SIGNS OF LIFE A film review by Jeff Meyer Copyright 1989 Jeff Meyer
[As some of you might know, the Seattle Film Festival finished about five months ago. Well, uh... I've been busy... -jm]
[Seen at the Seattle International Film Festival]
SIGNS OF LIFE (USA, 1989) Director: John David Coles Screenwriter: Mark Malone Cast: Arthur Kennedy, Kevin J. O'Conner, Vincent Philip D'Onofrio, Michael Lewis, Beau Bridges, Kate Reid, Mary Louise Parker
This is another American Playhouse production that has an almost trademarked style to it; I sometimes wonder if a film funded by the AP underwriters is required to have a setting in small-town or rural America. Its scope isn't exceptional: it examines the effects of closing a small boat-building firm in New England on its workers and their families and friends, and it takes the "weekend-in-the-life" approach of studying its characters in almost separate storylines. One of the workers wants to go to Florida to become a diver, but is hindered by having to care for his retarded brother; another wants to follow his friend on his diving expedition, but doesn't know how (or whether) to break up with his long-standing romantic relationship. The elderly owner of the company is desperately trying to drum up business for another boat in the town; meanwhile, he's having hallucinations of his dead father visiting him, trying to tell him something ... or are they hallucinations? And finally, Beau Bridges plays the company foreman, who is desperately trying to find work in the town to support his wife and >5 children.
The film takes a St. Elsewhere approach to its material: it's often funny and whimsical, but it seems as if disaster could be around the corner. The film is emotionally satisfying; it isn't hugely imaginative in its conclusion, but it didn't telegraph the ending at all, and worked nicely. The actors all give good performances, and carry off their roles well. (Several of them were also in MYSTIC PIZZA, and the almost-identical settings tends to merge the two in my memory.) The only exception is Bridges; his performance, and indeed his entire sub-plot, seems to have almost been scotch-taped on at the last minute.
Solid, enjoyable show; since it's an American Playhouse production, you'll probably be able to see it on PBS sometime next year.
Moriarty, aka Jeff Meyer INTERNET<EMAIL_ADDRESS>Manual UUCP: {uw-beaver, sun, microsoft, hplsla, uiucuxc}!fluke!moriarty .
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Cora, 6yo: "Dad this chili you warmed up for lunch..." Me: "Yes?" Cora: "It's chilly." Big grin... #soproud #dadjokesliveon
2/14/2015, 1:09:43 PM
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link--- layout: tweet ---
Cora, 6yo: "Dad this chili you warmed up for lunch..." Me: "Yes?" Cora: "It's chilly." Big grin... #soproud #dadjokesliveon
2/14/2015, 1:09:43 PM
Favs: 7
Retweets: 0
link


About Me
Hello my name is Rizkika, and I am 20 years old. I am from Jakarta, Indonesia and I my family moved to the states when I was 9 years old. I am now a student at Year Up studying Software Development and Testing. My professional background is mainly in the hospitality industry, and I am planning to grow more professionally in the tech industry. I also have a transfer Associate degree in Arts & Science and I plan to continue my degree after my internship with Year Up.
A few examples of technical skills I have learned:
If you have any questions or are interested in working with me, please connect with me on linkedIn and send me a message!


CSS grid container and items. Each box takes up calc(100 / 3) of screen
Box 10
Yada Yada Yada
Box 11
Yada Yada Yada
Box 12
Yada Yada Yada
Yada Yada Yada
Yada Yada Yada
Yada Yada Yada
Yada Yada Yada
Huh
Yada Yada Yada
Huh
Yada Yada Yada
Huh
Yada Yada Yada
Huh
Yada Yada Yada
Huh
Yada Yada Yada
Huh
Yada Yada Yada
Huh
Yada Yada Yada
Huh
Yada Yada Yada
Huh
Yada Yada Yada
Huh
Yada Yada Yada
Huh
Yada Yada Yada
Huh
Yada Yada Yada
Huh
Yada Yada Yada
Huh
We're the Loveliest Village Around
We promise a rainbow every day! No, really! We also have a huge variety of wild animals - buffalo, deer, chipmunks. And you get to drive around and look at them! You can also observe people doing things like getting out of their car to talk to the buffalo - which will make you feel infinitely smarter than they are. And might get you a viral video on Twitter!


Modal dialogs, or pop-up windows, are handy for prototyping and production. Streamlined but flexible dialog prompts with the minimum required functionality and smart defaults.
New to version 1.0.4: Modal animations (fade-in and slide down) were added by default.
NOTE: Reveal is the equivalent to Bootstrap Modals and uses the ModalModule
. See proper usage below.
Open a modal from service.
To be able to open modals from service, inject BsModalService to your constructor. Then, call .show() method of modal service. Pass a TemplateRef or a component as a first argument and config as a second (optionally).
.show() method returns an instance of BsModalRef class with .hide() method and content property where you'll find a component which you've passed to service.
Your couch. It is mine.
I'm a paragraph that lives inside of a modal.
Creating a modal as a service helps to keep your modals consistent across your app. Create a single unified structure then dynamically assign your content for use in your component(s). Follow the step-by-step directions below.
Nested modals are supported with ngx-foundation.
Modal #1 Yes/No Button Confirmation:
{{message}}
Yeah, first modal paragraph here.
This is a nested modal.
On small screens, a modal is always 100% of the width of the screen. On medium-sized screens and larger, the width changes to 600px (see the $reveal-width
setting).
The size of a modal can be changed with these sizing classes, which are added to the modal container:
.tiny
: 30% wide.small
: 50% wide.large
: 90% wide.full
: 100% width and heightRings of Uranus the sky calls to us science colonies extraordinary claims require extraordinary evidence from which we spring. Globular star cluster dream of the mind's eye with pretty stories for which there's little good evidence at the edge of forever made in the interiors of collapsing stars globular star cluster and billions upon billions upon billions upon billions upon billions upon billions upon billions.
Modal with opportunity to confirm
or decline
.
Do you want to confirm?


0.11.0
Tutorials
Gallery
Manual
API
FAQ
Cite
Site
Getting Started
What’s new
Cite MNE
Related publications
Tutorials
Examples Gallery
Manual
API Reference
Frequently Asked Questions
Advanced installation and setup
MNE with CPP
Page
Tutorials
Getting started
Working with MNE data structures
Preprocessing
Source localization
Statistics
Sensor space
Source space
Visualization and Reporting
Command line tools
Tutorials
Getting started
Working with MNE data structures
Preprocessing
Source localization
Statistics
Sensor space
Source space
Visualization and Reporting
Command line tools
« Related publi...
Basic MEG and... »
Tutorials
¶
Getting started
¶
Basic MEG and EEG data processing
Working with MNE data structures
¶
Creating MNE-Python’s data structures from scratch
The
Info
data structure
The
Raw
data structure: continuous data
The
Epochs
data structure: epoched data
Export epochs to Pandas DataFrame
Preprocessing
¶
Compute ICA on MEG data and remove artifacts
Source localization
¶
Basics of source localization
Statistics
¶
Sensor space
¶
Permutation t-test on toy data with spatial clustering
Spatiotemporal permutation F-test on full sensor data
Non-parametric 1 sample cluster statistic on single trial power
Non-parametric between conditions cluster statistic on single trial power
Source space
¶
Mass-univariate twoway repeated measures ANOVA on single trial power
2 samples permutation test on source data with spatio-temporal clustering
Repeated measures ANOVA on source data with spatio-temporal clustering
Permutation t-test on source data with spatio-temporal clustering
Visualization and Reporting
¶
Getting started with MNE report command
Command line tools
¶
Getting started with MNE command line


| Class and Description |
|---|
| SortBuilder |
| Class and Description |
|---|
| SortBuilder |
| SortOrder
A sorting order.
|
| Class and Description |
|---|
| SortBuilder |
| SortOrder
A sorting order.
|
| SortParseElement |
| Class and Description |
|---|
| SortBuilder |
| SortOrder
A sorting order.
|
| SortParseElement |
| Class and Description |
|---|
| SortBuilder |
| SortOrder
A sorting order.
|
| Class and Description |
|---|
| SortParseElement |
| Class and Description |
|---|
| FieldSortBuilder
A sort builder to sort based on a document field.
|
| GeoDistanceSortBuilder
A geo distance based sorting on a geo point like field.
|
| ScoreSortBuilder
A sort builder allowing to sort by score.
|
| ScriptSortBuilder
Script sort builder allows to sort based on a custom script expression.
|
| SortBuilder |
| SortOrder
A sorting order.
|
| SortParser |
Copyright © 2009–2016. All rights reserved.


Hi, I’m Ehsanul Hoque! I’m a software engineer and technology enthusiast living in Dhaka, Bangladesh.
I work mostly in TensorFlow , Python, C/C++ and Java.
My main interest is in server-side technology and massive scalability also I’ve been worked with other platforms such as Android, IoT Devices, etc.
Check out my GitHub page to see what I’ve been up to recently!
Contact
If you’d like to get in touch, you can find me on LinkedIn or reach me at contact@gamerrishad.com


Primo Charas
Brands
Dispensaries
Industry Stocks
Health & Safety
Doctors
Top Stories
CBD Extracted
Hemp vs marijuana production
... read more
Brand
Dispensary
Safety
Doctor
CIIX - Chineseinvestors.com, Inc.
closed at $0.645 on the first Friday of the last quarter of 2018. It has a target price of $3.75.
?
Symptoms:
Crohn's disease
nausea
anorexia
appetite loss
skin care
Head
Arm-Left
Torso
Arm-Right
Leg-Left
Leg-Right


SDK
Every project uses a Software Development Kit (SDK). For Java projects, the SDK is referred to as the JDK (Java Development Kit). The SDK determines which API library is used to build the project. If your project is multi-module, the project SDK by default is common for all modules within the project. Optionally, you can configure individual SDKs for each module. For more information about SDKs, see SDK and Configuring Global, Project and Module SDKs in the IntelliJ IDEA Web Help.
Getting project SDK information
Main information about the project SDK can be accessed via ProjectRootManager.java like the following example shows
String projectSdk = ProjectRootManager.getInstance(project).getProjectSdk();
Getting and setting project SDK attributes
-
To get the project level SDK
Sdk projectSDK = ProjectRootManager.getInstance(project).getProjectSdk();
-
To get the project level SDK name:
String projectSDKName = ProjectRootManager.getInstance(project).getProjectSdkName();
-
To set the project level SDK:
ProjectRootManager.getInstance(project).setProjectSdk(Sdk jdk);
-
To set the project level SDK name:
ProjectRootManager.getInstance(project).setProjectSdkName(String name);
See the following code sample to get more familiar with SDK manipulation tool set.


