Copyright Note
Java Card 2.1.1 API
Java Card 2.1.1 Application Programming Interface Notes
Referenced Standards
ISO - International Standards Organization
-
Information Technology - Identification cards - integrated circuit cards
with contacts: ISO 7816
-
Information Technology - Security Techniques - Digital Signature Scheme
Giving Message Recovery: ISO 9796
-
Information Technology - Data integrity mechanism using a cryptographic
check function employing a block cipher algorithm: ISO 9797
-
Information technology - Security techniques - Digital signatures with
appendix : ISO 14888
RSA Data Security, Inc.
-
RSA Encryption Standard: PKCS #1 Version 2.0
-
Password-Based Encryption Standard: PKCS #5 Version 1.5
EMV
-
The EMV '96 ICC Specifications for Payments systems Version 3.0
IPSec
-
The Internet Key Exchange ( IKE ) document RFC 2409 (STD 1)
Standard Names for Security and Crypto
-
SHA (also SHA-1): Secure Hash Algorithm, as defined in Secure Hash Standard,
NIST FIPS 180-1.
-
MD5: The Message Digest algorithm RSA-MD5, as defined by RSA DSI in RFC
1321.
-
RIPEMD-160 : as defined in ISO/IEC 10118-3:1998 Information technology
-- Security techniques -- Hash-functions -- Part 3: Dedicated hash-functions
-
DSA: Digital Signature Algorithm, as defined in Digital Signature Standard,
NIST FIPS 186.
-
DES: The Data Encryption Standard, as defined by NIST in FIPS 46-1 and
46-2.
-
RSA: The Rivest, Shamir and Adleman Asymmetric Cipher algorithm.


Why is JavaScript popular in web development?
If HTML is used to make text and images appear on our website and CSS is used to make those elements look fun, interesting, and beautiful, then what in the world is JavaScript (JS) and why would we ever need it? After all, we have really awesome looking text and sweet pictures... what else could we possibly need?
Well, what if your website required users to enter personal information about themselves? Wouldn't it be cool if the form could tell users they've spelt something wrong before they submit the entire form? Or what if you wanted some part of your page to be animated?  I think you might be recognizing a pattern here and seeing where I'm driving this code bus - that's right - if you wanted to do any of the aforementioned things, you need (drum roll please) JavaScript! So let's now complete the first line of this blog post... if HTML is used to make text and images appear on our website and CSS is used to make those elements look fun, interesting, and beautiful, then JavaScript is used to add behavior (or "action") to our page.
Now that we know what JavaScript is used for and some of the things it can do, let's take a look at a couple reasons why JavaScript is so popular in web development.
1. JavaScript and the web were made for each other. It is fully supported by all modern browsers and works seamlessly with HTML/DHTML, CSS3 and background services via AJAX. Take a look at the image below to get a sense of JavaScript's recent surge in popularity.
2. Bye bye flash, hello HTML5. In a world where light weight and flexible HTML5 canvas constructs are taking the place of load heavy Flash apps, JavaScript is finding itself in the spotlight again. How so, you might ask. Well, the HTML5 canvas element works by code that is also written as plain text and it's code that is fundamentally based on JavaScript and can interface with exisitng JavaScript code without any problems whatsoever. Pretty sweet, I know.
Aside from the fact that JavaScript can give our web pages some behavior, or action, in a light, flexible manner, what are some other great things about it? Take a peak below!
- JavaScript is easy to learn. There are books, online tutorials, courses, white papers, and a littany of other materials to help you learn the material.
- JavaScript is easy to debug. Who cares about debugging? Why is that important? Debugging is important because if something is wrong with the code, it's nice to know where the issue is as opposed to spending precious time trying to figure out what it is. JavaScript allows the user to trace the exact place where an error occurred!
- JavaScript is a fast, powerful dude that works well with others. While JavaScript can do some amazing things to your web page, some manipulations are tough to design and take quite a bit of time to develop. In light of that, developers have written a number of good libraries to support the language and allow it to easily manipulate web pages with just a little bit of code.
What are JavaScript's limitations?
Arguably the single most limiting thing about JavaScript is that it only works on the front end. That is, it's obviously great for tweaking web pages, creating User Interfaces and games, and fetching data from the server, but in order to fetch that data it still has to rely on other technologies such as PHP or .Net to run on the server and provide that information. In additon, the other great limitation is security. The way the script interacts with the DOM poses a risk for end users by enabling malicious hackers to deliver damaging code over the web and run it on client computers.


Root Conf is a conference on DevOps and Cloud Infrastructure. 2017 edition’s theme is service reliability. Following is my notes from Day 1.
-
State of the open source monitoring landscape
- The speaker of the session is the co-founder of Icinga monitoring system. I missed first ten minutes of the talk.-The talk is a comparison of all available OSS options for monitoring, visualization.
- Auto-discovery is hard.
- As per 2015 monitoring tool usage survey, Nagios is widely used.
- Nagios is reliable and stable.
- Icinga 2 is a fork of Nagios, rewrite in c++. It’s modern, web 2.0 with APIs, extensions and multiple backends.
- Sensu has limited features on OSS side and a lot of features on enterprise version. OSS version isn’t useful much.
- Zabbix is full featured, out of box monitoring system written in C. It provides logging and graphing features. Scaling is hard since all writes are written to single Postgres DB.
- Riemann is stream processor and written in Clojure. The DSL stream processing language needs knowledge of Clojure. The system is stateless.
- OpenNMS is a network monitoring tool written in Java and good for auto discovery. Using plugins for a non-Java environment is slow.
- Graphite is flexible, a popular monitoring tool for time series database.
- Prometheus is flexible rule-based alerting and time series database metrics.
- Elastic comes with Elastic search, log stash, and kibana. It’s picking up a lot of traction. Elastic Stack is extensible using X-PACK feature.
- Grafana is best for visualizing time series database. Easy to get started and combine multiple backends. - - Grafana annotations easy to use and tag the events.
- There is no one tool which fits everyone’s case. You have to start somewhere. So pick up a monitoring tool, see if it works for you else try the next one til you settle down.
-
Deployment strategies with Kubernetes
- This was talk with a live demo.
- Canary deployment: Route a small amount of traffic to a new host to test functioning.
- If new hosts don’t act normal roll back the deployment.
- Blue Green Deployment is a procedure to minimize the downtime of the deployment. The idea is to have two set of machines with identical configuration but one with the latest code, rev 2 and other with rev 1. Once the machines with latest code act correctly, spin down the machines with rev 1 code.
- Then a demo of
kubectl
with adding a new host to the cluster and roll back.
-
- The talk is on a story on developing, push to GitHub, merge and release.  Now, what to do?
- The problem is developer didn’t get the code reviewed.
- How can automation help here?
- Enforcing standard like I unreviewed merge is reverted using GitHub API, Slack Bot, Hubot.
- As soon as developer opens a PR, alice, the bot adds a comment to the PR with the checklist. When the code is merged, bot verifies the checklist, if items are unchecked, the bot reverts the merge.
- The bot can do more work. DM the bot in the slack to issue commands and bot can interact with Jenkins to roll back the deployed code.
- The bot can receive commands via slack personal message.
-
Necessary tooling and monitoring for performance critical applications
- The talk is about collecting metrics for German E-commerce company Otto.
- The company receives two orders/sec, million visitors per day.On an average, it takes eight clicks/pages to complete an order.
- Monitor database, response time, throughput, requests/second, and measure state of the system
- Metrics everywhere! We talk about metrics to decide and diagnose the problem.
- Metrics is a Clojure library to measure and record the data to the external system.
- The library offers various features like Counter, gauges, meters, timers, histogram percentile.
- Rather than extracting data from the log file, measure details from the code and write to the data store.
- Third party libraries are available for visualization.
- The demo used d3.js application for annotation and visualization. In-house solution.
- While measuring the metrics, measure from all possible places and store separately. If the web application makes a call to the recommendation engine, collect the metrics from the web application and recommendation for a single task and push to the data store.
-
What should be PID 1 in a container?
- In older version of Docker, Docker doesn’t reap child process correctly. As a result, for every request, docker spawns a new application and never terminated. This is called PID 1 zombie problem.
- This will eat all available PIDs in the container.
- Use
Sysctl-a | grep pid_max
to find maximum available PIDs in the container. - In the bare metal machine, PID 1 is
systemd
or any init program. - If the first process in the container is bash, then is PID 1 zombie process doesn’t occur.
- Using bash is to handle all signal handlers is messy.
- Yelp came up with Yelp/dumb-init. Now,
dumb-init
is PID 1 and no more zombie processes. - Docker-1.13, introduced the flag,
--init
. - Another solution uses
system
as PID 1 - Docker allows running
system
without privilege mode. - Running system as PID 1 has other useful features like managing logs.
-
‘Razor’ sharp provision for bare metal servers
- I attended only first half of the talk, fifteen minutes.
- When you buy physical rack space in a data server how will you install the OS? You’re in Bangalore and server is in Amsterdam.
- First OS installation on bare metal is hard.
- There comes Network boot!
- PXELinux is a syslinux derivative to boot OS from NIC card.
- Once the machine comes up, DHCP request is broadcasted, and DHCP server responds.
- Cobbler helps in managing all services running the network.
- DHCP server, TFTP server, and config are required to complete the installation.
- Microkernel in placed in TFTP server.
- Razor is a tool to automate provisioning bare metal installation.
- Razor philosophy, consume the hardware resource like the virtual resource.
- Razor components - Nodes, Tags, Repository, policy, Brokers, Hooks
-
FreeBSD is not a Linux distribution
- FreeBSD is a complete OS, not a distribution
- Who uses? NetFlix, WhatsApp, Yahoo!, NetApp and more
- Great tools, mature release model, excellent documentation, friendly license.
- Now a lot of forks NetBSD, FreeBSD, OpenBSD and few more
- Good file system. UFS, and ZFS. UFS high performance and reliable. - If you don’t want to lose data use ZFS!
- Jails - GNU/Linux copied this and called containers!
- No GCC only llvm/clang.
- FreeBSD is forefront in developing next generation tools.
- Pluggable TCP stacks - BBR, RACK, CUBIC, NewReno
- Firewalls - Ipfw , PF
- Dummynet - live network emulation tool
- FreeBSD can run Linux binaries in userspace. It maps GNU/Linux system call with FreeBSD.
- It can run on 256 cores machine.
- Hard Ware - NUMA, ARM64, Secure boot/UEFI
- Politics - Democratically elected core team
- Join the Mailing list and send patches, you will get a commit bit.
- Excellent mentor program - GSoC copied our idea.
- FreeBSD uses SVN and Git revision control.
- Took a dig at GPLV2 and not a business friendly license.
- Read out BSD license on the stage.
See also
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.


