|
libcoap
4.2.1
|
coap_attribute, coap_add_attr, coap_find_attr — Work with CoAP attributes
#include <coap2/coap.h>
coap_attr_t *coap_add_attr(coap_resource_t *resource, coap_str_const_t *name, coap_str_const_t *value, int flags);
coap_attr_t *coap_find_attr(coap_resource_t *resource, coap_str_const_t *name);
Link with -lcoap-2, -lcoap-2-gnutls, -lcoap-2-openssl or -lcoap-2-tinydtls depending on your (D)TLS library type.
CoAP Resources on a CoAP Server need to be created, updated etc. The URI in the request packet defines the resource to work with, with possibly the Query referring to a sub-resource. When resources are configured on the CoAP server, the URI to match against is specified. Callback Handlers are then added to the resource to handle the different request methods. Adding Attributes allows textual information to be added to the resource which can then be reported back to any client doing a "GET .well-known/core" request.
Attributes are automatically freed when a Resource is deleted.
The coap_add_attr() function registers a new attribute called name for the resource. The value of the attribute is value.
flags can be one or more of the following, which, if set, defines what is to be internally freed off when the attribute is deleted with coap_delete_resource().
|
COAP_ATTR_FLAGS_RELEASE_NAME |
Free off name when attribute is deleted with coap_delete_resource(). |
|
COAP_ATTR_FLAGS_RELEASE_VALUE |
Free off value when attribute is deleted with coap_delete_resource(). |
The coap_find_attr() function returns the attribute with the name, if found, associated with resource.
coap_add_attr() function return a pointer to the attribute that was created or NULL if there is a malloc failure.
coap_find_attr() function returns a pointer to the first matching attribute or NULL if the name was not found.
Initialize Resources
#include <coap2/coap.h> static void init_resources(coap_context_t *ctx) { coap_resource_t *r; /* Create a resource to return general information */ r = coap_resource_init(NULL, 0); coap_register_handler(r, COAP_REQUEST_GET, hnd_get_index); /* Document resource for .well-known/core request */ coap_add_attr(r, coap_make_str_const("ct"), coap_make_str_const("0"), 0); coap_add_attr(r, coap_make_str_const("title"), coap_make_str_const("\"General Info\""), 0); coap_add_resource(ctx, r); /* Create a resource to return return or update time */ r = coap_resource_init(coap_make_str_const("time"), COAP_RESOURCE_FLAGS_NOTIFY_CON); coap_resource_set_get_observable(r, 1); coap_register_handler(r, COAP_REQUEST_GET, hnd_get_time); coap_register_handler(r, COAP_REQUEST_PUT, hnd_put_time); coap_register_handler(r, COAP_REQUEST_DELETE, hnd_delete_time); /* Document resource for .well-known/core request */ coap_add_attr(r, coap_make_str_const("ct"), coap_make_str_const("0"), 0); coap_add_attr(r, coap_make_str_const("title"), coap_make_str_const("\"Internal Clock\""), 0); coap_add_attr(r, coap_make_str_const("rt"), coap_make_str_const("\"secs\""), 0); coap_add_attr(r, coap_make_str_const("if"), coap_make_str_const("\"clock\""), 0); coap_add_resource(ctx, r); }


ReplicaContext
tf.distribute.Strategy
API when in a replica context.
tf.compat.v1.distribute.ReplicaContext
tf.compat.v2.distribute.ReplicaContext
You can use tf.distribute.get_replica_context
to get an instance of
ReplicaContext
. This should be inside your replicated step function, such
as in a tf.distribute.Strategy.experimental_run_v2
call.
__init__
__init__( strategy, replica_id_in_sync_group )
Initialize self. See help(type(self)) for accurate signature.
devices
The devices this replica is to be executed on, as a tuple of strings.
num_replicas_in_sync
Returns number of replicas over which gradients are aggregated.
replica_id_in_sync_group
Returns the id of the replica being defined.
This identifies the replica that is part of a sync group. Currently we
assume that all sync groups contain the same number of replicas. The value
of the replica id can range from 0 to num_replica_in_sync
- 1.
strategy
The current tf.distribute.Strategy
object.
__enter__
__enter__()
__exit__
__exit__( exception_type, exception_value, traceback )
all_reduce
all_reduce( reduce_op, value )
All-reduces the given value Tensor
nest across replicas.
If all_reduce
is called in any replica, it must be called in all replicas.
The nested structure and Tensor
shapes must be identical in all replicas.
IMPORTANT: The ordering of communications must be identical in all replicas.
Example with two replicas:
Replica 0 value
: {'a': 1, 'b': [40, 1]}
Replica 1 value
: {'a': 3, 'b': [ 2, 98]}
If reduce_op
== SUM
:
Result (on all replicas): {'a': 4, 'b': [42, 99]}
If reduce_op
== MEAN
:
Result (on all replicas): {'a': 2, 'b': [21, 49.5]}
reduce_op
: Reduction type, an instance of tf.distribute.ReduceOp
enum.value
: The nested structure of Tensor
s to all-reduce. The structure must
be compatible with tf.nest
.A Tensor
nest with the reduced value
s from each replica.
merge_call
merge_call( merge_fn, args=(), kwargs=None )
Merge args across replicas and run merge_fn
in a cross-replica context.