--- layout: page title: About header-img: "img/about-bg.png" ---
This blog will guide you through my journey through learning to code. Brace yourself :)


Day 4
Today’s Progress
I have spent some time learning the basics of async, call stack and event loop.
I have started to make a weather app using the Google Maps API, and I have learned how to beautify JSON callbacks (see below).
Thoughts
async and call stack are quite intimidating at the moment and whilst it does make sense, I don’t think I understand it enough. I’m really excited to progress with the weather app and use the Google Maps API.
Code Snippets
Pretty print JSON callbacks
This works by adding spaces (2 in this case) to the string
request({
url: 'https://maps.googleapis.com/maps/api/geocode/json?address=1301%20lombard%20street%20philadelphia',
json: true
}, (error, response, body) => {
console.log(JSON.stringify(body, undefined, 2));
});
Written on April 24, 2018


Rangers 2-0 Porto: Steven Gerrard's men move closer to Europa League last 32
by Scott MullenTwo goals in four second-half minutes earned Rangers a rousing victory over Porto to surge closer to the Europa League knockout stages.
Little separated Steven Gerrard's men and last year's Champions League quarter-finalists before Alfredo Morelos and Steven Davis crashed in strikes at a raucous Ibrox.
The result means Rangers sit joint top of Group G along side Young Boys of Switzerland with two games remaining, while Porto and Feyenoord are three points behind.
It was yet another showing of strength in Europe under Gerrard, who has only lost three times - and never at Ibrox - across 26 matches in continental competition.
Rangers, who drew 1-1 with Porto two weeks ago, now go to Rotterdam on 28 November before welcoming Young Boys to Ibrox two weeks later in their final group game.
Rangers deliver another European message
Sitting contently at the top table in the Estadio do Dragao media room two weeks earlier, the Rangers manager beamed about what he had just witnessed from his team.
Fielding questions from the assembled journalists, Gerrard could not hide his pride after Morelos' equaliser had cancelled out Luis Diaz's opener to keep Rangers in the hunt in this group. "I think that was our best performance in Europe for large parts," he said. "I'm not going to get carried away but that is a big point."
While this showing, particularly in the first half, may not have been Rangers' best, the devastation wreaked on one of Europe's big names in a ruthless second half may well be causing some of the club's support to be reaching for the 'let's get carried away' button.
Not for the first time, Rangers faced a daunting task under a dark sky in Govan, only to send a household name away beleaguered and bemused into the chilled night. Spartak Moscow. Villarreal. Legia Warsaw. Feyenoord. And now Porto. All have come to Glasgow, all sent away again without even scoring a goal.
Plenty has been made of the European ties across the city at Celtic Park. And rightly so. Yet there is something fascinating about watching Rangers on these evenings. For a group largely untested at this level prior to last year, there is a swagger and courage to their play.
Against Sergio Conceicao's men, they survived an early scare to at least make the first half competitive, before fearlessly dictating the play once they had their noses in front.
Gerrard stuck with the same team which caused Porto so many problems in Portugal, although they were forced to ride their luck in the opening stages, Glen Kamara showing his acrobatic side to clear Pepe's glanced effort off the goal line. Conor Goldson would smash a ball clear in the second half, as well.
At the other end, Rangers struggled to show composure in the final third, a mixture of shots and crosses nestling somewhere in the Broomloan Road end. But after the break, flickering moments hinted at a spark coming with Pepe going off early for the visitors.
Ryan Kent was proving to be a nuisance. Past one player he turned. Then another. Then came the shot from the edge of the area. The save by Agustin Marchesin triggered a corner and rumble of hope in the stands.
And then the moment came. Courageous play from James Tavernier on the right led to the ball being nudged to Ryan Jack. Rangers' man of the moment did not fail, his cross from the right finding its target. Morelos allowed it to run across his body at the edge of the area with the merest of first touches.
As Ibrox held its breath, his left foot met the ball on the half volley just before it floated out of reach to arrow a shot across Marchesin and in. Ibrox erupted. Morelos roared back in a primal outpouring of emotion.
If one nearly lifted the roof, the second moments later almost launched it into orbit.
Morelos was the provider, this time out on the left. A pinpoint cutback was gathered 20 yards out by Davis. The Northern Irishman launched an effort on target, but the telling nick off of Ivan Marcano - the man who was rolled for the first - sent the ball careering by the stranded Porto goalkeeper.
There was nowhere for Porto to go. No way of Rangers letting this one slip. Another big European night at Ibrox. Now on to Feyenoord, and another foreign experience for Gerrard to potentially savour.
Man of the match - Alfredo Morelos
It seems like an easy option, but with a goal and an assist, there is only one winner.
There was plenty industry in the middle of the park, particularly from Davis and Jack, with the latter continuing to embrace life at Ibrox this season.
Again, though, it was Morelos' intervention that turned this game on its head. He earned Rangers a point in Portugal, and it was his clinical ability to create space and guide his volley at goal that set the teams apart in Glasgow.
That's 11 goals in Europe this season. While Gerrard says he's not for sale, Rangers' resolve may well be tested soon enough.
Match stats
- Rangers have lost just one of their last 12 games in Europe, winning eight and drawing three
- FC Porto are winless in their last eight Europa League away games (D2 L6), having won each of their previous six on the road before that
- Rangers have registered seven clean sheets in their last eight home games in Europe, shipping just one goal in that period
- Morelos is the first player to score in three consecutive appearances in major European competition for Rangers since Nacho Novo in the 2006-07 Uefa Cup
- Davis netted his first goal in competitive club football since scoring for Southampton against Everton in the Premier League in November 2017 - 711 days ago.
Line-ups
Rangers
- 1McGregor
- 2Tavernier
- 6Goldson
- 5Helander
- 31BarisicBooked at 31mins
- 8Jack
- 10Davis
- 18Kamara
- 21BarkerSubstituted forArfieldat 65'minutes
- 20MorelosSubstituted forDefoeat 85'minutes
- 14KentSubstituted forAriboat 83'minutes
Substitutes
- 4Edmundson
- 9Defoe
- 11Ojo
- 13Foderingham
- 15Flanagan
- 17Aribo
- 37Arfield
FC Porto
- 32Marchesín
- 18Manafá Jancó
- 19Mbemba
- 3PepeSubstituted forDíazat 49'minutesBooked at 90mins
- 5Marcano
- 13Telles
- 25OtávioBooked at 39minsSubstituted forSoares Silvaat 74'minutes
- 22DaniloBooked at 37mins
- 16Uribe
- 17JM Corona
- 29Francisco SoaresSubstituted forMendes Andradeat 64'minutes
Substitutes
- 6Almeida Costa
- 7Díaz
- 10Nakajima
- 20Mendes Andrade
- 24Saravia
- 31Meireles Costa
- 49Soares Silva
Referee:
Davide Massa
Match Stats
Home TeamRangersAway TeamFC Porto
Possession
Home51%
Away49%
Shots
Home8
Away9
Shots on Target
Home3
Away1
Corners
Home6
Away6
Fouls
Home12
Away17
Live Text
Posted at
Match ends, Rangers 2, FC Porto 0.
Full Time
Posted at 90'+5'
Second Half ends, Rangers 2, FC Porto 0.
Posted at 90'+3'
Offside, Rangers. Borna Barisic tries a through ball, but Jermain Defoe is caught offside.
Booking
Posted at 90'+3'
Luis Díaz (FC Porto) is shown the yellow card for a bad foul.
Posted at 90'+3'
Scott Arfield (Rangers) wins a free kick in the defensive half.
Posted at 90'+3'
Foul by Luis Díaz (FC Porto).
Posted at 90'+2'
Foul by Steven Davis (Rangers).
Posted at 90'+2'
Iván Marcano (FC Porto) wins a free kick on the right wing.
Posted at 90'+1'
Joe Aribo (Rangers) wins a free kick on the right wing.
Posted at 90'+1'
Foul by Luis Díaz (FC Porto).
Posted at 90'
Glen Kamara (Rangers) wins a free kick in the attacking half.
Posted at 90'
Foul by Luis Díaz (FC Porto).
Posted at 89'
Attempt missed. Zé Luís (FC Porto) header from the centre of the box misses to the right. Assisted by Alex Telles with a cross following a corner.
Posted at 88'
Corner, FC Porto. Conceded by Connor Goldson.
Posted at 88'
Corner, FC Porto. Conceded by James Tavernier.
Substitution
Posted at 85'
Substitution, Rangers. Jermain Defoe replaces Alfredo Morelos.
Posted at 85'
Attempt missed. Zé Luís (FC Porto) header from the centre of the box misses to the left. Assisted by Corona with a cross.
Posted at 85'
Foul by Connor Goldson (Rangers).
Posted at 85'
Luis Díaz (FC Porto) wins a free kick in the defensive half.
Posted at 83'
James Tavernier (Rangers) wins a free kick in the defensive half.
Posted at 83'
Foul by Zé Luís (FC Porto).
Substitution
Posted at 83'
Substitution, Rangers. Joe Aribo replaces Ryan Kent.
Posted at 81'
Alfredo Morelos (Rangers) wins a free kick in the defensive half.
Posted at 81'
Foul by Alex Telles (FC Porto).
Posted at 80'
Attempt blocked. Wilson Manafá (FC Porto) right footed shot from the left side of the box is blocked. Assisted by Mateus Uribe.
Posted at 75'
Alfredo Morelos (Rangers) wins a free kick in the defensive half.
Posted at 75'
Foul by Danilo Pereira (FC Porto).
Substitution
Posted at 74'
Substitution, FC Porto. Fabio Silva replaces Otávio.
Goal!
Posted at 73'
Goal! Rangers 2, FC Porto 0. Steven Davis (Rangers) right footed shot from outside the box to the centre of the goal. Assisted by Alfredo Morelos.
Posted at 71'
Ryan Kent (Rangers) wins a free kick in the attacking half.
Posted at 71'
Foul by Danilo Pereira (FC Porto).
Goal!
Posted at 69'
Goal! Rangers 1, FC Porto 0. Alfredo Morelos (Rangers) left footed shot from the centre of the box to the bottom left corner. Assisted by Ryan Jack with a cross.
Posted at 68'
Alfredo Morelos (Rangers) wins a free kick in the defensive half.
Posted at 68'
Foul by Otávio (FC Porto).
Posted at 68'
Attempt blocked. Iván Marcano (FC Porto) left footed shot from the left side of the six yard box is blocked. Assisted by Otávio.
Posted at 67'
Corner, FC Porto. Conceded by Connor Goldson.
Posted at 67'
Attempt missed. Wilson Manafá (FC Porto) left footed shot from the left side of the six yard box misses to the right. Assisted by Corona with a cross.
Substitution
Posted at 65'
Substitution, Rangers. Scott Arfield replaces Brandon Barker.
Posted at 64'
Attempt missed. Connor Goldson (Rangers) header from the centre of the box is close, but misses to the left. Assisted by Borna Barisic with a cross following a set piece situation.
Substitution
Posted at 64'
Substitution, FC Porto. Zé Luís replaces Tiquinho Soares.
Show more updates
goal


