I have been writing code and building computers since early childhood. I have 8 years of corporate IT experience holding such titles as Information Technology Specialist, IT Coordinator and System Administrator.
I'm available for web development projects, small business IT services and open to full time opportunities. I also like to build cars, play video games and do other geeky stuff with my sons.
HTML/CSS
Ruby/Rails
Javascript
React.js
Python
RESTful APIs
AWS
PC Build/Upgrade
Hardware Repair
Network Setup
Deployment
Help Desk
User Training


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Refer all employment inquiries to the Lafayette office of the Georgia Department of Labor. Click HERE to view Roper Corporation's Weather Policy.
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809 jobs in Lafayette, Georgia matched your search. . LA FAYETTE, GA. See who you know. Process Improvement Engineer. GE Home & Business Solutions .
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Since opening in 1974, Roper Corporation Employees Credit Union has offered financial services to members. The credit union serves members from 1 location.
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Roper Corporation - La Fayette, GA (Georgia) |<PHONE_NUMBER>
Manufacturer of GE, GE Profile, and Monogram cooking appliances.
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Locations - The Bank of LaFayette
101 West Patton Street, Post Office Box 1149, LaFayette, GA 30728 . A Moneybelt 24-Hour Cash Dispenser is located at the Roper Corporation plant on .
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Configuration
The VirtualBox provider exposes some additional configuration options that allow you to more finely control your VirtualBox-powered Vagrant environments.
GUI vs. Headless
By default, VirtualBox machines are started in headless mode, meaning there is no UI for the machines visible on the host machine. Sometimes, you want to have a UI. Common use cases include wanting to see a browser that may be running in the machine, or debugging a strange boot issue. You can easily tell the VirtualBox provider to boot with a GUI:
config.vm.provider "virtualbox" do |v|
v.gui = true
end
Virtual Machine Name
You can customize the name that appears in the VirtualBox GUI by setting the name
property. By default, Vagrant sets it to the containing folder of the Vagrantfile plus a timestamp of when the machine was created. By setting another name, your VM can be more easily identified.
config.vm.provider "virtualbox" do |v|
v.name = "my_vm"
end
VBoxManage Customizations
VBoxManage is a utility that can be used to make modifications to VirtualBox virtual machines from the command line.
Vagrant exposes a way to call any command against VBoxManage just prior to booting the machine:
config.vm.provider "virtualbox" do |v|
v.customize ["modifyvm", :id, "--cpuexecutioncap", "50"]
end
In the example above, the VM is modified to have a host CPU execution cap of 50%, meaning that no matter how much CPU is used in the VM, no more than 50% would be used on your own host machine. Some details:
-
The
:id
special parameter is replaced with the ID of the virtual machine being created, so when a VBoxManage command requires an ID, you can pass this special parameter. -
Multiple
customize
directives can be used. They will be executed in the order given.


"""
uart.py:
This is the adapter for projects that would choose to use a serial UART interface for sending data from an F prime
deployment. This handles sending and receiving data from the things like 'LinuxSerialDriver' and other standard UART
drivers.
@author lestarch
"""
import logging
import fprime_gds.common.communication.adapters.base
import serial
from serial.tools import list_ports
[docs]LOGGER = logging.getLogger("serial_adapter")
[docs]class SerialAdapter(fprime_gds.common.communication.adapters.base.BaseAdapter):
"""
Supplies a data source adapter that is pulling data off from a UART wire using PySerial. This is setup using a
device handle and a baudrate for the given serial device.
"""
[docs] BAUDS = [
50,
75,
110,
134,
150,
200,
300,
600,
1200,
1800,
2400,
4800,
9600,
19200,
38400,
57600,
115200,
230400,
460800,
500000,
576000,
921600,
1000000,
1152000,
1500000,
2000000,
2500000,
3000000,
3500000,
4000000,
]
def __init__(self, device, baud):
"""
Initialize the serial adapter using the default settings. This does not open the serial port, but sets up all
the internal variables used when opening the device.
"""
self.device = device
self.baud = baud
self.serial = None
[docs] def open(self):
"""
Opens the serial port based on previously supplied settings. If the port is already open, then close it first.
Then open the port up again.
"""
self.close()
self.serial = serial.Serial(self.device, self.baud)
return self.serial is not None
[docs] def close(self):
"""
Close the serial device, and ignore any errors that might arrive when attempting that closure.
"""
try:
if self.serial is not None:
self.serial.close()
finally:
self.serial = None
[docs] def write(self, frame):
"""
Send a given framed bit of data by sending it out the serial interface. It will attempt to reconnect if there is
was a problem previously. This function will return true on success, or false on error.
:param frame: framed data packet to send out
:return: True, when data was sent through the UART. False otherwise.
"""
try:
if self.serial is None:
self.open()
written = self.serial.write(frame)
# Not believed to be possible to not send everything without getting a timeout exception
assert written == len(frame)
return True
except serial.serialutil.SerialException as exc:
LOGGER.warning("Serial exception caught: %s. Reconnecting.", (str(exc)))
self.close()
return False
[docs] def read(self, timeout=0.500):
"""
Read up to a given count in bytes from the UART adapter. This may return less than the full requested size but
is expected to return some data.
:param timeout: timeout for reading data from the serial.
:return: data successfully read
"""
data = b""
try:
if self.serial is None:
self.open()
# Read as much data as possible, while ensuring to block if no data is available at this time. Note: as much
# data is read as possible to avoid a long-return time to this call. Minimum data to read is one byte in
# order to block this function while data is incoming.
self.serial.timeout = timeout
data = self.serial.read(1) # Force a block for at least 1 character
while self.serial.in_waiting:
data += self.serial.read(
self.serial.in_waiting
) # Drain the incoming data queue
except serial.serialutil.SerialException as exc:
LOGGER.warning("Serial exception caught: %s. Reconnecting.", (str(exc)))
self.close()
return data
@classmethod
[docs] def get_arguments(cls):
"""
Returns a dictionary of flag to argparse-argument dictionaries for use with argparse to setup arguments.
:return: dictionary of flag to argparse arguments for use with argparse
"""
available = list(
map(lambda info: info.device, list_ports.comports(include_links=True))
)
default = "/dev/ttyACM0" if not available else available[-1]
return {
("--uart-device",): {
"dest": "device",
"type": str,
"default": default,
"help": "UART device representing the FSW. Default: %(default)s",
},
("--uart-baud",): {
"dest": "baud",
"type": int,
"default": 9600,
"help": "Baud rate of the serial device. Default: %(default)s",
},
}
@classmethod
[docs] def check_arguments(cls, args):
"""
Code that should check arguments of this adapter. If there is a problem with this code, then a "ValueError"
should be raised describing the problem with these arguments.
:param args: arguments as dictionary
"""
ports = map(lambda info: info.device, list_ports.comports(include_links=True))
if not args["device"] in ports:
raise ValueError(
"Serial port '{}' not valid. Available ports: {}".format(
args["device"], ports
)
)
# Note: baud rate may not *always* work. These are a superset
baud = 0
try:
baud = int(args["baud"])
except ValueError:
raise ValueError(
"Serial baud rate '{}' not integer. Use one of: {}".format(
args["baud"], SerialAdapter.BAUDS
)
)
if not int(baud) in SerialAdapter.BAUDS:
raise ValueError(
"Serial baud rate '{}' not supported. Use one of: {}".format(
baud, SerialAdapter.BAUDS
)
)


Data Fields | |
| long | ulSize |
| Size of the structure. | |
| long | ulConnectorType |
| HDTV connector type. | |
| long | ulDeviceData |
| HDTV capabilities. | |
| long | ulOverridedDeviceData |
| Overridden HDTV capabilities. | |
| long | ulReserved |
| Reserved field. |
This structure is used to retrieve HDTV information information for display calls.
Size of the structure.
HDTV connector type.
HDTV capabilities.
Overridden HDTV capabilities.
Reserved field.