Optimize your JavaScript applications with this free 208 page advanced eBook
Studying JavaScript performance in depth will make you capable of tackling the complex and important tasks required to solve performance issues. With this full-length free eBook, discover how to optimize your JavaScript for both mobile and web applications.
- Create your own build system for JavaScript projects using Node.js and GulpJS
- Get to know best performance-focused practices when writing JavaScript code
- Use the DOM more efficiently by optimizing JavaScript with CSS3 animations
- Learn how to unit test JavaScript code using Jasmine


The ToDo APP
Created by GROUP 10:
Moyank Giri
Vaibhava Krishna D
T Tarun Aditya
A Python Project
Semester 1 of batch 2019-23
UE19CS102 - Section M
PES University, Bengaluru, Karnataka, India
For more details or source code go to:
Github-Enygmator-ToDo-PoC
IMPORT TODO DATABASE:
CREATE NEW TODO DATABASE
App Status and notification:
Everything looks good!


Simple terms for CronStorm
These terms are between you and CronStorm ("we", "us").
When you subscribe to the CronStorm API service to get a developer API key, you agree to these terms. If you cancel your subscription (which you can do at any time) we do not provide a refund for partial months, but instead your subscription (your API key) will remain operational until the end of that billing cycle. Billing cycles begin each month on the same date that you started your subscription, or the closest day to that.
If you provided an email address when you paid (you may have had to do so, since Stripe (a payment provider we use, not affiliated with us) may require it), therefore you agree that you consent to us sending you some promotion mails about your subscription, reminders about subscription, overdue payments, and support emails about your service, or other notifications over email. If you wish to not receive these mails then you can email us at this support address to opt out and we will no longer contact you, except as we are required to do so by law or to resolve or clarify any issues about payment that, if we were not to contact you, would possibly have adverse legal consequences.
When you buy an interval you purchase a number of invocations. But actually that number is only the maximum number of invocations. The actualy invocations effected are always less than that because each request always takes slightly longer than the interval duration ( for example, a 1 seconds request typically takes 1.1 seconds ), so that means that over a 5 minute ( 300 invocation ) duration, you will typically only see about 270 invocations. This is normal. What you are in effect paying for is utilizing of our system for the specificied duration of time, which is always exact. But the number of invocations we provide at time of sale is only a guide and you agree to take it as such. If you have a need for a specific number of invocations ( rather than a specific exact duration over which they occur ) please contact us via email or raise an issue on our github issue tracker.
This is the beta release of pocketwatch. Currently limitations include (but not limited to): GET requests only, cannot delete an interval once it starts without knowing it's tracking number (long, secret code which you need to save if you want to prove you created the interval and want to request its deletion, currently request is via email).
If you do request an interval deletion we do not currently offer any refunds. So please begin your intervals carefully. We can delete it for you if you request it (and have the tracking number) but you will not get any money back.
Use this tool at your own discretion. We are not responsible for how you use it.
We reserve the right to delete intervals with no refund if it appears they are harmful in any way.
In general we will not offer refunds. But in case we do, they will be determined on a case by case basis.
Don't use the names The Dosaygo Corporation, DOSY, Dosycorp, or CronStorm.com in anyway that suggests partnership or endoresment or other relationship when none in fact exists.
This product is offered by the Dosyago Corporation, any legal action will be decided with respect to the laws of the United States, and through the courts in that jurisdiction.
Unless otherwise made explicit, no content or other corpyrightable or protected work may be reused or incorporated without prior written permission from us.
If you need to contact us open an issue at: https://github.com/dosyago-corp/service-issues/issues
We do our best to place and fulfill your orders as requested, and to achieve the specified rates of requests for the specified duration as ordered in your interval, however we do not guarantee we shall achieve this, and we are not responsible for issues related to external network latency, routing or other such issues, and in the case your interval suffers degraded performance on account of such or other issues we have limited control over, we certainly offer no refunds. This service does not currently have any SLA ( service level agreement ).
If you need to contact CronStorm privately please email CronStorm for help.
These terms are a living document. We may update them at any time, with or without notice.


My Webpage
Home
Page 2
Page 3
This is my homepage
This is the contents of my homepage
This is the contents of my homepage -s
This is the contents of my homepage
This is the contents of my homepage
This is the contents of my homepage


| DOSE: T+ 0:00 |
500 mg |
oral |
DXM |
(liquid) |
| T+ 0:00 |
1 shot |
oral |
Alcohol |
(liquid) |
| T+ 0:30 |
350 mg |
oral |
DXM |
(capsule) |
| T+ 3:00 |
650 mg |
oral |
DXM |
(liquid) |
| T+ 3:00 |
1 shot |
oral |
Alcohol |
(liquid) |
I'd like to share a _very_ intense experience i had yesterday whilst experimenting (quite heavily, i might add) with DXM. Around 3 months ago, i'd pursued research into usage of DXM, especially into one state of consciousness known on the DXM faq as 'Plateau Sigma'. This seemed to be a very delusional and unpleasant state, but I was adiment that I would pursue it, just to see if I (a self named, and externally named subgenius) could control my mind, psyche and consciousness whilst in this state.
Here's how I dosed (note: i didn't follow the faq):
T+00h: ingested 500mg white (foul tasting) powder in vodka (1 shot)
waited awhile. after around 30min i felt first effects, at which time I took a gelcap containing 350mg more dxm. i took another dose later, but i'll get to that in a minute
I sat on my sofa in my room, having the odd cigartette and playing with my guitar (btw, this is very satisfying in conjunction with cannabis). at roughly the 3hr point, i snapped out of my guitar / cigarette trance, realising that i was struming away absolute bullcrap instead of trying to perfect 'riders on the storm'. fair enough. the world around me after this trance was so utterly alien it's really impossible to describe.
Imagine this: i'm around 10 inches tall, with really long legs and the room around me is the size of what literally seems like the Sydney opera house. in front of me is a coffee table, which although was right in ront of me seemed quite far away. I had the sensation that there was now no wall or sofa behind me and that at any given moment i could fall into nothingness, so i had to constantly check. there was nothing outside this little world i was in at the moment, reality beyond the room seemed incomprehensible and all that was there was what i thought was infinity. space and time, and even life itself would disappear in a nanosecond were i to leave this room. thanatos at my door, and a black hole behind me, how comforting. then i realised that my thoughts were completely delusional, so i snapped out of it and looked around again. still the same, but this time i realised it was my room and i was safe there. ok.
i'd prepared one final dose for myself on my bedside table, as another vodka shot (650mg DXM), and a paracetamol lying next to it (thoughts become more delusional as body temp rises) so down that went and i lay on my bed for a while. i sort of dozed off, & went to a world of mine which i keep in my mind for psychedelic trips. i was there, the place was vast & white, full of buildings, the floor a conveyor belt of geometric patterns and the sky a green entrance to the infinity. my quest this time was simply to explore as far as i could before awakening. i had the advantage over the world this time. my body needed no control and i had neither the inhibitions of emotion or pain.
Turning the corner i encountered what i thought was the god-entity. this, i thought was the entity ruling absolutely over the different realities and as such had unquestionnable power over me, my life and everything, everywhere. darkness came and went, different realities passed - planets were brought to me and disappeared just as quickly, full of different ecosystems, different florae and faunae, culture and religion. just as i got a glimpse into a new and wonderful world, another would appear, equally as intriguing. i now had no body, no soul and no attributes to my name. i was powerless to go forward from this point and my only choice was to observe what was happening around me.
My vision skipped and i was back to my bedroom. i looked at myself, then struggled to see the time on the clock. 1pm, four and a half hours since first ingestion. that meant, to my relief that I'd come back to what was my world, the world I was comfortable and familiar with. no, that was wrong. I looked out of my window. everything was different, the trees outside, the neighbourhood and people i saw were all monstrous. these realities i had seen were now combined, and no matter how i tried to bring myself back to reality, it stayed the same. oh crap. i broke down in tears, thinking the world would stay like that, and i'd be trapped with monsters, my emotions and my personality consumed by this god-entity. whereas i was a god of my own inner reality, this one was of far vaster knowlege, and in his eyes i was pathetic, so he took away what was me and replaced it with a dead, roaming soul.

At some point i fell fully into sleep, having very weird dreams, and awoke around 12 hours later, feeling very dry, and very hungry, i just sat on my bed, lit a cigarette and thought about what happened. i felt so glad to be where i was, to be smoking this cig, to be myself once again and that my friends, family and girlfriend were all out there, not knowing this state.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


A dik-dik is a tiny antelope that lives in the bush of East Africa, Namibia and Angola. They are named dik-diks because of the sound it makes when frightened. There are four different species of dik-diks. Dik-diks weigh between three and six kilograms.
Scientific name: Madoqua Height: 12 – 16 in. (At Shoulder) Mass: 6.6 – 13 lbs Speed: 26 mph (Maximum)


BONSAI Sparql Endpoint
Select a query, or write own
Activity Types
Locations
Flow Objects
Input Flows of an Activity by Location
Output Flows of an Activity by Location
Activities where Flowobject is an output
All flows given Location and Activity
Production of Steel from China
Chinese Activities using Steel
Chinese Particulate Matter Emission from Steel Production
Chinese Carbon Dioxide Emission from Steel Production
Is flow with objectType x a determining flow for Activity y
Is flow with objectType x a required for Activity Y
List objectTypes required for activities
Total flow of objectType in period
In which unit are flowObjects Measured
Which flows are overbalanced
Which flows are underbalanced
Amount of flowObject f used in Activity a in Location l
Amount of flowObject f as output of Activity a in Location l
What are the determining flows for all activities in locaiton l
What is the determining flow for Activity A in Location L
What units can be used for Balanceable properties
Provenance of graph
Provenance of single flow
User Defined


The maps will be stored in memory but all data will be lost when closing or reloading the current
page.
The maps will be stored within the browser storage. Data won't be lost when closing or reloading the current
page. But you won't access them using a Web API and share them.
The maps will be stored using a Web API. You will be able to access them using a Web API and share them.


