Upper School students are constantly emailing and communicating with teachers. Here’s a set of rules to follow. The interactivity above has examples to practice with.
A subject line is one of the most important parts of an email because this is what the teacher first sees. Your subject line needs to convey the message that the email needs to be opened.
Subject lines should give the reader an idea of what the message is about and whether it requires an action
Subject lines should not be BLANK. The teacher has no idea what to expect in the message and comes off as unprofessional.
Ex: Absent from class (9/21). Requesting Study Hall Pass
Ex: Missing class today and need homework pdf
A salutation is a greeting at the beginning of an email. You want to make sure it is appropriate for the situation.
It is safer to be overly formal than too casual.
Ex: Dear Mr./Mrs./Dr. _______,
short and sweet is best!
Ex: I will be absent today. May I please get a study hall pass to go to the library tomorrow, 9/22 Wed, during pd. 6?
Do not send an email
When you close your message, end with a “Thank you” to acknowledge you appreciate the reader’s time and help.
Ex:
"Thank you for your time,
STUDENT NAME"


New Delhi's Stats
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The one


#1
History
Based on a conference by
Chris Williams
4 months
JSConf
.
Budapest
2015
Sponsored by
Diversity Sponsorship
Maciej Małecki
Marek Stepien
Bartek Szopka
Speakers
Organizer companies
You
We're here to help
#jsconfbp
MC
Karolina Szczur
@fox
&yet


SAVIOR
Reviewed by Harvey Karten, Ph.D. Lions Gate Films/ Initial Entertainment Group Director: Predrag Antonijevic Writer: Robert Orr Cast: Dennis Quaid, Nastassja Kinski, Stellan Skarsgard, Natasa Ninkovic, Sergei Trifunovic, Nebojsa Glogovac, Vesna Trivalic, Jean-Marc Barr
"Savior" is a small-scale "Saving Private Ryan." While "Ryan"'s plot is propelled by an attempt of American soldiers physically to save a GI from possible death in a global war, "Savior"'s is nudged ahead by its emphasis on saving one soldier's soul. Filmed in the remote, exotic town of Budva, Montenegro, "Savior" explores aspects of the Bosnian War which began in earnest in 1992, a complex operation that pitted three rival groups against one another. In Predrag "Gaga" Antonijevic's film, Serbs are at first portrayed as the bad guys, inflicting brutal, unecessary cruelty on their enemies, but in the interest of balance, the Croats are shown inflicting similar malignity against Serbs and Muslims. The one group that appear to be victims throughout are the Muslims, but at least one of their ethnic group are portrayed as responsible for a senseless bombing against innocent people patronizing a Paris cafe. The film is an outspoken antiwar document, more dramatized than the much honoed "Welcome to Sarajevo" and more unadorned in form.
Based on a script by Robert Orr, "Savior" utilizes primarily the English language, using subtitles for limited moments that Serb is spoken. It opens in Paris as a member of an American mission, Joshua Rose (Dennis Quaid), is enjoying a happy moment with his beautiful wife Maria (Nastassja Kinski) and their small son, whom he has promised to take to the movies.  Intent on immediate vengeance, he walks angrily across the street to the local mosque and mows down a group of probably innocent Muslims who are praying there. Together with his best friend Peter (Stellan Skarsgard), he flees to Bosnia where he changes his name to Guy, joins the foreign legion, and heads off to fight a modern battle of Jericho.
Director Antonijevic occupies the rest of the movie with events leading to Joshua Rose's redemption from the horrible crime he perpetrated in Paris, though at first we wonder whether he is in fact becoming more hardened to the world, more inured to violence. At one point he even shoots a kid almost point blank through telescopic sights, but as Gaga Antonijevic soon points out, you can't trust even the most innocent youngsters any more than Americans could have trusted the women and children in Vietnam who appeared to be friends. The war, pitting the three groups against one another, appears so brutal that few prisoners are taken and civilians are treated with the same derision as are armed fighting men.
"Savior" is Dennis Quaid's movie. Nastassja Kinski gets second billing strictly for her name, in line with an objectionable habit of studios to prioritize casts more by audience recognition than by actual input into their pictures. About all that Kinski, as Joshua's wife Maria, gets to say, is "You promised to take him to the movies today!" when her husband must abandon his commitment, and Stellan Skarsgard, fast becoming a household name among moviegoers, is billed third and yet gets little exposure this time around.
The principal segment of "Savior" revolves around Joshua's relationship with Vera (Natasa Ninkovic), a Serbian woman who has become pregnant while in an enemy camp. She is now looked upon as a whore by her father and at first refuses to speak to Joshua (who saved her life when a Serb proceeded to beat and kick her) and declines as well to take care of her newly-born infant, the product of apparent rape. When the little girl is orphaned and Joshua determines to care for her, he is redeemed.
Antonijevic's portrayals of violence on both sides is wrenching, the sort of depiction that would strengthen American resolve to support NATO strikes anywhere in the world in which "ethnic cleansing" programs are being carried out.  As though these brutalities are not sufficient in themselves to evoke our sense of horror, director Antonijevic pumps up the music unnecessarily to manipulate audience emotions further.
Perhaps the principal reason to see "Savior" is to savor Dennis Quaid's performance as a man who has given himself over to thoughtless revenge, a hardened individual who both saves a helpless infant and who is himself redeemed by the little girl. Should we cheer his character's re-conversion to humanity? Perhaps. But what about the need to inflict justice on a man who has mercilessly murdered a group of blameless Muslims in a Paris mosque?
Rated R. Running Time: 103 minutes.(C) 1998 Harvey Karten
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


--- title: About ---
This is why I eat a plant-based diet and why I want to make it easier for others to as well.


Viewers can vote from a pre-set list of
sentiments when they watch a video to connote
how it made them feel.They can vote for more
than one. We chose six of those sentiments and
categorized them according to Ted Talk theme,
which are encoded by yellow boxes. Each represented
sentiment percentage is relative to all the
sentiments for each category.


My First Website by Anil
Welcome to my website! Here are some things I enjoy.
Web Development
learning all
surfing
Favourite Teams
Here is my fav team
MAy be may not be
......and another


Welcome to the kitchen sink example
Link Twitter + Authereum
Must be logged into authereum and twitter
Logout


Moderate command (Settings Menu)
General Settings
This property sheet allows you to set various configuration options in Moderate to suit the way you want to work. For Ameol, these options are saved into your current Ameol initialisation file in a section called [Moderate], for Ameol32 they are saved into the registry. (They can be edited by hand, but it is strongly recommended that you use this dialog to make any changes.)
Click on the controls in the property sheet for information on what they do.
Click on the tabs for help on file list and file directory settings.


class SessionsRepository
Properties
| $message | ||
| $errors |
Methods
|
__construct(DvsUser $DvsUser, UsersRepository $UsersRepository)
|
||
| User |
login(array $input)
Login user |
|
| Boolean |
logout()
Log user out |
|
| Boolean |
register(array $input)
Register new user |
|
| Void |
resendActivation(array $input)
Resends activate email using submitted email address |
|
| Response |
remind(array $input)
Handle a POST request for "remind password" |
|
| Response |
reset(array $credentials)
Handle POST data from reset (change) password form |
|
| False |
activate(int $userId, string $activateCode)
Process user activation request |
|
| Boolean |
removeUnactivatedUsers($daysOutstanding = 30)
Removes users which have been awaiting activation (after registering). |
|
| Boolean |
validateCredentials($credentials)
Validate user credentials (without logging user into system) |
|
| Boolean |
getRememberMe($input)
Get "remember_me" field value |
Details
at line 18
public
__construct(DvsUser $DvsUser, UsersRepository $UsersRepository)
at line 30
public User
login(array $input)
Login user
at line 56
public Boolean
logout()
Log user out
at line 70
public Boolean
register(array $input)
Register new user
at line 91
public Void
resendActivation(array $input)
Resends activate email using submitted email address
at line 124
public Response
remind(array $input)
Handle a POST request for "remind password"
at line 146
public Response
reset(array $credentials)
Handle POST data from reset (change) password form
at line 181
public False
activate(int $userId, string $activateCode)
Process user activation request
at line 205
public Boolean
removeUnactivatedUsers($daysOutstanding = 30)
Removes users which have been awaiting activation (after registering).
Currently, default is 30 days outstanding
at line 220
public Boolean
validateCredentials($credentials)
Validate user credentials (without logging user into system)
at line 230
public Boolean
getRememberMe($input)
Get "remember_me" field value


PDB objects and parsing¶
Parsing and printing of PDB files is more complex than that of the other files. There are following components:
mdk::pdb::Parser
parses and printsmdk::pdb::Data
objects;mdk::pdb::Model
represents a full-atomic model as derived from themdk::pdb::Data
, and allows for conversion from/tomdk::Model
andmdk::pdb::Data
mdk::pdb::Data
represents a parsed PDB file but in a raw format, i.e. not as a model;mdk::pdb::records::Record
is a single record in a PDB file, a variant type of many record types (see records for details).
In general, one takes an input stream, recovers mdk::pdb::Data
via
mdk::pdb::Parser
, converts it to a mdk::pdb::Model
,
then (after potentially modifying it) reduces it to a mdk::Model
to
pass to a mdk::Simulation
. If one wants to print
mdk::Model
, a mdk::pdb::Data
object can be generated
from one (via mdk::pdb::Model
), which can the written with the
parser.
We also provide a framework wherewith one can add custom records to parse, see parsing for details.


New Trade Defaults
Overview
Use this dialog to configure the settings you want to use for each new trade added to your database. This dialog will have the same settings as the Add a Trade dialog.
Once these settings are configured, they will be used to provide default values each time you access the Add a Trade or Bulk Add Trades dialogs. They will also be used as the settings for trades added
by end-users through the register.php script.


--- layout: page title: About header-img: "img/about-bg.png" ---
I am in the November 4th part-time online program and this is my project for Mod 1!


Fervour marks Chakra Theertha Mukkoti at Tirumala
Priests trek to Chakra Theertham amid thick Tirumala forests
by Special CorrespondentFervour marked the annual Chakra Theertha Mukkoti observed on Monday at the famous hill temple of Lord Venkateswara.
In connection with the religious event, the priests trekked all the way to the Chakra Theertham situated about two kilometres amid the thick Tirumala forests along with the temple paraphernalia and carried out special pujas to presiding deities of Lord Chakrathalwar, Narasimha Swamy and Hanuman.
Immense importance is attached to the sacred water body which according to ‘Varaha puranam’ is regarded as one of the top seven ‘theerthams’ atop the town.
After the completion of religious formalities, the authorities also carried out the distribution of ‘anna prasadam’ to the devotees who had thronged the place in thousands.


Description: Determine whether a Deferred object has been resolved.
As of jQuery 1.7 this API has been deprecated; please use deferred.state()
instead.
Returns true
if the Deferred object is in the resolved state, meaning that either deferred.resolve()
or deferred.resolveWith()
has been called for the object and the doneCallbacks have been called (or are in the process of being called).
Note that a Deferred object can be in one of three states: pending, resolved, or rejected; use deferred.isRejected()
to determine whether the Deferred object is in the rejected state. These methods are primarily useful for debugging, for example to determine whether a Deferred has already been resolved even though you are inside code that intended to reject it.


We're always available at #nixos on irc.freenode.net.
You can see the conversation about the wiki, and future plans at NixOS/nixpkgs#22599.
Additionally, the old, out of date, and generally inaccurate NixOS Wiki can be viewed on Archive.org at https://web.archive.org/web/20160829180446/https://nixos.org/wiki/Special:AllPages. As the wiki has been readonly since before 2016-08-29, this is the most recent version.