This allows communication and coordination when there are multiple calls
to the step_fn triggered by a call to
strategy.experimental_run_v2(step_fn, ...)
.
See tf.distribute.Strategy.experimental_run_v2
for an
explanation.
If not inside a distributed scope, this is equivalent to:
strategy = tf.distribute.get_strategy() with cross-replica-context(strategy): return merge_fn(strategy, *args, **kwargs)
merge_fn
: Function that joins arguments from threads that are given as
PerReplica. It accepts tf.distribute.Strategy
object as
the first argument.args
: List or tuple with positional per-thread arguments for merge_fn
.kwargs
: Dict with keyword per-thread arguments for merge_fn
.The return value of merge_fn
, except for PerReplica
values which are
unpacked.


I recently decided to attend a 48-hour hackathon organised by Founders of the Future. Straight away I received some interesting comments from friends who wanted to understand more. These included questions such as “I didn’t know you’re a hacker?!”. I have to admit stringing them on that one was enjoyable.
Firstly, let’s dispel some myths; hackathons are not for just for geeks who code or wannabe hackers. Anyone can attend a hackathon, not just software developers. Non-technical skills are just as useful.
Two things are key to enjoying a hackathon in my opinion. Stay open-minded and have a willingness to learn. If you don't have that, then at least make sure you can bounce a ball and catch it. More on that later :-)
So let’s address some burning questions.

Typical hackathons will ease you into the weekend with a few social ice-breaking activities. Potential favorites are rock-paper-scissors and forming random unrelated words into potential business ideas. For instance ‘sushi shaped marshmallows’! This is a real example of a quick 5-10 minute idea forming into a funny pitch.
The key at this point is for you to get out of your comfort zone and feel relaxed in a new creative environment.
Post ice-breaking activities, participants are encouraged to pitch ideas they wish to work on during the next 48 hours. At this preliminary stage pitches are generally high-level and half-baked around the theme set by the organisers.
Making the short list
Everyone gets to vote for 2 - 3 ideas each and the ones with the most votes are shortlisted. This is the chance to back the idea(s) that resonate best with you. It is also important that your personality does not clash with anyone on the team. One way to avoid that is by picking as diverse a team as possible, so you can truly bring something different to the table. After all you’ll be spending long hours with these people and be expected to deliver something under a pressing timeframe.
So ice-breaking is over. Ideas have been shortlisted. You've formed a team. Now the excitement can begin. Bring out those bouncy balls!(*) Especially for those awkward moments.
Enjoying the Process
Whatever you do, just don’t panic! If you fear that you cannot contribute much to the team; breathe. Create a role for yourself, or support someone else in a task. You should recognise the fear and move on. Get ready to learn by doing something you’re not used to doing and are probably not good at. Whether that is building a mobile prototype, identifying a target market for a product or creating a pitch slide deck, everyone has a role.
The earlier you get comfortable with these crazy like-minded people the better.
What can I gain from attending a hackathon?
#Difficult situations call for creative approaches.
Most Hackathons have a theme of some sort. For instance, the Founders of the Future was centered around ideas for improving gender diversity in the workplace. So this was a hackathon open to anyone interested in that theme. These like-minded people will all have different skills and backgrounds. Their professions may range from accountants, to business consultants, to software developers etc. What makes the experience worthwhile is adapting to different situations.
There is no room for egos. They should be left miles away from the door. Communication skills are essential and this is the ideal opportunity to enhance those skills. Especially when a team member proposes an idea at the eleventh hour, and you need to politely and diplomatically agree to disagree, diffusing the situation gracefully. Not everyone can do that without pulling an emoji face or bruising someone with a bouncy ball.
#Get out of your comfort zones
The time pressure to deliver something you have never done before is going to be exhilerating. Some people claim a weekend hackathon is equivalent to 6 months’ worth of a “day job” (that says a lot about some peoples' day jobs!).
You’ve never created a mobile prototype using a software you've never heard of? This is your chance. You’ve never developed a survey using Google Forms? Here is your opportunity.
Start where your comfort zone ends. That’s where the magic happens.
#Networking
Say goodbye to inhibitions. Exchanging ideas and learning something new may not sound like fun to everyone. This is also an opportunity to develop new connections. Not just the people in your team but other participantsas well. Lurk around the free food and coffee during the breaks and you’ll soon find someone who is working on something interesting.
Not forgetting the mentors who are present at key points during the process.
They offer insights into:
- how best to formulate ideas into a viable business.
- potential pitfalls to avoid
- how to tie things together into a convincing pitch.
Speak to them people offline if you have a burning idea, even unrelated to the hackathon. Generally people want to be helpful.