Uses of Interface
org.apache.qpid.protonj2.client.DeliveryState
-
Packages that use DeliveryState Package Description org.apache.qpid.protonj2.client org.apache.qpid.protonj2.client.exceptions org.apache.qpid.protonj2.client.impl -
-
Uses of DeliveryState in org.apache.qpid.protonj2.client
Methods in org.apache.qpid.protonj2.client that return DeliveryState Modifier and Type Method Description static DeliveryState
DeliveryState. accepted()
DeliveryState
Source. defaultOutcome()
DeliveryState
SourceOptions. defaultOutcome()
static DeliveryState
DeliveryState. modified(boolean failed, boolean undeliverable)
Create a new Modified outcome with the given failure state.static DeliveryState
DeliveryState. modified(boolean failed, boolean undeliverable, java.util.Map<java.lang.String,java.lang.Object> annotations)
Create a new Modified outcome with the given failure state.static DeliveryState
DeliveryState. rejected(java.lang.String condition, java.lang.String description)
Create a new Rejected outcome with the given condition and description.static DeliveryState
DeliveryState. rejected(java.lang.String condition, java.lang.String description, java.util.Map<java.lang.String,java.lang.Object> info)
Create a new Rejected outcome with the given condition and description.static DeliveryState
DeliveryState. released()
DeliveryState
Delivery. remoteState()
Gets the current remote state for the delivery.DeliveryState
Tracker. remoteState()
Gets the current remote state for the tracked delivery.DeliveryState
Delivery. state()
Gets the current local state for the delivery.DeliveryState
Tracker. state()
Gets the current local state for the tracked delivery.Methods in org.apache.qpid.protonj2.client with parameters of type DeliveryState Modifier and Type Method Description SourceOptions
SourceOptions. defaultOutcome(DeliveryState defaultOutcome)
Delivery
Delivery. disposition(DeliveryState state, boolean settle)
Updates the DeliveryState, and optionally settle the delivery as well.StreamDelivery
StreamDelivery. disposition(DeliveryState state, boolean settle)
Updates the DeliveryState, and optionally settle the delivery as well.StreamTracker
StreamTracker. disposition(DeliveryState state, boolean settle)
Updates the DeliveryState, and optionally settle the delivery as well.Tracker
Tracker. disposition(DeliveryState state, boolean settle)
Updates the DeliveryState, and optionally settle the delivery as well. -
Uses of DeliveryState in org.apache.qpid.protonj2.client.exceptions
Methods in org.apache.qpid.protonj2.client.exceptions that return DeliveryState Modifier and Type Method Description DeliveryState
ClientDeliveryStateException. getOutcome()
Constructors in org.apache.qpid.protonj2.client.exceptions with parameters of type DeliveryState Constructor Description ClientDeliveryStateException(java.lang.String message, java.lang.Throwable cause, DeliveryState outcome)
ClientDeliveryStateException(java.lang.String message, DeliveryState outcome)
-
Uses of DeliveryState in org.apache.qpid.protonj2.client.impl
Classes in org.apache.qpid.protonj2.client.impl that implement DeliveryState Modifier and Type Class Description class
ClientDeliveryState
Client internal implementation of a DeliveryState type.static class
ClientDeliveryState.ClientAccepted
Client definedAccepted
delivery state definitionstatic class
ClientDeliveryState.ClientModified
Client definedModified
delivery state definitionstatic class
ClientDeliveryState.ClientRejected
Client definedRejected
delivery state definitionstatic class
ClientDeliveryState.ClientReleased
Client definedReleased
delivery state definitionstatic class
ClientDeliveryState.ClientTransactional
Client definedTransactionalState
delivery state definitionMethods in org.apache.qpid.protonj2.client.impl that return DeliveryState Modifier and Type Method Description DeliveryState
ClientDelivery. remoteState()
DeliveryState
ClientNoOpTracker. remoteState()
DeliveryState
ClientStreamDelivery. remoteState()
DeliveryState
ClientDelivery. state()
DeliveryState
ClientNoOpTracker. state()
DeliveryState
ClientStreamDelivery. state()
Methods in org.apache.qpid.protonj2.client.impl with parameters of type DeliveryState Modifier and Type Method Description Delivery
ClientDelivery. disposition(DeliveryState state, boolean settle)
StreamTracker
ClientNoOpStreamTracker. disposition(DeliveryState state, boolean settle)
Tracker
ClientNoOpTracker. disposition(DeliveryState state, boolean settle)
StreamDelivery
ClientStreamDelivery. disposition(DeliveryState state, boolean settle)
StreamTracker
ClientStreamTracker. disposition(DeliveryState state, boolean settle)
-


 Training |
{% if user.passed_training %}
Training passed {% else %}Training not passed yet {% endif %} |
|---|---|
| 2. Homework |
{% if user.passed_swc_homework %}
SWC Homework accepted {% elif user.swc_homework_in_evaluation %}SWC Homework not evaluated yet Please wait while we evaluate your homework. {% else %}SWC Homework not submitted yet This step is necessary only if you want to become Software Carpentry Instructor. Submit link to your Pull Request: {% crispy swc_form %} {% endif %}{% if user.passed_dc_homework %} DC Homework accepted {% elif user.dc_homework_in_evaluation %}DC Homework not evaluated yet Please wait while we evaluate your homework. {% else %}DC Homework not submitted yet This step is necessary only if you want to become Data Carpentry Instructor. Submit link to your Homework: {% crispy dc_form %} {% endif %} |
| 3. Discussion Session |
{% if user.passed_discussion %}
Discussion Session passed {% else %}Discussion Session not passed yet Register for Discussion Session on this Etherpad. Register for only one session even if you want to become both SWC and DC Instructor. {% endif %} |
| 4. Demo Session |
{% if user.passed_swc_demo %}
SWC Demo Session passed {% else %}SWC Demo Session not passed yet {% endif %} {% if user.passed_dc_demo %}DC Demo Session passed {% else %}DC Demo Session not passed yet {% endif %} {% if not user.passed_swc_demo or not user.passed_dc_demo %}Register for Demo Session on this Etherpad. If you want to become both SWC and DC Instructor, you have to teach two 5-minutes sessions during one or two sessions. {% endif %} |
In the case of any questions, please send us email at team@carpentries.org


| 004:038 Khan | : |
And (also) those who spend of their substance to be seen of men, and believe not in Allah and the Last Day [they are the friends of Shaitan (Satan)], and whoever takes Shaitan (Satan) as an intimate; then what a dreadful intimate he has! |
| 004:038 Maulana | : |
And those who spend their wealth to be seen of men and believe not in Allah nor in the Last Day.  Whoso taketh Satan for a comrade, a bad comrade hath he. |
| 004:038 Rashad | : |
They give money to charity only to show off, while disbelieving in GOD and the Last Day. If one's companion is the devil, that is the worst companion. |
| 004:038 Sarwar | : |
those who spend their property out of a desire to show off and not because of their belief in God and the Day of Judgment, and (lastly) those who choose Satan for a friend; what an evil friend! |
| 004:038 Shakir | : |
And those who spend their property (in alms) to be seen of the people and do not believe in Allah nor in the last day; and as for him whose associate is the Shaitan, an evil associate is he! |
| 004:038 Sherali | : |
And as for those who spend their wealth in order to be seen of men, and believe not in ALLAH, nor the Last Day, they are the companions of Satan, an whoso has Satan for a companion, and evil companion is he. |
| 004:038 Yusufali | : |
Not those who spend of their substance, to be seen of men, but have no faith in Allah and the Last Day: If any take the Evil One for their intimate, what a dreadful intimate he is! |
Concepts/Themes Covered: أركان الإسلام > التوحيد > الكافرون > صفات الكفار Pillars of Islam> Islamic> The Disbelievers> The Characteristics of the Disbelievers أركان الإسلام > الزكاة والصدقات Pillars of Islam> Due Alms and Charities الإيمان > الغيب > الشيطان > وسوسته Faith> The Unseen World> Satan, the Devil> Satanic Temptations الإنسان والعلاقات الأخلاقية > الأخلاق الذميمة > الرياء Man and The Moral Relations>The Dispraised Morals>Hypocrisy تنظيم العلاقات المالية > إنفاقها Organizing Financial Relationships>Its Expenditure تنظيم العلاقات المالية > الزكاة Organizing Financial Relationships>Due Alms
This tool uses eight English parallel translations for the Holy Quran imported from Mr. Clay Smith. You can also visit Qurany Concepts for browsing the Quran themes or topics. If you want to search the Holy Quran for keywords, please visit Qurany Keywords.


{% extends 'common_app/base.html' %} {% load crispy_forms_filters %} {% block body_block %} {% if form.errors %}
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These Ranadu functions can be of use when constructing and modifying
data.frames:
- getNetCDF ( ): loads a data.frame with requested variables
- standardVariables ( ): defines a common set of variables
for input to getNetCDF(). This list is the default if no variables
are specified in the call to getNetCDF. The optional argument is a
list of character names that specify additional variables to include.
- DataDirectory ( ): This is system dependent and is intended
to aid in portability by providing the usual location of the netCDF
data files. It tries a set of likely names, but may need redefinition
for some file systems.
- getIndex ( ): finds the index for a specified time
- setRange ( ): sets a range of indices covering a specified
time interval.
- selectTimes ( ): produces a new data.frame with a limited
time range.
In addition, some functions provided by the package “dplyr” can
be very useful when working with data.frames. These actions can be
implemented by normal R subsetting also (e.g., using “[[...]]”
notation), but using these dplyr functions serves to clarify what
the steps in the code are doing:
- dplyr::select ( ): produces a new data.frame with only the
variables listed as arguments in the call to the function.
- dplyr::filter ( ): produces a subset data.frame where the
argument is a logical test applied to the data.frame rows that must
be met for the row to be included in the returned data.frame.
- dplyr::mutate ( ): adds new variables to the data.frame.