Introduction LINQ stands for Language Integrated Query. LINQ helps us in querying objects. Introduction of LINQ is a great advantage for developers because as a developer if you have ORM(Object Relation Mapping) framework like Entity Framework then you do not need to write SQL queries. Linq can help you query your business objects as well.
LINQ helps to query
Objects in memory (Collections) Databases (Entities) XML ADO .NET (DataSets) Why Linq?
Introduction Helper Methods are nothing but simply extension methods. If you are wondering what actually an extension method is then check this. This post will talk about different types of helpers -:
HtmlHelpers UrlHelpers Implementing both of them is same like implementing extension methods. Before implementing them it is important to know why we will implement them. Things can also work without them also.
Why Implement Helper Methods?
Introduction This post is about how we can take the advantage of Extension methods that is available in C#. This post will cover everything about C# extension methods. This is one of the posts under knowing C# better. In this post I will explain how we can create extension methods and how extension methods are used in real life situations.
Imagine there is a class file and some methods in the file which does some task.
Introduction S3 stands for Simple Storage Service. It is one of the many services provided by Amazon. This post is all from my experience and what I have learnt during my short professional career. If you are talking of Amazon storage services then it is a very big and unending topic to write on but here I will take example of a simple file to upload into the bucket. Not only uploading but is very important to write the code in an effective way such that it is easy to understand and make subsequent changes easily.
Introduction Of late I have not been writing posts that are related to coding. Of late things are mostly related to experiences. But keep calm. More posts related to coding are coming soon. Keep following for more updates as they say 😉
We destroyed it……Not knowing our infrastructure cost us BIG TIME!! This line goes way back to the late 2016’s and the early 2017’s when I was working with a startup as a software developer.
This post is all about the tips and tricks about cracking tech interviews. This is not at all a technical post. Nothing technical here but I myself have been in the situation where I was forced to look for jobs due to some unwanted situations and there I learned how to get a job. This post is meant for all who are looking to crack interviews and hence get a job.
Introduction This post is all from my experience and what I have learnt during my short professional career. Of course this is a very big topic and unending topic to write on but as a coder/developer/programmer it is very important to write the code in an effective way such that it is easy to understand and make subsequent changes easily.I will give you few tips which will certainly help you in writing code in a better way.
What is a Profiler? Profiling helps in gathering information about an executing application, i.e how much time is spend where which in turn helps allowing to determine those improvements that are required in the application. Anything that helps us do profiling is called as a profiler.
Read more about why profilers and good .NET profilers.
One of them is Miniprofiler
MiniProfiler Miniprofiler is developed by the famous team at StackOverflow.
This post is a continuation of my previous post.Here we will see how we can create a login and connect your web application to the database.
In real life scenarios when we are hosting the website on the IIS and we also have the application pointing to a database then we need to have proper logging and proper permissions set up.If not then we will get a yellow screen saying…
Prerequisites You already have installed IIS on your system.If IIS is not installed then go to
Control Panel -> Programs and features -> Turn on windows features on or off Steps Add a new MVC Web application.Here I have added it to the path E:\ Open the IIS by pressing Windows+R and then type inetmgr. Right click on folder Sites and click Add Website Enter the site name as you want and the host name as per your requirement.


| Buffer's | Memory buffers |
| Loggin | Logging information |
| Asynchrony | Asynchronous utility functions |
| File System | Asynchronous access to the file system |
| Streams | Streaming data |
| IP addresses | Raw IP4 or IP6 addresses |
| URL | URL parsing |
| MIME Types. | MIME type functionality |
| ▼TCP Connections | Communication over the TCP protocol (reliable streaming) |
| Low level TCP Connections | Low level functions to access TCP |
| ▼HTTP Connections | Communication over the HTTP 1.1 protocol |
| HTTP Setup | Initiating HTTP servers or clients |
| HTTP Input and Output. | HTTP incoming and outgoing data |
| HTTP Server Requests and Responses. | Implicit bindings for HTTP incoming- and outgoing data for a server |
| HTTP Static Content Server. | Serve static content over HTTP 1.1 |
| HTTPS secure connections | Secure connections over HTTPS |
| TTY | Terminal input and output |
| Various | Various utility functions |
| Memory Allocation. | Routines for safe memory allocation based on using |
| ▼Effect Handlers | Library for algebraic effect handlers in C |
| Generic Values | Generic value representations |
| Handlers | These are the primitives to define effect handlers and yield operations |
| Debugging | Helpers for debugging and custom allocators |
| Effect Definition helpers | Macros to define effect handlers |
| Linear Handlers | Linear handlers can be scoped inline and are very efficient |
| Exceptions and Finally clauses | Functions for standard exceptions and finally handlers |
| Implicit Parameters | Functions to easily define and use implicit parameters |


vignettes/async.Rmd
async.Rmd
Asynchronous requests with crul
.
There are two interfaces to asynchronous requests in crul
:
The first option takes less thinking, less work, and is good solution when you just want to hit a bunch of URLs asynchronously.
The second option is ideal when you want to set curl options/headers on each request and/or want to do different types of HTTP methods on each request.
One thing to think about before using async is whether the data provider is okay with it. It’s possible that a data provider’s service may be brought down if you do too many async requests.
Build request object with 1 or more URLs
(cc <- Async$new(
urls = c(
'https://httpbin.org/get?a=5',
'https://httpbin.org/get?a=5&b=6',
'https://httpbin.org/ip'
)
))
#> <crul async connection>
#> curl options:
#> proxies:
#> auth:
#> headers:
#> urls: (n: 3)
#> https://httpbin.org/get?a=5
#> https://httpbin.org/get?a=5&b=6
#> https://httpbin.org/ip
Make request with any HTTP method
(res <- cc$get())
#> [[1]]
#> <crul response>
#> url: https://httpbin.org/get?a=5
#> request_headers:
#> response_headers:
#> status: HTTP/1.1 200 OK
#> access-control-allow-credentials: true
#> access-control-allow-origin: *
#> content-encoding: gzip
#> content-type: application/json
#> date: Wed, 27 Mar 2019 23:59:51 GMT
#> server: nginx
#> content-length: 234
#> connection: keep-alive
#> params:
#> a: 5
#> status: 200
#>
#> [[2]]
#> <crul response>
#> url: https://httpbin.org/get?a=5&b=6
#> request_headers:
#> response_headers:
#> status: HTTP/1.1 200 OK
#> access-control-allow-credentials: true
#> access-control-allow-origin: *
#> content-encoding: gzip
#> content-type: application/json
#> date: Wed, 27 Mar 2019 23:59:51 GMT
#> server: nginx
#> content-length: 242
#> connection: keep-alive
#> params:
#> a: 5
#> b: 6
#> status: 200
#>
#> [[3]]
#> <crul response>
#> url: https://httpbin.org/ip
#> request_headers:
#> response_headers:
#> status: HTTP/1.1 200 OK
#> access-control-allow-credentials: true
#> access-control-allow-origin: *
#> content-encoding: gzip
#> content-type: application/json
#> date: Wed, 27 Mar 2019 23:59:51 GMT
#> server: nginx
#> content-length: 56
#> connection: keep-alive
#> status: 200
You get back a list matching length of the number of input URLs
Access object variables and methods just as with HttpClient
results, here just one at a time.
res[[1]]$url
#> [1] "https://httpbin.org/get?a=5"
res[[1]]$success()
#> [1] TRUE
res[[1]]$parse("UTF-8")
#> [1] "{\n \"args\": {\n \"a\": \"5\"\n }, \n \"headers\": {\n \"Accept\": \"application/json, text/xml, application/xml, */*\", \n \"Accept-Encoding\": \"gzip, deflate\", \n \"Host\": \"httpbin.org\", \n \"User-Agent\": \"R (3.5.3 x86_64-apple-darwin15.6.0 x86_64 darwin15.6.0)\"\n }, \n \"origin\": \"<IP_ADDRESS>, <IP_ADDRESS>\", \n \"url\": \"https://httpbin.org/get?a=5\"\n}\n"
Or apply access/method calls across many results, e.g., parse all results
lapply(res, function(z) z$parse("UTF-8"))
#> [[1]]
#> [1] "{\n \"args\": {\n \"a\": \"5\"\n }, \n \"headers\": {\n \"Accept\": \"application/json, text/xml, application/xml, */*\", \n \"Accept-Encoding\": \"gzip, deflate\", \n \"Host\": \"httpbin.org\", \n \"User-Agent\": \"R (3.5.3 x86_64-apple-darwin15.6.0 x86_64 darwin15.6.0)\"\n }, \n \"origin\": \"<IP_ADDRESS>, <IP_ADDRESS>\", \n \"url\": \"https://httpbin.org/get?a=5\"\n}\n"
#>
#> [[2]]
#> [1] "{\n \"args\": {\n \"a\": \"5\", \n \"b\": \"6\"\n }, \n \"headers\": {\n \"Accept\": \"application/json, text/xml, application/xml, */*\", \n \"Accept-Encoding\": \"gzip, deflate\", \n \"Host\": \"httpbin.org\", \n \"User-Agent\": \"R (3.5.3 x86_64-apple-darwin15.6.0 x86_64 darwin15.6.0)\"\n }, \n \"origin\": \"<IP_ADDRESS>, <IP_ADDRESS>\", \n \"url\": \"https://httpbin.org/get?a=5&b=6\"\n}\n"
#>
#> [[3]]
#> [1] "{\n \"origin\": \"<IP_ADDRESS>, <IP_ADDRESS>\"\n}\n"
req1 <- HttpRequest$new(
url = "https://httpbin.org/get?a=5",
opts = list(
verbose = TRUE
)
)
req1$get()
#> <crul http request> get
#> url: https://httpbin.org/get?a=5
#> curl options:
#> verbose: TRUE
#> proxies:
#> auth:
#> headers:
#> progress: FALSE
req2 <- HttpRequest$new(
url = "https://httpbin.org/post?a=5&b=6"
)
req2$post(body = list(a = 5))
#> <crul http request> post
#> url: https://httpbin.org/post?a=5&b=6
#> curl options:
#> proxies:
#> auth:
#> headers:
#> progress: FALSE
(res <- AsyncVaried$new(req1, req2))
#> <crul async varied connection>
#> requests: (n: 2)
#> get: https://httpbin.org/get?a=5
#> post: https://httpbin.org/post?a=5&b=6
Make requests asynchronously
Parse all results
res$parse()
#> [1] "{\n \"args\": {\n \"a\": \"5\"\n }, \n \"headers\": {\n \"Accept\": \"application/json, text/xml, application/xml, */*\", \n \"Accept-Encoding\": \"gzip, deflate\", \n \"Host\": \"httpbin.org\", \n \"User-Agent\": \"R (3.5.3 x86_64-apple-darwin15.6.0 x86_64 darwin15.6.0)\"\n }, \n \"origin\": \"<IP_ADDRESS>, <IP_ADDRESS>\", \n \"url\": \"https://httpbin.org/get?a=5\"\n}\n"
#> [2] "{\n \"args\": {\n \"a\": \"5\", \n \"b\": \"6\"\n }, \n \"data\": \"\", \n \"files\": {}, \n \"form\": {\n \"a\": \"5\"\n }, \n \"headers\": {\n \"Accept\": \"application/json, text/xml, application/xml, */*\", \n \"Accept-Encoding\": \"gzip, deflate\", \n \"Content-Length\": \"137\", \n \"Content-Type\": \"multipart/form-data; boundary=------------------------b22f9775d3239a86\", \n \"Host\": \"httpbin.org\", \n \"User-Agent\": \"libcurl/7.54.0 r-curl/3.3 crul/0.7.4\"\n }, \n \"json\": null, \n \"origin\": \"<IP_ADDRESS>, <IP_ADDRESS>\", \n \"url\": \"https://httpbin.org/post?a=5&b=6\"\n}\n"
lapply(res$parse(), jsonlite::prettify)
#> [[1]]
#> {
#> "args": {
#> "a": "5"
#> },
#> "headers": {
#> "Accept": "application/json, text/xml, application/xml, */*",
#> "Accept-Encoding": "gzip, deflate",
#> "Host": "httpbin.org",
#> "User-Agent": "R (3.5.3 x86_64-apple-darwin15.6.0 x86_64 darwin15.6.0)"
#> },
#> "origin": "<IP_ADDRESS>, <IP_ADDRESS>",
#> "url": "https://httpbin.org/get?a=5"
#> }
#>
#>
#> [[2]]
#> {
#> "args": {
#> "a": "5",
#> "b": "6"
#> },
#> "data": "",
#> "files": {
#>
#> },
#> "form": {
#> "a": "5"
#> },
#> "headers": {
#> "Accept": "application/json, text/xml, application/xml, */*",
#> "Accept-Encoding": "gzip, deflate",
#> "Content-Length": "137",
#> "Content-Type": "multipart/form-data; boundary=------------------------b22f9775d3239a86",
#> "Host": "httpbin.org",
#> "User-Agent": "libcurl/7.54.0 r-curl/3.3 crul/0.7.4"
#> },
#> "json": null,
#> "origin": "<IP_ADDRESS>, <IP_ADDRESS>",
#> "url": "https://httpbin.org/post?a=5&b=6"
#> }
#>
Status codes