PyFlink: Introducing Python Support for UDFs in Flink's Table API
April 9, 2020 - Jincheng Sun (@sunjincheng121) Markos Sfikas (@MarkSfik)Flink 1.9 introduced the Python Table API, allowing developers and data engineers to write Python Table API jobs for Table transformations and analysis, such as Python ETL or aggregate jobs. However, Python users faced some limitations when it came to support for Python UDFs in Flink 1.9, preventing them from extending the system’s built-in functionality.
In Flink 1.10, the community further extended the support for Python by adding Python UDFs in PyFlink. Additionally, both the Python UDF environment and dependency management are now supported, allowing users to import third-party libraries in the UDFs, leveraging Python’s rich set of third-party libraries.
Python Support for UDFs in Flink 1.10 #
Before diving into how you can define and use Python UDFs, we explain the motivation and background behind how UDFs work in PyFlink and provide some additional context about the implementation of our approach. Below we give a brief introduction on the PyFlink architecture from job submission, all the way to executing the Python UDF.
The PyFlink architecture mainly includes two parts — local and cluster — as shown in the architecture visual below. The local phase is the compilation of the job, and the cluster is the execution of the job.
For the local part, the Python API is a mapping of the Java API: each time Python executes a method in the figure above, it will synchronously call the method corresponding to Java through Py4J, and finally generate a Java JobGraph, before submitting it to the cluster.
For the cluster part, just like ordinary Java jobs, the JobMaster schedules tasks to TaskManagers. The tasks that include Python UDF in a TaskManager involve the execution of Java and Python operators. In the Python UDF operator, various gRPC services are used to provide different communications between the Java VM and the Python VM, such as DataService for data transmissions, StateService for state requirements, and Logging and Metrics Services. These services are built on Beam’s Fn API. While currently only Process mode is supported for Python workers, support for Docker mode and External service mode is also considered for future Flink releases.
How to use PyFlink with UDFs in Flink 1.10 #
This section provides some Python user defined function (UDF) examples, including how to install PyFlink, how to define/register/invoke UDFs in PyFlink and how to execute the job.
Install PyFlink #
Using Python in Apache Flink requires installing PyFlink. PyFlink is available through PyPI and can be easily installed using pip:
$ python -m pip install apache-flink
Define a Python UDF #
There are many ways to define a Python scalar function, besides extending the base class ScalarFunction
. The following example shows the different ways of defining a Python scalar function that takes two columns of BIGINT
as input parameters and returns the sum of them as the result.
# option 1: extending the base class `ScalarFunction`
class Add(ScalarFunction):
def eval(self, i, j):
return i + j
add = udf(Add(), [DataTypes.BIGINT(), DataTypes.BIGINT()], DataTypes.BIGINT())
# option 2: Python function
@udf(input_types=[DataTypes.BIGINT(), DataTypes.BIGINT()], result_type=DataTypes.BIGINT())
def add(i, j):
return i + j
# option 3: lambda function
add = udf(lambda i, j: i + j, [DataTypes.BIGINT(), DataTypes.BIGINT()], DataTypes.BIGINT())
# option 4: callable function
class CallableAdd(object):
def __call__(self, i, j):
return i + j
add = udf(CallableAdd(), [DataTypes.BIGINT(), DataTypes.BIGINT()], DataTypes.BIGINT())
# option 5: partial function
def partial_add(i, j, k):
return i + j + k
add = udf(functools.partial(partial_add, k=1), [DataTypes.BIGINT(), DataTypes.BIGINT()],
DataTypes.BIGINT())
Register a Python UDF #
# register the Python function
table_env.register_function("add", add)
Invoke a Python UDF #
# use the function in Python Table API
my_table.select("add(a, b)")
Below, you can find a complete example of using Python UDF.
from pyflink.datastream import StreamExecutionEnvironment
from pyflink.table import StreamTableEnvironment, DataTypes
from pyflink.table.descriptors import Schema, OldCsv, FileSystem
from pyflink.table.udf import udf
env = StreamExecutionEnvironment.get_execution_environment()
env.set_parallelism(1)
t_env = StreamTableEnvironment.create(env)
add = udf(lambda i, j: i + j, [DataTypes.BIGINT(), DataTypes.BIGINT()], DataTypes.BIGINT())
t_env.register_function("add", add)
t_env.connect(FileSystem().path('/tmp/input')) \
.with_format(OldCsv()
.field('a', DataTypes.BIGINT())
.field('b', DataTypes.BIGINT())) \
.with_schema(Schema()
.field('a', DataTypes.BIGINT())
.field('b', DataTypes.BIGINT())) \
.create_temporary_table('mySource')
t_env.connect(FileSystem().path('/tmp/output')) \
.with_format(OldCsv()
.field('sum', DataTypes.BIGINT())) \
.with_schema(Schema()
.field('sum', DataTypes.BIGINT())) \
.create_temporary_table('mySink')
t_env.from_path('mySource')\
.select("add(a, b)") \
.insert_into('mySink')
t_env.execute("tutorial_job")
Submit the job #
Firstly, you need to prepare the input data in the “/tmp/input” file. For example,
$ echo "1,2" > /tmp/input
Next, you can run this example on the command line,
$ python python_udf_sum.py
The command builds and runs the Python Table API program in a local mini-cluster. You can also submit the Python Table API program to a remote cluster using different command lines, (see more details here).
Finally, you can see the execution result on the command line:
$ cat /tmp/output 3
Python UDF dependency management #
In many cases, you would like to import third-party dependencies in the Python UDF. The example below provides detailed guidance on how to manage such dependencies.
Suppose you want to use the mpmath
to perform the sum of the example above. The Python UDF may look like:
@udf(input_types=[DataTypes.BIGINT(), DataTypes.BIGINT()], result_type=DataTypes.BIGINT())
def add(i, j):
from mpmath import fadd # add third-party dependency
return int(fadd(i, j))
To make it available on the worker node that does not contain the dependency, you can specify the dependencies with the following commands and API:
$ cd /tmp
$ echo mpmath==1.1.0 > requirements.txt
$ pip download -d cached_dir -r requirements.txt --no-binary :all:
t_env.set_python_requirements("/tmp/requirements.txt", "/tmp/cached_dir")
A requirements.txt
file that defines the third-party dependencies is used. If the dependencies cannot be accessed in the cluster, then you can specify a directory containing the installation packages of these dependencies by using the parameter “requirements_cached_dir
”, as illustrated in the example above. The dependencies will be uploaded to the cluster and installed offline.
Conclusion & Upcoming work #
In this blog post, we introduced the architecture of Python UDFs in PyFlink and provided some examples on how to define, register and invoke UDFs. Flink 1.10 brings Python support in the framework to new levels, allowing Python users to write even more magic with their preferred language. The community is actively working towards continuously improving the functionality and performance of PyFlink. Future work in upcoming releases will introduce support for Pandas UDFs in scalar and aggregate functions, add support to use Python UDFs through the SQL client to further expand the usage scope of Python UDFs, provide support for a Python ML Pipeline API and finally work towards even more performance improvements. The picture below provides more details on the roadmap for succeeding releases.


China keeps the easing cycle at a gradual pace – UOB
by Pablo PiovanoSuan Teck Kin, CFA, Head of Research at UOB Group, reviewed the recent move in the Chinese Loan Prime Rate (LPR).
Key Quotes
“China’s Loan Prime Rate (LPR) latest fixings (Wed, 20 Nov) dipped a modest 5bps to 4.15% for the 1Y tenure) and to 4.80% for 5Y and above tenure, which was at the lower end of the range of our call of 5-10bps decline”.
“While it is clear that China’s easing cycle has started, the pace of declines seen in the MLF, 7D reverse repo rate and the latest LPR fixings has been milder than we had anticipated, suggesting the authorities are taking a measured and cautious approach to guide interest rates lower”.
“With the US-China trade tensions appearing to be easing and major central banks on pause mode, we are adjusting our forecasts to factor in a less aggressive downtrend in the LPR. We now project the 1Y LPR at 3.80% by mid-2020, instead of our earlier call of 3.65%”.


One of my big tasks since coming into NYU last August was to work on the ReproZip project. My role is largely outreach and education: I was tasked with teaching ReproZip and general reproducibility principles, gathering use cases in a wider variety of disciplines (when I arrived, the use cases were largely in computer science), and supporting users in general.
ReproZip kind of blew my mind when I arrived; it's an open source software tool that simplifies the process of creating reproducible experiments. Basically it tracks operating system calls and creates a package that contains all the binaries, files, and dependencies required to reproduce the experiment. A reviewer can then extract the experiment on their own machine using ANY operating system (even if it's different from the original one!!) to reproduce the results. As a librarian, I was like "OH MY GOD. THE DIGITAL PRESERVATION GAME JUST GOT UPPED." Anyway, here's basically how ReproZip works -- in 2 steps:
Step 1: Packing
Step 2: Unpacking
Rémi Rampin, the current developer of ReproZip, Fernando Chirigati, the former developer, and I created this great GitHub repository called ReproZip Examples, dedicated to showcasing examples and use cases from different domains using ReproZip. We have everything from digital humanities (a history paper used ReproZip!) to archived websites and client-server architecture, to machine learning. It's awesome -- check it out and try to unpack stuff if you want!
I'm really advocating hard for libraries to start at least investigating using ReproZip for their digital collections -- there is so much unused potential for this it's actually crazy, which brings us around to the title. Getting use cases is hard.
In May, Rémi and I will be at the Data and Software and Preservation for Open Science workshop, Container Strategies for Data Software Preservation that Promote Open Science. I'm serving as an external organizer, but the two of us will be doing some extensive work with ReproZip while there.
The DASPOS project, NSF funded, "represents a collective effort to explore the realization of a viable data, software, and computation preservation architecture for High Energy Physics (HEP)." But at this point, it's grown FAR beyond HEP -- the workshop so far is slotted to have representation from a variety of fields and professions (like libraries!!).
In addition to a talk/demo during the conference proceedings, Rémi and I are leading three breakout sessions that will allow people to try out ReproZip for themselves, using their research if they brought some. I'm hoping that, with the new ReproZip-Examples, we can get some people at the DASPOS workshop to add their own .rpz packages for us to try and reproduce! This would be the best-case scenario, but it depends a lot on the research of the participants.
Anyway. I'm really looking forward to learning more about some other containerizing tools like Umbrella and meeting some other folks (hopefully a lot of librarians!!) who are involved in the reproducibility and preservation space. The community doing active tool development in this area seems fairly small, so it'll be great for fostering interoperability having us all in a room.