These should be enough reasons to attend your first hackathon and give you some idea of what you can expect. I went in with no expectations other than to be around energetic, like-minded, crazy people who I can learn from.
Written by Angela Kakiiza


DeepMatcher documentation¶
DeepMatcher is a deep learning library for entity matching.
Tutorials¶
Tutorials are located here
The deepmatcher package contains high level modules used in the construction of deep learning modules for entity matching.
-
class
deepmatcher.
MatchingModel
(attr_summarizer='hybrid', attr_condense_factor='auto', attr_comparator=None, attr_merge='concat', classifier='2-layer-highway', hidden_size=300)[source]¶ A neural network model for entity matching.
Refer to the Matching Models tutorial for details on how to customize a MatchingModel. A brief intro is below:
This network consists of the following components:
- Attribute Summarizers
- Attribute Comparators
- A Classifier
Creating a MatchingModel instance does not immediately construct the neural network. The network will be constructed just before training based on metadata from the training set:
- For each attribute (e.g., Product Name, Address, etc.), an Attribute Summarizer is constructed using the specified attr_summarizer template.
- For each attribute, an Attribute Comparator is constructed using the specified attr_summarizer template.
- A Classifier is constructed based on the specified classifier template.
Parameters: - attr_summarizer (string or
AttrSummarizer
or callable) – The attribute summarizer. Takes in two word embedding sequences and summarizes the information in them to produce two summary vectors as output. Options listed here. Defaults to ‘hybrid’, i.e., theHybrid
model. - attr_comparator (string or
Merge
or callable) – The attribute comparator. Takes as input the two summary vectors and applies a comparison function over those summaries to obtain the final similarity representation of the two attribute values. Argument must specify a Merge operation. Default is selected based on attr_summarizer choice. - attr_condense_factor (string or int) – The factor by which to condense each attribute similarity vector. E.g. if attr_condense_factor is set to 3 and the attribute similarity vector size is 300, then each attribute similarity vector is transformed to a 100 dimensional vector using a linear transformation. The purpose of condensing is to reduce the number of parameters in the classifier module. This parameter can be set to a number or ‘auto’. If ‘auto’, then the condensing factor is set to be equal to the number attributes, but if there are more than 6 attributes, then the condensing factor is set to 6. Defaults to ‘auto’.
- attr_merge (string or
Merge
or callable) – The operation used to merge the (optionally condensed) attribute similarity vectors to obtain the input to the classifier. Argument must specify a Merge operation. Defaults to ‘concat’, i.e., concatenate all attribute similarity vectors to form the classifier input. - classifier (string or
Classifier
or callable) – The neural network to perform match / mismatch classification based on attribute similarity representations. Options listed here. Defaults to ‘2-layer-highway’, i.e., use a two layer highway network followed by a softmax layer for classification. - hidden_size (int) – The hidden size to use for the attr_summarizer and the classifier modules,
if they are string arguments. If a module or
callable
input is specified for a component, this argument is ignored for that component.
-
forward
(input)[source]¶ Performs a forward pass through the model.
Overrides
torch.nn.Module.forward()
.Parameters: input ( MatchingBatch
) – A batch of tuple pairs processed into tensors.
-
initialize
(train_dataset, init_batch=None)[source]¶ Initialize (not lazily) the matching model given the actual training data.
Instantiates all sub-components and their trainable parameters.
Parameters: - train_dataset (
MatchingDataset
) – The training dataset obtained usingdeepmatcher.data.process()
. - init_batch (
MatchingBatch
) – A batch of data to forward propagate through the model. If None, a batch is drawn from the training dataset.
- train_dataset (
-
load_state
(path)[source]¶ Load the model state from a file in a certain path.
Parameters: path (string) – The path to load the model state from.
-
run_eval
(dataset, batch_size=32, device=None, progress_style='bar', log_freq=5, sort_in_buckets=None)[source]¶ Evaluate the model on the specified dataset.
Parameters: - dataset (
MatchingDataset
) – The evaluation dataset obtained usingdeepmatcher.data.process()
. - batch_size (int) – Mini-batch size for SGD. For details on what this is see this video. Defaults to 32. This is a keyword only param.
- device (int) – The device index of the GPU on which to train the model. Set to -1 to use CPU only, even if GPU is available. If None, will use first available GPU, or use CPU if no GPUs are available. Defaults to None. This is a keyword only param.
- progress_style (string) – Sets the progress update style. One of ‘bar’ or ‘log’. If ‘bar’, uses a progress bar, updated every N batches. If ‘log’, prints training stats every N batches. N is determined by the log_freq param. This is a keyword only param.
- log_freq (int) – Number of batches between progress updates. Defaults to 5. This is a keyword only param.
- sort_in_buckets (bool) – Whether to batch examples of similar lengths together. If True, minimizes
amount of padding needed while producing freshly shuffled batches for each
new epoch. Implemented using
torchtext.data.pool()
. Defaults to True. This is a keyword only param.