1. Peptization is a process of
precipitation of colloidal particles.
purification of colloids.
dispersing precipitate into colloidal solution.
movement of colloidal particles in the electric field.


| Daniel |
Programador |
Brasil |
24 |
01/05/2020 |
R$5,000 |
| Tiger Nixon |
System Architect |
Edinburgh |
61 |
2011/04/25 |
$320,800 |
| Garrett Winters |
Accountant |
Tokyo |
63 |
2011/07/25 |
$170,750 |
| Ashton Cox |
Junior Technical Author |
San Francisco |
66 |
2009/01/12 |
$86,000 |
| Cedric Kelly |
Senior Javascript Developer |
Edinburgh |
22 |
2012/03/29 |
$433,060 |
| Airi Satou |
Accountant |
Tokyo |
33 |
2008/11/28 |
$162,700 |
| Brielle Williamson |
Integration Specialist |
New York |
61 |
2012/12/02 |
$372,000 |
| Sonya Frost |
Software Engineer |
Edinburgh |
23 |
2008/12/13 |
$103,600 |
| Jena Gaines |
Office Manager |
London |
30 |
2008/12/19 |
$90,560 |
| Quinn Flynn |
Support Lead |
Edinburgh |
22 |
2013/03/03 |
$342,000 |
| Charde Marshall |
Regional Director |
San Francisco |
36 |
2008/10/16 |
$470,600 |
| Haley Kennedy |
Senior Marketing Designer |
London |
43 |
2012/12/18 |
$313,500 |
| Tatyana Fitzpatrick |
Regional Director |
London |
19 |
2010/03/17 |
$385,750 |
| Michael Silva |
Marketing Designer |
London |
66 |
2012/11/27 |
$198,500 |
| Paul Byrd |
Chief Financial Officer (CFO) |
New York |
64 |
2010/06/09 |
$725,000 |
| Gloria Little |
Systems Administrator |
New York |
59 |
2009/04/10 |
$237,500 |
| Bradley Greer |
Software Engineer |
London |
41 |
2012/10/13 |
$132,000 |


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My First Presentation
Youngmin Kim<EMAIL_ADDRESS>Introduction
What is the information security?
Gawon Kim
Who is she?


Rockstar updated the Collectors Role. New cycles has been added.
New Version
/ Day
/
/ Enable route
Yes
No


This page demostrates how to embed a test runner. It serves for testing the runner and additionally for providing a simple example of how to embed it in some code. if anyone wants to.
This is the iframe that contains the test runner. The test code is "just" pushed to this component so the one using that component can be completely ignorant about how the runner itself does the execution. The test runner reports back the status after all tests have run.


@* * Copyright 2023 HM Revenue & Customs * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. *@ @import uk.gov.hmrc.helptosavefrontend.views.html.helpers._ @import uk.gov.hmrc.helptosavefrontend.config.FrontendAppConfig @()(implicit messages: play.api.i18n.Messages, appConfig: FrontendAppConfig)
@Messages("hts.help-information.section.bonuses.s1.p1")
@Messages("hts.help-information.section.bonuses.s1.p2")
@Messages("hts.help-information.section.bonuses.s1.p3")
@Messages("hts.help-information.section.bonuses.s1.p4")


I'm a recently graduated Computer Science and Engineering major and have been accepted for Santa Clara University's Masters Program for Computer Science and Engineering for Spring 2020. I am currently looking for jobs in Software Engineering. I am highly experienced with programming in C, C++, and Java. Furthermore, I have beginner to intermediate experience in Python, JavaScript, HTML, CSS, SQL, Android Studio mobile development, and web development, and have completed multiple projects using these languages and tools. Examples of my work can be found on my github: https://github.com/Dctaylor.
Other important information about me: I'm a quick learner, detail-oriented and have excellent communication skills, particularly in technical communication. I'm happy to work independently or on a team. I'm excited to grow with a company and contribute to its success!
Worked as a Student Assistant in the Provost's office, where I:
- Supported the faculty onboarding process, including contract flow management and new hire supporting documents
- Helped redesign part of the STEM portion of the Provost’s website
- Assisted staff with analysis of faculty compensation
Worked as a QA Intern, where I:
- Automated document creation for product tolerance reports
- Developed and programmed shortcuts to improve the efficiency of submitting Certificate of Conformance documents to customers
- Streamlined processes and solved issues with the speed and accuracy of the current documentation generation and storage systems.
Santa Clara University Capstone Project completed with Vineet Joshi and Terry Shih. Bronco Books is a mobile Textbook E-Commerce platform for Santa Clara University students to buy and sell textbooks. This project was written in Java and Swift, and made use of Firebase, a "backend as a service", in order to handle authentication, image storage, and our realtime NoSQL database. Firebase was also used for easy access to ML Kit, a mobile SDK that provided access to barcode scanning. This project earned our team The School of Engineering 49th Annual Senior Design Conference Design Presentation Award.
View ProjectA working prototype for an Alumni Business Database website made for Santa Clara University. A site to find information about SCU alumni-owned businesses. Alumni may submit their businesses to be added to the site, and all submission must be approved by the SCU Alumni Office. Created by myself and two other students using Meteor, React, and MongoDB.
View ProjectA companion app for managing a tracker for your Pokemon Collection in Google Sheets with my custom template. This app facilitates the mass addition of (primarily shiny) Pokemon in your collection to the sheet, whether they are meant for trade, battle, or just for showing off. Created by myself in Python and PyQt5.
View Project


App Store Strategy
iOS and Android app store images strategy.
Role
Lead UX/UI Designer. In charge of designing user experience and UI elements
Overview
This body of work was to discover how to best present the products and services to USAA members and prospects through images showcasing the organization's app in the iOS app Store and the Google Play store.
We took a deeper look at what the organization was currently featuring in the stores compared to what other apps are doing. In collaboration with other designers we first looked at others in the industry by doing a competitive analysis and uncovered some key areas for improvement. As the organization had done a full redesign of their app it was the ideal time to re-evaluate how to best present themselves in the app store.
The process
The app store images are one of the first touch-points for prospects and users who are trying to decide what financial institution to use. At the current state the app store images for the organization lacked alignment with the current branding and failed to highlight features that reinforced how the organization best delivers the mission through the native platforms. Redesigning the app store presence would create a unified vision and strategy aligning with the new overall branding and starting a benchmark for success measures in the app store.
Old store screenshots public images
We started the work by doing a competitive research so that we may present this work to business partners and get a buy in for the project to do an overall redesign. We looked across the iOS and Android play stores at the breath of competitors. Our analysis looked at banking and insurance competition, as well as what companies in other industries are doing.
We then identified 4 ranges of design the industry was representing with these images.I then conducted a facilitated workshop with people for different sectors of the organization to align on features. Making sure to include every aspect of our industry we created transparency and collaboration in a way that we can still have a unified message.
I then synthesized the information gathered with partners and came up with some possible visual solutions that would include the features and design that came out of the collaboration workshop. With this work we needed to make sure that our internal assumptions of features was what our users would value as initial information from our organization.
Research
So the next step was to conduct a small user research where we looked at past research conducted by outside agencies (Kylime FEB 2021 Credit card competitive review, Keylime mAY 2021 Auto Competitive Review, U.S Insurance Digital Experience Study, ) These research findings gave us enough data to identify features that users deemed important in their digital tools both in bank and insurance industry.
Using this information and our internal feature list we then conducted a research where we had users rank the features from most important to least. We also included open questions on app store behavior to learn more from their interaction with this environment.
Using the rank of features we determined the order, hierarchy and visual alignment of the app store images providing those most important features in the beginning of the collection.
Final Design
By conducting research and presenting the data back to our partners we have now established a need to continue to update this space, spend time and resources in this area to in the future help increase users and provide another area for marketing and acquisition. We also stepped away from device specific so that the work in the future is easier to change and not tied to any specific device type.
The strategy also included rules and standardization so when a team needs to make an update there is a single unified message and it is consistent with the branding.
This work is owned and all the rights of work is reserved by USAA


The package BayHap performs simultaneous estimation of uncertain haplotype frequencies and association with haplotypes based on generalized linear models for quantitative, binary and survival traits. Bayesian statistics and Markov Chain Monte Carlo techniques are the theoretical framework for the methods of estimation included in this package. Prior values for model parameters can be included by the user. Convergence diagnostics and statistical and graphical analysis of the sampling output can be also carried out.
| Version: | 1.0.1 |
| Depends: | boa |
| Published: | 2013-03-13 |
| Author: | Raquel Iniesta and Victor Moreno |
| Maintainer: | Raquel Iniesta <riniesta at pssjd.org> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | yes |
| CRAN checks: | BayHap results |
| Reference manual: | BayHap.pdf |
| Package source: | BayHap_1.0.1.tar.gz |
| Windows binaries: | r-devel: BayHap_1.0.1.zip, r-release: BayHap_1.0.1.zip, r-oldrel: BayHap_1.0.1.zip |
| OS X binaries: | r-release: BayHap_1.0.1.tgz, r-oldrel: BayHap_1.0.1.tgz |
| Old sources: | BayHap archive |
Please use the canonical form https://CRAN.R-project.org/package=BayHap to link to this page.


amass enum
Find subdomains of a domain.
More information: home page
- Passively find subdomains of a domain:
amass enum -passive -d domain_name
- Find subdomains of a domain and actively verify them attempting to resolve the found subdomains:
amass enum -active -d domain_name -p 80,443,8080
- Do a brute force search for subdomains:
amass enum -brute -d domain_name
- Save the results to a text file:
amass enum -o output_file -d domain_name
- Save the results to a database:
amass enum -o output_file -dir path/to/database_directory


Title is required & must be at least 5 characters!
Content is required & must be at least 15 characters!
{{formError}}


In this unit we focused on various types of printing. This included garment printing, UV printing, and sublimation printing.
For garment printing, I created a t-shirt of an outline of Ohio with the Western Resere Academy logo inside of it. The hardest challenge of this was getting a high quality version of the WRA logo into CorelDraw, which I eventually acomplished by turing the logo into a bitmap.
For the UV printing, I printed on a popsocket. I had misjudged the size of my image, which resulted in the image not fully fitting on the popsocket. It was mostly on the popsocket, however, and was printed properly.
For sublimaation printing, I printed on a neopreme pencil pouch. I had to becareful suring the transfer because after being put in the heat press, it was hot to handle and I had to avoid toughing the zipper so I wouldn't be burned. Thankfully, I managed to not burn myself and the design transfered with no problems.