At Monarch, we want the overal expirience of travel to be frictionless; from booking to arriving at your destination.
Our staff team is comprised of highly qualified individuals who love working with customers on a daily basis.
Open Positions (8)
- Senior Full Stack Developer (5+ yrs) [1]
- Project Manager (3+ yrs) [2]
- Risk Analyst (3+ yrs) [1]
- A321-NEO FO (varies) [3]
- Social Media Manager (3+ yrs) [1]


Geonosis Version 1.0 - Adding the User Password Manager.
The User Password Manager provides Geonosis with the automatic ability to ensure all users are provided with an email that requires the user to "activate" a new user account prior to allowing a new user to log-in.
The really cool thing about Geonosis is that any module or programmatic function that is part of Geonosis can be made accessible to users of Geonosis and can therefore be made part of any web app that Geonosis can generate. This means any web app that Geonosis generates will be fully able to manage users using the same technique that Geonosis uses to manage users.
Got a need to deploy a web based user management system ? Well sure you do because any web site that need to manage users or subscribers will need to manage users. No more need to code this functionality for every single site one needs to deploy. No more need to code this ever again. Simply use Geonosis to generate all your web apps that need to manage users.
Stay tuned for more posts about Geonosis and this exciting new way to create and manage web based content.


public class LandPlot
extends java.lang.Object
| Modifier and Type | Field and Description |
|---|---|
private boolean |
hasRoboticon |
private int |
IndexEnergy |
private int |
IndexFood |
private int |
IndexOre |
private Roboticon |
installedRoboticon |
private com.badlogic.gdx.maps.tiled.TiledMapTileLayer.Cell |
mapTile |
private boolean |
owned |
private Player |
owner |
private com.badlogic.gdx.maps.tiled.TiledMapTileLayer.Cell |
playerTile |
private int[] |
productionAmounts
The base production amounts.
|
(package private) float[] |
productionModifiers
Saved modifiers for LandPlot.
|
private com.badlogic.gdx.maps.tiled.TiledMapTileLayer.Cell |
roboticonTile |
(package private) int |
x |
(package private) int |
y |
| Constructor and Description |
|---|
LandPlot(int ore,
int energy,
int food)
Initialise LandPlot with specific base amount of resources.
|
| Modifier and Type | Method and Description |
|---|---|
com.badlogic.gdx.maps.tiled.TiledMapTileLayer.Cell |
getMapTile() |
Player |
getOwner() |
com.badlogic.gdx.maps.tiled.TiledMapTileLayer.Cell |
getPlayerTile() |
int |
getResource(ResourceType resource)
Gets the index of the specific resource
|
com.badlogic.gdx.maps.tiled.TiledMapTileLayer.Cell |
getRoboticonTile() |
int |
getX() |
int |
getY() |
boolean |
hasOwner()
Returns the state of the land plots ownership
|
boolean |
hasRoboticon()
Checks if the tile contains a roboticon
|
boolean |
installRoboticon(Roboticon roboticon)
Install a roboticon to this LandPlot.
|
int |
produceResource(ResourceType resource)
Calculate the amount of resources to be produced for specific resource.
|
int[] |
produceResources()
Calculate the amount of resources to be produced.
|
void |
removeOwner()
Removes the owner of the tile
|
private int |
resourceTypeToIndex(ResourceType resource)
Get the type index from the
ResourceType |
void |
setHasRoboticon(boolean roboticonInstalled)
Setter for hasRoboticon
|
boolean |
setOwner(Player player)
Sets the owner of the land plot to the specified player
|
void |
setupTile(PlotManager plotManager,
int x,
int y)
Retrieves the overlays for the specific tile
|
private final int IndexOre
private final int IndexEnergy
private final int IndexFood
int x
int y
float[] productionModifiers
private com.badlogic.gdx.maps.tiled.TiledMapTileLayer.Cell mapTile
private com.badlogic.gdx.maps.tiled.TiledMapTileLayer.Cell playerTile
private com.badlogic.gdx.maps.tiled.TiledMapTileLayer.Cell roboticonTile
private Player owner
private int[] productionAmounts
private boolean owned
private Roboticon installedRoboticon
private boolean hasRoboticon
public LandPlot(int ore, int energy, int food)
ore
- Amount of oreenergy
- Amount of energyfood
- Amount of foodpublic com.badlogic.gdx.maps.tiled.TiledMapTileLayer.Cell getMapTile()
public com.badlogic.gdx.maps.tiled.TiledMapTileLayer.Cell getPlayerTile()
public com.badlogic.gdx.maps.tiled.TiledMapTileLayer.Cell getRoboticonTile()
public Player getOwner()
public int getX()
public int getY()
public boolean setOwner(Player player)
player
- The player to be set as ownerpublic boolean hasOwner()
public void removeOwner()
public void setupTile(PlotManager plotManager, int x, int y)
plotManager
- The plotmanager storing the images of the current maox
- The x coordinate of the tiley
- The y coordinate if the tileprivate int resourceTypeToIndex(ResourceType resource)
ResourceType
resource
- The ResourceType
InvalidResourceTypeException
- Exception is thrown if the resource index is invalid.public boolean installRoboticon(Roboticon roboticon)
roboticon
- The roboticon to be installed.public int[] produceResources()
public int produceResource(ResourceType resource)
resource
- The resource type to be calculated.public int getResource(ResourceType resource)
resource
- The resource selectedpublic boolean hasRoboticon()
public void setHasRoboticon(boolean roboticonInstalled)
roboticonInstalled
- The boolean that hasRoboticon is to be changed to


NamePurge Settings
NamePurge allows you to easily moderate users names when they join or change their names. This is useful to prevent users trying to advertise servers or using profanity in their name
When enabled, bots names will not be filtered. This is useful if certain bots contain a discord link in their name
List of text that is not allowed in a users name. Each text should be separated by a line


myquery channel
All options but query are optional.
Query (q):
Limit (l):
Offset (o):
Mya Deployment (m):


93
The LORD Reigns
1 The LORD reigns; he is robed in majesty;
the LORD is robed; he has put on strength as his belt.
Yes, the world is established; it shall never be moved.
2 Your throne is established from of old;
you are from everlasting.
3 The floods have lifted up, O LORD,
the floods have lifted up their voice;
the floods lift up their roaring.
4 Mightier than the thunders of many waters,
mightier than the waves of the sea,
the LORD on high is mighty!
5 Your decrees are very trustworthy;
holiness befits your house,
O LORD, forevermore.


flutter_parse library
Classes
- FlutterParse
- The {@code Parse} class contains static functions that handle global configuration for the Parse library.
- ParseDateFormat
- This is the currently used date format. It is precise to the millisecond.
- ParseDecoder
- A ParseDecoder can be used to transform JSON data structures into actual objects, such as ParseObject
- ParseEncoder
- A ParseEncoder can be used to transform objects such as ParseObject into Map data structures.
- ParseFile
- ParseFile is a local representation of a file that is saved to the Parse cloud. [...]
- ParseGeoPoint
- ParseGeoPoint represents a latitude / longitude point that may be associated with a key in a {@link ParseObject} or used as a reference point for geo queries. This allows proximity based queries on the key. [...]
- ParseObject
- The ParseObject is a local representation of data that can be saved and retrieved from the Parse cloud. [...]
-
ParseQuery<
T extends ParseObject> -
The ParseQuery class defines a query that is used to fetch ParseObject. The most
common use case is finding all objects that match a query through the findAsync
method. For example, this sample code fetches all objects of class
MyClass
. It calls a different function depending on whether the fetch succeeded or not. - ParseTextUtils
- ParseUser
- The ParseUser is a local representation of user data that can be saved and retrieved from the Parse cloud.
- PointerOrLocalIdEncoder
- Encoder for pointer or ParseObject.
Properties
- parseDateFormat → ParseDateFormat
-
final
- parseDecoder → ParseDecoder
-
final
- parseEncoder → ParseEncoder
-
final
- pointerOrLocalIdEncoder → PointerOrLocalIdEncoder
-
final
Exceptions / Errors
- ParseException
- A ParseException gets raised whenever a ParseObject issues an invalid request, such as deleting or editing an object that no longer exists on the server, or when there is a network failure preventing communication with the Parse server.