In the history of modern astronomy, there is probably no one greater leap forward building and launch.
In the history of modern astronomy, there is probably no one greater leap forward building and launch.
In the history of modern astronomy, there is probably no one greater leap forward building and launch.


Authorization
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FROM: MRS, FATIMA RASHEED KHALIFA ADDRESS:NORTH ZONE BAZRA IRAQ.) Please permit me your little endulgence to solicit for your unreserved attention, based on my urgly condition of life though one with almighty Allah is with majority. I am Mrs Fatima R. Khalifa, a widow to Late Sheik Mohammed Rasheed Khalifa. I am 64 years old of a born muslim faith. Presently i am suffering from long time cancer of the breast, from all indications, my condition is really deteriorating and is quite obvious that I won't live more than six months according to my doctor,because the cancer stage has gotten to a very bad stage. My late husband was killed during the invasion of collition forces from American and Britain in Iraq, unfortunately he died leaving behind a huge sum of money which he had looted from a supply of ammunition to the ex- Saddam Hussein Government. This money is now in the vault of a private security company oversea to be precise in Holland, but due to my present bad condition of health as I am suffering from Cancer of the breast its quite impossible for me to travel that distance to receive this money. To this effect I have writen a letter throgh my family lawyer requesting for the transfer of this box to Dubai United Arab Emirates pending when I can succeed in getting a trust and reliable person to utilize this money. Although, today, I have received a signal from the security company notifying me of the approval to transfer the consignment through their agent. Therefore, let me receive your total support and security assurance that the consignment will be save in your hand . I want you to understand that a neither have any son nor have any person to entrust this money to which I know that it is better make use of a total foreigner to achieve my aim. Atleast had it been that during the period of our marriage we could produce any child, my late husband was very wealthy and after his death, his offspring will inherited all his business and wealth. I got your contact through a desperate and personal search on the web, for someone I could really trust and I have prayed over it. Therefore my desire now is to contribute part of this wealth to the propagation of islam and in assisting the less-privileged Muslim faithful in Africa,Asia,Europe and in all Arab-Nation's . I have discussed this with my Family Lawyer hence I am willing to donate part of it atleast 40% of the total sum of US$16,250,00000. (sixteen Million two hundred and fifty thousand US Dollars)for the development of the holy work of islam, as well as distribution's to aid muslim's faithfuls. Please I want you to note that, this fund is lying in a Security Company in Holland The Netherlands so it can be transferred to you upon your readiness to co-join with me in this transaction and secure the money. For that, my lawyer will file application's that will back you up for the transfer of the money in your name as the beneficiary only if you promise to use this funds/my donations judiciouly for the said purpose. Therefore,I have decided to set-aside 40% for the promotion of holy islam and the work of Allah. while 30% will be retuned back to me through my lawyer so that I can offset my bills in the hospital because of the cancer treatment.  May the Grace of Allah be our guide and be my refuge when i leave this world. Moreso,I further decided to offer you 20% While the remaining 10% is for the expenses incured during the facillitation of the project. Yours in Allah, Mrs Fatima Rasheed Khalifa


Climate Change Indicators: River Flooding
Key Points
- Floods have generally become larger in rivers and streams across large parts of the Northeast and Midwest. Flood magnitude has generally decreased in the West, southern Appalachia, and northern Michigan (see Figure 1).
- Large floods have become more frequent across the Northeast, Pacific Northwest, and northern Great Plains. Flood frequency has decreased in some other parts of the country, especially the Southwest and the Rockies (see Figure 2).
- Increases and decreases in frequency and magnitude of river flood events generally coincide with increases and decreases in the frequency of heavy rainfall events.2,3
Background
Rivers and streams experience flooding as a natural result of large rain storms or spring snowmelt that quickly drains into streams and rivers. Although the risk for flooding varies across the United States, most areas are susceptible to floods, even in dry and mountainous regions. The size, or magnitude, of flood events is influenced by how much water enters the waterway upstream—and how quickly. Flood frequency largely depends on the frequency of weather events.
Large flood events can damage homes, roads, bridges, and other infrastructure; wipe out farmers’ crops; and harm or displace people. Although regular flooding helps to maintain the nutrient balance of soils in the flood plain, larger or more frequent floods could disrupt ecosystems by displacing aquatic life, impairing water quality, and increasing soil erosion. By inundating water treatment systems with sediment and contaminants, and promoting the growth of harmful microbes, floods can directly affect the water supplies that communities depend on.
Climate change may cause river floods to become larger or more frequent than they used to be in some places, yet become smaller and less frequent in other places. As warmer temperatures cause more water to evaporate from the land and oceans, changes in the size and frequency of heavy precipitation events may in turn affect the size and frequency of river flooding (see the Heavy Precipitation indicator).1 Changes in streamflow, the timing of snowmelt (see the Streamflow indicator), and the amount of snowpack that accumulates in the winter (see the Snowpack indicator) can also affect flood patterns.
About the Indicator
The U.S. Geological Survey maintains thousands of stream gauges across the United States. Each gauge measures water level and discharge—the amount of water flowing past the gauge. This indicator uses total daily discharge data from about 500 long-term stream gauge stations where trends are not substantially influenced by dams, reservoir management, wastewater treatment facilities, or land-use change.
One way to determine whether the magnitude of flooding has changed is by studying the largest flood event from each year. This indicator examines the maximum discharge from every year at every station to identify whether peak flows have generally increased or decreased. This indicator also analyzes whether large flood events have become more or less frequent over time, based on daily discharge records.
This indicator starts in 1965 because flood data have been available for a large number of sites to support a national-level analysis since then.
Indicator Notes
This indicator is based on U.S. stream gauges that have recorded data consistently since 1965. Besides climate change, many other types of human influences could affect the frequency and magnitude of floods—for example, dams, floodwater management activities, agricultural practices, and changes in land use. To remove these influences, this indicator focuses on a set of sites that are not heavily influenced by human activities, in watersheds that do not have a large proportion of impervious surfaces such as concrete and asphalt. Increased flooding does not necessarily result in an increased risk to people or property if an area has protective infrastructure, such as levees or floodwalls.
Data Sources
Daily stream gauge data were collected by the U.S. Geological Survey. These data came from a set of gauges in watersheds with minimal human impacts, which have been classified as reference gauges.6 Daily discharge data are stored in the National Water Information System and are publicly available at: http://waterdata.usgs.gov/nwis.
Technical Documentation
References
1 Mallakpour, I., G. Villarini. 2015. The changing nature of flooding across the central United States. Nature Climate Change 5:250–254.
2 Mallakpour, I., G. Villarini. 2015. The changing nature of flooding across the central United States. Nature Climate Change 5:250–254.
3 Melillo, J.M., T.C. Richmond, and G.W. Yohe (eds.). 2014. Climate change impacts in the United States: The third National Climate Assessment. U.S. Global Change Research Program. http://nca2014.globalchange.gov.
4 Slater, L., and G. Villarini. 2016 update and expansion to data originally published in: Mallakpour, I., G. Villarini. 2015. The changing nature of flooding across the central United States. Nature Climate Change 5:250–254.
5 Slater, L., and G. Villarini. 2016 update and expansion to data originally published in: Mallakpour, I., G. Villarini. 2015. The changing nature of flooding across the central United States. Nature Climate Change 5:250–254.
6 Lins, H.F. 2012. USGS Hydro-Climatic Data Network 2009 (HCDN-2009). U.S. Geological Survey Fact Sheet 2012-3047.


Almost all modern online services run on geo-distributed data centers, and fault tolerance is one of the primary requirement that decides the revenue of the service provider. Recent experiences have shown that the failure of a data center (at a site) is inevitable. In order to mask the failure, spare compute capacity needs to be provisioned across the distributed data center, which leads to additional cost. While the existing literature addresses the capacity provisioning problem only to minimize the number of servers, we argue that the operating cost needs to be considered as well. Since the operating cost and client demand vary both across space and time, we consider cost-aware capacity provisioning to account for their impact on the operating cost of data centers.


I will try to leave all masks behind
At least for those facets presented here and
Behind the curtains of my shyness
There lives a savage bull
And If I'm to be perceived as perverse
May I at least be clear on my ways.
Behind my rationality,
There always lived burning desires,
Often misregarded by most
Or else very misunderstood.
Behind all my actions,
Behind all my ideas,
Behind all my fantasies,
What do I desire the most?
Everyone will have an answer
For me it's rather simple.
I desire what is mine,
Mine and mine only,
No one can touch,
No one can get close,
Mine and mine only.
In all the cues
All physical gifts
All small and tiny details
My mind and body burns
For that which yells as fully mine
My signature
My land
My garden
My home.
Nothing else
Awakens my bull
Nothing else
Really gets my desire
If anything gets close to what's mine
Even if remotely in my mind
My desire is not the same
If I am offered something many had
I simply find no sparkle
A sense of repel and disgust strikes me
To expose myself to that
Which holds the remainers of others
Would you kiss a sidewalk?

I wouldn't either
But I'm smart enough
To see the similiarity,
With any drugs given
I would never be able to consumate
Because I have no desire
My bull is retracted
It simply doesn't find the whistle
To feed that disguting land
If anything
If I ever felt my land had been invaded by someone else
I'd start trying to validate my beauty
Mistakengly, I once used those trash humans for validation
"Am I beautiful enough?"