Contrary to popular belief, Lorem Ipsum is not simply random text.  Richard McClintock, a Latin professor at Hampden-Syndey College in Virginia, looked up one of the more obscure Latin words, consectetur, from a Lorem Ipsum passage, and going through the cities of the word in classical literature, discovered the undoubtable source.
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Sometimes you just sit around doing nothing and this thought just
comes to knock your head at the back that life is just so beautiful
even after that you are broken from the inside,
have a sense of emptiness and loneliness, you should probably stop and
give up on life but if you have just a single drop of hope in your
heart for a better tomorrow then you can look past all your
inadequacies and still would be able to see how beautiful the world
is.
The single fact that my action will always change who I would be in
the future is just so inspiring to me. In the time of crisis,
this is the single most important as well as difficult thing to do and
that is taking 100% responsibility that everything that is happening
in your life is because of you.
Will make you so small but will give you the power to make a different
decision and therefore a different life. We all want to be special but
why?
what is that being special is going to give us that not being doesn't
or is there even something we need or
it's just something we think we NEED?


Changing Genres through Synergy
-
gryphonent
I would love to have a feature where I can change genres through Synergy. Similar to changing the rating for a song, though I guess you
-
Greg Hurrell
Genre in the floater:
https://wincent.com/a/support/bugs/show_bug.cgi?id=103
Changeable genre:
-
iwan
what is the status on this feature ?
btw above links are dead.
-
Greg Hurrell
I expect the links should be https://wincent.com/issues/103 and https://wincent.com/issues/384 respectively.
No status updates. This is an 8-year-old thread. I am unlikely to implement either of these things myself, although Synergy is open source so it's possible someone may decide to contribute a patch.
Reply
This topic is now closed.


“Reality is for people that lack imagination.” — Hayao Miyazaki
Through my career I've had the privilage to work on many projects ranging from start ups, to internet marketing firms, to agency work, and large scale corporations. My experiences have kept me well rounded in design and development while keeping me on my toes with new and different technologies.
I work well individually and with groups. I've also had the privilage to mentor and lead teams when it was needed of me. My experience has given me the tools to engage with my peers with enthusiasm and insight.
HTML
CSS
LESS/SASS
Javascript
Sketch
Adobe Creative Suite
Web Design
Branding
Marketing
Mobile
Prototyping
BootStrap
Github
Leadership
Mentoring
Always excited to learn more
Organize
Planning
Collaborate
Brain Storm
Time Management
Observation
Analysis
Interviews
Surveys and Questionnaires
Usability Tests
Research
Conceptualize
Wireframe
Prototype
Execute
Pseudo Code
Version Tracking
Testing
Optimize
Publish
Below are concept designs. User experiences were made but *screens for prototypes are not present*
Process: Sketch and Mockup for Prototype Below >
Sketch Wireframe and Prototype >
I would love to hear from you to discuss any needs or problems you may have and what solutions we can come up with.
818-245-0017


This is not the only way to do a table, there are simpler and more complex ways, but this seems good and works nicely. The data is all made up.
| ID | Name | Description |
|---|---|---|
| A01 | Oliver | Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. |
| A02 | Amelia | Mattis pellentesque id nibh tortor id aliquet. |
| A03 | Emily | Bibendum neque egestas congue quisque egestas. |
| A04 | George | Posuere morbi leo urna molestie. |
| A05 | Harry | Phasellus egestas tellus rutrum tellus pellentesque eu tincidunt tortor aliquam. |
It is well worth reading <table>: The Table element - HTML: Hypertext Markup Language | MDN to get a clear understanding of how to work with tables and what has been deprecated.
The <style> section of this file is the correct way to add borders and colours, but is only an introduction.


{% extends 'base.html' %} {% block head %}
It was founded in the year 1841. The university is named after physiologist Alexander A. Bogomolets. The university provides medical training for over 10,000 students, including about 1,300 foreigners from 56 countries. It is one of the best medical universities in Ukraine. Among its 1,500 teaching staff, 855 are PhD and 205 Doctors of Sciences. So the education quality is topmost in this University
Mbbs course duration is 5.8 years which also includes the internship. In all medical universities in Ukraine, course duration for MBBS is 5.8 years.
Have a look at the area your university is in.


{{ form.choose_topic(class="form-control flatpickr-input flatpickr-mobile", type="datetime-local", placeholder="Select date and time") }}
{{ form.submit_work_for_review(class="form-control flatpickr-input flatpickr-mobile", type="datetime-local", placeholder="Select date and time") }}
{{ form.upload_reviews(class="form-control flatpickr-input flatpickr-mobile", type="datetime-local", placeholder="Select date and time") }}
{{ form.pre_defense(class="form-control flatpickr-input flatpickr-mobile", type="datetime-local", placeholder="Select date and time") }}
{{ form.defense(class="form-control flatpickr-input flatpickr-mobile", type="datetime-local", placeholder="Select date and time") }}


DARKTABLE(1) darktable DARKTABLE(1) NAME darktable - a digital photography workflow application SYNOPSIS "darktable [options] [{IMG_1234.RAW,/path/to/load/}]" Options: --help --version -d {all,cache,camctl,control,dev,fswatch,memory,opencl,perf,pwstorage,sql} --library override.db --disable-opencl -t num_threads DESCRIPTION darktable is a digital photography workflow application for Linux and Mac OS X in the lines of Adobe Lightroom and Apple Aperture. The application is designed to ease editing and consistent processing of large photo sessions and provides a easy to use digital light-table and a set of sophisticated postprocessing tools. Most processing is done in 32-bit floating point per channel mode in device independent CIE L*a*b* color space. darktable is also fully color managed, which gives you full control over the look of the photos. The application relies on a modern plug-in architecture thus making it easy for 3rd party developers to extend the existing capabilities of the application. All light-table and darkroom features are implemented as plug-ins, so you can create your plug-ins reusing existing code. COMMAND LINE ARGUMENTS IMG_1234.CR2 or /path/to/load/ Loads the given image in darkroom mode, or imports the given folder. -d control Enable job queue debugging. If you redirect darktables output to control.log and call ./tools/create_control_svg.sh control.log, you will get a nice control.svg with a visualization of the threads' work. -d cache This will give you a lot of debugging info about the mipmap cache for light table mode. If compiled in debug mode, this will also tell you where in the code a certain buffer has last been locked. -d perf Use this for performance tweaking your darkroom modules. It will rdtsc-measure the runtimes of all plugins and print them to stdout. -d all Enable all debugging output. --library override.db Uses override.db instead of the gconf/darktablerc specified library file.  Disables GPU computing at runtime. Useful to check for bugs in our opencl code and to avoid buggy drivers on certain machines. -t num_threads For openmp builds only. Overrides the default number of threads (= number of cores). OTHER INFO Please visit darktable's website for news, bug tracker and forum: <http://www.darktable.org> KEYBINDINGS All modes: F11 Switch between fullscreen and normal modes of the application's window Tab Show/hide sidebars e Switch between light-table and darkroom modes F7 Decrease contrast F8 Increase contrast Lighttable mode: alt-1 Zoom in on image under cursor alt-2, 3 Adjust zoom alt-4 Zoom out completely q Zoom out to fit all images in one row inside viewport, center on image under cursor a, s, d, w Move around (arrows also work) 1, 2, 3, 4, 5 Star rating ctrl-backspace Strip all stars (reject image) F1, F2, F3, F4, F5 Color labels: toggle red, yellow, green, blue and magenta ctrl-g, ctrl-shift-g Move to top, bottom, respectively ctrl-e Export selected images Darkroom mode: alt-1, 2, 3 Zoom to 1:1, fill, and fit, respectively ctrl-f show/hide film strip while cropping: Enter/Backspace apply/revert currently selected cropping frame RELATED darktable-faster first copies the library to ram and then starts dt. On close, it is copied back, so even in case of crashes no data loss is anticipated. darktable-viewer screensaver version of dt. Shows the last active collection in full screen as a slideshow. BUGS Please use the mailing list to report all the bugs you run into. AUTHORS The principal developer of darktable is Johannes Hanika. The (hopefully) complete list of contributors to the project is: * developers: Henrik Andersson, Johannes Hanika, Tobias Ellinghaus. * ubuntu packaging, color management, video tutorials: Pascal de Bruijn. * opencl pipeline: Ulrich Pegelow. * networking, battle testing, translation expert: Alexandre Prokoudine. * translation: Alexandre Prokoudine, Daniele Giorgis, Ger Siemerink, Henrik Andersson, Josep Puigdemont, MichaÅ PrÄdotka, Mikko Ruohola, Olivier Tribout, Roberto Quintero, Sebastien Delcoigne, Thomas Costis, Tobias Ellinghaus. * contributors: Alexandre Prokoudine Alexey Dokuchaev Ammon Riley Antony Dovgal Boucman Brian Teague Bruce Guenter Cherrot Luo Denis Cheremisov Edouard Gomez Edward Herr FrantiÅ¡ek Å idÃ¡k Ger Siemerink Gianluigi Calcaterra James C. McPherson Joao Trindade Jose Carlos Garcia Sogo MichaÅ PrÄdotka Moritz Lipp Olivier Tribout Pascal de Bruijn Petr Styblo Robert Bieber Rostyslav Pidgornyi Sergey Pavlov Simon Spannagel Terry Jeffress Tim Harder Tom Vanderpoel Ulrich Pegelow jan maigl And all those of you that made previous releases possible This man page was written by Alexandre Prokoudine<EMAIL_ADDRESS>HISTORY The project was started by Johannes Hanika in early 2009 to fill the gap (or, rather, a black hole) of a digital photography workflow tool on Linux. COPYRIGHT AND LICENSE Copyright (C) 2009-2010 by Authors. darktable is free software; you can redistribute it and/or modify it under the terms of the GPL v3 or (at your option) any later version. darktable-1.0.4 1986d DARKTABLE(1)


Monthly Archives: November 2015
HAW Meeting Minutes
HAW Meeting Minutes (March 15, 2015)
HAW Meeting Minutes (April 4, 2015)
HAW Meeting Minutes (April 19, 2015)
HAW Meeting Minutes (May 3, 2015)
HAW Meeting Minutes (May 30, 2015)
HAW Meeting Minutes (July 25, 2015)
HAW Meeting Minutes (August 9, 2015)
HAW Meeting Minutes (September 20, 2015)
HAW Meeting Minutes (September 27, 2015)
HAW Meeting Minutes (October 3, 2015)
HAW Meeting Minutes (October 18, 2015)
40 Voices: We Are Hmong
We had the privilege of working on a special project title, "40 Voices: We are Hmong" for the Seattle Hmong New Year 2015. “40 Voices: We Are Hmong” was created to capture the journey, stories, and the voices of the Hmong community in the Northwest. As we celebrate our "40 Years in America" we encourage the community to engage and share our struggles and stories but most importantly our hopes and dreams. The three questions asked of the participants are as follow: 1. What doe it mean to be Hmong? 2. What do you appreciate about the Hmong culture? 3. What would you like to see improve in the Hmong community in the future? Some videos are Hmong speaking only and if you do not understand Hmong, the transcriptions for the people speaking Hmong are under the description on the youtube channel. Please watch, share, and enjoy! "Only together can we learn and grow as a community."
Spokane Hmong New Year
Please join the Spokane Hmong Association for their New Year Celebration.
WHSA College NightWestern Hmong Student Association is hosting it's annual 'College Night'! Here, college students from Western Washington University will be holding informational sessions for local high school students to help with the transition into college and give more information regarding higher education. The whole program will be from 5 - 7 PM at the Kirkland Heights Apartment Complex. Our Program: - Financial Aid/Loans - Scholarships/Workstudy - Admissions - Community College vs. Transfer vs. Tech School - High School / Running Start, University We can answer any other questions regarding higher education as well! There'll be a FREE RAFFLE for all attendees. Come and you'll get a chance to win some awesome gear! FREE FOOD provided too! COME JOIN US!! For more information or concerns, contact us at<EMAIL_ADDRESS>https://www.facebook.com/events/1678239289083448/


This is an example marketing or informational website using the MFDC beta button. It includes a large callout called a jumbotron and three supporting pieces of content. Use it as a starting point to create something more unique.
To use the MFDC beta-button just include the code to the right somewhere in the header of your page.