A collection of functions to plot acid/base titration curves (pH vs. volume of titrant), complexation titration curves (pMetal vs. volume of EDTA), redox titration curves (potential vs.volume of titrant), and precipitation titration curves (either pAnalyte or pTitrant vs. volume of titrant). Options include the titration of mixtures, the ability to overlay two or more titration curves, and the ability to show equivalence points.
| Version: | 0.1.0 |
| Suggests: | knitr, rmarkdown |
| Published: | 2016-02-11 |
| Author: | David Harvey [aut, cre] |
| Maintainer: | David Harvey <harvey at depauw.edu> |
| License: | GPL-2 |
| NeedsCompilation: | no |
| Materials: | README |
| In views: | ChemPhys |
| CRAN checks: | titrationCurves results |
| Reference manual: | titrationCurves.pdf |
| Vignettes: |
Titration Curves |
| Package source: | titrationCurves_0.1.0.tar.gz |
| Windows binaries: | r-devel: titrationCurves_0.1.0.zip, r-release: titrationCurves_0.1.0.zip, r-oldrel: titrationCurves_0.1.0.zip |
| OS X binaries: | r-release: titrationCurves_0.1.0.tgz, r-oldrel: titrationCurves_0.1.0.tgz |
Please use the canonical form https://CRAN.R-project.org/package=titrationCurves to link to this page.


DevConnector
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A collection is a container or folder which contains one or more digital objects. The user is able to add and remove collections from the tool.
Object here means a complex digital object is made up of interdependent simple digital objects or components (e.g. files, software applications, hardware specifications). In the media domain, it a combination of a digital video file and its playback environment. In science, it is the raw data, scripts and outputs of the experiment.
Object Name: The name of the digital object.
Creation Date: Date the object was uploaded to the system.
Risk Level: Combined risk proximity and impact measure: 1 – low, 10 – high.
Primary Risk: Type of risk of nearest proximity to the object.
Date for action: The predicted date that action is required.
Confidence: confidence interval of the prediction
Impact: Confidence interval for the date for action.
Delete Selected: Select one or more objects for deletion.
1. Click the "New" button on the sidebar on the left side of the page.
2. Click "Collection".
3. Enter the name of the new collection
4. Click "Create" button.
1. Click "Collections" on the sidebar on the left side of the page.
2. Click the bin icon next to the collection to be deleted.
3. Click "Yes" to confirm your selection when the warning message is displayed.
1. Click the "New" button on the sidebar on the left side of the page.
2. Click "Object".
3. Select the "Collection" for the new object from the dropdown list.
4. Enter the name of the new object.
5. Select the "Accession Date".
6. Select the "Database" from the dropdown list.
7. Select the "Scripting Language" from the dropdown list.
8. Select the "Image Format" from the dropdown list.
9. Select the "Image Viewer" from the dropdown list.
10. Select the "Document Format" from the dropdown list.
11. Select the "Document Viewer" from the dropdown list.
Once all the information for the object is provided, click the "Create" button.
1. Click the checkbox of the object(s) to be deleted.
2. Click "Delete Selected".
3. Click "Yes" to confirm your selection when the warning message is displayed.


<EMAIL_ADDRESS>New York, NY 10128 United States
Chat on Google Talk
Twitter, Facebook, Dribbble, Vimeo


This research project was conducted under my supervision at Rensselaer Polytechnic Institute (RPI), My students worked on this project under my guidlines and mentoring is: Karthik Dusi. This research project was a part of the Data Analytics course that I taught during Fall 2019 at Rensselaer Polytechnic Institute.
Outcome of this project was published as a conference paper in AAAI Symposium on AI for Social Good in August, 2020.
Access to water is one of the fundamental human rights. Clean water is an issue plaguing many countries worldwide and is one of the world’s largest health concerns. The poor are those who suffer significantly from access to improved water sources and often contract other infectious diseases from unsafe water. This paper examines how the AI community can further research into clean water data that is available and investigate the socio-economic factors that prevent some communities from gaining access to safe water sources. Preliminary and Exploratory Data Analysis were done on the UN data to understand the patterns, relations, and trends between related variables. Key correlations were investigated between different socioeconomic factors such as GDP, Corruption, and Infrastructure to understand what has the greatest effect on access to improved water sources. To do so, visualizations were built using Python and the Seaborn package, as well as using the Pandas package to curate the data.
Over 1.1 billion people in the world lack access to a general water source according to the World Wildlife Organization, 2020 report. According to Worldwildlife.org, 2.7 billion people suffer from water scarcity at least one month a year, and 2.4 billion people are victims to clean water inadequately. These numbers have been on the rise and continue to be as scientists predict that by 2025, over two-thirds of the world’s population will face water shortage issues (World Wildlife Organization 2020).
The UN provides datasets that they have collected as well as datasets from related organizations like the WHO at UN Data Repository . Other sites like Our World in Data also has relevant data towards understanding the problems behind lack of access to clean water. To understand basic correlations and present ideas for the reader, a dataset with information on populations using improved water sources was explored(World Health Organization 2014). This dataset has a percentage of a population using improved water source for 192 countries, and further divides the percentages into whether they live in rural or urban areas. The rural areas are defined as areas not part of major metropolitan areas, which are defined by population density and distance from the metropolitan city, and the rural data reflects data collected on those areas. Similarly, the urban data reflects data collected in areas part of major metropolitan areas. There is also historical data ranging from 1990 up to 2012 for these countries, giving ample data to explore and analyze.
The project workflow as shown in Figure 1, project starts with obtaining the data from the sources, cleaning the data, and storing the data on a local repository.
EDA was done on the collected dataset to understand correlations between GDP per capita on percentage of total population’s access to improved water source, as well as understanding the correlations between GDP and percentage of urban populations and percentage of rural populations’ access to the improved water sources.
Combining this initial dataset with other indicators provided by the World Bank (The World Bank 2018, 2020) resulted in variables measuring Government Effectiveness, Overall Infrastructure, and the Corruption Perception Index. The initial dataset ranged from 1990 to 2012, but the World Bank dataset had data from 1995 to 2012. For preliminary purposes, the following analyses were done on data collected on the year 2012. Looking at only 124 countries in 2012, the following heatmap in Figure 6 to investigate correlations was generated.
we can see a strong blue color means a higher correlation between the two variables. We see a medium to a strong correlation between the corruption perception index (Corruption Perceptions Index 2020) and percent of the rural population with access. This can be perceived as certain rural populations not having access to improved water sources because of a higher corruption perception index. We can also observe that there is a strong relationship between government effectiveness and percentage value of rural population that has access to improved water sources, which makes sense given that more effective governments are able to provide water sources to all parts of the country. There is a medium correlation between infrastructure rating and percentage of the total population with access to improved water sources. This could be because this infrastructure rating considers all infrastructure in the country, and it may be more prudent just to observe water-related infrastructure, like drainage basins, sewers, reservoirs, etc.
The AI community can help leverage this data and turn it into a usable tool for governments and relief organizations by helping them predict where resources must be allocated first to enhance access to improved water sources. Using it as a model to predict where clean water sources will deplete given trends in GDP, infrastructure, and other socioeconomic factors would be very useful as several scholars assert that by 2025, two-thirds of the world’s population will face water shortage.(World Wildlife Organization 2020).
Additionally, models can be used to investigate where water quality is low. With machines and water filters that continuously check whether the water is safe to drink or not, a data collection feature could be added and could provide data for data scientists to use in narrowing down where the water contamination is happening. Prototypes of devices that can detect whether water quality is low and can report the data to a database exist, and could be used for this application. By using machine learning techniques and neural networks, this existing data coupled with other socio-economic datasets can be used for the further analysis and develop prediction models. Lack of clean water leads to many infectious diseases, such as deadly diarrheal diseases, cholera, and typhoid, and by using a model to see where there is no clean water available, medical professionals can help try to prevent the spread of infectious diseases in those areas utilizing those models. Stakeholders for this type of application would be public policy experts, healthcare professionals, and infrastructure professionals who could help provide data and insights regarding what sort of socioeconomic factors are most prevalent in prohibiting access to clean water.
This paper presents a preliminary understanding of what could be done to collect and explore the data to help solve access to improved water sources. Looking at correlations between key indicators and populations with access to water sources provides a basic understanding of what features to use in future models. Additionally, looking at rural populations over urban populations may be more productive since urban populations tend to be well developed and have good water sources. We plan to use the insights gained from this initial analysis to test out different hypotheses and research questions in the future.