Amazon Textract - Automatically extract text and structured data from documents
Documents are a primary tool for record keeping, communication, collaboration, and transactions across many industries, including financial, medical, legal, and real estate. The millions of mortgage applications and hundreds of millions of W2 tax forms processed each year are just a few examples of such documents. A lot of information is locked in unstructured documents. It usually requires time-consuming and complex processes to enable search and discovery, business process automation, and compliance control for these documents.
In this post, I show how you can take advantage of Amazon Textract to automatically extract text and data from scanned documents without any machine learning (ML) experience. While AWS takes care of building, training, and deploying advanced ML models in a highly available and scalable environment, you take advantage of these models with simple-to-use API actions. Here are the use cases that I cover in this post:
- Text detection from documents
- Multi-column detection and reading order
- Natural language processing and document classification
- Natural language processing for medical documents
- Document translation
- Search and discovery
- Form extraction and processing
- Compliance control with document redaction
- Table extraction and processing
- PDF document processing
Amazon Textract
Before I get started with the use cases, let me review and introduce some of the core features. Amazon Textract goes beyond simple optical character recognition (OCR) to also identify the contents of fields in forms and information stored in tables. This allows you to use Amazon Textract to instantly “read” virtually any type of document and accurately extract text and data without the need for any manual effort or custom code.
The following images show an example document and corresponding extracted text, form, and table data using Amazon Textract in the AWS Management Console.
The following image shows the lines extracted as raw text from the document.
The following image shows the extracted form fields and their corresponding values.
The following image shows the extracted table, cells, and the text in those cells.
To quickly download a zip file containing the output, choose Download results. You can choose various formats, including raw JSON, text, and CSV files for forms and tables.
In addition to the detected content, Amazon Textract provides additional information, like confidence scores and bounded boxes for detected elements. It gives you control on how you consume extracted content and integrate it into various business applications.
Amazon Textract provides both synchronous and asynchronous API actions to extract document text and analyze the document text data.
Synchronous APIs can be used for single-page documents and low latency use cases such as mobile capture. Asynchronous APIs can be used for multi-page documents such as PDF documents with thousands of pages. For more information, see the Amazon Textract API Reference.
Use cases
Now, write some code to take advantage of Amazon Textract API operations using the AWS SDK and see how easy it is to build power-smart applications. I will also use the JSON Parser Library for some of the below use cases.
Text detection from documents
I start with a simple example on how to detect text from a document. Use the following image as an input document to Amazon Textract. As you can see, the sample image is not of good quality, but Amazon Textract can still detect the text with accuracy.
The following code example shows how to use a few lines of code to send this sample image to Amazon Textract and get a JSON response back. You then iterate over the blocks in JSON and print the detected text, as shown below.
import boto3
# Document
s3BucketName = "ki-textract-demo-docs"
documentName = "simple-document-image.jpg"
# Amazon Textract client
textract = boto3.client('textract')
# Call Amazon Textract
response = textract.detect_document_text(
Document={
'S3Object': {
'Bucket': s3BucketName,
'Name': documentName
}
})
#print(response)
# Print detected text
for item in response["Blocks"]:
if item["BlockType"] == "LINE":
print ('\033[94m' + item["Text"] + '\033[0m')
The following JSON response is what you receive from Amazon Textract, with blocks representing detected text in the document.
{
"Blocks": [
{
"Geometry": {
"BoundingBox": {
"Width": 1.0,
"Top": 0.0,
"Left": 0.0,
"Height": 1.0
},
"Polygon": [
{
"Y": 0.0,
"X": 0.0
},
{
"Y": 0.0,
"X": 1.0
},
{
"Y": 1.0,
"X": 1.0
},
{
"Y": 1.0,
"X": 0.0
}
]
},
"Relationships": [
{
"Type": "CHILD",
"Ids": [
"2602b0a6-20e3-4e6e-9e46-3be57fd0844b",
"82aedd57-187f-43dd-9eb1-4f312ca30042",
"52be1777-53f7-42f6-a7cf-6d09bdc15a30",
"7ca7caa6-00ef-4cda-b1aa-5571dfed1a7c"
]
}
],
"BlockType": "PAGE",
"Id": "8136b2dc-37c1-4300-a9da-6ed8b276ea97"
}.....
],
"DocumentMetadata": {
"Pages": 1
}
}
The following image shows the output of the detected
Multi-column detection and reading order
Traditional OCR solutions read left to right, do not detect multiple columns, and end up generating incorrect reading order for multi-column documents. In addition to detecting text, Amazon Textract provides additional geometry information that can be used to detect multiple columns and print the text in reading order. The following image is a two-column document. Similar to the earlier example, the image is not good quality but Amazon Textract still performs well.
The following example code shows processing the document with Amazon Textract and taking advantage of geometry information to print the text in reading order.
import boto3
# Document
s3BucketName = "ki-textract-demo-docs"
documentName = "two-column-image.jpg"
# Amazon Textract client
textract = boto3.client('textract')
# Call Amazon Textract
response = textract.detect_document_text(
Document={
'S3Object': {
'Bucket': s3BucketName,
'Name': documentName
}
})
#print(response)
# Detect columns and print lines
columns = []
lines = []
for item in response["Blocks"]:
if item["BlockType"] == "LINE":
column_found=False
for index, column in enumerate(columns):
bbox_left = item["Geometry"]["BoundingBox"]["Left"]
bbox_right = item["Geometry"]["BoundingBox"]["Left"] + item["Geometry"]["BoundingBox"]["Width"]
bbox_centre = item["Geometry"]["BoundingBox"]["Left"] + item["Geometry"]["BoundingBox"]["Width"]/2
column_centre = column['left'] + column['right']/2
if (bbox_centre > column['left'] and bbox_centre < column['right']) or (column_centre > bbox_left and column_centre < bbox_right):
#Bbox appears inside the column
lines.append([index, item["Text"]])
column_found=True
break
if not column_found:
columns.append({'left':item["Geometry"]["BoundingBox"]["Left"], 'right':item["Geometry"]["BoundingBox"]["Left"] + item["Geometry"]["BoundingBox"]["Width"]})
lines.append([len(columns)-1, item["Text"]])
lines.sort(key=lambda x: x[0])
for line in lines:
print (line[1])
The following image shows the output of the detected text in the correct reading order.
Natural language processing and document classification
Customer emails, support tickets, product reviews, social media, even advertising copy all represent insights into customer sentiment that can be put to work for your business.
A lot of such content contains images or scanned versions of documents. After text is extracted from these documents, you can use Amazon Comprehend to detect sentiment, entities, key phrases, syntax and topics. You can also train Amazon Comprehend to detect custom entities based on your business domain. These insights can then be used to classify documents, automate business process workflows, and ensure compliance.
The following example code shows processing the first image sample used earlier with Amazon Textract to extract text and then using Amazon Comprehend to detect sentiment and entities.
import boto3
# Document
s3BucketName = "ki-textract-demo-docs"
documentName = "simple-document-image.jpg"
# Amazon Textract client
textract = boto3.client('textract')
# Call Amazon Textract
response = textract.detect_document_text(
Document={
'S3Object': {
'Bucket': s3BucketName,
'Name': documentName
}
})
#print(response)
# Print text
print("\nText\n========")
text = ""
for item in response["Blocks"]:
if item["BlockType"] == "LINE":
print ('\033[94m' + item["Text"] + '\033[0m')
text = text + " " + item["Text"]
# Amazon Comprehend client
comprehend = boto3.client('comprehend')
# Detect sentiment
sentiment = comprehend.detect_sentiment(LanguageCode="en", Text=text)
print ("\nSentiment\n========\n{}".format(sentiment.get('Sentiment')))
# Detect entities
entities = comprehend.detect_entities(LanguageCode="en", Text=text)
print("\nEntities\n========")
for entity in entities["Entities"]:
print ("{}\t=>\t{}".format(entity["Type"], entity["Text"]))
The following image shows the output text along with the text analysis from Amazon Comprehend. You can see that it found the sentiment to be “Neutral” and detected “Amazon” as an organization, “Seattle, WA” as a location and “July 5th, 1994” as a date, along with other entities.
Natural language processing for medical documents
One of the important ways to improve patient care and accelerate clinical research is by understanding and analyzing the insights and relationships that are “trapped” in free-form medical text. These can include hospital admission notes and a patient’s medical history. In this example, use the following document to extract text using Amazon Textract. You then use Amazon Comprehend Medical to extract medical entities, such as medical condition, medication, dosage, strength, and protected health information (PHI).
The following example code shows how different medical entities are detected.
import boto3
# Document
s3BucketName = "ki-textract-demo-docs"
documentName = "medical-notes.png"
# Amazon Textract client
textract = boto3.client('textract')
# Call Amazon Textract
response = textract.detect_document_text(
Document={
'S3Object': {
'Bucket': s3BucketName,
'Name': documentName
}
})
#print(response)
# Print text
print("\nText\n========")
text = ""
for item in response["Blocks"]:
if item["BlockType"] == "LINE":
print ('\033[94m' + item["Text"] + '\033[0m')
text = text + " " + item["Text"]
# Amazon Comprehend client
comprehend = boto3.client('comprehendmedical')
# Detect medical entities
entities = comprehend.detect_entities(Text=text)
print("\nMidical Entities\n========")
for entity in entities["Entities"]:
print("- {}".format(entity["Text"]))
print (" Type: {}".format(entity["Type"]))
print (" Category: {}".format(entity["Category"]))
if(entity["Traits"]):
print(" Traits:")
for trait in entity["Traits"]:
print (" - {}".format(trait["Name"]))
print("\n")
The following image and text block shows the output of the detected text with information categorized by type. It detected “40yo” as the age with category “Protected Health Information”. It also detected different medical conditions, including sleeping trouble, rash, inferior turbinates, erythematous eruption, and others. It recognized different medications and anatomy information.
Medical Entities
========
- 40yo
Type: AGE
Category: PROTECTED_HEALTH_INFORMATION
- Sleeping trouble
Type: DX_NAME
Category: MEDICAL_CONDITION
Traits:
- SYMPTOM
- Clonidine
Type: GENERIC_NAME
Category: MEDICATION
- Rash
Type: DX_NAME
Category: MEDICAL_CONDITION
Traits:
- SYMPTOM
- face
Type: SYSTEM_ORGAN_SITE
Category: ANATOMY
- leg
Type: SYSTEM_ORGAN_SITE
Category: ANATOMY
- Vyvanse
Type: BRAND_NAME
Category: MEDICATION
- Clonidine
Type: GENERIC_NAME
Category: MEDICATION
- HEENT
Type: SYSTEM_ORGAN_SITE
Category: ANATOMY
- Boggy inferior turbinates
Type: DX_NAME
Category: MEDICAL_CONDITION
Traits:
- SIGN
- inferior
Type: DIRECTION
Category: ANATOMY
- turbinates
Type: SYSTEM_ORGAN_SITE
Category: ANATOMY
- oropharyngeal lesion
Type: DX_NAME
Category: MEDICAL_CONDITION
Traits:
- SIGN
- NEGATION
- Lungs
Type: SYSTEM_ORGAN_SITE
Category: ANATOMY
- clear Heart
Type: DX_NAME
Category: MEDICAL_CONDITION
Traits:
- SIGN
- Heart
Type: SYSTEM_ORGAN_SITE
Category: ANATOMY
- Regular rhythm
Type: DX_NAME
Category: MEDICAL_CONDITION
Traits:
- SIGN
- Skin
Type: SYSTEM_ORGAN_SITE
Category: ANATOMY
- erythematous eruption
Type: DX_NAME
Category: MEDICAL_CONDITION
Traits:
- SIGN
- hairline
Type: SYSTEM_ORGAN_SITE
Category: ANATOMY
Document translation
Many organizations localize content for international users, such as websites and applications. They must translate large volumes of documents efficiently. You can use Amazon Textract along with Amazon Translate to extract text and data and then translate them into other languages.
The following code example shows translating the text in the first image to German.
import boto3
# Document
s3BucketName = "ki-textract-demo-docs"
documentName = "simple-document-image.jpg"
# Amazon Textract client
textract = boto3.client('textract')
# Call Amazon Textract
response = textract.detect_document_text(
Document={
'S3Object': {
'Bucket': s3BucketName,
'Name': documentName
}
})
#print(response)
# Amazon Translate client
translate = boto3.client('translate')
print ('')
for item in response["Blocks"]:
if item["BlockType"] == "LINE":
print ('\033[94m' + item["Text"] + '\033[0m')
result = translate.translate_text(Text=item["Text"], SourceLanguageCode="en", TargetLanguageCode="de")
print ('\033[92m' + result.get('TranslatedText') + '\033[0m')
print ('')
The following image shows the output of the detected text, translated to German line by line.
Search and discovery
Extracting structured data from documents and creating a smart index using Amazon Elasticsearch Service (Amazon ES) allows you to search through millions of documents quickly. For example, a mortgage company could use Amazon Textract to process millions of scanned loan applications in a matter of hours and have the extracted data indexed in Amazon ES. This would allow them to create search experiences like searching for loan applications where the applicant name is John Doe, or searching for contracts where the interest rate is 2 percent.
The following code example shows how you can extract text from the first image, store it in Amazon ES, and then search it using Kibana. You can also build a custom UI experience by taking advantage of the Amazon ES APIs. Later in the post, as you learn how to extract forms and tables, that structured data can then be indexed similarly to enable smart search.
import boto3
from elasticsearch import Elasticsearch, RequestsHttpConnection
from requests_aws4auth import AWS4Auth
def indexDocument(bucketName, objectName, text):
# Update host with endpoint of your Elasticsearch cluster
#host = "search--xxxxxxxxxxxxxx.us-east-1.es.amazonaws.com
host = "searchxxxxxxxxxxxxxxxx.us-east-1.es.amazonaws.com"
region = 'us-east-1'
if(text):
service = 'es'
ss = boto3.Session()
credentials = ss.get_credentials()
region = ss.region_name
awsauth = AWS4Auth(credentials.access_key, credentials.secret_key, region, service, session_token=credentials.token)
es = Elasticsearch(
hosts = [{'host': host, 'port': 443}],
http_auth = awsauth,
use_ssl = True,
verify_certs = True,
connection_class = RequestsHttpConnection
)
document = {
"name": "{}".format(objectName),
"bucket" : "{}".format(bucketName),
"content" : text
}
es.index(index="textract", doc_type="document", id=objectName, body=document)
print("Indexed document: {}".format(objectName))
# Document
s3BucketName = "ki-textract-demo-docs"
documentName = "simple-document-image.jpg"
# Amazon Textract client
textract = boto3.client('textract')
# Call Amazon Textract
response = textract.detect_document_text(
Document={
'S3Object': {
'Bucket': s3BucketName,
'Name': documentName
}
})
#print(response)
# Print detected text
text = ""
for item in response["Blocks"]:
if item["BlockType"] == "LINE":
print ('\033[94m' + item["Text"] + '\033[0m')
text += item["Text"]
indexDocument(s3BucketName, documentName, text)
# You can view index documents in Kibana Dashboard
The following image shows the output of extracted text in Kibana search results.
Form extraction and processing
Amazon Textract can provide the inputs required to automatically process forms without human intervention. For example, a bank could write code to read PDFs of loan applications. The information contained in the document could be used to initiate all of the necessary background and credit checks to approve the loan so that customers can get instant results for their application rather than having to wait several days for manual review and validation. The following image is an employment application with form fields and a table.
The following code example shows how to extract forms from the employment application and process different fields.
import boto3
from trp import Document
# Document
s3BucketName = "ki-textract-demo-docs"
documentName = "employmentapp.png"
# Amazon Textract client
textract = boto3.client('textract')
# Call Amazon Textract
response = textract.analyze_document(
Document={
'S3Object': {
'Bucket': s3BucketName,
'Name': documentName
}
},
FeatureTypes=["FORMS"])
#print(response)
doc = Document(response)
for page in doc.pages:
# Print fields
print("Fields:")
for field in page.form.fields:
print("Key: {}, Value: {}".format(field.key, field.value))
# Get field by key
print("\nGet Field by Key:")
key = "Phone Number:"
field = page.form.getFieldByKey(key)
if(field):
print("Key: {}, Value: {}".format(field.key, field.value))
# Search fields by key
print("\nSearch Fields:")
key = "address"
fields = page.form.searchFieldsByKey(key)
for field in fields:
print("Key: {}, Value: {}".format(field.key, field.value))
The following image is the output of detected form for the employment application.
Compliance control with document redaction
Because Amazon Textract identifies data types and form labels automatically, AWS helps secure infrastructure so that you can maintain compliance with information controls. For example, an insurer could use Amazon Textract to feed a workflow that automatically redacts personally identifiable information (PII) for review before archiving claim forms. Amazon Textract recognizes the important fields that require protection.
The following code example shows extracting all the form fields in the employment application used earlier, and then redacting all the address fields.
import boto3
from trp import Document
from PIL import Image, ImageDraw
# Document
s3BucketName = "ki-textract-demo-docs"
documentName = "employmentapp.png"
# Amazon Textract client
textract = boto3.client('textract')
# Call Amazon Textract
response = textract.analyze_document(
Document={
'S3Object': {
'Bucket': s3BucketName,
'Name': documentName
}
},
FeatureTypes=["FORMS"])
#print(response)
doc = Document(response)
# Redact document
img = Image.open(documentName)
width, height = img.size
if(doc.pages):
page = doc.pages[0]
for field in page.form.fields:
if(field.key and field.value and "address" in field.key.text.lower()):
#if(field.key and field.value):
print("Redacting => Key: {}, Value: {}".format(field.key.text, field.value.text))
x1 = field.value.geometry.boundingBox.left*width
y1 = field.value.geometry.boundingBox.top*height-2
x2 = x1 + (field.value.geometry.boundingBox.width*width)+5
y2 = y1 + (field.value.geometry.boundingBox.height*height)+2
draw = ImageDraw.Draw(img)
draw.rectangle([x1, y1, x2, y2], fill="Black")
img.save("redacted-{}".format(documentName))
The following image is the output redacted version of employment application.
Table extraction and processing
Amazon Textract can detect tables and their content. A company can extract all the amounts from an expense report and apply rules, such as any expense more than $1000 needs extra review.
The following code example uses the expense report sample document and prints the content of each cell, along with a warning message if any expense is more than $1000.
import boto3
from trp import Document
# Document
s3BucketName = "ki-textract-demo-docs"
documentName = "expense.png"
# Amazon Textract client
textract = boto3.client('textract')
# Call Amazon Textract
response = textract.analyze_document(
Document={
'S3Object': {
'Bucket': s3BucketName,
'Name': documentName
}
},
FeatureTypes=["TABLES"])
#print(response)
doc = Document(response)
def isFloat(input):
try:
float(input)
except ValueError:
return False
return True
warning = ""
for page in doc.pages:
# Print tables
for table in page.tables:
for r, row in enumerate(table.rows):
itemName = ""
for c, cell in enumerate(row.cells):
print("Table[{}][{}] = {}".format(r, c, cell.text))
if(c == 0):
itemName = cell.text
elif(c == 4 and isFloat(cell.text)):
value = float(cell.text)
if(value > 1000):
warning += "{} is greater than $1000.".format(itemName)
if(warning):
print("\nReview needed:\n====================\n" + warning)
The following text is the output of the table cells and the text within.
Table[0][0] = Expense Description
Table[0][1] = Type
Table[0][2] = Date
Table[0][3] = Merchant Name
Table[0][4] = Amount (USD)
Table[1][0] = Furniture (Desks and Chairs)
Table[1][1] = Office Supplies
Table[1][2] = 5/10/1019
Table[1][3] = Merchant One
Table[1][4] = 1500.00
Table[2][0] = Team Lunch
Table[2][1] = Food
Table[2][2] = 5/11/2019
Table[2][3] = Merchant Two
Table[2][4] = 100.00
Table[3][0] = Team Dinner
Table[3][1] = Food
Table[3][2] = 5/12/2019
Table[3][3] = Merchant Three
Table[3][4] = 300.00
Table[4][0] = Laptop
Table[4][1] = Office Supplies
Table[4][2] = 5/13/2019
Table[4][3] = Merchant Three
Table[4][4] = 200.00
Table[5][0] =
Table[5][1] =
Table[5][2] =
Table[5][3] =
Table[5][4] =
Table[6][0] =
Table[6][1] =
Table[6][2] =
Table[6][3] =
Table[6][4] =
Table[7][0] =
Table[7][1] =
Table[7][2] =
Table[7][3] =
Table[7][4] =
Table[8][0] =
Table[8][1] =
Table[8][2] =
Table[8][3] = Total
Table[8][4] = 2100.00
Review needed:
====================
Furniture (Desks and Chairs) is greater than $1000.
PDF document processing (async API operations)
For the earlier examples, you used images with the sync API operations. Now, see how you can process PDF files using the async API operations.
First, use StartDocumentTextDetection or StartDocumentAnalysis to start an Amazon Textract job. As the job completes, Amazon Textract publishes the results of an Amazon Textract request, including completion status, to Amazon SNS. You can then use GetDocumentTextDetection or GetDocumentAnalysis to get the results from Amazon Textract.
The following code example shows how to start a job, get job status, and then process the results. Click here for the sample PDF document. For more information, see Calling Amazon Textract Asynchronous Operations.
import boto3
import time
def startJob(s3BucketName, objectName):
response = None
client = boto3.client('textract')
response = client.start_document_text_detection(
DocumentLocation={
'S3Object': {
'Bucket': s3BucketName,
'Name': objectName
}
})
return response["JobId"]
def isJobComplete(jobId):
# For production use cases, use SNS based notification
# Details at: https://docs.aws.amazon.com/textract/latest/dg/api-async.html
time.sleep(5)
client = boto3.client('textract')
response = client.get_document_text_detection(JobId=jobId)
status = response["JobStatus"]
print("Job status: {}".format(status))
while(status == "IN_PROGRESS"):
time.sleep(5)
response = client.get_document_text_detection(JobId=jobId)
status = response["JobStatus"]
print("Job status: {}".format(status))
return status
def getJobResults(jobId):
pages = []
client = boto3.client('textract')
response = client.get_document_text_detection(JobId=jobId)
pages.append(response)
print("Resultset page recieved: {}".format(len(pages)))
nextToken = None
if('NextToken' in response):
nextToken = response['NextToken']
while(nextToken):
response = client.get_document_text_detection(JobId=jobId, NextToken=nextToken)
pages.append(response)
print("Resultset page recieved: {}".format(len(pages)))
nextToken = None
if('NextToken' in response):
nextToken = response['NextToken']
return pages
# Document
s3BucketName = "ki-textract-demo-docs"
documentName = "Amazon-Textract-Pdf.pdf"
jobId = startJob(s3BucketName, documentName)
print("Started job with id: {}".format(jobId))
if(isJobComplete(jobId)):
response = getJobResults(jobId)
#print(response)
# Print detected text
for resultPage in response:
for item in resultPage["Blocks"]:
if item["BlockType"] == "LINE":
print ('\033[94m' + item["Text"] + '\033[0m')
The following image shows the job status as the API call proceeds.
Conclusion
In this post, I showed you how to use Amazon Textract to automatically extract text and data from scanned documents without any machine learning (ML) experience. I covered use cases in fields such as finance, healthcare, and HR, but there are many other opportunities where the ability to unlock text and data from unstructured documents could be most useful. To learn more about Amazon Textract, read about processing single-page and multi-page documents, working with block objects, and code samples.
You can start using Amazon Textract in US East (N. Virginia), US East (Ohio), US West (Oregon), and EU (Ireland) today.