Returns: The F1 score obtained by the model on the dataset.
Return type: - dataset (
-
run_prediction
(dataset, output_attributes=False, batch_size=32, device=None, progress_style='bar', log_freq=5, sort_in_buckets=None)[source]¶ Use the model to obtain predictions, i.e., match scores on the specified dataset.
Parameters: - dataset (
MatchingDataset
) – The dataset (labeled or not) obtained usingdeepmatcher.data.process()
ordeepmatcher.data.process_unlabeled()
. - output_attributes (bool) – Whether to include all attributes in the original CSV file of the dataset in the returned pandas table.
- batch_size (int) – Mini-batch size for SGD. For details on what this is see this video. Defaults to 32. This is a keyword only param.
- device (int) – The device index of the GPU on which to train the model. Set to -1 to use CPU only, even if GPU is available. If None, will use first available GPU, or use CPU if no GPUs are available. Defaults to None. This is a keyword only param.
- progress_style (string) – Sets the progress update style. One of ‘bar’ or ‘log’. If ‘bar’, uses a progress bar, updated every N batches. If ‘log’, prints training stats every N batches. N is determined by the log_freq param. This is a keyword only param.
- log_freq (int) – Number of batches between progress updates. Defaults to 5. This is a keyword only param.
- sort_in_buckets (bool) – Whether to batch examples of similar lengths together. If True, minimizes
amount of padding needed while producing freshly shuffled batches for each
new epoch. Implemented using
torchtext.data.pool()
. Defaults to True. This is a keyword only param.
Returns: - A pandas DataFrame containing tuple pair IDs (in the “id”
column) and the corresponding match score predictions (in the “match_score” column). Will also include all attributes in the original CSV file of the dataset if output_attributes is True.
Return type: - dataset (
-
run_train
(train_dataset, validation_dataset, best_save_path, epochs=30, criterion=None, optimizer=None, pos_neg_ratio=None, pos_weight=None, label_smoothing=0.05, save_every_prefix=None, save_every_freq=None, batch_size=32, device=None, progress_style='bar', log_freq=5, sort_in_buckets=None)[source]¶ Train the model using the specified training set.
Parameters: - train_dataset (
MatchingDataset
) – The training dataset obtained usingdeepmatcher.data.process()
. - validation_dataset (
MatchingDataset
) – The validation dataset obtained usingdeepmatcher.data.process()
. This is used for early stopping. - best_save_path (string) – The path to save the best model to. At the end of each epoch, if the new model accuracy (F1) is better than all previous epochs, then it is saved to this location.
- epochs (int) – Number of training epochs, i.e., number of times to cycle through the entire training set. Defaults to 50.
- criterion (
torch.nn.Module
) – The loss function to use. Refer to the losses section of the PyTorch API for options. By default, deepmatcher will output a 2d tensor of shape (N, C) where N is the batch size and C is 2 - the number of classes. Keep this in mind when picking the loss. Defaults toSoftNLLLoss
with label smoothing. - optimizer (
Optimizer
) – The optimizer to use for updating the trainable parameters of theMatchingModel
neural network after each iteration. If not specified anOptimizer
with Adam optimizer will be constructed. - pos_neg_ratio (int) – The weight of the positive class (match) wrt the negative class (non-match). This parameter must be specified if there is a significant class imbalance in the dataset.
- label_smoothing (float) – The label_smoothing parameter to constructor of
SoftNLLLoss
criterion. Only used when criterion param is None. Defaults to 0.05. - save_every_prefix (string) – Prefix of the path to save model to, after end of every N epochs, where N is determined by save_every_freq param. E.g. setting this to “/path/to/saved/model” will save models to “/path/to/saved/model_ep1.pth”, “/path/to/saved/model_ep2.pth”, etc. Models will not be saved periodically if this is None. Defaults to None.
- save_every_freq (int) – Determines the frequency (number of epochs) for saving models periodically to the path specified by the save_every_prefix param (has no effect if that param is not set). Defaults to 1.
- batch_size (int) – Mini-batch size for SGD. For details on what this is see this video. Defaults to 32. This is a keyword only param.
- device (int) – The device index of the GPU on which to train the model. Set to -1 to use CPU only, even if GPU is available. If None, will use first available GPU, or use CPU if no GPUs are available. Defaults to None. This is a keyword only param.
- progress_style (string) – Sets the progress update style. One of ‘bar’ or ‘log’. If ‘bar’, uses a progress bar, updated every N batches. If ‘log’, prints training stats every N batches. N is determined by the log_freq param. This is a keyword only param.
- log_freq (int) – Number of batches between progress updates. Defaults to 5. This is a keyword only param.
- sort_in_buckets (bool) – Whether to batch examples of similar lengths together. If True, minimizes
amount of padding needed while producing freshly shuffled batches for each
new epoch. Implemented using
torchtext.data.pool()
. Defaults to True. This is a keyword only param.
