 2, 9, 5]] , ‘5’ , [3, 6, 7, ‘2’, 9, 5] . If’chack_A’ is’True’, it means that ‘listA’ does not need to be changed; if it is’False’, the input ‘listA’ is changed to a one-dimensional list of numerical data.ListB has the same judgment and processing method as listA.TypeError: object of type ‘NoneType’ has no len()Solution: ‘listA’ or ‘listB’ is None. Check that the input list is a one-dimensional list, for example, [1,5,8,6,3].
-
Math.Statistics.
variance
(listA) → float¶ Calculate the variance of all elements in a list.
Variance:The average of the squared distance of each number from its mean. Algorithm: All the numerical data in the list are subtracted one by one from the mean value and then squared, after adding them up and dividing by the length of the list minus one.
- Parameters
listA (list ,ndarray, pandas.Series and pandas.DataFrame. One-dimensional numerical list.) –
- Returns
- Return type
float. The variance of all elements in a list.
Error
ValueError: The list must be one-dimensional numerical data, and there is at least one numerical data in it.Solution: The entered’listA’ is an empty list. Please change to a one-dimensional list of numerical data.ValueError: The list must be one-dimensional numerical data.Solution: ‘listA’ contains a string or is two-dimensional list. Check that the input list is a one-dimensional list, for example, [1,5,8,6,3].TypeError: object of type ‘NoneType’ has no len()Solution: ‘listA’ is None. Check that the input list is a one-dimensional list, for example, [1,5,8,6,3].References
Variance: https://en.wikipedia.org/wiki/Variance


So, why did this matter?
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Illinois languished without a budget for
So, why did this matter?
Don't just read — here are some ways you can act
Contact Illinois politicians and tell them that Illinois needs a budget ASAP.
Call Gov Rauner's office at<PHONE_NUMBER>, House Speaker Madigan at<PHONE_NUMBER>, or look up your state reps.
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--- layout: post title: Apprentice in software date: 2010-07-14 08:25:28.000000000 +02:00 type: post published: true status: publish categories: - Ideas tags: [] meta: _edit_last: '2' _wp_old_slug: '' dsq_thread_id: '3772314349' _jetpack_related_posts_cache: a:0:{} author: login: admin email<EMAIL_ADDRESS>display_name: gerbrand first_name: Gerbrand last_name: van Dieijen ---
When you want to become a pilot, you'll first have to be a co-pilot (after your training). You'll won't become a medical professional without working with an experienced doctor first. As software engineering is a profession that also combines deep knowledge, analytical skills and experience as well, why would this be any difference?
My company, Xebia, has started a master-apprentice program in the Netherlands. See Apprenticeship Program for more information.


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