Regardless, deep in my heart
If I were to fantasize
With that which I desire the most
The only thing, in the end, I desire
I'd find pleasure in that which is often disregarded
But for me it finds all my certainty
The soul that I belong to
All of myself
All of my body,
And in such
The body that belongs to me
Each tiny detail
The skin that is mine with my scent
The hand that is made perfectly for my body
The mouth that is mine, clean, reserved,all for me
The legs that I gently kiss
And as such feel the smell of the water
Coming through the gates
All my water
All made for me
For my tongue, for my mouth, for my body,
It is clean, pure, made for me
Dripping through my garden
Scratching and carving my soul
The smoothness and warmthness of the skin
That only I have it
That is my home
Locked for myself
The gentleness
The texture made for me,
My flowers and tress
All mine, for myself
I slider and enter myself in the home which I own
I give her that which is hers and only hers
Beautifully combined
So much desire I find
So much strength I gave
The fire burns my skin
I want to grab, hold, get her close to me
I want to unite our souls into one
Feel the beating of our hearts
Burn her with the heat of my skin
Bites, slaps, very strong squeezes
Hugging
Grabbing
I tame my savage lioness
I spread her wings to bring my gift
I prepare her with all that is needed
For the ultimate climax
Make my lioness fly,
So high
So high
So high
I have no limit
I go as far as I have to
She is mine
And it is my duty to give her what she desires
I never stop
I use all my strength
All my fire
And our time
Comes
I leave a part of myself in her
She has part of me
I have part of her
My mind can't stop racing
The desire is overwhelming
The ultimate act of ownership
Our bodies are flooded
The smell, the texture, the warmth,
The final sound...
She would always apologize
Unaware of the pleasure I feel
Everything is perfect
My bull absorbs it all
It is his
All his
They belong to each other
My essence, her essence
Our smell
Our river
All perfectly melted together
I can only feel proud, joyful, completed
There is nothing but contempt
Peace
I feel gravitated towards
No matter what might come from this
Any water, any liquid, anything
In any dirt we might do, I never feel any repel
Quite the opposite, I feel connected, gravitating, glued to her
Because she is part of me
My body and soul accepts everything that comes out of her.
And it's not a perversion I acquired
I was like that since the first time I touched her body
Never was I naive,
Never did I lack a strong sexuality
Indeed,
I think I was born with it,
Deep in my heart and soul,
It is part of my DNA,
I never had to learn,
Never did I change,
Never did I become unpure,
I always just feel it all
It flows from my soul to my body
My bull when secure of his land
It aggressively and instinctively comes out
No technique, practice or experience required
It all is the movement of the soul
The strong
The bold
The builder
The fertilizer
In my biggest desire
Instinctively
Existentially
Sexually
My biggest desire is to feel home
To grab, squeeze and hold
That land which is mine
And no one else gets to touch.


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- recordScore(String, int, long, String) - Method in class eic.beike.projectx.network.projectXServer.Database
-
Record a score in the database, yet unclear when a score should be recorded.
- recordScore(String, int, long, String) - Method in interface eic.beike.projectx.network.projectXServer.IDatabase
-
- register(String, String) - Method in class eic.beike.projectx.network.projectXServer.Database
-
Register a name for the given id.
- register(String, String) - Method in interface eic.beike.projectx.network.projectXServer.IDatabase
-
Register a name for the given id.
- REQUEST_OK - Static variable in class eic.beike.projectx.util.Constants
-
- Resource - Enum in eic.beike.projectx.network.busdata
-
The name of the resources from the bus coupled with their sensor.
- ResponseEntry - Class in eic.beike.projectx.network.busdata
-
Small data class used by gson, comparation on timestamp
- ResponseEntry() - Constructor for class eic.beike.projectx.network.busdata.ResponseEntry
-
- RetrieveReader - Class in eic.beike.projectx.network
-
Used to retrieve a buffered readers from URLs.
- RetrieveReader() - Constructor for class eic.beike.projectx.network.RetrieveReader
-
- RoundTracker - Class in eic.beike.projectx.model
-
Thread that keeps of the rounds, for now it ends the round every time the bus goes from
not being at a stop to being at a stop.
- RoundTracker() - Constructor for class eic.beike.projectx.model.RoundTracker
-
- ROW - Static variable in class eic.beike.projectx.android.event.Keys
-
- ROW_1 - Static variable in class eic.beike.projectx.android.event.Keys
-
- ROW_2 - Static variable in class eic.beike.projectx.android.event.Keys
-
- run() - Method in class eic.beike.projectx.model.RoundTracker
-


Get(SystemNICAddress)Values in the list returned by this function are separated by carriage returns. The address consists of 6 bytes displayed in hexadecimal separated by colons. In Windows, find this address by typing the command “ipconfig /All” in a DOS window. In OS X, find this address under Network Overview in the System Profile tab under Applications/Utilities/Apple System Profiler.
• Returns 00:07:34:4e:c2:0d, for example.


Quick and simple projects that I made at the beginning of my coding journey.
This simple clicking game utilized HTML CSS and JQuery
This simple Show Hide Memory Game utilized HTML CSS and JQuery.
Travel recommendation and review site built with Django and the Google Maps API.


Data changes minds. We might start out with a set of assumptions about how the world works, but as we gather more data, we may have to update our opinions.
Probabilities too can be updated as information comes in. As you saw in Data 8, Bayes' Rule is one way to update probabilities. Let's recall the rule in the context of an example, and then we will state it in greater generality.
In a population there is a rare disease: only 0.4% of the people have it. There is a laboratory test for the disease that has a 99% chance of returning a positive result when run on a person that has the disease. When run on someone who doesn't have the disease, it has a 99.5% chance of returning a negative result. So overall, it's a pretty good test.
One person is picked at random from this population. Given that the person tests positive, what is the chance that the person has the disease?
Here is the tree diagram we drew in Data 8, to summarize the information in the problem.
To solve the problem, we will use the division rule as we did in Data 8. In this kind of application, where we are finding probabilities for an earlier phase of an experiement given the result of a later phase, the division rule is called Bayes' Rule.
Let $D$ be the event that the patient has the disease, and, with some abuse of math notation, let $+$ be the event that the patient tests positive. Then what we're looking for is $P(D \mid +)$. By the division rule,
$$ P(D \mid + ) = \frac{P(D \text{ and } +)}{P(+)} = \frac{0.004 \cdot 0.99}{0.004 \cdot 0.99 + 0.996 \cdot 0.005} = 44.3\% $$(.004*.99)/(0.004*.99 + 0.996*.005)
In general, if the entire outcome space can be partitioned into events $A_1, A_2 \ldots , A_n$, and $B$ is an event of positive probability, then for each $i$,
\begin{align*} P(A_i \mid B) &= \frac{P(A_iB)}{P(B)} ~~~~ \text{(division rule)} \\ \\ &= \frac{P(A_iB)}{\sum_{j=1}^n P(A_j B)} ~~~~ \text{(the }A_j\text{'s partition the whole space)} \\ \\ &= \frac{P(A_i)P(B \mid A_i)}{\sum_{j=1}^n P(A_j)P(B \mid A_j)} ~~~~ \text{(multiplication rule)} \end{align*}This calculation, called Bayes' Rule, is an application of the division rule in a setting where the events $A_1, A_2, \ldots , A_n$ can be thought of as the results of an "earlier" stage of an experiment and $B$ the result of a "later" stage. The calculation allows us to find "backwards in time" conditional chances of an earlier event given a later one, by writing the chance in terms of the "forwards in time" conditional chances of the later event given the earlier ones.
Now let's take a look at the numerical value of the answer we got in our example. It's a bit disconcerting: it says that even though the person has tested positive, there is less than 50% chance they have the disease: the disease is so rare that the proportion of people who have the disease and test positive is actually a bit smaller than the people who don't have the disease and get a bad test result.
This is not a fault of the test. It's due to our premise, that the person "is picked at random from the population." That's not what happens when people go to get tested. Usually they go because they or their doctors think they should. And in that case they are no longer "randomly picked" members of the population.
If a person thinks they might have the disease, then their subjective probability of having the disease should be larger than the probability for a random member of the population. Let's take the following steps to see how much difference the prior makes.
prior = make_array(0.004, 0.01, 0.05, 0.1, 0.5)
Table().with_columns(
'Prior P(D)', prior,
'Posterior P(D|+)', (prior*0.99)/(prior*0.99 + (1-prior)*0.005)
)
You can see that the posterior chance that the person has the disease, given that they tested positive, depends heavily on the prior. For example, if the person thinks they even have a 10% chance of the disease, then, given that they test positive, their probability of having the disease gets updated to over 95%.
Another way to visualize this is by defining a Bernoulli random variable $X$ that is 1 if the person has the disease and 0 otherwise. We say that "$X$ is the indicator of the person having the disease."
Suppose the person starts out with a 10% prior probability that they have the disease. Then $X$ is Bernoulli $(0.1)$.
How you update this distribution based on test results depends not only on the prior but also on the likelihood that the person tests positive given that they have or don't have the disease. This likelihood derives from the accuracy of the test, which is 99% if the person has the disease and 99.5% if they don't.
To see how all these different aspects fit together, define a new Bernoulli random variable $T$ that is 1 if the test result is positive and 0 if the test result is negative. So $T$ is the indicator of the person testing positive. The joint distribution of $X$ and $T$ is displayed in the following table.
x = make_array(0, 1)
t = make_array(0, 1)
def jt(t, x):
if x == 1:
if t == 1:
return 0.1*0.99
if t == 0:
return 0.1*0.01
if x == 0:
if t == 1:
return 0.9*0.005
if t == 0:
return 0.9*0.995
dist_xt_tbl = Table().values('T', t, 'X', x).probability_function(jt)
dist_xt = dist_xt_tbl.toJoint()
dist_xt
The entry in each $(x, t)$ cell is $P(X=x)P(T=t \mid X=x)$, that is, the "prior times the likelihood".
Each entry in the T=1
column of the joint distribution table has the form prior times likelihood, and the conditional distribution of $X$ given $T=1$ is obtained by dividing those products by the same quantity $P(T=1)$. This conditional distribution, called the posterior distribution of $X$ given $T=1$, thus satisfies
dist_xt.conditional_dist('X', 'T')
You can see that the posterior distribution of $X$ given $T=1$ is consistent with the posterior probability $P(D \mid +)$ we calculated in an earlier table in the case $P(D) = 0.1$.