Returns: The best F1 score obtained by the model on the validation dataset.
Return type: - train_dataset (
-
save_state
(path, include_meta=True)[source]¶ Save the model state to a certain path.
Parameters: - path (string) – The path to save the model state to.
- include_meta (bool) – Whether to include training dataset metadata along with
the model parameters when saving. If False, the model will not be
automatically initialized upon loading - you will need to initialize
manually using
initialize()
.
-
class
deepmatcher.
AttrSummarizer
(word_contextualizer, word_comparator, word_aggregator, hidden_size=None)[source]¶ The Attribute Summarizer.
Summarizes the two word embedding sequences of an attribute. Refer this tutorial for details. Sub-classes that implement various built-in options for this module are defined in
deepmatcher.attr_summarizers
.Parameters: - word_contextualizer (string or
WordContextualizer
or callable) – Module that takes a word embedding sequence and produces a context-aware word embedding sequence as output. Options listed here. - word_comparator (string or
WordComparator
or callable) – Module that takes two word embedding sequences, aligns words in the two sequences, and performs a word by word comparison. Options listed here. - word_aggregator (string or
WordAggregator
or callable) – Module that takes a sequence of vectors and aggregates it into a single vector. Options listed here. - hidden_size (int) – The hidden size used for the three sub-modules (i.e., word_contextualizer, word_comparator, and word_aggregator). If None, uses the input size, i.e., the size of the last dimension of the input to this module as the hidden size. Defaults to None.
- word_contextualizer (string or
-
class
deepmatcher.
WordContextualizer
[source]¶ The Word Contextualizer.
Takes a word embedding sequence and produces a context-aware word embedding sequence. Refer this tutorial for details. Sub-classes that implement various options for this module are defined in
deepmatcher.word_contextualizers
.
-
class
deepmatcher.
WordComparator
[source]¶ The Word Comparator.
Takes two word embedding sequences, aligns words in the two sequences, and performs a word by word comparison. Refer this tutorial for details. Sub-classes that implement various options for this module are defined in
deepmatcher.word_comparators
.
-
class
deepmatcher.
WordAggregator
[source]¶ The Word Aggregator.
Takes a sequence of vectors and aggregates it into a single vector. Refer this tutorial for details. Sub-classes that implement various options for this module are defined in
deepmatcher.word_aggregators
.
-
class
deepmatcher.
Classifier
(transform_network, hidden_size=None)[source]¶ The Classifier Network.
Predicts whether a tuple pair matches or not given a representation of all the attribute summarizations. Refer this tutorial for details.
Parameters: - transform_network (string or
Transform
or callable) – The neural network to transform the input vector of the classifier to a hidden representation of size hidden_size. Argument must specify a Transform operation. - hidden_size (int) – The size of the hidden representation generated by the transformation network. If None, uses the size of the input vector to this module as the hidden size.
- transform_network (string or


Welcome to the 7-Iron Site
This site is a placeholder for you to replace as needed.
Info:
Site docroot: /home/vagrant/workspace/site/docroot


Real*4 XNO Catalog number of star Real*8 SRA0 B1950 Right Ascension (radians) Real*8 SDEC0 B1950 Declination (radians) Character*2 IS Spectral type (2 characters) Integer*2 MAG V Magnitude * 100 Real*4 XRPM R.A. proper motion (radians per year) Real*4 XDPM Dec. proper motion (radians per year)
The first and last entries in the Yale Bright Star (BSC5) catalog file are:
sbsc5 WCSTools 3.8.1, 30 April 2010, Doug Mink SAO BSC_number RA2000 Dec2000 Mag Type 000001 00:05:09.900 +45:13:45.00 6.70 A1 009110 00:05:06.200 +61:18:51.00 5.80 B8
The first and last entries in the RA-sorted BSC5ra catalog file are:
BSC_num RA2000 Dec2000 Mag Type 009077 00:00:19.200 -44:17:26.00 6.29 G3 009076 23:59:55.000 -65:34:38.00 4.50 B9


27 March 1996
Disease Outbreak Reported
In a recent report from the WHO Office for the African Region the accumulated number of cases of meningitis had increased to 51 541 with 6 504 deaths (case fatality rate 12.6%). The increase was largely from the over 9 000 additional cases reported in Nigeria. More than 4 000 of them were new cases reported in the past week. Major increases were also reported in Burkina Faso, Mali, and Togo.
The following number of cases (deaths) have been reported in:
Benin: 424 (56); Burkina Faso: 11 866 (1 064); Cameroon: 5 (1); Central African Republic: 155 (22); Chad: 505 (53); Ghana: 18 (0); Mali: 1 404 (227); Niger: 6 518 (676); Nigeria: 30 212 (4 346); Tanzania: 99(8); Togo: 505 (53), Zaire: 69 (5). No cases have been reported in Burundi, Côte d'Ivoire, Eritrea, Mauritania, and Senegal.