In this document
Starting from Android 6.0 (API level 23), Android introduces two power-saving features that extend battery life for users by managing how apps behave when a device is not connected to a power source. Doze reduces battery consumption by deferring background CPU and network activity for apps when the device is unused for long periods of time. App Standby defers background network activity for apps with which the user has not recently interacted.
Doze and App Standby manage the behavior of all apps running on Android 6.0 or higher, regardless whether they are specifically targeting API level 23. To ensure the best experience for users, test your app in Doze and App Standby modes and make any necessary adjustments to your code. The sections below provide details.
Understanding Doze
If a user leaves a device unplugged and stationary for a period of time, with the screen off, the device enters Doze mode. In Doze mode, the system attempts to conserve battery by restricting apps' access to network and CPU-intensive services. It also prevents apps from accessing the network and defers their jobs, syncs, and standard alarms.
Periodically, the system exits Doze for a brief time to let apps complete their deferred activities. During this maintenance window, the system runs all pending syncs, jobs, and alarms, and lets apps access the network.
At the conclusion of each maintenance window, the system again enters Doze, suspending network access and deferring jobs, syncs, and alarms. Over time, the system schedules maintenance windows less and less frequently, helping to reduce battery consumption in cases of longer-term inactivity when the device is not connected to a charger.
As soon as the user wakes the device by moving it, turning on the screen, or connecting a charger, the system exits Doze and all apps return to normal activity.
Doze restrictions
The following restrictions apply to your apps while in Doze:
- Network access is suspended.
- The system ignores wake locks.
- Standard
AlarmManager
alarms (includingsetExact()
andsetWindow()
) are deferred to the next maintenance window. -
- If you need to set alarms that fire while in Doze, use
setAndAllowWhileIdle()
orsetExactAndAllowWhileIdle()
. - Alarms set with
setAlarmClock()
continue to fire normally — the system exits Doze shortly before those alarms fire.
- If you need to set alarms that fire while in Doze, use
- The system does not perform Wi-Fi scans.
- The system does not allow sync adapters to run.
- The system does not allow
JobScheduler
to run.
Doze checklist
- If possible, use GCM for downstream messaging.
- If your users must see a notification right away, make sure to use a GCM high priority message.
- Provide sufficient information within the initial message payload, so subsequent network access is unnecessary.
- Set critical alarms with
setAndAllowWhileIdle()
andsetExactAndAllowWhileIdle()
. - Test your app in Doze.
Adapting your app to Doze
Doze can affect apps differently, depending on the capabilities they offer and the services they use. Many apps function normally across Doze cycles without modification. In some cases, you must optimize the way that your app manages network, alarms, jobs, and syncs. Apps should be able to efficiently manage activities during each maintenance window.
Doze is particularly likely to affect activities that AlarmManager
alarms and
timers manage, because alarms in Android 5.1 (API level 22) or lower do not fire when the system
is in Doze.
To help with scheduling alarms, Android 6.0 (API level 23) introduces two new
AlarmManager
methods: setAndAllowWhileIdle()
and setExactAndAllowWhileIdle()
. With these methods,
you can set alarms that will fire even if the device is in Doze.
Note: Neither setAndAllowWhileIdle()
nor setExactAndAllowWhileIdle()
can fire alarms more
than once per 9 minutes, per app.
The Doze restriction on network access is also likely to affect your app, especially if the app relies on real-time messages such as tickles or notifications. If your app requires a persistent connection to the network to receive messages, you should use Google Cloud Messaging (GCM) if possible.
To confirm that your app behaves as expected with Doze, you can use adb commands to force the system to enter and exit Doze and observe your app’s behavior. For details, see Testing with Doze and App Standby.
Understanding App Standby
App Standby allows the system to determine that an app is idle when the user is not actively using it. The system makes this determination when the user does not touch the app for a certain period of time and none of the following conditions applies:
- The user explicitly launches the app.
- The app has a process currently in the foreground (either as an activity or foreground service, or in use by another activity or foreground service).
- The app generates a notification that users see on the lock screen or in the notification tray.
When the user plugs the device into a power supply, the system releases apps from the standby state, allowing them to freely access the network and to execute any pending jobs and syncs. If the device is idle for long periods of time, the system allows idle apps network access around once a day.
Using GCM to Interact with Your App While the Device is Idle
Google Cloud Messaging (GCM) is a cloud-to-device service that lets you support real-time downstream messaging between backend services and apps on Android devices. GCM provides a single, persistent connection to the cloud; all apps needing real-time messaging can share this connection. This shared connection significantly optimizes battery consumption by making it unnecessary for multiple apps to maintain their own, separate persistent connections, which can deplete the battery rapidly. For this reason, if your app requires messaging integration with a backend service, we strongly recommend that you use GCM if possible, rather than maintaining your own persistent network connection.
GCM is optimized to work with Doze and App Standby idle modes by means of high-priority GCM messages. GCM high-priority messages let you reliably wake your app to access the network, even if the user’s device is in Doze or the app is in App Standby mode. In Doze or App Standby mode, the system delivers the message and gives the app temporary access to network services and partial wakelocks, then returns the device or app to idle state.
High-priority GCM messages do not otherwise affect Doze mode, and they don’t affect the state of any other app. This means that your app can use them to communicate efficiently while minimizing battery impacts across the system and device.
As a general best practice, if your app requires downstream messaging, it should use GCM. If your server and client already uses GCM, make sure that your service uses high-priority messages for critical messages, since this will reliably wake apps even when the device is in Doze.
Support for Other Use Cases
Almost all apps should be able to support Doze by managing network connectivity, alarms, jobs, and syncs properly, and using GCM high-priority messages. For a narrow set of use cases, this might not be sufficient. For such cases, the system provides a configurable whitelist of apps that are partially exempt from Doze and App Standby optimizations.
An app that is whitelisted can use the network and hold
partial wake locks during Doze and
App Standby. However, other restrictions still apply to the
whitelisted app, just as they do to other apps. For example, the whitelisted
app’s jobs and syncs are deferred, and its regular AlarmManager
alarms do not
fire. An app can check whether it is currently on the exemption whitelist by
calling isIgnoringBatteryOptimizations()
.
Users can manually configure the whitelist in Settings > Battery > Battery Optimization. Alternatively, the system provides ways for apps to ask users to whitelist them.
- An app can fire the
ACTION_IGNORE_BATTERY_OPTIMIZATION_SETTINGS
intent to take the user directly to the Battery Optimization, where they can add the app. - An app holding the
REQUEST_IGNORE_BATTERY_OPTIMIZATIONS
permission can trigger a system dialog to let the user add the app to the whitelist directly, without going to settings. The app fires aACTION_REQUEST_IGNORE_BATTERY_OPTIMIZATIONS
Intent to trigger the dialog. - The user can manually remove apps from the whitelist as needed.
Before asking the user to add your app to the whitelist, make sure the app matches the acceptable use cases for whitelisting.
Note: Google Play policies prohibit apps from requesting direct exemption from Power Management features in Android 6.0+ (Doze and App Standby) unless the core function of the app is adversely affected.
Testing with Doze and App Standby
To ensure a great experience for your users, you should test your app fully in Doze and App Standby.
Testing your app with Doze
You can test Doze mode by following these steps:
- Configure a hardware device or virtual device with an Android 6.0 (API level 23) or higher system image.
- Connect the device to your development machine and install your app.
- Run your app and leave it active.
 (The app remains active.)
- Force the system to cycle through Doze modes by running the following
commands:
$ adb shell dumpsys battery unplug $ adb shell dumpsys deviceidle step
You may need to run the second command more than once. Repeat it until the device state changes to idle.
- Observe the behavior of your app after you reactivate the device. Make sure the app recovers gracefully when the device exits Doze.
Testing your app with App Standby
To test the App Standby mode with your app:
- Configure a hardware device or virtual device with an Android 6.0 (API level 23) or higher system image.
- Connect the device to your development machine and install your app.
- Run your app and leave it active.
- Force the app into App Standby mode by running the following commands:
$ adb shell dumpsys battery unplug $ adb shell am set-inactive <packageName> true
- Simulate waking your app using the following commands:
$ adb shell am set-inactive <packageName> false $ adb shell am get-inactive <packageName>
- Observe the behavior of your app after waking it. Make sure the app recovers gracefully from standby mode. In particular, you should check if your app's Notifications and background jobs continue to function as expected.
Acceptable Use Cases for Whitelisting
The table below highlights the acceptable use cases for requesting or being on the Battery Optimizations exceptions whitelist. In general, your app should not be on the whitelist unless Doze or App Standby break the core function of the app or there is a technical reason why your app cannot use GCM high-priority messages.
For more information, see Support for Other Use Cases .
| Type | Use-case | Can use GCM? | Whitelisting acceptable? | Notes |
|---|---|---|---|---|
| Instant messaging, chat, or calling app. | Requires delivery of real-time messages to users while device is in Doze or app is in App Standby. | Yes, using GCM | Not Acceptable | Should use GCM high-priority messages to wake the app and access the network. |
| Yes, but is not using GCM high-priority messages. | ||||
| Instant messaging, chat, or calling app; enterprise VOIP apps. | No, can not use GCM because of technical dependency on another messaging service or Doze and App Standby break the core function of the app. | Acceptable | ||
| Task automation app | App's core function is scheduling automated actions, such as for instant messaging, voice calling, new photo management, or location actions. | If applicable. | Acceptable | |
| Peripheral device companion app | App's core function is maintaining a persistent connection with the peripheral device for the purpose of providing the peripheral device internet access. | If applicable. | Acceptable | |
| App only needs to connect to a peripheral device periodically to sync, or only needs to connect to devices, such as wireless headphones, connected via standard Bluetooth profiles. | If applicable. | Not Acceptable |