It was a fine summer’s day and fox was out for a stroll when he spotted a crow sitting on a high branch of a tree. The fox couldn’t help but notice that the crow had in his beak a very large chunk of cheese! The fox being a crafty fox, and the fact that he’d not yet had his breakfast, decide that this was too good an opportunity to miss, and sat down at the foot of the tree. He watched the crow as he thought to come up with a ruse which would deliver the cheese to him. The crow eyed him suspiciously, being very much aware of the fox’s reputation. But she considered herself quite safe as it is well known that foxes do not climb trees.
“My! My!” Said the fox at last, looking up towards the crow, “What a magnificent noble bird I see before me! Your beauty is indeed without equal, mistress, and the effect of the sunlight on your feathers is surely beyond compare! I heard from my friends that it is your voice, however, that is your finest attribute, and I have been longing to hear you sing!”
Now the crow had never really given much thought to the quality of her voice,
but
she was mightily flattered by the fox’s compliments. As a result she
forgot all about her suspicions, and threw caution to the wind. She opened her mouth,
and
started singing a tune, as loudly as she could, “caw craw craw!”
As soon as she did so, of course, down fell the cheese, and the fox put his paw
over it.
Aesop


@()(implicit request: MessagesRequestHeader)
'Pot' a Red then a Colour until the reds are cleared, then pot the colours in the correct order.
Full Adult class rules'Pot' all the Reds then then pot the colours in the correct order.
Full Under 16 rulesStandard 'Score'. Punch as many you can in any order, but don't repeat any control.
Full Open class rules


Preamble
This page is under construction
Frequently Asked Questions
How real are the simulated datasets?
How does EAGLE compare to other existing simulators?
How real are the simulated datasets?
very! [, with a bit of effort]
This page is under construction
How real are the simulated datasets?
How does EAGLE compare to other existing simulators?
very! [, with a bit of effort]


{% extends "layout.html" %} {% block content %} {% include "includes/phase_banner_prototype.html" %} {% include "includes/backlink_examples.html" %} Why {{ 'isn’t their partner' if isPartnerFlow else 'aren’t they' }} working? Why {{ 'isn’t their partner' if isPartnerFlow else 'aren’t they' }} working? They were made redundant recently They stopped working recently due to an illness Other reason Continue {% endblock %}