Created By:
Elaine Nerea
Github Username/URL:
missnerea
Level 2 test for:
Java
-Numbers divisible by 3 are replaced with "ping"
-Numbers divisible by 5 are replaced with "pong"
-Numbers divisible by 15 are replaced with "pingpong"


-
Javascript Concurrency : Web Workers
The Problem: JavaScript Concurrency
…
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Intro to CSS layout
Using max-width instead of width will improve the browser’s handling of small windows. This is important when making a site usable on mobile. …
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Creating restful CRUD app with Angularjs $resource
Most single page applications involve CRUD operations. If you are trying to build CRUD operations using AngularJS, then you can leverage the power of
$resource
…
-
Angular vs React vs React Native
Most funny thing today: …
-
Getting Started with Stylus
Stylus is a very popular css preprocessor, with lots features like extending class, creating mixins, importing spreadsheets and setting variables. Let’s check out the basics of stylus and get into css programming. …


Load the "previous_original.json" file:
Load the "original.json" file:
Load the "[language].json" file:
Start
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public final class NVHalfFloat
extends java.lang.Object
| Modifier and Type | Field and Description |
|---|---|
static int |
GL_HALF_FLOAT_NV
Accepted by the <type> argument of VertexPointer, NormalPointer,
ColorPointer, TexCoordPointer, FogCoordPointerEXT,
SecondaryColorPointerEXT, VertexWeightPointerEXT, VertexAttribPointerNV,
DrawPixels, ReadPixels, TexImage1D, TexImage2D, TexImage3D, TexSubImage1D,
TexSubImage2D, TexSubImage3D, and GetTexImage:
|
| Modifier and Type | Method and Description |
|---|---|
static void |
glColor3hNV(short red,
short green,
short blue) |
static void |
glColor4hNV(short red,
short green,
short blue,
short alpha) |
static void |
glFogCoordhNV(short fog) |
static void |
glMultiTexCoord1hNV(int target,
short s) |
static void |
glMultiTexCoord2hNV(int target,
short s,
short t) |
static void |
glMultiTexCoord3hNV(int target,
short s,
short t,
short r) |
static void |
glMultiTexCoord4hNV(int target,
short s,
short t,
short r,
short q) |
static void |
glNormal3hNV(short nx,
short ny,
short nz) |
static void |
glSecondaryColor3hNV(short red,
short green,
short blue) |
static void |
glTexCoord1hNV(short s) |
static void |
glTexCoord2hNV(short s,
short t) |
static void |
glTexCoord3hNV(short s,
short t,
short r) |
static void |
glTexCoord4hNV(short s,
short t,
short r,
short q) |
static void |
glVertex2hNV(short x,
short y) |
static void |
glVertex3hNV(short x,
short y,
short z) |
static void |
glVertex4hNV(short x,
short y,
short z,
short w) |
static void |
glVertexAttrib1hNV(int index,
short x) |
static void |
glVertexAttrib2hNV(int index,
short x,
short y) |
static void |
glVertexAttrib3hNV(int index,
short x,
short y,
short z) |
static void |
glVertexAttrib4hNV(int index,
short x,
short y,
short z,
short w) |
static void |
glVertexAttribs1NV(int index,
java.nio.ShortBuffer attribs) |
static void |
glVertexAttribs2NV(int index,
java.nio.ShortBuffer attribs) |
static void |
glVertexAttribs3NV(int index,
java.nio.ShortBuffer attribs) |
static void |
glVertexAttribs4NV(int index,
java.nio.ShortBuffer attribs) |
static void |
glVertexWeighthNV(short weight) |
public static final int GL_HALF_FLOAT_NV
public static void glVertex2hNV(short x, short y)
public static void glVertex3hNV(short x, short y, short z)
public static void glVertex4hNV(short x, short y, short z, short w)
public static void glNormal3hNV(short nx, short ny, short nz)
public static void glColor3hNV(short red, short green, short blue)
public static void glColor4hNV(short red, short green, short blue, short alpha)
public static void glTexCoord1hNV(short s)
public static void glTexCoord2hNV(short s, short t)
public static void glTexCoord3hNV(short s, short t, short r)
public static void glTexCoord4hNV(short s, short t, short r, short q)
public static void glMultiTexCoord1hNV(int target, short s)
public static void glMultiTexCoord2hNV(int target, short s, short t)
public static void glMultiTexCoord3hNV(int target, short s, short t, short r)
public static void glMultiTexCoord4hNV(int target, short s, short t, short r, short q)
public static void glFogCoordhNV(short fog)
public static void glSecondaryColor3hNV(short red, short green, short blue)
public static void glVertexWeighthNV(short weight)
public static void glVertexAttrib1hNV(int index, short x)
public static void glVertexAttrib2hNV(int index, short x, short y)
public static void glVertexAttrib3hNV(int index, short x, short y, short z)
public static void glVertexAttrib4hNV(int index, short x, short y, short z, short w)
public static void glVertexAttribs1NV(int index, java.nio.ShortBuffer attribs)
public static void glVertexAttribs2NV(int index, java.nio.ShortBuffer attribs)
public static void glVertexAttribs3NV(int index, java.nio.ShortBuffer attribs)
public static void glVertexAttribs4NV(int index, java.nio.ShortBuffer attribs)
Copyright © 2002-2010 lwjgl.org. All Rights Reserved.