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October 7th, 2019 - Derek Rodriguez
During my undergrad I took a data science class. It was a great class where we dove into topics such as EDA, lasso regression, SVMs, visualizations, and many other good things that other people across the Internet love to blog about ad nauseum. The one thing I had a terrible experience with was R. Let me clarify—R is a fantastic environment for statisticians to explore advanced computation methods on cleaned data. It is also a great environment for developing great insights on clean data using their DataFrame type. If you haven't caught the hint yet, R's true potential is so much more immediate if your data is already great.
The sad reality of most exciting data science projects is that they more often than not aren't based on a clean, well-studied dataset. Thank God Tensorflow changed their default tutorial away from digit recognition, because MNIST has quickly become the Wonderwall of computer vision. The reality is half of data science is just cleaning data, and the other half is complaining about how much of data science is just cleaning data. You can add another half's worth of time for debugging your neural net, if you were coaxed into using one that isn't off-the-shelf.
 Thirty minutes of of some good cURL mixed with awk and/or some sed perform miracles on poorly-formatted plaintext source. Python's scientific libraries (PySpark, pandas, etc.) do a decent job of copying R's DataFrame magic and even allowed for it to scale to multi-node architectures. That being said, criticizing R for all the awkwardness that comes from a language developed by a bunch of a statisticians is absolutely not the point of the post.
I came to an important realization. R is a Unix tool. Throwing an R snippet into a command just like you would with awk or sed opens a whole new world of possibility. It blows my mind that isn't R isn't shown in this context more often. If R is bad at cleaning your data, you can just clean it in awk and then just pipe to R. I've actually reduced my R usage down to mostly just two aliases (written here in fish syntax):
alias stats 'Rscript -e "summary(as.numeric(readLines(\"stdin\")))"' alias boxplot 'Rscript -e "boxplot(as.numeric(readLines(\"stdin\")))"'
The first produces a quick numeric summary of a series of numbers on stdin. The second produces a boxplot with that same data in PDF format. For example, let's say I've just bought a new server and I want to see how long it takes to spin up a web app after a crash. I would write a script that simulates a crash, and then logs the recovery time after each crash and saves it in a file called output.log. If I wanted a boxplot of the total recovery times, all I would have to do is:
$ grep "Recovery time:" output.log | awk '{print $2}' | boxplot
No spinning up Rstudio. No messing with ggplot settings. No more oh-my-god-why-how-do-I-just-get-this-into-a-list. Just results that help you figure do some quick-and-dirty EDA. I'd love it if people e-mail me some R snippets like the one I shared here so I can add them to my running list.
Update: I got some really great feedback from this article on HN. I've taken a stab at rewriting the post a little to emphasize the original intent of recontextualizing R as a great command-line tool, instead of as the core language for a data science / statistics programming ecosystem.