You can override the existing button set by specifying

somewhere in your page body.
You will also need to include the main script in the header as above.
Adding mfdc-beta-button-hide
as a style to the main body
element will hide the button until 'm' is pressed

<a<EMAIL_ADDRESS>src="/img/email.png"/>

The following icons are included by default and can be used in a custom button list.


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java.lang.Object edu.cmu.sphinx.decoder.scorer.ThreadedAcousticScorer
An acoustic scorer that breaks the scoring up into a configurable number of separate threads. All scores are maintained in LogMath log base
| Field Summary | |
static java.lang.String |
PROP_ACOUSTIC_GAIN
A sphinx property that controls the amount of acoustic gain. |
static float |
PROP_ACOUSTIC_GAIN_DEFAULT
The default value for the PROP_ACOUSTIC_LOOKAHEAD_FRAMES property. |
static java.lang.String |
PROP_FRONTEND
Property the defines the frontend to retrieve features from for scoring |
static java.lang.String |
PROP_IS_CPU_RELATIVE
A sphinx property name that controls whether the number of available CPUs on the system is used when determining the number of threads to use for scoring. |
static boolean |
PROP_IS_CPU_RELATIVE_DEFAULT
The default value for PROP_IS_CPU_RELATIVE. |
static java.lang.String |
PROP_MIN_SCOREABLES_PER_THREAD
A Sphinx Property name that controls the minimum number of scoreables sent to a thread. |
static int |
PROP_MIN_SCOREABLES_PER_THREAD_DEFAULT
The default value for PROP_MIN_SCOREABLES_PER_THREAD. |
static java.lang.String |
PROP_NUM_THREADS
A SphinxProperty name that controls the number of threads that are used to score hmm states. |
static int |
PROP_NUM_THREADS_DEFAULT
The default value for PROP_NUM_THREADS. |
static java.lang.String |
PROP_SCOREABLES_KEEP_FEATURE
A SphinxProperty specifying whether the scoreables should keep a reference to the scored features. |
static boolean |
PROP_SCOREABLES_KEEP_FEATURE_DEFAULT
The default value for PROP_SCOREABLES_KEEP_FEATURE. |
| Constructor Summary | |
ThreadedAcousticScorer()
|
| Method Summary | |
void |
allocate()
Allocates resources for this scorer |
Scoreable |
calculateScores(java.util.List scoreableList)
Scores the given set of states |
void |
deallocate()
Deallocates resouces for this scorer |
java.lang.String |
getName()
Retrieves the name for this configurable component |
void |
newProperties(PropertySheet ps)
This method is called when this configurable component has new data. |
void |
register(java.lang.String name,
Registry registry)
Register my properties. |
void |
startRecognition()
Initializes the scorer |
void |
stopRecognition()
Performs post-recognition cleanup. |
| Methods inherited from class java.lang.Object |
equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
| Field Detail |
public static java.lang.String PROP_FRONTEND
public static final java.lang.String PROP_NUM_THREADS
public static final int PROP_NUM_THREADS_DEFAULT
public static final java.lang.String PROP_IS_CPU_RELATIVE
public static final boolean PROP_IS_CPU_RELATIVE_DEFAULT
public static final java.lang.String PROP_MIN_SCOREABLES_PER_THREAD
public static final int PROP_MIN_SCOREABLES_PER_THREAD_DEFAULT
public static final java.lang.String PROP_SCOREABLES_KEEP_FEATURE
public static final boolean PROP_SCOREABLES_KEEP_FEATURE_DEFAULT
public static final java.lang.String PROP_ACOUSTIC_GAIN
public static final float PROP_ACOUSTIC_GAIN_DEFAULT
| Constructor Detail |
public ThreadedAcousticScorer()
| Method Detail |
public void allocate() throws java.io.IOException
AcousticScorer
allocate
in interface AcousticScorer
java.io.IOException
public void deallocate()
AcousticScorer
deallocate
in interface AcousticScorer
public void register(java.lang.String name, Registry registry) throws PropertyException
Configurable
register
in interface Configurable
name
- the name of the componentregistry
- the registry for this component
PropertyException
public void newProperties(PropertySheet ps) throws PropertyException
Configurable
newProperties
in interface Configurable
ps
- a property sheet holding the new data
PropertyException
- if there is a problem with the properties.public java.lang.String getName()
Configurable
getName
in interface Configurable
public void startRecognition()
startRecognition
in interface AcousticScorer
public Scoreable calculateScores(java.util.List scoreableList)
calculateScores
in interface AcousticScorer
scoreableList
- a list containing scoreable objects to be scored
public void stopRecognition()
stopRecognition
in interface AcousticScorer
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"So powerful is the light of unity that it can illuminate the whole earth."
~ Bahá'u'lláh
Images of Bahá'ís engaged in prayer from around the world. Copyright © Bahá'í International Community.
The Bahá'í Faith is a religion that follows the teachings of Bahá'u'lláh. These teachings stem from the central core beliefs that all people are members of one human family, that we are meant to live together in peace and harmony, and that there is one God and all religions come from that Divine Source, bringing guidance to humanity that reflect the needs of the day and age.
The Bahá'í community of Irving is one of many throughout the Dallas-Fort Worth Area. Together with our friends and neighbors, we are engage in community-building activities throughout Irving and North Texas to create a more spiritually and materially balanced and unified future for everyone. We invite anyone who shares in this vision to participate.
Like Bahá'í communities across the United States and throughout the world, here in Irving we work to apply the teachings of Bahá'u'lláh to the realities of our community and daily lives through:


Resources
Topics
AdaBoost
- Lecture 21: AdaBoost
- Lecture 21: AdaBoost [Notebook]
ADD TAGS HERE
Aggregate
- Lecture 18: Random Forest
- Lecture 18: Random Forest [Notebook]
- Lecture 18: Random Forest [Notebook]
- Lecture 18: Random Forest [Notebook]
Bagging
- Lecture 18: Random Forest
- Lecture 18: Random Forest [Notebook]
- Lecture 18: Random Forest [Notebook]
- Lecture 18: Random Forest [Notebook]
- Lecture 17: Bagging
- Lecture 17: Bagging [Notebook]
- Lecture 17: Bagging [Notebook]
- Lecture 17: Bagging [Notebook]
Bayes’ Theorem
- Lecture 15: Logistic Regression II
- Lecture 15: Logistic Regression II [Notebook]
- Lecture 15: Logistic Regression II [Notebook]
Bias
- Lecture 6: Regularization Ridge and Lasso Regression
- Lecture 6: Regularization Ridge and Lasso Regression [Notebook]
- Lecture 6: Regularization Ridge and Lasso Regression [Notebook]
- Lecture 6: Regularization Ridge and Lasso Regression [Notebook]
- Lecture 6: Regularization Ridge and Lasso Regression [Notebook]
- Lecture 6: Regularization Ridge and Lasso Regression [Notebook]
big data
Binary Response
- Lecture 15: Logistic Regression II
- Lecture 15: Logistic Regression II [Notebook]
- Lecture 15: Logistic Regression II [Notebook]
- Lecture 14: Logistic Regression I
- Lecture 14: Logistic Regression I [Notebook]
Binomial
- Lecture 7: Probability
- Lecture 7: Probability [Notebook]
- Lecture 7: Probability [Notebook]
Bootstrap
- Lecture 18: Random Forest
- Lecture 18: Random Forest [Notebook]
- Lecture 18: Random Forest [Notebook]
- Lecture 18: Random Forest [Notebook]
Bootstrapping
- Lecture 8: Inference in Regression and Hypothesis Testing
- Lecture 8: Inference in Regression and Hypothesis Testing [Notebook]
- Lecture 8: Inference in Regression and Hypothesis Testing [Notebook]
- Lecture 8: Inference in Regression and Hypothesis Testing [Notebook]
- Lecture 8: Inference in Regression and Hypothesis Testing [Notebook]
- Lecture 8: Inference in Regression and Hypothesis Testing [Notebook]
CART
Case Study
Classification boundaries
- Lecture 15: Logistic Regression II
- Lecture 15: Logistic Regression II [Notebook]
- Lecture 15: Logistic Regression II [Notebook]
clustering
Comparison of Models
Confidence Intervals
- Lecture 8: Inference in Regression and Hypothesis Testing
- Lecture 8: Inference in Regression and Hypothesis Testing [Notebook]
- Lecture 8: Inference in Regression and Hypothesis Testing [Notebook]
- Lecture 8: Inference in Regression and Hypothesis Testing [Notebook]
- Lecture 8: Inference in Regression and Hypothesis Testing [Notebook]
- Lecture 8: Inference in Regression and Hypothesis Testing [Notebook]
Constant Variance
- Lecture 8: Inference in Regression and Hypothesis Testing
- Lecture 8: Inference in Regression and Hypothesis Testing [Notebook]
- Lecture 8: Inference in Regression and Hypothesis Testing [Notebook]
- Lecture 8: Inference in Regression and Hypothesis Testing [Notebook]
- Lecture 8: Inference in Regression and Hypothesis Testing [Notebook]
- Lecture 8: Inference in Regression and Hypothesis Testing [Notebook]
Content
Cross Validation
- Lecture 5: Model Selection and Cross Validation
- Lecture 5: Model Selection and Cross Validation [Notebook]
- Lecture 5: Model Selection and Cross Validation [Notebook]
- S-Section 04: Regularization and Model Selection
Data Plotting
- Lecture 3: Introduction to Regression kNN and Linear Regression
- Lecture 3: Introduction to Regression kNN and Linear Regression [Notebook]
- Lecture 3: Introduction to Regression kNN and Linear Regression [Notebook]
- Lecture 3: Introduction to Regression kNN and Linear Regression [Notebook]
- Lecture 3: Introduction to Regression kNN and Linear Regression [Notebook]
- Lecture 3: Introduction to Regression kNN and Linear Regression [Notebook]
Decision Boundaries
Decision Trees
- Lecture 17: Bagging
- Lecture 17: Bagging [Notebook]
- Lecture 17: Bagging [Notebook]
- Lecture 17: Bagging [Notebook]
- Lecture 16: Decision Trees
- Lecture 16: Decision Trees [Notebook]
dimensionality reduction
EDA
Effective Visualization
ensemble methods
Entropy
Error
- Lecture 6: Regularization Ridge and Lasso Regression
- Lecture 6: Regularization Ridge and Lasso Regression [Notebook]
- Lecture 6: Regularization Ridge and Lasso Regression [Notebook]
- Lecture 6: Regularization Ridge and Lasso Regression [Notebook]
- Lecture 6: Regularization Ridge and Lasso Regression [Notebook]
- Lecture 6: Regularization Ridge and Lasso Regression [Notebook]
EthiCS
explained variance
Exponential Loss
- Lecture 21: AdaBoost
- Lecture 21: AdaBoost [Notebook]
F-score
Feature Scaling
- Lecture 4: Multi-linear and Polynomial Regression
- Lecture 4: Multi-linear and Polynomial Regression [Notebook]
- Lecture 4: Multi-linear and Polynomial Regression [Notebook]
- Lecture 4: Multi-linear and Polynomial Regression [Notebook]
Generalization
- Lecture 6: Regularization Ridge and Lasso Regression
- Lecture 6: Regularization Ridge and Lasso Regression [Notebook]
- Lecture 6: Regularization Ridge and Lasso Regression [Notebook]
- Lecture 6: Regularization Ridge and Lasso Regression [Notebook]
- Lecture 6: Regularization Ridge and Lasso Regression [Notebook]
- Lecture 6: Regularization Ridge and Lasso Regression [Notebook]
Gini Index
Gradient Boosting
- Lecture 20: Boosting, Gradient Boosting
- Lecture 20: Boosting, Gradient Boosting [Notebook]
- Lecture 19: Random Forrest II
- Lecture 19: Random Forrest II [Notebook]
Gradient Descent
Graphical Integrity
Help
High Dimensionality
- Lecture 10: Principal Component Analysis
- Lecture 10: Principal Component Analysis [Notebook]
- Lecture 10: Principal Component Analysis [Notebook]
- Lecture 10: Principal Component Analysis [Notebook]
hyper-parameters
- Lecture 19: Random Forrest II
- Lecture 19: Random Forrest II [Notebook]
- Lecture 18: Random Forest
- Lecture 18: Random Forest [Notebook]
- Lecture 18: Random Forest [Notebook]
- Lecture 18: Random Forest [Notebook]
Imbalanced classes
Imputation
- Lecture 9: Missing Data & Imputation
- Lecture 9: Missing Data & Imputation [Notebook]
- Lecture 9: Missing Data & Imputation [Notebook]
Imputation with uncertainty
- Lecture 9: Missing Data & Imputation
- Lecture 9: Missing Data & Imputation [Notebook]
- Lecture 9: Missing Data & Imputation [Notebook]
Independence
- Lecture 8: Inference in Regression and Hypothesis Testing
- Lecture 8: Inference in Regression and Hypothesis Testing [Notebook]
- Lecture 8: Inference in Regression and Hypothesis Testing [Notebook]
- Lecture 8: Inference in Regression and Hypothesis Testing [Notebook]
- Lecture 8: Inference in Regression and Hypothesis Testing [Notebook]
- Lecture 8: Inference in Regression and Hypothesis Testing [Notebook]
Instructor
interaction effect
- Lecture 4: Multi-linear and Polynomial Regression
- Lecture 4: Multi-linear and Polynomial Regression [Notebook]
- Lecture 4: Multi-linear and Polynomial Regression [Notebook]
- Lecture 4: Multi-linear and Polynomial Regression [Notebook]
Interaction Terms
- Lecture 10: Principal Component Analysis
- Lecture 10: Principal Component Analysis [Notebook]
- Lecture 10: Principal Component Analysis [Notebook]
- Lecture 10: Principal Component Analysis [Notebook]
Intro
K-Fold
- Lecture 5: Model Selection and Cross Validation
- Lecture 5: Model Selection and Cross Validation [Notebook]
- Lecture 5: Model Selection and Cross Validation [Notebook]
Knn
- Lecture 3: Introduction to Regression kNN and Linear Regression
- Lecture 3: Introduction to Regression kNN and Linear Regression [Notebook]
- Lecture 3: Introduction to Regression kNN and Linear Regression [Notebook]
- Lecture 3: Introduction to Regression kNN and Linear Regression [Notebook]
- Lecture 3: Introduction to Regression kNN and Linear Regression [Notebook]
- Lecture 3: Introduction to Regression kNN and Linear Regression [Notebook]
Knn Regression
- Lecture 3: Introduction to Regression kNN and Linear Regression
- Lecture 3: Introduction to Regression kNN and Linear Regression [Notebook]
- Lecture 3: Introduction to Regression kNN and Linear Regression [Notebook]
- Lecture 3: Introduction to Regression kNN and Linear Regression [Notebook]
- Lecture 3: Introduction to Regression kNN and Linear Regression [Notebook]
- Lecture 3: Introduction to Regression kNN and Linear Regression [Notebook]
Lasso
- Lecture 6: Regularization Ridge and Lasso Regression
- Lecture 6: Regularization Ridge and Lasso Regression [Notebook]
- Lecture 6: Regularization Ridge and Lasso Regression [Notebook]
- Lecture 6: Regularization Ridge and Lasso Regression [Notebook]
- Lecture 6: Regularization Ridge and Lasso Regression [Notebook]
- Lecture 6: Regularization Ridge and Lasso Regression [Notebook]
- S-Section 04: Regularization and Model Selection
Learning Rate
Leave-One-Out
- Lecture 5: Model Selection and Cross Validation
- Lecture 5: Model Selection and Cross Validation [Notebook]
- Lecture 5: Model Selection and Cross Validation [Notebook]
Likelihood
- Lecture 15: Logistic Regression II
- Lecture 15: Logistic Regression II [Notebook]
- Lecture 15: Logistic Regression II [Notebook]
- Lecture 14: Logistic Regression I
- Lecture 14: Logistic Regression I [Notebook]
- Lecture 7: Probability
- Lecture 7: Probability [Notebook]
- Lecture 7: Probability [Notebook]
Linearity
- Lecture 8: Inference in Regression and Hypothesis Testing
- Lecture 8: Inference in Regression and Hypothesis Testing [Notebook]
- Lecture 8: Inference in Regression and Hypothesis Testing [Notebook]
- Lecture 8: Inference in Regression and Hypothesis Testing [Notebook]
- Lecture 8: Inference in Regression and Hypothesis Testing [Notebook]
- Lecture 8: Inference in Regression and Hypothesis Testing [Notebook]
Logistic Estimation
- Lecture 15: Logistic Regression II
- Lecture 15: Logistic Regression II [Notebook]
- Lecture 15: Logistic Regression II [Notebook]
- Lecture 14: Logistic Regression I
- Lecture 14: Logistic Regression I [Notebook]
Logistic Regression
- Lecture 15: Logistic Regression II
- Lecture 15: Logistic Regression II [Notebook]
- Lecture 15: Logistic Regression II [Notebook]
- Lecture 14: Logistic Regression I
- Lecture 14: Logistic Regression I [Notebook]
- Lab 06: Principal Components Analysis (PCA)
Loss function
- Lecture 15: Logistic Regression II
- Lecture 15: Logistic Regression II [Notebook]
- Lecture 15: Logistic Regression II [Notebook]
Matplotlib
MDI
- Lecture 19: Random Forrest II
- Lecture 19: Random Forrest II [Notebook]
- Lecture 18: Random Forest
- Lecture 18: Random Forest [Notebook]
- Lecture 18: Random Forest [Notebook]
- Lecture 18: Random Forest [Notebook]
Missing at Random (MAR)
- Lecture 9: Missing Data & Imputation
- Lecture 9: Missing Data & Imputation [Notebook]
- Lecture 9: Missing Data & Imputation [Notebook]
Missing Completely at Random (MCAR)
- Lecture 9: Missing Data & Imputation
- Lecture 9: Missing Data & Imputation [Notebook]
- Lecture 9: Missing Data & Imputation [Notebook]
Missing Data
- Lecture 9: Missing Data & Imputation
- Lecture 9: Missing Data & Imputation [Notebook]
- Lecture 9: Missing Data & Imputation [Notebook]
Missing Not at Random (MNAR)
- Lecture 9: Missing Data & Imputation
- Lecture 9: Missing Data & Imputation [Notebook]
- Lecture 9: Missing Data & Imputation [Notebook]
MNIST
Model Interpretation
- Lecture 4: Multi-linear and Polynomial Regression
- Lecture 4: Multi-linear and Polynomial Regression [Notebook]
- Lecture 4: Multi-linear and Polynomial Regression [Notebook]
- Lecture 4: Multi-linear and Polynomial Regression [Notebook]
model selection
MSE
- Lecture 5: Model Selection and Cross Validation
- Lecture 5: Model Selection and Cross Validation [Notebook]
- Lecture 5: Model Selection and Cross Validation [Notebook]
- Lecture 4: Multi-linear and Polynomial Regression
- Lecture 4: Multi-linear and Polynomial Regression [Notebook]
- Lecture 4: Multi-linear and Polynomial Regression [Notebook]
- Lecture 4: Multi-linear and Polynomial Regression [Notebook]
- Lecture 3: Introduction to Regression kNN and Linear Regression
- Lecture 3: Introduction to Regression kNN and Linear Regression [Notebook]
- Lecture 3: Introduction to Regression kNN and Linear Regression [Notebook]
- Lecture 3: Introduction to Regression kNN and Linear Regression [Notebook]
- Lecture 3: Introduction to Regression kNN and Linear Regression [Notebook]
- Lecture 3: Introduction to Regression kNN and Linear Regression [Notebook]
Multi-Linear Regression
- Lecture 4: Multi-linear and Polynomial Regression
- Lecture 4: Multi-linear and Polynomial Regression [Notebook]
- Lecture 4: Multi-linear and Polynomial Regression [Notebook]
- Lecture 4: Multi-linear and Polynomial Regression [Notebook]
Nan
- Lecture 9: Missing Data & Imputation
- Lecture 9: Missing Data & Imputation [Notebook]
- Lecture 9: Missing Data & Imputation [Notebook]
Normal
- Lecture 7: Probability
- Lecture 7: Probability [Notebook]
- Lecture 7: Probability [Notebook]
Normality
- Lecture 8: Inference in Regression and Hypothesis Testing
- Lecture 8: Inference in Regression and Hypothesis Testing [Notebook]
- Lecture 8: Inference in Regression and Hypothesis Testing [Notebook]
- Lecture 8: Inference in Regression and Hypothesis Testing [Notebook]
- Lecture 8: Inference in Regression and Hypothesis Testing [Notebook]
- Lecture 8: Inference in Regression and Hypothesis Testing [Notebook]
normalization
NPL
- Lecture 23: Natural Language Processing
- Lecture 23: Natural Language Processing [Notebook]
- Lecture 23: Natural Language Processing [Notebook]
One hot encoding
- Lecture 4: Multi-linear and Polynomial Regression
- Lecture 4: Multi-linear and Polynomial Regression [Notebook]
- Lecture 4: Multi-linear and Polynomial Regression [Notebook]
- Lecture 4: Multi-linear and Polynomial Regression [Notebook]
Out of Bag Error (OOB)
- Lecture 18: Random Forest
- Lecture 18: Random Forest [Notebook]
- Lecture 18: Random Forest [Notebook]
- Lecture 18: Random Forest [Notebook]
Overfitting
- Lecture 18: Random Forest
- Lecture 18: Random Forest [Notebook]
- Lecture 18: Random Forest [Notebook]
- Lecture 18: Random Forest [Notebook]
- Lecture 17: Bagging
- Lecture 17: Bagging [Notebook]
- Lecture 17: Bagging [Notebook]
- Lecture 17: Bagging [Notebook]
- Lecture 5: Model Selection and Cross Validation
- Lecture 5: Model Selection and Cross Validation [Notebook]
- Lecture 5: Model Selection and Cross Validation [Notebook]
P-Value
- Lecture 8: Inference in Regression and Hypothesis Testing
- Lecture 8: Inference in Regression and Hypothesis Testing [Notebook]
- Lecture 8: Inference in Regression and Hypothesis Testing [Notebook]
- Lecture 8: Inference in Regression and Hypothesis Testing [Notebook]
- Lecture 8: Inference in Regression and Hypothesis Testing [Notebook]
- Lecture 8: Inference in Regression and Hypothesis Testing [Notebook]
pandas
- Lab 4 [Notebook]
- Lab 4 [Notebook]
- Lab 3 [Notebook]
- Lab 2 [Notebook]
- Lab 2 [Notebook]
- Lab 2 [Notebook]
- Lecture 2: Introduction to PANDAS and EDA
- Lecture 2: Introduction to Data Science [Notebook]
- Lecture 2: Introduction to PANDAS 2 [Notebook]
- Lecture 2: Introduction to PANDAS [Notebook]
- Lab 1 [Notebook]
PDF/PMF
- Lecture 7: Probability
- Lecture 7: Probability [Notebook]
- Lecture 7: Probability [Notebook]
PMF
- Lecture 15: Logistic Regression II
- Lecture 15: Logistic Regression II [Notebook]
- Lecture 15: Logistic Regression II [Notebook]
- Lecture 14: Logistic Regression I
- Lecture 14: Logistic Regression I [Notebook]
Polynomial Regression
- Lecture 4: Multi-linear and Polynomial Regression
- Lecture 4: Multi-linear and Polynomial Regression [Notebook]
- Lecture 4: Multi-linear and Polynomial Regression [Notebook]
- Lecture 4: Multi-linear and Polynomial Regression [Notebook]
Prediction Intervals
- Lecture 8: Inference in Regression and Hypothesis Testing
- Lecture 8: Inference in Regression and Hypothesis Testing [Notebook]
- Lecture 8: Inference in Regression and Hypothesis Testing [Notebook]
- Lecture 8: Inference in Regression and Hypothesis Testing [Notebook]
- Lecture 8: Inference in Regression and Hypothesis Testing [Notebook]
- Lecture 8: Inference in Regression and Hypothesis Testing [Notebook]
principal components analysis
Principal Components Analysis (PCA)
- Lecture 10: Principal Component Analysis
- Lecture 10: Principal Component Analysis [Notebook]
- Lecture 10: Principal Component Analysis [Notebook]
- Lecture 10: Principal Component Analysis [Notebook]
Probability
- Lecture 15: Logistic Regression II
- Lecture 15: Logistic Regression II [Notebook]
- Lecture 15: Logistic Regression II [Notebook]
- Lecture 14: Logistic Regression I
- Lecture 14: Logistic Regression I [Notebook]
Pruning
- Lecture 17: Bagging
- Lecture 17: Bagging [Notebook]
- Lecture 17: Bagging [Notebook]
- Lecture 17: Bagging [Notebook]
Random Forest
- Lecture 19: Random Forrest II
- Lecture 19: Random Forrest II [Notebook]
- Lecture 18: Random Forest
- Lecture 18: Random Forest [Notebook]
- Lecture 18: Random Forest [Notebook]
- Lecture 18: Random Forest [Notebook]
Random Variable
- Lecture 7: Probability
- Lecture 7: Probability [Notebook]
- Lecture 7: Probability [Notebook]
Regression Trees
- Lecture 17: Bagging
- Lecture 17: Bagging [Notebook]
- Lecture 17: Bagging [Notebook]
- Lecture 17: Bagging [Notebook]
Regularization
- Lecture 6: Regularization Ridge and Lasso Regression
- Lecture 6: Regularization Ridge and Lasso Regression [Notebook]
- Lecture 6: Regularization Ridge and Lasso Regression [Notebook]
- Lecture 6: Regularization Ridge and Lasso Regression [Notebook]
- Lecture 6: Regularization Ridge and Lasso Regression [Notebook]
- Lecture 6: Regularization Ridge and Lasso Regression [Notebook]
- S-Section 04: Regularization and Model Selection
Residuals
Review
Ridge
- Lecture 6: Regularization Ridge and Lasso Regression
- Lecture 6: Regularization Ridge and Lasso Regression [Notebook]
- Lecture 6: Regularization Ridge and Lasso Regression [Notebook]
- Lecture 6: Regularization Ridge and Lasso Regression [Notebook]
- Lecture 6: Regularization Ridge and Lasso Regression [Notebook]
- Lecture 6: Regularization Ridge and Lasso Regression [Notebook]
- S-Section 04: Regularization and Model Selection
Standard Errors
- Lecture 8: Inference in Regression and Hypothesis Testing
- Lecture 8: Inference in Regression and Hypothesis Testing [Notebook]
- Lecture 8: Inference in Regression and Hypothesis Testing [Notebook]
- Lecture 8: Inference in Regression and Hypothesis Testing [Notebook]
- Lecture 8: Inference in Regression and Hypothesis Testing [Notebook]
- Lecture 8: Inference in Regression and Hypothesis Testing [Notebook]
standarization
Stopping Conditions
- Lecture 17: Bagging
- Lecture 17: Bagging [Notebook]
- Lecture 17: Bagging [Notebook]
- Lecture 17: Bagging [Notebook]
Stopping Conditions & Prunin
synergy effect
- Lecture 4: Multi-linear and Polynomial Regression
- Lecture 4: Multi-linear and Polynomial Regression [Notebook]
- Lecture 4: Multi-linear and Polynomial Regression [Notebook]
- Lecture 4: Multi-linear and Polynomial Regression [Notebook]
T-Test
- Lecture 8: Inference in Regression and Hypothesis Testing
- Lecture 8: Inference in Regression and Hypothesis Testing [Notebook]
- Lecture 8: Inference in Regression and Hypothesis Testing [Notebook]
- Lecture 8: Inference in Regression and Hypothesis Testing [Notebook]
- Lecture 8: Inference in Regression and Hypothesis Testing [Notebook]
- Lecture 8: Inference in Regression and Hypothesis Testing [Notebook]
Teaching Fellows
Train Validation Test
- Lecture 5: Model Selection and Cross Validation
- Lecture 5: Model Selection and Cross Validation [Notebook]
- Lecture 5: Model Selection and Cross Validation [Notebook]
Underfitting
- Lecture 18: Random Forest
- Lecture 18: Random Forest [Notebook]
- Lecture 18: Random Forest [Notebook]
- Lecture 18: Random Forest [Notebook]
- Lecture 5: Model Selection and Cross Validation
- Lecture 5: Model Selection and Cross Validation [Notebook]
- Lecture 5: Model Selection and Cross Validation [Notebook]
Uniform
- Lecture 7: Probability
- Lecture 7: Probability [Notebook]
- Lecture 7: Probability [Notebook]
Variable Importance
- Lecture 18: Random Forest
- Lecture 18: Random Forest [Notebook]
- Lecture 18: Random Forest [Notebook]
- Lecture 18: Random Forest [Notebook]
Variance
- Lecture 6: Regularization Ridge and Lasso Regression
- Lecture 6: Regularization Ridge and Lasso Regression [Notebook]
- Lecture 6: Regularization Ridge and Lasso Regression [Notebook]
- Lecture 6: Regularization Ridge and Lasso Regression [Notebook]
- Lecture 6: Regularization Ridge and Lasso Regression [Notebook]
- Lecture 6: Regularization Ridge and Lasso Regression [Notebook]
Variance vs Bias
weights
- Lecture 21: AdaBoost
- Lecture 21: AdaBoost [Notebook]