SQL Injection
SQL stands for Structural Query Language. It’s a standardized language used to access and manipulate data in databases. SQL is exercised by users who have a question or query that they want answered. Their query is executed against a database, commonly structured in a table with columns and rows, retrieved and returned to the user from a database.
SQL Injection is a security vulnerability where the attacker can issue SQL commands in order to gain access to the back-end SQL server. It is the most common web-app risks, perhaps because of how simple it is to achieve and has the potential to compromise virtually any website that is SQL database driven.
Most commonly, attackers utilize login screens to check if a website is employing a SQL database. Checking this is almost effortless because one can exploit special characters in the SQL language which can return an error, if the website has poorly written code that doesn’t check for them. Once they have established that a SQL database is handling their users data, they are capable of tricking the site into thinking that the information passed in is correct. All one needs to do is pass in a statement that will return a ‘true’ value. For example, passing in ‘1=1’
will always evaluate to true
and will force the selection of a valid username and BOOM! Now they have access to whatever user data (ususally senstive) is stored in that instance of user. Because the range of command manipulations is vast, the attacker can access data from several users, a specific user, or even perform complete table deletions.
Considering how easy it is to perform an injection, developers need to protect themselves and their users from potentially supplying attackers with their information. There are a few ways one can prevent the manipulation or removal of data.


This package defines a mocked-up version of the LabWizard login dialog, and is the main example used in Chapter 7.


sortable-list (open console to see event logs)
Basic Demo
First
Second
Third
Fourth
Fifth
Sixth
Animated Sorting
First
Second
Third
Fourth
Fifth
Sixth
Grouped Lists
First
Second
Third
Fourth
Fifth
Sixth
Cloning from first to second
FirstX
SecondX
ThirdX
FourthX
FifthX
SixthX


Getting Started¶
Requirements¶
Steam Audio supports the following platforms:
Windows 7 or later (32-bit and 64-bit)
Linux (32-bit and 64-bit, tested with Ubuntu 16.04 LTS)
macOS 10.7 or later (64-bit Intel)
Android 5.0 or later (32-bit ARM, 64-bit ARM, 32-bit Intel, and 64-bit Intel)
Steam Audio supports the following C/C++ compilers on different platforms:
Platform |
Compiler Version |
|---|---|
Windows |
Microsoft Visual Studio 2015 or later |
Linux |
GCC 5.0 or later |
macOS |
Xcode 7 or later |
Android |
Android NDK r10e or later, Clang 3.6 or later |
Compiling and linking with Steam Audio¶
Before you can access any Steam Audio API functionality, you must point your compiler to the Steam Audio headers. To do this, add SDKROOT/include
to your compiler’s header file search path.
Note
In all instructions on this page, SDKROOT
refers to the directory in which you extracted the steamaudio.zip
file.
Next, you must include the necessary header file:
#include <phonon.h>
This will pull in all the Steam Audio API functions, and make them available for use in your program.
Finally, you must point your compiler to the Steam Audio libraries. This involves setting the library search path, and specifying the libraries to link against:
Platform |
Library Directory |
Library To Link |
|---|---|---|
Windows 32-bit |
|
|
Windows 64-bit |
|
|
Linux 32-bit |
|
|
Linux 64-bit |
|
|
macOS |
|
|
Android ARMv7 |
|
|
Android ARMv8/AArch64 |
|
|
Android x86 |
|
|
Android x64 |
|
|
For more instructions on how to configure search paths and link libraries in your development environment, refer to the documentation for your compiler or IDE.
Example: Spatializing an audio clip¶
This example shows how to use the Steam Audio SDK to apply HRTF-based 3D audio to a mono (single-channel) audio clip.
To avoid complicating this example with code related to graphical rendering, audio engines, and codecs, this example is a C++ command-line tool that loads audio data from a file, applies 3D audio effects to it, and saves it to another file.
Before using any Steam Audio functionality, include the necessary headers:
#include <phonon.h>
Initialization: Context¶
The first step in initializing Steam Audio is to create a context object. This object keeps track of various optional global settings of Steam Audio, and will be used to create all other Steam Audio API objects.
To create a context object, first initialize an IPLContextSettings
structure with the settings we want to use when creating the context:
IPLContextSettings contextSettings{};
contextSettings.version = STEAMAUDIO_VERSION;
The only setting we need to specify is version
. This is the version of the Steam Audio API that your program is compiled with, and will typically be set to STEAMAUDIO_VERSION
. There are other optional settings that you can specify, such as custom memory allocation functions that Steam Audio should use whenever it needs to allocate or free memory. For the purposes of this example, though, we’ll leave everything at their default (zero) values.