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December
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d.s.Correli
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lj.7926
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FAIRCHILD
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GARDEN
1624912
Legend -
Level of confidence that token is an accurately-transcribed word
extremely low
very low
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undetermined
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viral HAE Fig. nasal bronchial SARS-CoV- OpenUrl Scale bar: cells virions Wang apical VeroE CrossRef antiviral hpi basal infection COVID- containing replication electron-dense observed respiratory treatment Enlargement human immune infected remdesivir airway cell combination compared results vesicles (white [[#xB]]m. accumulation cells. expression genome novel surface µm. Liu data model models quantification showing (V). MRC. analysis ciliated cytoplasmic double-membrane expressed induced log reconstituted study titers AP, Huang OT, Remdesivir Zhang clinical diltiazem double effect effects epithelium experimental genes high hpi, intracellular material patients potential production response shown spherules time van (Ba) (Ci) (available Chen Data HAE, Representative Section area condition. coronavirus epithelia features hpi. independent membranes numerous using viruses <;strong>;(C)<;/strong>; <;strong>;(D)<;/strong>; (C) (D) RNA Trans-epithelial Zhou cases cellular clusters component cytopathic described different double-membraned drug electron formed innate kinetics lower materials measured mitochondria new panel). performed perinuclear pieces region relative remdesivir-diltiazem resistance virus <;strong>;(A)<;/strong>; <;strong>;(B)<;/strong>; (A) (B) (DMVs) (TEER (V), (m) (mi) ;<;div BL, COVID-. Cao Clinical Download Gao III Influenza Materials Microvili MucilAir™ Nanostring Open Relative Results Song Vero Wei Wuhan, Xie Zhao areas arrow) arrow). change cilia classic clustered compartments confirmed current data-hide-link-title=""> data-icon-position="" figure fold genes) indicated infection, infection. infectious interferon levels magnification mainly major medical membrane microscopy mock molecular patient points. pole poles provide reductions remodeling signatures strong tab terms therapeutic total transcriptional transmission transverse treatments type ultrastructure upper values virus-induced vitro xmlns=";http://www.w.org//xhtml";>;<;span


--- layout: post title: Stunt truck date: '2015-04-27T19:30:29+02:00' categories: - Set Design - Props picture_url: https://film-Storyboards.tumblr.com/post/117560817853 --- The House of Dancing Water, Macau


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virsh pool-undefine
Delete the configuration file in `/etc/libvirt/storage` for a stopped virtual machine storage pool.
See also: `virsh`, `virsh-pool-destroy`.
More information: home page
- Delete the configuration for the storage pool specified name or UUID (determine using `virsh pool-list`):
virsh pool-undefine --pool name|uuid


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This is the last resting place for the work of the data team at Digital First Media's Project Thunderdome. Our team--Peggy Bustamante, Vaughn Hagerty, Nelson Hsu, Tom Meagher and MaryJo Webster--built interactive graphics and news applications and supported computer-assisted reporting projects for the nation's second largest newspaper company. In the spring of 2014, the company decided to shutter Thunderdome and the data team along with it. Before we left, we went back and open sourced many of the projects that we built over our team's 21 months of existence.
In the repos of our Github organization, you'll find the code and data behind many of the projects we built in our short lifespan. These are charts and maps, graphs and quizzes, news applications and games that ran on the websites of Digital First Media's 75 daily papers. Many of these we created as a result of partnering with reporters and editors in those local newsrooms on stories they were pursuing. Others we reported and built ourselves.
Most of these repos are just the code for the various one-off stories and projects, including the bombings at the Boston Marathon, coverage of the Newtown school shooting, an investigation of Connecticut school superintendent salaries and more data tables than you can shake a stick at, like all of the ads aired during the Super Bowl for five years. You're welcome to peruse the code, look at the projects and use anything you find interesting.
Some of these tools and templates we found ourselves using over and over again. We think these repos in particular will be of interest to other newsrooms looking to kickstart their news development.
Here's where you come in.
We have provided bare bones documentation on every project, often inline in the code. For many of these reusable templates we singled out above, a little more work needs to be done to truly abstract them and to guide other users through the process of bootstrapping them, in order to spin them up. We would love it if other newsroom developers would fork repos they find intriguing and help us make them easier for everyone to use. We accept pull requests!
There are many ways you can reach us. Feel free to file issues in any of the repos with questions. You can ping the data team on Twitter, or you can email us at hello+github@canihireathunderdomer.com.