Contents:
Content's sections are entitled and auto-numbered by this CSS.
1: Heading is taken from the attribute name
of this section
.
1-1: Heading is taken from the first header
child element of this section
. It makes
adding inner HTML formatting to the heading text possible.
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of this section
.
2: Heading is taken from the first header
child element of this section
.
3: Heading is taken from the first header
child element of this section
. It makes adding
inner HTML formatting to the heading text possible.
3-1: Heading is taken from the first header
child element of this section
.
3-1-1: Heading is taken from the attribute name
of this section
.
3-1-2: Heading is taken from the first header
child element of this section
. It
makes adding inner HTML formatting to the heading text possible.
3-2: Heading is taken from the first header
child element of this section
.
4: Heading is taken from the first header
child element of this section
.
CSS:
/* This CSS file is critically important for content: */ @import "../auto-numbering/auto-numbering.css"; /* Custom styles... */ #tocHeader, header, section:before { font-weight: bold; } nav ul { list-style-type: none; } /* to remove bullets from TOC */ nav>ul { margin: 0; padding: 0; }
HTML body:

Contents:

First entitling method: simple text title using "name" attribute.

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 Some Formatting can be used
Second entitling method: "header" as first child element of a section.

Some content…

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For further detail and ideas, please see source HTML code of this document.


Hello. I am a first-year student in the Mathematics/Finacial Analysis and Risk Mangement program at the University of Waterloo. I am a serious, dedicated, and persistent person.
During my free time, I enjoy various different activities. A Capella singing is one of the most spectacular extracurricular activities on campus. I am currently in the A Capella Ensemble, UW ACE. I also enjoy board games such as Dominion, Citiadels, and Villages. These board games are all games where the players have to collect the game's resources in order to gain points. Lastly, my biggest hobby is competitively playing the game Dota2. With over 12 million concurrent players monthly, I rank above the 1% of players according to the match making rating system.
Introduction to Financial Accouting: An introductory course to Financial Accouting. In this class, I studied various financial statements such as: Balance Sheets, Income Statements and Statement of Cash Flow. Futher analysis of these statements included analysis of: Bad Debt, Bonds, and Depreciation. Thoughout the course, I completed weekly analysis of the items above, enhancing my understanding and knowledge.
Introduction to Functional Programing: A course on Functional Programing through Scheme and DrRacket. Over the term, I analyzed and practised using the functional programs Sceme and DrRacket. The course mainly focused on the abstraction of Lists, Trees, Graphs, and Structures. A prerequisite to my current class, Introduction to Imperative Porgraming.
Linear Algebra for Honors Mathematics: A great course on linear algebra as well as fundamentals to mathematics. Mathematical logic behind proofs were studied with great detail. Throughout the course, various proof techniques such as induction and contradiction were practiced.


There are a series of operations that we can perform on statements to get new statements. This is analogous to taking two numbers and adding, subtracting, or multiplying to get a new number. In algebra, the letters \(x\text{,}\) \(y\text{,}\) and \(z\) are often used to represent numbers. These are called variables. In logic we typically use the variables \(p\text{,}\) \(q\text{,}\) and \(r\) to represent a statement. When we perform an operation on one or more statements, we will call the result a compound statement. It is important to note that the resulting statement is, in fact, a statement. This means that a compound statement must either be true or false, but not both at the same time.
in-context


Black Marketeer of Mammon:
If you are finished thinking, select one. Which class would you like to be?
Bishop.
Prophet.


Dashboard
Instructions: Upload new Excel (.xls) spreadsheet(s) for each company. Make sure:
- File is named "
Company
.xls", i.e.Company
is Icertis - Spreadsheet contains data for at least 12 months (1 full year)
- Spreadsheet tabs are labeled: "ARR by Customer", "IS (
year
)", "BS (year
)", and "CF (year
)", i.e.year
is 2020 The last year does not need to have all 12 months of data, but it should be in a separate tab. - Each sheet has compact columns, a.k.a. there should be no empty columns in between label columns and month columns (rows can have empty rows in between)
- There are only raw data values and no intermediate calculations, i.e. totals, averages, quarterly aggregations
- For the ARR by Customer input, the leftmost column for customers is named "Customer"
- For the financial statement inputs, i.e. IS, BS, CF, the column used for identifying fields is named "Labels"
- All labels in the "Labels" column are exactly the same (case-sensitive), i.e. no typos, no extra spaces, same capitalization
- For all month column labels, they are in the format
dd-Mmm-yy
, i.e.dd
is 31,Mmm
is Jan,yy
is 20 - The dates used for each month column label are the same across all input sheets, i.e. ARR by Customer has
29-Feb-16
so IS (2016) must have29-Feb-16
, not28-Feb-16
or any other date
Output Data
Charts


TOC
Sidebar page - Table of Contents
en
,
fr
Updated
Sep 28, 2011
by
<EMAIL_ADDRESS>Concepts
Introduction To Objectify
Best Practices
Advanced Patterns
Frequently Asked Questions
Annotation Reference
Release Notes
Maven Repository
API Javadoc
Objectify With GWT
Caching Without Objectify
Extensions
Contributing To Objectify


--- layout: page title: About header-img: "img/about-bg.png" ---
I'm a learning software engineer at Flatiron School, sharing my trials, tribulations, and overall experiences on my career path.


Welcome to the University of Newcastle's Orientation Web App!
We hope that you are very excited to start your future with us and will provide you any help you may need with your time at University, especially during your first semester! Below is a video of what's to come for you and your University experience.