Entity Extractors In One Hour - Created with Haiku Deck, presentation software that inspires
MTurk hits can be run in sandbox (developer) or live environment. Hits can be created with/without qualifications. All the possibilities are explained below.
We will be definining an extraction which is named; the name forms the identifier for this effort. This identifier must be chose as a string which can be safely used as a directory component with only lower case letters, digits and the underscore character. For example, we might define a catalog
extraction for getting information from online stores, or a makemodel
for extracting atributes of automobiles for sale.
Within our extraction we will be defining one or more categories. A category is a class of entities to be recognized and extracted. For example, in the catalog
extractor, we might define categories price
, description
, and modelnumber
. The category names should be simple identifiers with only lower case letters, digits, and the underscore character. Each category also has an English label, suitable for presentation to users, which can include spaces and upper case letters. For example, in the catalog
extractor, we might define the category labels as "Price", "Item Description", and "Model Number".
We will define a corpus of example texts too train and test our extractors. These are conventionally called sentences but they may be fragments of a sentence, multiple sentences, etc., as long as they can be annotated by users in the interface without scrolling. These sentences should have a fair number of instances of the categories of this extraction.
Mechanical Turk microwork tasks are conventionally termed hits and we will use that terminology as well.
Some Mechanical Turk extractions will benefit from having the workers undergo training and vetting. This project includes capabilities to define and populate an optional special task, called a qualification task, to carry this out.
pip install requests
pip install boto
pip install nltk
pip install awscli
$HOME
.~/.bashrc
, ~/.profile
, or ~/.bash_profile
export GITHUB=<your github repository root folder>
export PATH="$PATH:$GITHUB/dig-mturk/generic/scripts"
source <shell init file>
cd $GITHUB/dig-mturk; git checkout master; git pull; cd mturk; mvn clean install
cd $GITHUB/Web-Karma; git checkout development; git pull; mvn clean install
aws configure
cd $GITHUB/dig-mturk
export EXTRACTION=<extraction>
newExtraction.sh $EXTRACTION
This will create $GITHUB/dig-mturk/extractions/$EXTRACTION
and copy/adapt a few files from $GITHUB/dig-mturk/extractions/boilerplate/
to $GITHUB/dig-mturk/extractions/$EXTRACTION/
$GITHUB/dig-mturk/extractions/$EXTRACTION/sentences.txt
to contain the sentence data, one sentence per line.$GITHUB/dig-mturk/extractions/$EXTRACTION/fetchSentences.sh
and/or $GITHUB/dig-mturk/extractions/$EXTRACTION/query.json
to specify the index name, desired hit count, credentials, query fields, and query path. Then execute the script:fetchSentences.sh $EXTRACTION $TRIAL $SIZE
where $SIZE is the number of sentences to be fetched from ES. This will generate $GITHUB/dig-mturk/extractions/$EXTRACTION/sentences.json
.$GITHUB/dig-mturk/extractions/$EXTRACTION/create.cfg
is a configuration file with parameters defining how Mechanical Turk tasks are created.[create]
section, edit parameters to specify the desired number of hits to be created, the number of sentences in each hit, etc. If your data comes from ElasticSearch, be sure the [field]
parameter specifies how data is obtained from each ElasticSearch result 'hit'. In the [boto]
section, edit the parameters to specify the AWS/S3 configurations specified above.$GITHUB/dig-mturk/extractions/$EXTRACTION/hitdata.pyfmt
is a template file specifying details of Mechanical Turk hits. For this file, you should edit the task description, title, and categories. You should leave the scratch_category
as it is. You may wish to edit the keywords. You should leave the bracketed expressions {instructions}
, {qualifications}
and {sentences}
as they are: they will be filled in by the page generator.$GITHUB/dig-mturk/extractions/$EXTRACTION/head.html
, $GITHUB/dig-mturk/extractions/$EXTRACTION/instructions.html
, and $GITHUB/dig-mturk/extractions/$EXTRACTION/tail.html
will be combined to become an instructions page for the users.
$GITHUB/dig-mturk/extractions/$EXTRACTION/head.html
: no change.$GITHUB/dig-mturk/extractions/$EXTRACTION/instructions.html
: Change the sample content and the categories to conform to your categories and likely data. There are five necessary modifications in $GITHUB/dig-mturk/extractions/$EXTRACTION/instructions.html
. Each is introduced by a comment suggesting what needs to be changed.$GITHUB/dig-mturk/extractions/$EXTRACTION/tail.html
: Change the $GITHUB/dig-mturk/extractions/$EXTRACTION/tail.html
if you consider the standard boilerplate text confusing in context when presented to a user as part of the instructions.cd $GITHUB/dig-mturk/extractions/$EXTRACTION; cat head.html instructions.html tail.html > page.html; open page.html
$GITHUB/dig-mturk/extractions/$EXTRACTION/karma/
folder contains information so that the Karma information integration system can transform the Mechanical Turk workers' outputs into a format useful for training a CRF model.
$GITHUB/dig-mturk/extractions/$EXTRACTION/karma/preloaded-ontologies/mturk-ontology.ttl
: Define the categories as relations (see embedded example) and use the labels as the rdfs:label
.$GITHUB/dig-mturk/extractions/$EXTRACTION/karma/python/mturk.py
: In the definition of data structure categoryToAnnotationClass
at the top of the file, insert a mapping between your categories and their labels.$GITHUB/dig-mturk/extractions/$EXTRACTION/qualification/
folder contains specifications and files used to specify a qualification task. (See below.)The qualification task is a small test where the user practices annotating examples before performing the real, paid tasks. The objective of the test is to teach the user by practicing, so for each wrong answer the user receives feedback with the explanation of how to correctly answer it. Once the user has successfully completed the qualification task, he/she will be allowed to continue to the actual tasks. A user who is not successful at the qualification task can try again after a specified delay.
If an extraction requires qualification, the qualification task has to be generated before creating hits. A user wishing to perform a hit belong to your extraction will be first directed to satisfy the qualification if you have specified one.
To generate a qualification task, you need the following:
$EXTRACTION
as the name of the qualification.newQualification.sh $EXTRACTION
- this will copy config JSON from $GITHUB/dig-mturk/extractions/boilerplate/qualification/
to $GITHUB/dig-mturk/extractions/$EXTRACTION/qualification/qual_${EXTRACTION}.json.
It will also generate qualification.cfg.$GITHUB/dig-mturk/extractions/$EXTRACTION/qualification.cfg
to reflect your organization name, Amazon S3 bucket name, Amazon access_key_id and Amazon secret_access_key.$GITHUB/dig-mturk/extractions/$EXTRACTION/qual_${EXTRACTION}.json
to reflect categories, sentences, answers, etc. to define your qualification domain. Note: the JSON key explanation.wrong
needn't be edited. The script will populate this field as required before generating HTML.For each sentence, change:
\t
. If there is no answer for this category, set as "".newQualification.sh $EXTRACTION
- this will copy config JSON from $GITHUB/dig-mturk/extractions/boilerplate/qualification/
to $GITHUB/dig-mturk/extractions/$EXTRACTION/qualification/qual_${EXTRACTION}.json
generateQualification.sh $EXTRACTION --publish --upload
. This will generate and publish the qualification page HTML and assets to Amazon S3, based on the qual_${EXTRACTION}.json
data.generateQualification.sh $EXTRACTION --render
(optional) can be used to preview the qualification test sentences and correction essages (message presented to user after incorrect response).deployQualification.sh -sandbox $GITHUB/dig-mturk/extractions/$EXTRACTION/qualification.cfg
to create qualification in sandbox. This will update qualification.cfg with the qualification ID generated by Amazon for sandbox trials of the qualification test.deployQualification.sh -live $GITHUB/dig-mturk/extractions/$EXTRACTION/qualification.cfg
to create qualification in live. This will update qualification.cfg with the qualification ID generated by Amazon for live use by any interested Turkers.$GITHUB/dig-mturk/mturk/src/main/python/respondQualification.py
every 5 minutes. This job approves/rejects all qualification requests.You may want to edit $GITHUB/dig-mturk/extractions/$EXTRACTION/create.cfg
for a small number of hits, small number of sentences/hit.
export TRIAL=<trial>
mkdir $GITHUB/dig-mturk/extractions/$EXTRACTION/$TRIAL
createHits.sh sandbox $EXTRACTION $TRIAL
. This will create config json for hits and upload it to S3 at extractions/$EXTRACTION/$TRIAL/config/.deployHits.sh -sandbox $EXTRACTION $TRIAL
. This will create all files requires to create a HIT and publish those to $BUCKET/extractions/$EXTRACTION/$TRIAL/hits/
in S3 and will also yield a few sandbox URLs, which you can annotate yourself to test things out. If hit was created requiring qualification you need to take the qualification test to be qualified before you are allowed to do the hits, even for sandbox deployment.hitResults.sh -sandbox $EXTRACTION $TRIAL
. This will approve the unapproved assignments of the hits and create tab separated result files in S3 in $BUCKET/extractions/$EXTRACTION/$TRIAL/hits/
.When your task well formed, you are ready to create live hits for the Mechanical Turk community to perform for you.
You may want to edit $GITHUB/dig-mturk/extractions/$EXTRACTION/create.cfg
to significantly increase the number of hits and/or to standardize the number of sentences/hit.
export TRIAL=<trial>
mkdir $GITHUB/dig-mturk/extractions/$EXTRACTION/$TRIAL
createHits.sh $EXTRACTION $TRIAL
. This will create config json for hits and upload it to S3 at extractions/$EXTRACTION/$TRIAL/config/.deployHits.sh -live $EXTRACTION $TRIAL
. This will create all files requires to create a HIT and publish those to extractions/$EXTRACTION/$TRIAL/hits/ in S3 and yield a few sandbox URLs, which you can annotate yourself to test things out. If hit was created using qualification you need to take the qualification test, get qualified before you are allowed to do the hits.hitResults.sh -live $EXTRACTION $TRIAL
. This will create all files requires to create a HIT and publish those to $BUCKET/extractions/$EXTRACTION/$TRIAL/hits/
in S3.Since these are processing done on results fetched from hits and stored in S3, these steps don't depend on the staging area (live/sandbox) of the hits.
consolidateResults.sh $EXTRACTION $TRIAL
# Note: no -sandbox since it is independent of Mturk. This will consolidate the result files for all hits in a particular trial.fetchConsolidated.sh $EXTRACTION $TRIAL
. This will download consolidated result file from S3.modelConsolidated.sh $EXTRACTION $TRIAL
. This will do offline karma model for the results fetched from S3.adjudicate.sh $EXTRACTION $TRIAL
. This will compute the agreement amongst the Turkers for each annotation. Adjudicated results are in $GITHUB/dig-mturk/extractions/$EXTRACTION/$TRIAL/adjudicated_$EXTRACTION_$TRIAL.jsonThis research is supported by the Defense Advanced Research Projects Agency (DARPA) and the Air Force Research Laboratory (AFRL) under contract number FA8750-14-C-0240.
1 The student Lidia Ferreira thank to Science Without Borders Program, CAPES Scholarship - Proc. Nº 88888.030514/2013-00.