Async Loading.
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Here we don't have an initial data. The tree will try to fetch init data with ID == 'root' (node's name is 'root' as well)
Loading indicator and delay (5000ms)
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What's your favorite color?
What's your favorite kind of music?


We neither rent nor sell your Personal Information in personally identifiable form to anyone. However, we may share your Personal Information with third parties as described in this section:
Information that’s no longer personally identifiable. We may anonymize your Personal Information so that you are not individually identified, and provide that information to our partners. We may also provide aggregate usage
information to our partners, who may use such information to understand how often and in what ways people use our Services, so that they, too, can provide you with an optimal online experience. However, we never disclose aggregate
usage information to a partner in a manner that would identify you personally, as an individual.
Advertisers: We allow advertisers and/or merchant partners (“Advertisers”) to choose the demographic information of users who will see their advertisements and/or promotional offers and you agree that we may provide any of
the information we have collected from you in non-personally identifiable form to an Advertiser, in order for that Advertiser to select the appropriate audience for those advertisements and/or offers. For example, we might use
the fact you are located in San Francisco to show you ads or offers for San Francisco businesses, but we will not tell such businesses who you are. Or, we might allow Advertisers to display their ads to users with similar usage
patterns to yours, but we will not disclose usage information to Advertisers except in aggregate form, and not in a manner that would identify you personally. Note that if an advertiser asks us to show an ad to a certain audience
or audience segment and you respond to that ad, the advertiser may conclude that you fit the description of the audience they were trying to reach.
We may deliver a file to you through the Services (known as a “web beacon”) from an ad network. Web beacons allow ad networks to provide anonymized, aggregated auditing, research and reporting for us and for advertisers.
Web beacons also enable ad networks to serve targeted advertisements to you when you visit other websites. Because your web browser must request these advertisements and web beacons from the ad network’s servers, these companies
can view, edit, or set their own cookies, just as if you had requested a web page from their site. You may be able to opt-out of web beacon tracking conducted by third parties through our Services by adjusting the Do Not Track
settings on your browser; please note that we don’t control whether or how these third parties comply with Do Not Track requests.
Affiliated Businesses: In certain situations, businesses or third party websites we’re affiliated with may sell or provide products or services to you through or in connection with the Services (either alone or jointly with
us). You can recognize when an affiliated business is associated with such a transaction or service, and we will share your Personal Information with that affiliated business only to the extent that it is related to such transaction
or service. One such service may include the ability for you to automatically transmit Third Party Account Information to your Services profile or to automatically transmit information in your Services profile to your third party
account; for example, accessing you friend list from Facebook. We have no control over the policies and practices of third party websites or businesses as to privacy or anything else, so if you choose to take part in any transaction
or service relating to an affiliated website or business, please review all such business’ or websites’ policies.
Agents: We employ other companies and people to perform tasks on our behalf and need to share your information with them to provide products or services to you; for example, we may use a payment processing Company to receive
and process your credit card transactions for us. Unless we tell you differently, our agents do not have any right to use the Personal Information we share with them beyond what is necessary to assist us.
Business Transfers: We may choose to buy or sell assets. In these types of transactions, customer information is typically one of the business assets that would be transferred. Also, if we (or our assets) are acquired, or
if we go out of business, enter bankruptcy, or go through some other change of control, Personal Information could be one of the assets transferred to or acquired by a third party.
Protection of Company and Others: We reserve the right to access, read, preserve, and disclose any information that we reasonably believe is necessary to comply with law or court order; enforce or apply our
Terms of Use and other agreements; or protect the rights, property, or safety of Company, our employees, our users, or others.


Noun; A word pronounced the same as another but differing in meaning whether spelled the same way or not.
Please enter a word!


DevOps is a term that was coined by Patrick Debois
DevOps describes the framework of breaking communications silos and encouraging inter-disciplinary cooperation and integration.
DevOps acknowledges that problems in software/IT projects is not the responsibility of Devs or IT alone.
DevOps can be thought of as the convergence of three specific movements:
DevOps is about CAMS - Culture, Automation, Measurement, and Sharing.
Avoid local optima - strive to improve the whole process.
Get the results back in the hands of those who can use them. Quickly!
Be willing to change things, and keep changing.
The key to a successful DevOps environment is culture.
One major component to that is the reducing the barriers to communication.
Improving communication between teams is critical.
Fortunately, technology helps make this easy.
It's people that make this difficult.
We use a variety of tools to help with communication. In no particular order:
When you think of an outage in a traditional IT environment, this is what comes to mind.
Someone, usually upper management, is calling for the head of whomever is responsible.
People scramble, not only to resolve the outage, but to make sure their backsides are covered.
Not only does this impede recovery time, but limits the organizations ability to learn from the outage.
Since there isn't an incentive to show culpablity and there are major disincentives to acknowledging it, getting to the true root cause can be rough.
At Stack Exchange, blame is used only to find the root of a problem.
We've even made it somewhat of a game...
Since there isn't a stigma to blame, people are willing to disclose changes they've made or actions they've taken.
This allows you to get to root cause that much faster.
And focus on resolving the problem, rather than worrying about our job.
Nick Craver (developer), Geoff Dalgas (developer), Peter Grace (Sysadmin) and myself (sysadmin) working on a database outage in the plaza in front of our offices in NYC.
Notes:
You may have noticed that I included configuration with source code.
This is not an accident.
Our server configurations are versioned and managed like applications.
By treating our servers this way, we can recreate our environment
physically, virtually, or on some cloud provider with minimal modification.
All the members of our technical staff have access to our build server(s).
Whom ever need to can build, but if you build, be prepared to deal with the consequences.
Stack Exchange offers an interesting example of a DevOps environment,
where business representatives, developers and operations personnel all collaborate to deliver innovative, highly available solutions.


--- short: e-filesfromlocal type: extractor color: red title: Files from local description: Allows files in local file system to be used in the pipeline. ---
Extractor, allows files in local file system to be used in the pipeline.
The {{page.title}} component provides access from the pipeline to the local file system containing input files. This is useful for local instances of LP-ETL, for example on a developer's laptop. It is not recommended to use this component on remote servers as the access to local file system can be a security risk.


Welcome to the MERCURY!
| Mercury Profile: |
| Diameter: 4,879 km |
| Mass: 3.29 × 10^23 kg (0.06 Earths) |
| Moons: None |
| Orbit Distance: 57,909,227 km (0.39 AU) |
| Orbit Period: 88 days |
| Surface Temperature: -173 to 427°C |
| Mercury Profile: |
| Diameter: 4,879 km |
| Mass: 3.29 × 10^23 kg (0.06 Earths) |
| Moons: None |
| Orbit Distance: 57,909,227 km (0.39 AU) |
| Orbit Period: 88 days |
| Surface Temperature: -173 to 427°C |
Mercury is the smallest planet in the Solar System and the one closest to the Sun,[a] with an orbital period of about 88 Earth days, which is much faster than any other planet in the Solar System. Seen from Earth, it appears to move around its orbit in about 116 days. It has no known natural satellites. It is named after the Roman deity Mercury, the messenger to the gods.
Partly because it has almost no atmosphere to retain heat, Mercury's surface temperature varies diurnally more than any other planet in the Solar System, ranging from 100 K (-173 Celsius) at night to 700 K (427 Celsius) during the day in some equatorial regions. The poles are constantly below 180 K (-93 Celsius). Mercury's axis has the smallest tilt of any of the Solar System's planets (about 1/30 of a degree), and its orbital eccentricity is the largest of all known planets in the Solar System. At aphelion, Mercury is about 1.5 times as far from the Sun as it is at perihelion. Mercury's surface is heavily cratered and similar in appearance to the Moon, indicating that it has been geologically inactive for billions of years.
Mercury is one of four terrestrial planets in the Solar System, and is a rocky body like Earth. It is the smallest planet in the Solar System, with an equatorial radius of 2,439.7 kilometres (1,516.0 mi). Mercury is also smaller-albeit more massive-than the largest natural satellites in the Solar System, Ganymede and Titan. Mercury consists of approximately 70% metallic and 30% silicate material. Mercury's density is the second highest in the Solar System at 5.427 g/cm3, only slightly less than Earth's density of 5.515 g/cm3. If the effect of gravitational compression were to be factored out, the materials of which Mercury is made would be denser, with an uncompressed density of 5.3 g/cm3 versus Earth's 4.4 g/cm3.
Mercury's density can be used to infer details of its inner structure. Although Earth's high density results appreciably from gravitational compression, particularly at the core, Mercury is much smaller and its inner regions are not as compressed. Therefore, for it to have such a high density, its core must be large and rich in iron.
Mercury's surface is similar in appearance to that of the Moon, showing extensive mare-like plains and heavy cratering, indicating that it has been geologically
inactive for billions of years. Because knowledge of Mercury's geology has been based only on the 1975 Mariner 10 flyby and terrestrial observations, it is the least
understood of the terrestrial planets. As data from MESSENGER orbiter are processed, this knowledge will increase. For example, an unusual crater with radiating
troughs has been discovered that scientists called "the spider". It was later named Apollodorus.
Albedo features are areas of markedly different reflectivity, as seen by telescopic observation. Mercury has dorsa (also called "wrinkle-ridges"), Moon-like highlands,
montes (mountains), planitiae (plains), rupes (escarpments), and valles (valleys).
Despite its small size and slow 59-day-long rotation, Mercury has a significant, and apparently global, magnetic field. According to measurements taken by Mariner 10,
it is about 1.1% the strength of Earth's. The magnetic-field strength at Mercury's equator is about 300 nT. Like that of Earth, Mercury's magnetic field is dipolar.
Unlike Earth, Mercury's poles are nearly aligned with the planet's spin axis. Measurements from both the Mariner 10 and MESSENGER space probes have indicated
that the strength and shape of the magnetic field are stable.
It is likely that this magnetic field is generated by a dynamo effect, in a manner similar to the magnetic field of Earth. This dynamo effect would result from
the circulation of the planet's iron-rich liquid core. Particularly strong tidal effects caused by the planet's high orbital eccentricity would serve to keep the
core in the liquid state necessary for this dynamo effect.
Graph showing relative strength of Mercury's magnetic field
Mercury has the most eccentric orbit of all the planets; its eccentricity is 0.21 with its distance from the Sun ranging from 46,000,000 to 70,000,000 km
(29,000,000 to 43,000,000 mi). It takes 87.969 Earth days to complete an orbit. The diagram on the right illustrates the effects of the eccentricity,
showing Mercury's orbit overlaid with a circular orbit having the same semi-major axis.
Mercury's higher velocity when it is near perihelion is clear from the
greater distance it covers in each 5-day interval. In the diagram the varying distance of Mercury to the Sun is represented by the size of the planet,
which is inversely proportional to Mercury's distance from the Sun. This varying distance to the Sun, combined with a 3:2 spin-orbit resonance of the planet's
rotation around its axis, result in complex variations of the surface temperature. This resonance makes a single solar day on Mercury last exactly two Mercury years,
or about 176 Earth days