About
BilderSkript
BilderSkript is a compound created from the following words.
Bilder [ˈbɪldɐ], (German), images
Skript [skʁɪpt], (German), a written document or notes
BilderSkript automatically summarizes a lecture as a sequence of interesting images scenes.
Technically, it trains a deep neural net on objection recognition and extracts interesting scenes from a large sequence of still image recordings. Theses scenes compile to the BilderSkript
Project Definition
Motivation and Goal
A lecturer usually provides a lot of supplemental course material such as a written script, slides, reading material, exercises.
However, students visiting the course’s lecture experience an additional channel, which helps them to sort and weight the supplemental material. The way how a lecturer presents the content will let attendees realize the weight and importance of certain parts of the content. Hence, it supports the creation of a red thread throughout entire supplemental material.
In contrast to full lecture recording, the BilderSkript approach condenses the recording to find those parts within a lecture which contribute to the understanding of the supplemental material.
Primarily, BilderSkript aims at course attendees, however, it could also be valuable for remote attentees.
BilderSkript has the following goals
- re-create partially the course experience
- support attenting students to follow-up on the content
- support students who have not attended all lessions to catch-up on the course
- improve the efficiency when working with the supplemental course material
Objectives
There are a couple of activities to perform
- record lectures as a sequence of wide angle still images
- train a computer to identify objects within still images
- define a metric of interestingness from the sequence of recognized objects
- extract interesting images and compile a lecture notebook from which ultimately creates the BilderSkript
Research Questions
BilderSkript relies on widely available open-source ML software. However, there are a couple of interesting research questions on the way.
- How to automate image preprocessing? - Image recording activity does not happen in a controlled environment, i.e. lecture rooms change, the light varies, the camera positions are not fixed (resulting in varying field of views), varying colors of background, cloths etc.
- Can we successfully transfer other object identfication models to lecture recordings? Object identfication utilizes labeled data from one or few lectures for training. Since labeling is laborious, we are interested to transfer trained models from other areas and apply for object identfication in our lecture recordings.
Introductionary Example
BilderSkript takes a series of still images as input and compiles visual lecture notes as output. Below we illustrate a step-by-step walkthrough how the software processes the data.
Image recording: A 360 camera records the entire room. However, the lens towards the audience is covered to maintain privacy. The images have the typical distorted appearance due to the camera’s fisheye lenses. Nevertheless, it creates an approx. 200 degrees, wide-angle recording.
Image preparation: The BilderSkript projects fisheye images as equirectangular images to correct for the distortion.
Finally, the perspective control corrects the deformation of vertical and horizontal lines when the camera records the projection wall or blackboard from an inclinded position.
Object identfication: In this step BilderSkript identifies typical objects, such as blackboard, lecturer, video projection, within each image.
tbd. include image
Classification: In particular, if there is a blackboard, it is interesting to know whether the board is empty or not. The classifiers utilizes the identified blackboard from the previous step and assigns class labels, which correspond to the blackboard’s state. This step may filter images before it inputs them to the classifier.
| Label | Original Image | Filtered Image |
|---|---|---|
| empty | ||
| full |
Sequencing: Utilizing the object id, it transforms the set of images into a sequence of object compositions. A step consists of all identified objects in a single image. Please note, each step links back to the original image.
tbd. include image
Interesting sequences: This steps only operates on the sequence of identified objects. Based on a definition on interestingness, it quantifies the previously created sequences on this metric. The idea is that interestingness emerges from changes in the composition of identified objects.
tbd. include image
Compilation: By the degree of interestingness BilderSkript applies a threshold to extract the best ones. Once it has found an interesting sequence, it links each step within this sequence back to its image where it originates from. Finally, it compiles the scene from these images.
tbd. include image
System Design
This use case diagram depicts BilderSkript’s main services. Note, the shown use cases do not imply an execution order.
The engineer performs the system setup, configures and trains the system for the user to be beneficial.
Docker Container Toolchain
Docker Images and Volumes
Docker images contain the various software pipelines from the tool chain. The responsibilities within the overall system design motivate the system boundaries induced by the distribution of pipelines across docker images. The following table list the docker images and their respective pipeline functionalities.
| Image | Pipeline | Notes |
|---|---|---|
| vscode | n/a | IDE VS code |
| builder | Build | makefile, doc, versioning via git and dvc, mlflow exp. |
| hugin | Data prep | image data preparation using hugin |
| hugin-vnc | n/a | like hugin, but provides VNC server for GUI interaction |
| mrcnn | ML training | object detection |
| ludwig | ML training | classification |
| cicd | Deployment pipeline | not yet implemented |
When started as docker containers, they run scripts and communicate with each other utilizing shared volumes on the filesystem. The image below illustrates the docker toolchain.
The most important volumes are:
- ${APP_ROOT}/ipc : used to store sockets for IPCs between containers
- ${APP_ROOT}/pipelines : contains all BilderSkript pipelines
- ${APP_ROOT}/images : data directory
- ${APP_ROOT}/src : stores source codes
- ${APP_ROOT}/scripts : scripts
- ${APP_ROOT}/ludwig : stores experiments, models and prediction
- ${APP_ROOT}/vscode : maintains the state of VS code
- ${APP_ROOT}/docs : github’s webserver serves the docu blog from this directory
- ${APP_ROOT}/docs_site : the docu blog’s source
By default ${APP_ROOT}
is set to /bilderskript
. For instance, one accesses the pipeline scripts under /bilderskript/pipelines
.
Build and Startup
Docker Compose creates all images, containers and volumes. The configuration is defined in the docker-compose.yml file
Building all images using the following command
docker-compose build
Start a container from builder
image and get a bash
shell. The default container name is bilderskript_builder_1
docker-compose up -d builder
docker exec -it bilderskript_builder_1 /bin/bash
Pipelines
Pipelines are the fundamental building blocks of the BilderSkript system.
Pipeline Definitions
The term pipeline is heavily used in machine learning (ML). It generally refers to a sequence of steps to run in order to perform transformations. There are various kinds of pipelines, e.g. data pipelines, machine learning pipelines, deployment pipelines and others. Depending on the pipeline type, it takes a a certain ressource type as input and produces output ressources by the application of the pipeline’s transformation steps. For instance, a data pipeline takes data files as an input and prepares them to be fed into a machine learning algorithm of the ML pipeline.
BilderSkript stores pipelines in the pipelines
volume usually accessed under /bilderscript/pipelines
. They are encoded as snakemake makefiles for version control.
Snakemake
Snakemake is a workflow management system to implement data analysis pipelines.
Snakemake compares the input and output ressources. These ressources are files. If the modification date of any of the input files is newer than the output file, snakemake runs the shell command. This behavior is encoded as rules, which transform input files into output files. All rules of a pipeline are defined in a snakefile. We list some important snakemake commands.
Run the snakemake pipeline
snakemake <pipeline name>
Generate a report
snakemake <pipeline name> --report <snakefile.html>
Generate a summary table displaying the current state of input and output files.
snakemake <pipeline name> --summary
Before the snakefile is run, snakemake generates a DAG which shows the dependencies. This command visualizes the DAG.
snakemake <pipeline name> --dag | dot -Tpng > <pipeline name>.png
BilderSkript Pipelines
The BilderSkript pipelines follow the guidelines of The Snakemake-Workflows project. There is a central Snakemake
file which includes the configuration and the concrete workflows for the data and ML pipelines.
The workflow’s design separates the pipeline specific parameters from the dataset specific ones. A separate file stores each parameter set
config.yaml
contains the pipeline specific parameters. This file exists only once and contains the parameters for all pipelines.dataset.csv
stores the parameters relevant for the dataset a pipeline processes. There may be several of these files, one for each class of data for a pipeline. There is no fixed schema. The file’s name depends on the content and the pipeline, which sources the file. A pipeline which processes various ML models may utilize amodels.csv
file to store the set of available models and additional parameters for each model.
The approach increases the flexibility to apply the pipelines to various datasets. The figure below depicts this separation
The pipelines are named after their snakefile. All pipelines files stay in the /bilderskript/pipelines
directory relative to the BilderSkript’s project dir.
doc.snakefile: describes the pipeline for documentation generations. You may view the pipeline’s report.
data_prep.snakefile: prepares the image files for the ML pipeline. It’s a complex pipeline because it utilizes interprocess communication (IPC) with the
hugin
container. You may view the pipeline’s report.data_prep.snakefile
requires parameters inconfig.yaml
:- datasets_idx: line in
datasets.csv
defining the data to process - out_dir: the directory where prep’ed images as a result of the pipeline execution are stored
The pipeline’s default behavior is started by the
data_prep.sh
script. The pipeline’s rule DAG is shown indata_prep.snakefile.png
- datasets_idx: line in
ludwig.snakefile: detects the blackboard’s state of writing, i.e. whether the writing on the blackboard fills out the blackboard completely, partially, or alternatively, the blackboard is empty.
The pipeline’s default behavior is started by the [
ludwig.sh
]
Run the following script to create reports for all BilderSkript pipelines
snakemake_report.sh
[data_prep.snakefile] Pipeline
The data preparation pipeline, data_prep.snakefile
, comprises of two phases
- Configure the pipeline’s parameters
config.yaml
defines pipeline specific parametersdatasets.csv
defines the data input and data-specific processing parameters
- Run the pipeline on the images from the lecture recording
1. Configuration
The following activity diagram describes the steps to configure the data preparation pipeline. Configuration is stored in config.yaml
and dataset.csv
.
Precondition:
builder
andhugin-vnc
container up and running- at least one image from lecture recording available
Postcondition:
- images directories set
.pto
files created- (optionally) crop specification defined
The parameter values from above need to be stored in the config.yaml
and dataset.csv
file. The activity diagram indicates to which file each parameter belongs to.
Commands
Spin up the hugin-vnc
container.
docker-compose up -d hugin-vnc
Afterwards, use a vnc client to get Hugin’s display and create the .pto
files. Modify the pipeline’s dataset.csv
and config.yaml
to tell the scripts where to look for the files. The activity diagram below displays the steps to create the files.
2. Run the Pipeline
Finally, the engineer starts the data prep pipeline and the pipeline processes the input images from the img_dir and places it in the out_dir.
Precondition:
builder
andhugin
container up and running.pto
files created- img_dir and out_dir image directories set
Parameter values from above are sourced from the the dataset.csv
file.
Postcondition:
- images processed placed in out_dir
Pipeline Start
The following commands start the pipeline. The activity diagram belows depicts the details of the pipeline run.
Spin up the builder
and hugin
container and get an interactive shell to the builder
container.
docker-compose up -d builder hugin
docker exec -it bilderskript_builder_1 /bin/bash
Within the builder
container run
cd BilderSkript/pipelines
./data_prep.sh
[ludwig.snakefile] Pipeline for Blackboard Classification
The ludwig.snakefile
pipeline takes blackboard images as input and assigns labels like full, partial, empty. It indicates the whether the writing on the blackboard fills out the blackboard completely, partially, or alternatively, the blackboard is empty.
Train the Blackboard Image Classifier
The image classifier detects the blackboard’s state of writing. It is an interesting BilderSkript context to know whether the writing on the blackboard fills out the blackboard completely, partially, or alternatively, the blackboard is empty.
Experiment
We learn a model, a classifier, which maps images to class labels, e.g. full, partial, empty.
- utilizes the Ludwig docker container
- runs from command line;
- script tests whether the container already runs and if appropriate spins it up
- utilizes comet.ml to store experiment results
- manually shutdown the container
For utilizing comet.ml you need to provide a .comet.env
file in the project’s directory. The file conatins the API key and the comet’s project name.
Walkthrough
Start an experiment from scripts/ludwig
.
STEP 1: Prepare the input dataset
Precondition:
- `.png’ images as input for training the model
- within the
<training image path>
directory the images are organized in subdirs, where subdir names are the class labels - experiment name, e.g. stats_lecture
Command:
./ludwig_experiment.sh datacsv <experiment name> <training image path>
Postcondition:
- directory created:
ludwig/experiements/<experiment name>
- directory contains
<experiment name>.csv
, which links images to class labels
STEP 2: Learn the model
Precondition: see above
Command:
./ludwig_experiment.sh experiment <experiment name> <training image path>
Postcondition:
- Results available in
ludwig/experiements/<experiment name>/results
- comet.ml website contains experiment’s results
STEP 3: Visualize and Shutdown
Precondition: the experiment name from above
Command:
./ludwig_experiment.sh visualize <experiment name>
./ludwig_experiment.sh shutdown <experiment name>
Postcondition:
- comet.ml website contains for addtional images from the experiment run
- the Ludwig container is removed.
Multi Experiments
Multi experiements are repeated experiements which varying parameters. Particularly, we vary the parameters for the image pre-processing.
Start a multi experiment from scripts/ludwig
. All directories created during an regular experiment are prefixed by exp<num>_
indicating the run number within the multi experiment.
Precondition:
- see initial preconditions of a single experiment
- edit
ludwig_multi_experiments.sh
for- training image path
- parameters to to loop through
Command:
./ludwig_multi_experiments.sh <experiment root name>
Postcondition:
- directories with results created:
ludwig/experiements/exp<num>_<experiment root name>
- comet.ml website contains all experiments’ results
Training Process
The following activity diagram illustrates the overall sequence of actions for running one or more experiments. The swimlane Experiments
depicts all artifacts which relate to an experiment. When an experiment is repeated with different parameters the filtered images, the datacsv file and the resulting model are created under the new experiment name.
Labeling
This activity assigns names or descriptors to components within an image. Any supervised ML algorithm requires labeled data for training. Successfully trained on the labeled data, the ML algorithm is afterwards enabled to identify and name similar components in images which do not have labels.
Labeling is usually done manually. A user marks regions within images and assigns labels therewith identifying objects in the image. Labeled data is not questioned and provides the algorithm with ground truth data.
Problem
BilderSkript delivers images from various lectures. We will need to repeat the labeling process to find appropriate features which let the ML based object identification algorithm generalize sufficiently well in order to identify objects in recordings from other lectures.
Tool Support
Labeling objects within images has a long history in the computer vision research community. For ML-based products there are a couple of online services available which support labeling activties distributed across a crowd workers, integrate semi-automated quality checks and other functions when it come to large-scale applications.
Quora list not-complete list of labeling tools and services; some are comercial, some are open-source. Check out https://www.quora.com/What-is-the-best-image-labeling-tool-for-object-detection.
For BilderSkript we found the following open tools tools attractive and discuss them briefly.
- ImgLab: web based tool; Online service for immediate use available under https://imglab.in
- LabelImg: the classic one, often mentioned on towardsdatascience.com. Google finds approx. 45 hits. It is a desktop tool.
- Alp’s Labeling Tool (ALT): more than just a labeling tool; it’s a desktop tool for Windows as well as Linux / Ubuntu.
- Computer Vision Annotation Tool (CVAT): web based online tool. Works with videos, too. Apart from the web GUI, there is also a REST API to programmatically access the tool.
ImgLab to annotate Labels
BilderSkript utilizes the ImgLab for label annotations. The web based tool exports label annotation in multiple formats
- dlib XML
- dlib pts
- Pascal VOC: standardized, however, the annotation data export only works for the current image. One needs to proceed to next image to export the next image’s annotation.
- COCO: standardized, however, the exportet annotation text seems to be incomplete
The dlib XML format originates from the dlib toolkit containing various machine learning algorithms. Here is an example using the python API for using dlib tools.
The XML format is reduced to the very basic tags required. An example is shown below.
<?xml version='1.0' encoding='ISO-8859-1'?>
<?xml-stylesheet type='text/xsl' href='image_metadata_stylesheet.xsl'?>
<dataset>
<name>dlib face detection dataset generated by ImgLab</name>
<comment>
This dataset is manually crafted or adjusted using ImgLab web tool
Check more detail on https://github.com/NaturalIntelligence/imglab
</comment>
<images> <image file='IMG_20191219_094800_001_flat_pc_resize_mirror.png'>
<box top='36' left='119' width='278' height='208'>
slide
</box>
<box top='31' left='-2' width='206' height='319'>
person
</box>
</image>
<image file='IMG_20191219_094802_002_flat_pc_resize_mirror.png'>
<box top='44' left='1' width='204' height='304'>
person
</box>
</image>
</images></dataset>
BilderSkript Labels
We create rectangular shapes and define the following labels to annotate the shapes:
Development IDE - VS code
We use VS code as the BilderSkript’s development IDE. A docker image encapsulates the IDE and makes it accessible through the web browser. An developer’s software and programming effort focuses mostly on writing and editing shell scripts. As a consequence, the docker image provides appropriate extensions to support this actitity.
Web-based VS code
Start VS code
docker-compose up -d vscode
and point the web browser to http://localhost:8080.
The vscode container starts with the BilderSkript repo mounted and opened. It stores its state, e.g. last file open, extensions, other settings, in the vscode
directory within the BilderSkript’s project dir.
Extensions
BilderSkript’s vscode docker image and repo comes pre-installed with following extentions especially for working with shell scripts.
The shellchecker runs on-the-fly and provides quick fixes for better coding quality of shell scripts. The shfmt tool reformats the shell script. Use shift + alt + f
to reformat the script.
Git
Git version control within the vscode image misses user.name
and user.email
for git actions like commit. There is an extensive discussion on https://stackoverflow.com/questions/42318673/changing-the-git-user-inside-visual-studio-code. The main message is
Changing the git user inside Visual Studio Code, is not inside rather outside.
This would require a developer to run git config --global user....
commands on within vscode’s CLI.
BilderSkript’s docker-compose.yml
file maps a .gitconfig file from the project dir onto the vscode’s container $HOME/.gitconfig
. As a result, it makes git configuration data available to the vscode container.
By default, the .gitconfig
is included in the project’s .gitignore
file to avoid accidentially committing private git configuration details into the public repo.
Versioning and Repository Management
Pipeline Code and Data Versioning
The builder
image takes care of versioning using [git]() and [dvc]().
Pipelines are commonly shared in various volumes.
Repository Management
The project’s repository is mounted as separate volume in the builder
container. As a consequence, only makefiles in the project’s root dir are able to commit changes to the repository. Scripts in subdirectories are not aware that the project’s root is under git version control. The volumes are organized under the following two mount points.
Mount point: /BilderSkriptRepo
Scripts which perform pipeline and data versioning start under this mount point. It contains all files and directories from the project repository:
.git/
Dockerfiles/
- …
docker_compose.yml
README.md
Mount point: /bilderskript
Scripts which actually execute pipelines start under this mount point. It contains the following directories:
docs/
docs_site/
images/
scripts/
src/
Documentation Generation
Docu Blog
The project’s documentation is a form of a single-page blog using the Hugo static webpage generator. It lists notes taken during the development and justifies design decisions. The builder
docker image takes care of the generation utilizing the snakemake
script, pipelines/doc.snakemake
.
The blog utilizes the OneDly theme. This theme is excluded from git versioning because it originates from a separate repository. One include it as a git submodule.
cd docs_site
git add submodule https://github.com/cdeck3r/OneDly-Theme.git themes/onedly
The pipeline doc.snakemake
generates the complete documentation calling
cd /bilderskript/pipelines
snakemake -s doc.snakemake
The docu blog is available under http://cdeck3r.com/BilderSkript/.
APPENDIX: Pipeline Interprocess Communication (IPC)
The distribution of pipelines across various docker containers requires interprocess communication between the docker containers. Let’s discuss the design for the BilderSkript pipelines.
IPC Control Problem
We have the builder
container which controls the execution of all pipelines. A pipeline implements a sequence of processs executions, where some of the processes run in their respective containers. As an example consider the data preparation pipeline. The pipeline in the builder
container initiates the image data preparation in the hugin
container. Once the process in the hugin
container completes the pipeline can continues with the next step.
Basically, the sequence diagram above depicts a synchronous remote procedure call (RPC). This is a classical function in many programming languages, e.g. Java RMI.
IPC control problem formulation:
Implement a synchronous RPC to start processes across containers and that seamlessly integrates into pipelined workflows.
Contraints / requirements:
- low effort for additional network setup
- low effort for access and securing the access control
- script based, preferrably in bash, as it makes it easy to integrate the RPC as shell command.
In the following sections, we discuss some IPC approaches for their qualification as synchronous RPC.
File-based IPC
A very simple, but effective approach is the use of files for exchanging messages between container processes. The process in container A writes a message character string into a file on a shared volume from which the process in container B can read the file’s content. In this scenario, a single file serves as a buffer between those processes. However, the file access needs to be coordinated, otherwise the processes would overwrite each other’s messages. Additionally, to get informed about new messages, the processes need to poll the commonly shared file.
Client / Server IPC
Process within different containers may communicate as networked client and servers. Subsequently, some options:
- REST or similar RPC-alike calls across the network
- SSH remote script execution
- Shared UNIX sockets
REST requests are easy to implement, addtionally, a webserver such as nginx or apache is required to receive the requests. Alternatively, one could implement a simple webserver using python and flask. SSH remote script execution needs accounts to login to the remote containers. One needs to enter a password before execution, which limits automation capabilities. Public key access can resolve this problem, but needs caution in handling public and private keys. Both options expose containers to the network. As a consequence, it requires network definition between containers and a firewall to block unauthorized access.
Finally, UNIX sockets appear to be an ideal approach to interprocess communication.  However, this type of interprocess communication is limited between containers on the same host.
Message Queues and Brokers
A message broker maintains a message queue (MQ), where distirbuted processes can register themselve to exchange messages without directly knowing each other.
Message brokers are a prefered way for decoupled distributed processes, in particular for long running processes executed asynchronously. However, the setup and managment of a MQ takes effort. Processes need to agree on the message format and containers need to reach the broker’s queue via the network.
Databases for IPC
Databases store information, which are accessible by clients. As such, this is related to the file-based IPC. Process information is organized in tables and table attributes store process states, e.g. process execution start or whether a process successfully completed.
Similar to file-based IPC, processes are required to poll the database to receive updates on the process state. Addtionally, the setup and maintenance of even simple DBMS is significant. A simple file based database, e.g. sqlite, is an attractive solution for this last issue. However, write and read access must be coordinated to avoid (orphaned) file locks.
Final Design Decision
As a result of the discussion, BilderSkript implements an interprocess communication using shared UNIX sockets. It combines the charm of file based approaches with the advantage of bi-directional communication. Tool support for scripting is very mature. The next section provides more details how this approach implemented.
Limitation. The use of UNIX limits IPC between containers on the same host. To overcome one may use socat
to forward the UNIX socket to a TCP one. See this stackoverflow post to get an idea how this can be implemented. However, this would re-open the discussion on network access issues, but enables a migration path to evolve the local host approach to distributed network approach.
Further Ressources:
There are a couple of ressources to follow up on these issues.
- https://www.linode.com/docs/security/authentication/use-public-key-authentication-with-ssh/
- Dockerize an SSH service and ubuntu sshd
- sshpass and Docker secret
- Docker ssh considered evil
An interesting op-ed from Bozho’s tech blog is You Probably Don’t Need a Message Queue. It inspired some of the disussion above.
APPENDIX: IPC via Shared UNIX Sockets
This is a technical design description on the usage of shared UNIX socket for docker container IPC. Both containers connect to a socket stored on a shared volume, e.g. under the mount point ipc
. They exchange simple control messages to run scripts and read and write data from the shared volume.
The approach utilizes
socat
for creating sockets and socket communicationss
to check for the presence of the listening socket.
It follows a classical client / server approach where the server initializes the socket and waits for the client to start the communication. The following sequence diagrams depicts the interaction over the socket.
Here are the script calls for the server and client.
./ipc_socket_server.sh <socket name> <path/to/server_app.sh>
./ipc_socket_client.sh <socket name> <path/to/client_app.sh>
Cross Container IPC Example
We illustrate the container IPC between the builder
container and the hugin
container. The hugin
container processes all images taken with the 360 camera and converts the fisheye lens images in regular ones using a equirectangular projection. The server script runs on the hugin
container while the client script runs on the builder
container. The socket between both resides on a shared volume.
It starts by the snakemake file on the builder
calling the hugin.sh
script.
Ressources:
A brief tutorial on socat
is the Socat Cheatsheet from Travis Clarke.