Hydro Data
Logged By
Sample Depth
Date
Time
Longitude
Latitude
pH
Temperature
Hardness
Total Dissolved Solids
Alkalinity
Free Chlorine
Nitrates
Nitrites
Ammonia
Turbidity
Flow Rate
Depth
Other
General Notes:
Submit


First, you need to install OEmbed. It is available at http://github.com/worldcompany/djangoembed/
git clone git://github.com/worldcompany/djangoembed/
cd djangoembed
python setup.py install
After installing, adding OEmbed consumption to your projects is a snap. First, add it to your projects’ INSTALLED_APPs and run ‘syncdb’:
# settings.py
INSTALLED_APPS = [
...
'oembed'
]
djangoembed uses a registration pattern like the admin’s. In order to be sure all apps have been loaded, djangoembed should run autodiscover() in the urls.py. If you like, you can place this code right below your admin.autodiscover() bits:
# urls.py
import oembed
oembed.autodiscover()
Now you’re ready to start consuming OEmbed-able objects. There are a couple of options depending on what you want to do. The most straightforward way to get up-and-running is to add it to your templates:
{% load oembed_tags %}
{% oembed %}blog.content{% endoembed %}
{# or use the filter #}
{{ blog.content|oembed }}
{# maybe you're working with some dimensional constraints #}
{% oembed "600x600" %}blog.content{% endoembed %}
{{ blog.content|oembed:"600x600" }}
You can consume oembed objects in python as well:
import oembed
oembed.autodiscover()
# just get the metadata
resource = oembed.site.embed('http://www.youtube.com/watch?v=nda_OSWeyn8')
resource.get_data()
{u'author_name': u'botmib',
u'author_url': u'http://www.youtube.com/user/botmib',
u'height': 313,
u'html': u'<object width="384" height="313"><param name="movie" value="http://www.youtube.com/v/nda_OSWeyn8&fs=1"></param><param name="allowFullScreen" value="true"></param><param name="allowscriptaccess" value="always"></param><embed src="http://www.youtube.com/v/nda_OSWeyn8&fs=1" type="application/x-shockwave-flash" width="384" height="313" allowscriptaccess="always" allowfullscreen="true"></embed></object>',
u'provider_name': u'YouTube',
u'provider_url': u'http://www.youtube.com/',
u'title': u'Leprechaun in Mobile, Alabama',
u'type': u'video',
u'version': u'1.0',
u'width': 384}
# get the metadata and run it through a template for pretty presentation
from oembed.consumer import OEmbedConsumer
client = OEmbedConsumer()
embedded = client.parse_text("http://www.youtube.com/watch?v=nda_OSWeyn8")

<object width="384" height="313">
<param name="movie" value="http://www.youtube.com/v/nda_OSWeyn8&fs=1"></param>
<param name="allowFullScreen" value="true"></param>
<param name="allowscriptaccess" value="always"></param>
<embed src="http://www.youtube.com/v/nda_OSWeyn8&fs=1"
type="application/x-shockwave-flash"
width="384"
height="313"
allowscriptaccess="always"
allowfullscreen="true">
</embed>
</object>

Leprechaun in Mobile, Alabama
by
botmib

'


{{content}}
Do you have what it takes to become new level developer ? Do you want to improve your skills and get new titile? Here you can find all kind of information and tests to make it happen!
There is also a room for improvement. you can be better and achieve much more ahead.We are here to help you with this.
Forgot something and want to refresh your knowledge? Wait no more, our library always grows and detailed filters can help you in no time.


Tamar Braxton’s Boyfriend Shares Video Of The Couple Packing The PDA – She ‘Can’t Get Enough Of This Sugar!’
by Nick MarkusTamar Braxton and her significant other, David Adefeso seem happier and more in love than ever! The two packed quite a lot of PDA while at his birthday party as proven by the footage the man shared on social media.
The pair shared some passionate kisses at the bash all the while people around them cheered and celebrated.
In reality, David is set to turn 50 years old in a couple of days but it looks like Tamar started the celebrations early!
She then took to social media to share a clip from the party in which she was poking some fun at her pals by singing about their veganism.
The songstress looked gorgeous in her Fashion Nova outfit and she definitely knew it as well since she made sure to properly show it off in a separate post.
The birthday bash looked like a lot of fun since a lot of the guests could be seen having the time of their lives, dancing the night away.
David also took to his own platform to share a romantic vid in which he and his woman were packing the PDA at that same party.
When one pal toasted for the birthday boy at some point, the pair kissed no less than four times in the span of that short clip!
In the caption, Tamar’s man wrote: ‘Another amazing Taco Tuesday, this time during [my] birthday week. Watch how my @tamarbraxton is grabbing my shirt at the end… lil mamma can’t get enough of this sugar. And baby, I can’t ever seem to get enough of you too, thanks for a terrific start to birthday week.’ Aww….