The natural numbers are here displayed as a grid, and the "grid number" is shown.
Each box (square) in this grid represents a number. Prime numbers are shown as red squares.
The number of rows and columns of the numerical grid can be changed by typing in the text box or by dragging the lower left and right margins.
The buttons on the left allow you to scroll up and down. Click on the button with two arrows to scroll up or down two lines. Click on the other two buttons to scroll one line up or down.
In the box "start value" enter the number you wish to specify as the first number in the grid.


I led the redesign of the solar dashboard — from defining problems, scoping work, ideating design, planning and conducting user research, and partnering with engineers to polish the final product.
The team consisted of a lead designer (me), a product manager, an engineering manager, a user researcher, and five engineers.
Community solar is a program for customers to redirect their energy bill towards a remote solar farm in exchange for monthly savings. The existing solar dashboard lacked information and didn't clarify the steps to being fully enrolled on a farm.
As a result, customers were cancelling due to misaligned expectations about what they had signed up for, with the average attrition rate for the first 60-90 days after sign-up around 2.4%. We needed to create a comprehensive dashboard experience for community solar.
Our goal was to improve customer retention and address misaligned expectations by:
Overview of solar page once enrolled
I led a cross-functional brainstorming session to gather ideas across product, engineering, marketing, and customer experience, and created a list of ideas to explore.
Brainstorming ideas
I mapped a user’s journey to being enrolled on a live solar farm, which helped prioritize the information we could show at each step. This also highlighted the need to show updates pre-enrollment as well as impact post-enrollment. Prior to being enrolled, we could also show evergreen content around the community impact they were making.
The three steps to joining a farm
I broke down the sections to design based on the steps of the enrollment journey. First, I explored Welcome and Explainer sections for the initial Pending step. There were technical constraints around having an onboarding tour, so I pushed for a design that communicated a user's status and linked out to an in-depth explainer blog.
Welcome and Explainer section iterations
From there, I designed evergreen modules that we could show to customers before their solar farm was live. We landed on an interactive map of your state’s solar farms to demonstrate the community of Arcadia solar members. We also knew customers rarely thought about where their energy was coming from every time they turned on their lights, so I designed an energy breakdown vizualization for people to explore how clean or dirty their zip code’s energy is.
Solar farm map and energy breakdown iterations
We had real-time solar data once a farm launched, so I created a graph visualization of solar energy produced. An edge case was brought to my attention around empty graphs in initial time intervals, so I paired with an engineer to design various time interval variations based on how long the farm had been turned on for.
Solar energy production chart iterations
I drafted a research brief and conducted user tests with 8 people to understand how they interacted with these designs and to test comprehension of community solar. A few learnings were:
Overall, the designs increased comprehension around the enrollment waiting period for joining a community solar project, and we addressed the points of confusion through clarifying the pending step with wait time and an empty state of their savings.
User testing brief and script
I provided our engineering team with a spec for web and mobile designs, and I partnered with them to ensure the final outcome was polished.
Preview of final design specs
The designs were built and released iteratively, and these improvements helped reduce customer attrition and cut down the number of customer service calls regarding pre-enrollment confusion.
Looking back, I would have loved to have designed more for the end-to-end experience, with improvements made across the website, sign-up, and emails, but we were limited by those cross-functional teams’ capacities.
Overall, this was an investment in making the dashboard oriented around our community solar product, which would pave the path to support iterative features in the future, such as our advocacy tool.


Limiting Scope
Sometimes you may only want to retrieve a subset of data. This is governed at two levels. At the top are any scope limitations dictated by your contract; these can only be changed by digi.me support so please contact support if you want to discuss this further.
At a code level, you can restrict the scope of a Private Sharing session by passing in a custom DMEScope
.
Defining DMEScope
DMEScope
is comprised of three properties. timeRanges
is a list of DMETimeRange
objects (more on this below) to limit the breadth of time for which applicable data will be returned. serviceGroups
is a list of DMEServiceGroup
objects, which nests it’s own list of DMEServiceType
objects, nesting DMEObjectType
objects; this is also detailed below. context
is not currently used and should be left as the default.
Scoping by time range:
DMETimeRange
has 3 properties. fromDate
, toDate
and last
. These are all optional but at least one is required.
Valid configurations are:
To restrict data to a certain point, forwards:
fromDate
: A valid Date
object representation of the earliest date you want data for.
toDate
: null
last
: null
To restrict data from a certain point, backwards:
fromDate
: null
toDate
: A valid Date
object representation of the latest date you want data for.
last
: null
To restrict data from a certain point, to a certain point:
fromDate
: A valid Date
object representation of the earliest date you want data for.
toDate
: A valid Date
object representation of the latest date you want data for.
last
: null
To restrict data to a rolling period, defined by the current date:
fromDate
: null
toDate
: null
last
: A string literal describing the period of time back from now you wish to allow.
NB: Valid last
strings take the format of a integer followed by d
, m
, y
, representing days, months and years respectively. For example: 6m
.
Scoping by service group, service type or object type:
To restrict scope at an object level, your scope must be ‘fully described’; that is to say that a service group must comprise at least one service type, which must comprise at least one object type. Furthermore, a service group may only contain service types belonging to it and said service types may only contain object types belonging to them.
Service Groups, Service Types and Objects are all listed here in the developer documentation. Their relationships are also shown (what belongs to what).
Below is an example of a valid scope to retrive only Playlists
and Followed Artists
from Spotify
:
val playlistObjectType = DMEServiceObjectType(403) // 403 is the ID for a Playlist object.
val followedArtistObjectType = DMEServiceObjectType(407) // 407 is the ID for a Followed Artist object.
val spotifyServiceType = DMEServiceType(19, listOf(playlistObjectType, followedArtistObjectType)) // 19 is the ID for Spotify.
val entertainmentServiceGroup = DMEServiceGroup(5, listOf(spotifyServiceType)) // 5 is the ID for Entertainment.
val scope = DMEScope() // This scope is valid, as no restrictions have been imposed.
scope.serviceGroups = listOf(entertainmentServiceGroup) // The scope is still valid, as it conforms to the rules listed above.
Providing DMEScope
When calling authorize
on your DMEPullClient
, simply pass in your DMEScope
object:
val scope = DMEScope(listOf(DMETimeRange(last = "6m")))
pullClient.authorize(this, scope)
NB: this
represents the activity which is setup to forward onActivityResult
, as described in Getting Started.
The data received from any subsequent calls to getSessionData
will be limited by the scope of the session defined above.


Indicates if record multi select is allowed.
Boolean, default = true
If multi select is allowed, user can select more then one record at a time. This property can be defined
during object creation:
Or any time after the grid has been created:
You can optionally enable .show.selectColumn. See w2grid.show for
more information.


Lisa Stoddard is a human-environment geographer who is interested in the intersection of nature, society, and social justice. Stoddard’s interest and research on social justice extends into the classroom, including research on critical pedagogies, STEM and social justice, and creating inclusive spaces and practices for learning. Stoddard has been the recipient of multiple grants to examine issues of bias and stereotyping on student academic project teams, and the impact this has on student learning and experience. Through this work, Stoddard, and colleague Geoff Pfeifer, have developed a set of modules and tools to create more equitable team dynamics for use by both students and faculty. A description of some of this work can be found in the article Stoddard and Pfeifer (2018) "Working Toward More Equitable Team Dynamics: Mapping Student Assets to Minimize Stereotyping and Task Assignment Bias". American Society for Engineering Education and in Pfeifer and Stoddard (2019) Equitable and Effective Student Teams: Creating and Managing Team Dynamics for Equitable Learning Outcomes, in Wobbe and Stoddard (co-editors),


This is my babe Milky,me and my girlfriend's baby. Now u can see hes really comfortable bcs im combiing him with a comb
second one named Libro its a boy too. he is the quietest car in three cats,and he love to sleep around me and my girlfriend.The last one named Dodo ,sorry i dont have img to show u but anyway these three are my sweet heart !!!


| Stock Name | Date of Purchase | Amount(EURO) | |
|---|---|---|---|
| {{investment.name}} |
{{investment.date | date:'dd-MM-yyyy'}}
{{investment.dateSelectionTooOldFlag ? 'Date may predate Yahoo data for stock.' : ''}}{{investment.dateSelectionTooOldFlag ? 'Defaulting to earliest available date.' : ''}} |
{{investment.amount}} |


Create a new actionable infoscreen
Page to be shown on the screen
Action title
Action URL (for the QR code)
Action title #2
Action URL (for the QR code) #2
Action title #3
Action URL (for the QR code) #3
Your new infoscreen:
The URL goes here
Test it out
Created with ♥ by
Jouni Kaplas


(A) This paragraph is displayed because the condition is false.
(B) Although the condition is true, this paragraph is displayed because myUnless is set to false.