Automatic License Plate Recognition, or ALPR, take images of vehicles in public spaces, analyze the text on their license plates, and record the timestamp and position of the car's plates (based on GPS coordinates of the camera.)
Unknown as Chicago owns both fixed and mobile ALRPs. Each of Chicago's 25 districts has at least six ALPR-equipped police vehicles. In 2019 the Chicago Police Department purchased an additional 200 ALPRs, bringing the total to over 240. In addition, LPL is currently suing for the locations of stationary ALPRs. Chicago can also use their video surveillance network to scan license plates.
CPD has spent more than $500,000 for software to manage and analyze data from ALPRs. In addition, each device costs $25,000, which could bring the total expenditure to over $1.1 million.
Chicago Police Department purchases Motorola / PAGIS automated license plate readers. These devices scan license plates and check them against a Hot List of plates of interest. According to FOIA documents acquired by the author, there might be as many as 300,000 license plates stored in the Hot List at any one time in Chicago - meaning that approximately 1 in 10 Chicagoans has their vehicle on the Hot List. Each plate's information is stored for 365 days unless it is deemed 'pertinent to criminal or civil matters,' in which case it is retained until a court orders it to be removed.
According to the Chicago Police Department, a vehicle mounted ALPR can capture 3,200 images of license plates in one shift. Documents from the CPD acquired by LPL show that over a two year period, ALPRs recorded over 200 million hits. CPD claims that "the recovery of stolen vehicles has increased significantly" due to ALPRs. From 2016 to 2020, carjackings have decreased by 20%, although it is unclear to what extent this can be attributed to ALPR.
Even an incomplete record of where an individual's car has been provides detailed information about the activities of that person. In Oakland, over 4.6 million license plate scans from ALPR systems were released in a FOIA request, providing detailed information about Oakland residents' activities - including activities protected by the First Amendment.
Concerns over data retention policies and data sharing also should be addressed and publicly disclosed. The criteria that lands a given plate on the Hot List should be publicly disclosed. Check out the EFF's excellent FAQ on ALPR technology for more information.
In 2017, Immigration and Customs Enforcement (ICE) signed a contract with Thomson Reuters which gave them access to a database with billions of nationwide license plate records gleaned from ALPRs, which they can use to track and target immigrants. In addition, ICE has signed contracts with over 80 local law enforcement agencies for their data, including four in the Chicago area.
At the state level in Illinois, there are currently several bills that have been introduced (but not yet passed) in recent years to regulate ALPR use. A couple of these bills have been shot down, and one has been sitting in the Senate for over a year.
In 2018 the CPD issued a directive that sets procedural standards for ALPR use, yet which does not clarify what purposes ALPRs can or cannot be used for.
From documents acquired by Jennifer Helsby
Automatic License Plate Reader on Chicago Police Vehicle
Image courtesy Freddy Martinez
From documents acquired by Jennifer Helsby


---
layout: post
title: Kamikaze-QSCM Version 0.2 Released
categories: []
tags: []
status: publish
type: post
published: true
meta:
_original_post_id: ''
_original_import_author: John Duprey
author:
---
http://kamikaze-qscm.tigris.org/
A number of bugs were revealed when I tested this at work. At work we had several large CVS repositories that were imported into Subverion. I also changed the web page to only show the query form or the results - not both. I added a "Modify" option which allows you to edit the query from the results page.
The bugs fixed since .1:
You can read the release notes here.
You can download the tarball here: Kamikaze-QSCM 0.2
Alternatively, you can get this release via CVS - tagged: tKAMIKAZE-QSCM_0_2
Be sure to read the README.txt for installation instructions. You can view the README here.
As always, I'd appreciate your feedback. Did it work for you? What features (existing or new) interest you?
Thanks!
-John


Morgan Freeman
"I have to remind myself that some birds aren't meant to be caged. Their feathers are just too bright. And when they fly away, the part of you that knows it was a sin to lock them up does rejoice" - Morgan Freeman


--- layout: post title: Take the blog out of your own eye date: 2011-12-27 tags: - Critique - Luke 6v42 meta: pvc_views: "18075" views: "115" dsq_thread_id: "761302835" author: jcuenod permalink: /content/take-blog-out-your-own-eye/ excerpt: As evidenced by the fact that you are reading what I have written, I am part of the generation that blogs. Those who find themselves a part of this generation probably blog thanks to a slightly overinflated ego.   Those who find themselves a part of this generation probably blog thanks to a slightly overinflated ego.
In Luke 6:42 Jesus argues, "How can you say to your brother, 'Brother, let me take out the speck that is in your eye,' when you yourself do not see the log that is in your own eye? You hypocrite, first take the log out of your own eye, and then you will see clearly to take out the speck that is in your brother's eye." It is lamentable to me that I am associated with this generation of critics who do not take heed of Jesus' advice. They are so quick to point out specks in the eyes of others that they don't have time to pull the logs out of their own. "Bloggers" is the perfect term for these poor wretches: derived from "web" and "loggers" because they write on the web and they are the lumberjacks of the ocular world. I write this because, people of my generation, I am more than willing to help you remove those logs.
I find myself surrounded by hypocrites.
'God, I thank you that I am not like other men'


This library provides an Encrypted File System (EFS)
Chunks consist of a an acutal data 'block' that is encrypted. It is also prepended with the salt, initialization vector and authorisation tag used to encrypt the data.
Below is a diagram showing the layout of the encrypted chunks.
|||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| || || || || || || Salt || Initialization Vector || Authorisation Tag || Encrypted Block ... -> || || || || || || ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
This is a constant sized amount (optionally user-specified) of business data. A large file is split into several block of block_size (generall 4k). This is to to allow random access reads and writies. For example to read a small section of a file, the entire file does not need to be decrpted Only the block(s) that contain the section you want to read. This does mean however, that there needs to be an IV for each block. This is because reusing IVs, or having predictable IVs is a security threat. It can lead to the ___ attack. TODO: which attack again? Perhaps for large executables, where you need to always read the file in its entirely, We can get rid of the block and IVs. But consider if it's really worth it because you're only saving kilobytes here.
Some amount of data equal or smaller than a block.
EFS uses AES-GCM symmetric encryption, which requires the derivation of a symmetric encryption key using pbkdf
and a randomly generated salt and init vector. Sizes for these parameters follow NIST recommendations: https://csrc.nist.gov/publications/detail/sp/800-38d/final.
An authorisation tag based on chunk encryption is stored along side the salt and init vector. This provides a basic chunk level integrity gurantee that can be verified upon decryption in accordance with the AES-GCM algorithm.
TODO: Write instructions
TODO: Write instructions
TODO: Write instructions
This library uses Jest for testing.
Generated using TypeDoc


--- layout: post status: publish published: true title: AppSpeed update author: display_name: iguy login: iguy email<EMAIL_ADDRESS>url: '' author_login: iguy author_email<EMAIL_ADDRESS>wordpress_id: 203 wordpress_url: http://itsjustanotherlayer.com/?p=203 date: !binary |- MjAwOS0wOC0yOCAwMjo1MDoyOSAtMDUwMA== date_gmt: !binary |- MjAwOS0wOC0yOCAwMjo1MDoyOSAtMDUwMA== categories: - VMware tags: - appspeed - licensing - VMware comments: [] ---
I've had the AppSpeed demo setup and running in one of my clusters. When you get the demo temporary license, its for 16 cores worth. My smallest environment to test in is 160 cores deep.
I figured no issue, I'll just see what I get for a couple different apps to see how it works. That didn't work out so well either. Any actually interesting app is multi-tier which happens to bounce across multiple cores/hosts in the environment.
So I haven't forgotten and I'm not ignoring the statement. I've put in a request for a temporary license of 160 cores worth and I'm waiting for that to come through. 3 weeks and waiting now.
I've had the AppSpeed demo setup and running in one of my clusters. When you get the demo temporary license, its for 16 cores worth. My smallest environment to test in is 160 cores deep.
I figured no issue, I'll just see what I get for a couple different apps to see how it works. That didn't work out so well either. Any actually interesting app is multi-tier which happens to bounce across multiple cores/hosts in the environment.
So I haven't forgotten and I'm not ignoring the statement. I've put in a request for a temporary license of 160 cores worth and I'm waiting for that to come through. 3 weeks and waiting now.


15 references to Closed
Microsoft.Management.Infrastructure.CimCmdlets (1)
CimSessionOperations.cs (1)
831
case RunspaceState.
Closed
:
Microsoft.PowerShell.Commands.Utility (1)
commands\utility\GetRandomCommand.cs (1)
122
case RunspaceState.
Closed
:
Microsoft.PowerShell.ConsoleHost (1)
host\msh\ConsoleHost.cs (1)
660
case RunspaceState.
Closed
:
System.Management.Automation (12)
engine\debugger\debugger.cs (3)
3150
case RunspaceState.
Closed
:
3649
rsState == RunspaceState.
Closed
||
3740
case RunspaceState.
Closed
:
engine\hostifaces\Connection.cs (1)
1161
case RunspaceState.
Closed
:
engine\hostifaces\ConnectionBase.cs (2)
333
if (RunspaceState == RunspaceState.
Closed
||
341
SetRunspaceState(RunspaceState.
Closed
, null);
engine\hostifaces\LocalConnection.cs (2)
896
SetRunspaceState(RunspaceState.
Closed
);
1440
if (_remoteRunspace.RunspaceStateInfo.State == RunspaceState.
Closed
||
engine\hostifaces\RunspacePool.cs (1)
158
return RunspaceState.
Closed
;
engine\remoting\commands\newrunspacecommand.cs (1)
577
case RunspaceState.
Closed
:
engine\remoting\commands\PSRemotingCmdlet.cs (2)
3630
case RunspaceState.
Closed
:
4191
result = (runspace.RunspaceStateInfo.State == RunspaceState.
Closed
);


Assessment Data Aggregator for Game Environments (ADAGE) is an open-source data collection and analysis framework to
transform telemetry data into evidence of learning.
ADAGE provides information for developers, educators and players. Open source ADAGE tools for data
analysis and visualization will create a vibrant development community around games for learning.


Name: Prym Geybriel C. Pineda
Profile: Full Stack Web Developer
Email<EMAIL_ADDRESS>Phone: (+63) 918-7396-400
Hi, I am Prym Geybriel Capulong Pineda. A graduate of Bachelor of Science in Information Technology at Tarlac State University. A former Junior IT Support Officer at an Australian based Company. I was able to developed websites showcasing the knowledge and technical skills that a web developer must possess. That is why I changed my career path from IT Support Officer to Full Stack Developer. I am very passionate about what I do and have the willingness to learn and to be trained for improvement.
I'm well-experienced as Full Stack Developer, working in the IT Industry for more than 2 years now and handling different projects. Also, capable on creating websites and web base application as satisfying the design and functionality of the system from the clients demand and able to integrate data from various front-end/back-end services and databases. Responsible for maintaining the performance of the site, fixing and debugging, implementing different features and payment gateway as well.
Software Development
PHP Laravel 90%Building clients' websites from concept all the way to completion from the bottom up, fashioning everything from the homepage to site layout and functions, responsible for maintaining the performance of the system and integrating new features, APIs and Payment Gateway as well.
Creating Websites and Web Base Application/Systems as satisfying the design and functionality of the system from my clients' demand.
Building clients' websites from concept all the way to completion from the bottom up, fashioning everything from the homepage to site layout and function.
Integrate data from various front-end/back-end services and databases.
Resolving network issues, configuring operating systems and servers and using remote desktop connections to provide immediate support.
Creating a website that is well designed, testable, efficient code by using the best software development practices.
24
WORKS COMPLETED2
YEARS OF EXPERIENCE14
TOTAL CLIENTS2
AWARD WONFeel free to check some of my works.