Each of them bringing their own skillset and unique mindset. A team of 20 brilliant students and alumni working together to craft the next rendition of One Line A Day journaling. Lambda School is a revolutionary coding environment that has made all of this possible within their Build Week of 2019.


Play It to the Bone
Play It to the Bone is a movie starring Woody Harrelson, Antonio Banderas, and Lolita Davidovich. Two best friends and former middleweight contenders travel to Las Vegas to fight each other for the first time.


I'm a curious self-taught developer. I've started learning software development
using Java, and web development using JavaScript libraries such as React and
Angular.
At the moment I'm employed at Chainwave
Working in an Agile team of dedicated developers helped me grow, learn and
implement new technologies. Together we built viable deliverable products. I
took upon researching and implementing a testing framework solution (Cypress)
thus obtaining good code coverage.
I have good communication skills, I'm
patient, curios and I'm very motivated to succeed in every project I get
involved.
Below you'll see some of the projects that were built as a part
of my learning path.
A complex e-commerce progressive web app built with React, Redux, Hooks, GraphQL, Stripe, Firebase and more.
A complex app that allows users to registrate, signin, reset their password, create campgrounds all over the world and add comments about the existing campgrounds on the page. The app uses GoogleMaps API in order to locate each campground, and Cloudinary API for image upload.
A small React app that uses Redux for state management. It uses RoboHash API, that generates robot images from a given list of users. This was created to illustrate to power and speed of React and Redux.
A complex React app that allows users to registrate and signin in order to test an image recognition app using a Machine Learning API (a subset of Artificial Intelligence).


public class SlotInfo
extends java.lang.Object
| Modifier and Type | Field and Description |
|---|---|
protected Version |
firmwareVersion_
The version of the slot's firmware.
|
protected Version |
hardwareVersion_
The version of the slot's hardware.
|
protected boolean |
hwSlot_
Indicate, if this slot is a hardware device or if it is just pure software; i.e.
|
protected java.lang.String |
manufacturerID_
A string identifying the manufacturer of this slot.
|
protected boolean |
removableDevice_
Indicates, if the token's in this slot are removable or not.
|
protected java.lang.String |
slotDescription_
A short descrption of this slot.
|
protected boolean |
tokenPresent_
Indicates, if a token is present.
|
| Modifier | Constructor and Description |
|---|---|
protected |
SlotInfo(CK_SLOT_INFO ckSlotInfo)
Constructor that takes the CK_SLOT_INFO object as given by PKCS11.C_GetSlotInfo().
|
| Modifier and Type | Method and Description |
|---|---|
boolean |
equals(java.lang.Object otherObject)
Compares all member variables of this object with the other object.
|
Version |
getFirmwareVersion()
Get the version of the slot's firmware.
|
Version |
getHardwareVersion()
Get the verion of the slot's hardware.
|
java.lang.String |
getManufacturerID()
Get an identifier for the manufacturer of this slot.
|
java.lang.String |
getSlotDescription()
Get a short description of this slot.
|
int |
hashCode()
The overriding of this method should ensure that the objects of this class work correctly in a
hashtable.
|
boolean |
isHwSlot()
Indicate, if the token is a hardware device or if it is just a pure software implementation;
e.g.
|
boolean |
isRemovableDevice()
Indicate, if the token is removalbe from this slot or not.
|
boolean |
isTokenPresent()
Indicates, if there is a token present in this slot.
|
java.lang.String |
toString()
Returns the string representation of this object.
|
protected java.lang.String slotDescription_
protected java.lang.String manufacturerID_
protected Version hardwareVersion_
protected Version firmwareVersion_
protected boolean tokenPresent_
protected boolean removableDevice_
protected boolean hwSlot_
protected SlotInfo(CK_SLOT_INFO ckSlotInfo)
ckSlotInfo
- The CK_SLOT_INFO object as given by PKCS11.C_GetSlotInfo().public java.lang.String getSlotDescription()
public java.lang.String getManufacturerID()
public Version getHardwareVersion()
public Version getFirmwareVersion()
public boolean isTokenPresent()
public boolean isRemovableDevice()
public boolean isHwSlot()
public java.lang.String toString()
toString
in class java.lang.Object
public boolean equals(java.lang.Object otherObject)
equals
in class java.lang.Object
otherObject
- The other SlotInfo object.public int hashCode()
hashCode
in class java.lang.Object
IAIK JavaSecurity Website http://jce.iaik.tugraz.at/
IAIK at Graz University of Technology, Austria, Europe
Copyright 2001-2004, IAIK, Graz University of Technology, Inffeldgasse 16a, 8010 Graz, Austria. All Rights Reserved.
version 1.4


Magister Xenovia:
(The Dark Crystal dissolves in the solution.
The mixture begins to bubble.
Suddenly, something emerges from the bubbles...
The experiment was a success!)