{{post.title}} {{post.meta_description}} {{post.published_at | date:'longDate'}} Written by: Rick H Posted in: {{post.slug}}


The most influential scientist of the 20th century epoch, Albert Einstein (1879-1955) is known as the Father of Modern Physics. He may have revolutionized scientific thought, but what made the public adore him was his down-to-earth sense of humor. Known for making short quips, Einstein was the people’s scientist. Despite being one of the most brilliant men of the 20th century, Einstein appeared approachable, partly because he always had uncombed hair, disheveled clothing, and a lack of socks.
During his entire life, Einstein worked diligently to understand the world around him and in so doing, developed the Theory of Relativity, which opened the door for the creation of the atomic bomb. He received a Nobel Prize for his contribution to the field of Theoretical Physics in the year 1921.
"He was almost wholly without sophistication and wholly without worldliness ... There was always with him a wonderful purity at once childlike and profoundly stubborn."
Robert Oppenheimer Click here to find more informations about Albert Einstein


BABY BOY RATING: 8/10
For more reviews and movie trailers, visit http://www.joblo.com/
PLOT: A big-time momma's boy can't seem to leave his mother's crib and make good on the babies that he himself has produced in the world. He visits his kids (and their mommas) every now and then, but his main goal in life is...well, he doesn't have any goals. Then one day, his mother gets an ex-gangsta boyfriend and suddenly he isn't as welcome anymore in his own house.
CRITIQUE: First things first! This movie ain't about the "hood" at all, it's an intelligent film about love, relationships, family, friendship, maturity and growing up. Do not avoid this film because you think it's about gangbangers and stuff, because it isn't at all.  This is an extremely well-written film, with some of the most developed characters that I've seen on the screen all year, with superb acting performances to match. Granted, the film does start a little slow (and go a little long), but hang in there, because once you appreciate and get to know all of its characters, you will be glad that you did. This isn't a kid's movie. This is about the step that you need to take in order to pass into full "adulthood" and some might not appreciate the grander theme behind that step. I certainly did, because I too passed, and am passing, through many of these very same things right now (yeah, yeah, I still live with my momma!!) and related to a lot of the events that went down in this flick (oh yeah, and for the record, I'm not black but that made absolutely zero difference in this case).
But that's not even half the story.  I was crackin' up at almost every other scene, and they weren't "set-up jokes" like your typical comedies either, just day-to-day things that we could all relate to (like the scene in which Ving Rhames is "drinking Koolaid" in the kitchen and his infamously improvised "African Squat F**k" scene, which you just gotta see to believe!) And there are the performances, too. Ving Rhames, now here's a man who has already established an early lead in the race for best supporting actor at the end of this year.  Tyrese Gibson, I have no idea where this guy came from (an R&B singer and MTV VJ, you say...hmmmm?), but other than his uncanny resemblance to Taye Diggs, he nailed this part to the tee. From scene one on, I was glued to his performance and sympathized with his character as he molded and shaped himself into the person that he hoped to become one day. And everyone else, too. Gibson's girlfriend in the film, Taraji Henson, made me a backer with her many subtle touches, all of which helped raise her character several levels above the "typical" bitchie girlfriend that we've seen in many of these relationship movies.
But that's the thing that I liked the most about this movie. Everyone seemed real, the situations felt authentic, the characters all came across as people like you and me with issues and struggles and hopes and fears. I liked this movie a lot because it was masterfully written, wonderfully acted and genuinely entertaining to watch. And yeah, I even liked the whole homage to BOYZ 'N THE HOOD that Singleton pulled off in the end (he called this movie a "companion piece" to that film). A very solid follow-up to an ever greater original, BABY BOY is a film to be seen by anyone who feels like they never wanna grow up.
Note: The guy who plays Jody's best friend in this movie is Omar Gooding, Cuba Gooding Jr.'s younger brother (Cuba starred in 1991's BOYZ 'N THE HOOD).
Where's JoBlo coming from? The Best Man (8/10) - Brothers (5/10) - Shaft (7/10) - Summer of Sam (7/10) - What's Cooking (7/10)
Review Date: June 25, 2001 Director: John Singleton Writer: John Singleton Producer: John Singleton Actors: Tyrese Gibson as Jody Ving Rhames as Melvin Taraji P. Henson as Yvette Genre: Drama Year of Release: 2001 ------------------------------------ JoBlo's Movie Emporium http://www.joblo.com/ ------------------------------------ (c) 2001 Berge Garabedian
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


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Components simulation
Scheduling
Prediction
Deadlock
Producer - Consumer
Scheduling
Prediction
Deadlock - Mutex & Semaphores
Producer - Consumer


Example 8.7.3.
Determine if you think the following event pairs are mutually exclusive or not. Explain to yourself why you think they are or are not mutually exclusive. What information did you use to make your decisions?
Event Pair A: Being Black and Getting suspended from school
Event Pair B: Being truant and Having regular school attendance
Event Pair C: Making good grades and Being disciplined in school
Following up: Based on models we have looked at, we realize that being Black and getting suspended can happen together, so those events are not mutually exclusive. When I know that being truant means not attending school regularly, I realize that the events in Event pair B are complements and have to be mutually exclusive. Event pair C is the most difficult to really take apart. Common sense-wise and experience-wise it seems like maybe the people who got good grades didn’t get in real trouble at school, but then again there is no reason why they couldn’t get into trouble. This means that they have to be not mutually exclusive events.


November 23, 2014
Week 4, the first BlogEnumerable Map
The Enumerable mixin provides collection classes with several traversal and searching methods, and with the ability to sort. The class must provide a method each, which yields successive members of the collection. If Enumerable#max, #min, or #sort is used, the objects in the collection must also implement a meaningful <=> operator, as these methods rely on an ordering between members of the collection.
Enumerable map returns the results of a running block once for every element in the enumerator. This is useful when you need to write code to modify everything one time. Maps is the way to go.
Enumerable gives you lots of useful ways of doing something to every element of a collection object (an array or hash, for instance), which is a very common thing to do when you're building programs and websites.
Cheers
Keenan Sturtevant
.
November 23, 2014
Supply on demand is a term used often in the business our family owns. We created the "chain link" system that many new companys use. And a big problem with tech industries is that, the supply or demand is not right. Some places the supply is to great, and sites (or apps) crash and cannot work well. Then the demand for your product goes down. Or maybe demand was too high and supply couldnt keep up. Supply and Demand needs to be balanced perfectly and I feel it isn't so balanced in the tech world. yet....
Facebook is a great example of this, when they had great demand and the supply to go with it they ruled. Facebood is valued at 200 billion dollars. So they know what is going on, however in recent years they have been struggling. This is because they supply is there. But the demand is not. If Facebook wants to get back on top. It needs to create demand again. If it doesnt, I fear that the company may tank.
This is a problem with many tech companies. And it is hard to gauge how big your app will be. So its hard to determine how much supply you will need to keep up with the unknown amount of demand you will get. I have an Idea of how to fix this, but thats going to be another blog, another day.
Cheers
Keenan Sturtevant
.


About
Montréal Computational & Quantitative Linguistics Lab
One of the most important ideas of modern linguistics is that it is possible to understand the way people understand, learn, and use language using mathematically precise, computational models. In recent years, such models have found their way into nearly every aspect of our lives, powering much of our interaction with our phones, tablets, and computers. Computational linguistics refers to the set of methods and tools used to develop such models.
These methods are generally deployed in one of two ways. First, they can be used scientifically as an aid to understand the consequences of the complex assumptions that are often made in theories of language learning or use. By formalizing hypotheses, we can deduce outcomes that would be difficult or impossible to understand without simulation and/or proof and we can often derive new kinds of predictions from our theories. Second, such implemented computational models are increasingly important as engineering tools in applications across the spectrum of technology.
At McGill, research in computational linguistics is strong in both scientific applications here in the linguistics department and engineering applications in the university more broadly, with strong links between the two. Here in the department we use a variety of computational tools and methods to study fundamental questions about language acquisition, processing, use in society, and change over time.
A strong focus of the research in the department is on structured probabilistic models of phonology, morphology, and syntax which make critical use of linguistic theories in these domains. We are also interested in how computational models of linguistic structure can interface with computational models of other areas of cognition, such as vision, intuitive physics, folk psychology, and audition. To build such models we often draw on the framework of probabilistic programming languages, but also make use a wide variety of other areas of machine learning and artificial intelligence. Our research also includes other areas of computational linguistics, such as formal language and automata theory and dynamical systems models of language variation and change.
Our department has a special interest in formal tools and techniques for understanding the acoustic and articulatory realization of phonetics and phonology. In order to carry out “big data” studies of phonetics and phonology, we also build and apply tools for querying and analyzing large speech datasets. These tools use contemporary databases, machine learning, and speech recognition technology, in line with our more general interest in adapting powerful methods from computer science to analyze linguistic data. Some projects integrate and query large speech datasets (Speech Corpus Tools), align text and speech (Montréal Forced Aligner), and automatically measure phonetic variables (AutoVOT).
On the engineering side of computational linguistics, Jackie Cheung (Computer Science) uses statistical methods from artificial intelligence and machine learning to generate text and speech that is fluent and appropriate to context. Several of his group’s current projects include summarizing fiction, extracting events from text, and adapting language across genres. Derek Ruths (Computer Science) analyzes large-scale human behavior using data from Twitter and other online forums. McGill also has a strong Digital Humanities community, led by faculty Andrew Piper and Stéfan Sinclair (Languages, Literatures, and Cultures), who analyze a variety of types of textual data using computational methods to address questions about literature, culture, and society.
News
| Apr 17, 2023 | Lab meeting - Michaela Socolof |
| Apr 4, 2023 | Lab meeting - Jacob Louis Hoover |
| Mar 21, 2023 | Lab meeting - Benjamin LeBrun |
| Mar 7, 2023 | Lab meeting - Gaurav Kamath |
| Feb 21, 2023 | Lab meeting - Massimo Lipari |
| Feb 7, 2023 | Lab meeting - Irene Smith |


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