Items are objects that can be picked up by the player:
| #bowl | |
| (name *) | small bowl |
| (descr *) | It's a small bowl. |
| (item *) | %% This is what allows the player to pick up the bowl. |
| (* is #on #table) |
As a general rule, the standard library doesn't call attention to game objects. The story author is expected to mention them as part of the description of their parent object (such as the room).
But there is an important exception: As soon as the player picks up an object (or otherwise moves it from its original location), the responsibility for mentioning that object is transferred to the library. We say that the object has become handled or, equivalently, that it is no longer pristine.
There's a compelling pragmatic reason for this: When players are able to move objects around, those objects will eventually end up in strange locations, unanticipated by the story author. Players will put the flower pot on top of the bed, or take off their clothes and put them inside the ashtray, and the story author cannot be expected to provide custom text for every combination the player can think of. So, once objects start moving around, the library takes over the initiative in calling attention to them, using bland, default messages. They story author may then choose to override those messages on a case-by-case basis, and we will soon see how that's done in practice.
For now, the important thing to remember is that items (and wearable objects) can move around, and therefore we should only call them out in room descriptions (and in the descriptions of containers and supporters) when they are in their pristine state. Whenever we include a movable object in our story, we have a responsibility to check this. It can be done with an if-statement as in the following example:
| #bowl | |
| (name *) | small bowl |
| (descr *) | It's a small bowl. |
| (item *) | |
| (* is #on #table) | |
| (descr #table) | |
| It's wooden; possibly mahogany. | |
| (if) (#bowl is pristine) (then) | |
| A small bowl is placed exactly in its centre. | |
| (endif) |
Non-movable objects will remain pristine forever, so they can be described without a check.
Here is a complete example game with movable objects:
| (current player #player) | |
| #room | |
| (name *) | tutorial room |
| (room *) | |
| (#player is #in *) | |
| (look *) | This is a very nondescript room, dominated by a |
| wooden table. (notice #table) | |
| #table | |
| (name *) | wooden table |
| (dict *) | mahogany %% Add a synonym. |
| (supporter *) | |
| (* is #in #room) | |
| (descr *) | It's wooden; possibly mahogany. |
| (if) (#bowl is pristine) (then) | |
| A small bowl is placed exactly in its centre. | |
| (endif) | |
| #sapphire | |
| (name *) | sapphire |
| (stone *) | %% This is a custom, story-specific trait. |
| #amethyst | |
| (an *) | %% The indefinite article should be 'an'. |
| (name *) | amethyst |
| (stone *) | |
| #bowl | |
| (name *) | small bowl |
| (item *) | |
| (container *) | |
| (* is #on #table) | |
| (descr *) | It's a small bowl. |
| %% Some generic properties of stones: | |
| (item (stone $)) | |
| ((stone $) is #in #bowl) | |
| (descr (stone $Obj)) | |
| (The $Obj) looks very pretty. | |
| (dict (stone $)) | |
| precious stone gem | |
| (plural dict (stone $)) | |
| stones |
Try it! You might want to LOOK, X TABLE, LOOK IN BOWL, GET ALL, PUT STONE IN BOWL, PUT STONE ON TABLE, DROP ALL...
(dict $) and (plural dict $) can be used to add synonyms to objects. In the example above, we added both singular and plural synonyms to all objects belonging to the (stone $) category. A command such as GET STONES will result in every stone being picked up, due to the plural form. In contrast, GET STONE triggers a disambiguating question, where the game asks the player whether they meant to pick up the amethyst or the sapphire.
You may have noticed a problem with the last example: When the player examines the bowl, there is no mention of the stones within. For an oldschool game, it may be acceptable to expect the player to SEARCH or LOOK IN the bowl in order to find them. But for modern, narrative-driven games, that approach is generally frowned upon. We could mention the stones in the description of the bowl. But there are two stones, so how do we do that? Do we check whether they are both pristine? I.e.:
| (descr #bowl) | |
| It's a small bowl. | |
| (if) | |
| (#sapphire is pristine) | |
| (#amethyst is pristine) | |
| (then) | |
| There are two precious stones in it. | |
| (endif) |
But what if the player only picks up the amethyst, and then puts it back? The sapphire is still in the bowl, but the story doesn't mention it, and the library only prints a stock message about the amethyst (because it is no longer pristine). Another option is to add lots of special cases:
| (descr #bowl) | |
| It's a small bowl. | |
| (if) | |
| (#sapphire is pristine) | |
| (#amethyst is pristine) | |
| (then) | |
| There are two precious stones in it. | |
| (elseif) | |
| *($Stone is one of [#sapphire #amethyst]) | |
| ($Stone is pristine) | |
| (then) | |
| There's (a $Stone) in it. | |
| (endif) |
But this doesn't scale well, if there were more than two precious stones in the bowl to begin with. We also have the option to cop out entirely, and tell the library to narrate these objects already from the start:
| (descr #bowl) | |
| It's a small bowl. | |
| (#sapphire is handled) | |
| (#amethyst is handled) |
Remember, handled is the opposite of pristine in this context. Now, when the player first examines the bowl, the game responds:
But that's decidedly clunky. A somewhat better approach, although still a cop-out, is to print a vague message that encourages the player to look inside the bowl, without mentioning any details about what's inside:
| (descr #bowl) | |
| It's a small bowl. | |
| (if) ($ is #in #bowl) (then) | |
| There appears to be something inside. | |
| (endif) |
But this will backfire, in a sense, if the player takes the amethyst and then puts it back. Examining the bowl would then result in the following output:
Here, the library called attention to the amethyst (handled), but not to the sapphire (pristine). The printed text is technically correct, but while the first paragraph encourages the player to look inside the bowl, the second paragraph takes that incentive away, and the player is mislead to believe that there's nothing in the bowl apart from the amethyst.
Instead, the recommended way to handle this situation is to selectively override the appearance text that's printed for handled objects by the library. The message “An amethyst is in the small bowl” originates from a predicate called (appearance $Object $Relation $Parent). The first step is to tell the library to refrain from printing such a message about any object that's currently in the bowl:
| (appearance $ #in #bowl) | |
| %% Objects in the bowl have a blank appearance. |
Now that we have silenced those particular messages from the standard library, we must provide our own variant in the description of the bowl. But we have to be careful: With the rule above, we turned off automatic descriptions for any object in the bowl, not just the amethyst and the sapphire. So we have to take care of any foreign objects that might end up there too. In some situations, it might be sufficient to drop a vague hint:
| (descr #bowl) | |
| It's a small bowl. | |
| (if) ($ is #in #bowl) (then) | |
| There appears to be something inside. | |
| (endif) | |
| (appearance $ #in #bowl) |
A more advanced technique is to use a multi-query and a collect statement to print a list of all objects currently inside the bowl:
| (descr #bowl) | |
| It's a small bowl. | |
| (collect $Obj) | |
| *($Obj is #in #bowl) | |
| (into $List) | |
| You can see (a $List) in it. | |
| (appearance $ #in #bowl) |
An entirely different approach is to allow objects to call attention to themselves, but to replace the stock message with a custom one. This is done by overriding (appearence $ $ $) with a rule that prints text:
| #sapphire | |
| (appearance * $ $) | |
| (* is handled) | |
| A gleaming sapphire catches your eye. | |
| (notice *) %% Bind “it” |
That rule did not check the location of the sapphire, so it would override the default message also when the sapphire makes an appearance as part of a room description, or in any other place. Without the line (* is handled), the message would also be printed while the object is still pristine.
To summarize, movable items are more complicated than other objects, because there is a transfer of responsibility for calling attention to them. At first, while they are pristine, the story author should mention them as a natural part of the prose describing nearby objects (e.g. the room). As soon as they are handled, the library takes over, unless the story author explicitly reclaims control over their appearance.


My name is Navraj Narula and I am an undergraduate student at Boston University majoring in English Literature, Secondary Education, and Computer Science. I enjoy writing creatively on multiple levels, given my various academic fields of interest. Yet I constantly find myself to be most enamored with syntax as it relates to programming languages.
For that reason, I am choosing to apply to graduate programs in computer science/engineering. I am most interested in database design and data analytics. In my spare time, I create relational databases and develop front-end tools to access them for the purpose of information retrieval. I am also fascinated by algorithms, particular those pertaining to graph theory and how they can be implemented to solve problems. My research portfolio is also documented on this page.
Deciding to pursue my passion mid-junior year has led me to this conclusion: Nothing has to remain confusing. I have made the greatest decision of all: to continue to study what I love! I am happily exploring classes, research areas, and events pertaining to computer science. My curiosity encourages me to ask questions, re-read, and despite all barriers: push through.
Please click here to read my full story.


Our beautiful girl has matured nicely and easily attained her championship, finishing in style with a 5 point Best of Breed win from the classes at a Sporting Dog Specialy. She is what every breeder dreams of producing. Several Staghorn generations deep on both sides of her pedigree, we couldn't be happier. Very high hopes for this girlie!!!


Here are my top 5 favorite albums:
Rank
Artist
Album Title
Cover
1
Drake
Take Care
2
The Weeknd
Starboy
3
J.Cole
2014 Forest Hills Drive
4
Rihanna
Anti
5
Khalid
American Teen


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EPCR: Premiership Ruling Will Not Affect Saracens European Standing
by Marisa KennedyThe EPCR, the governing body of European club rugby, have confirmed that today’s decision by Premiership Rugby to hit Saracens with a strong penalty will not affect the club’s European standing.
The Heineken Champions Cup champions received a 35-point deduction as well as £5.36 million fine for breaching the Premiership Rugby Salary Cap.
Following a nine-month investigation, Premiership Rugby this morning confirmed that Saracens failed to disclose payments to players in each of the seasons 2016-17, 2017-18 and 2018-19, in addition to having exceeded the ceiling for payments to senior players in each of the three seasons.
The EPCR, however, have confirmed to Pundit Arena that the decision does not affect Saracens’ record in Europe.
“The Saracens decision is based on Gallagher Premiership Rugby regulations and does not affect the club’s European record or current status in the Heineken Champions Cup,” their statement read.
Saracens have denied the allegations saying they are “shocked and disappointed” at the outcome of the investigation.
Meanwhile, Saracens owner Nigel Wray said the club “never deliberately sought to mislead anyone.”
“For over 25 years, I have put my heart and soul into the game I love. Together we have created something incredibly special with the Saracens family, both on and off the field.
“This is absolutely devastating for everyone associated with this amazing group of players, staff, partners and fans.
“It has been acknowledged by the Panel that we never deliberately sought to mislead anyone or breach the cap and that’s why it feels like the rug is being completely pulled out from under our feet. We will appeal all the findings.”


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