Note
Creating any Steam Audio object follows the same pattern: first, you set up a structure with all the relevant settings for the object, then you call a function to create the object.
Next, we create the context by calling iplContextCreate
:
IPLContext context = nullptr;
iplContextCreate(&contextSettings, &context);
Here, context
is a handle to the Steam Audio context object. You will typically create a single context, which will persist throughout the lifetime of your application.
Initialization: HRTF¶
After creating the context, we need to load Head-Related Transfer Function (HRTF) data. An HRTF is a set of filters that is applied to audio in order to spatialize it. Steam Audio provides a default HRTF, and that’s what we’ll use for this example:
IPLHRTFSettings hrtfSettings{};
hrtfSettings.type = IPL_HRTFTYPE_DEFAULT;
We also need to specify some key properties that define how we will process audio. All audio processing in Steam Audio is done using uncompressed Pulse Code Modulated (PCM) audio data. Audio signals are represented as a series of samples measured at discrete, regularly-spaced points in time. Multi-channel audio is represented as multiple audio signals: for example, stereo (2-channel) audio contains 2 audio signals, so there are 2 samples for any given point in time.
The audio processing properties we need are:
Sampling rate. This is the frequency (in Hz) at which audio data is sampled. Typical sampling rates on most platforms are 44100 Hz (CD quality) or 48000 Hz.
Frame size. Most audio engines process audio in frames (also known as chunks or buffers). The frame size is the number of samples in a single frame of a single channel of audio. Typical values are 512 or 1024 samples per frame.
We specify these properties using a separate structure, since it will be needed in multiple places:
IPLAudioSettings audioSettings{};
audioSettings.samplingRate = 44100;
audioSettings.frameSize = 1024;
Now, we create the HRTF:
IPLHRTF hrtf = nullptr;
iplHRTFCreate(context, &audioSettings, &hrtfSettings, &hrtf);
This loads and initializes Steam Audio’s default HRTF. You can also use custom HRTFs that are loaded from SOFA files. For more information, see Custom HRTFs.
You will typically load one or more HRTFs, which will persist throughout the lifetime of your application.
Initialization: Binaural Effect¶
The actual work of spatializing an audio signal is performed by a binaural effect. This is an object that contains all the state that must persist from one audio frame to the next, for a single audio source.
As usual, we first populate a settings structure:
IPLBinauralEffectSettings effectSettings{};
effectSettings.hrtf = hrtf;
The only setting we need to specify is the HRTF that we want to use for spatialization. Now we can create the binaural effect:
IPLBinauralEffect effect = nullptr;
iplBinauralEffectCreate(context, &audioSettings, &effectSettings, &effect);
We are now ready to spatialize some audio! But first, we need to load our audio signal and allocate some buffers for audio processing.
Initialization: Audio Buffers¶
To avoid dealing with codecs and audio file loaders in this sample application, we assume that the input audio data is stored in a “raw” audio file. This file contains nothing but the contents of the input audio signal, stored in PCM format with 32-bit single-precision floating point samples. You can use free tools like Audacity to create and listen to such audio files.
std::vector<float> inputaudio = load_input_audio("inputaudio.raw");
This loads the audio data from the file inputaudio.raw and stores it in an std::vector<float>
. For more details on how the load_input_audio
function is implemented, see the complete code listing at the end of this page.
We also create a std::vector<float>
to store the final output data:
std::vector<float> outputaudio;
Next, we create IPLAudioBuffer
structures that define audio buffers that will be used as input to and output from the binaural effect. First, the input buffer:
float* inData[] = { inputaudio.data() };
IPLAudioBuffer inBuffer{};
inBuffer.numChannels = 1;
inBuffer.numSamples = audioSettings.frameSize;
inBuffer.data = inData;
The input buffer contains 1 channel, and the number of samples per channel is equal to the frame size. Audio buffers in Steam Audio are always deinterleaved, i.e., the samples for the first channel are stored contiguously in one array, the samples for the second channel are store contiguously in a second array, and so on. In the IPLAudioBuffer
structure, the data
field should point to an array of pointers, each of which points to the data for a single channel. Here, the input buffer points to the first 1024 samples of data loaded from the input file.
For the output buffer, we need to allocate memory for 1024 samples of 2-channel data, and set up an IPLAudioBuffer
structure to point to it. We can do this using the iplAudioBufferAllocate
function:
IPLAudioBuffer outBuffer{};
iplAudioBufferAllocate(context, 2, audioSettings.frameSize, &outBuffer);
This allocates a deinterleaved audio buffer that can store a single frame of stereo audio. Since we’ll want to save interleaved audio to disk, we need to allocate memory for that, too:
std::vector<float> outputaudioframe(2 * audioSettings.frameSize);
Main Loop¶
The main processing loop applies 3D audio effects to the input audio one frame at a time, accumulating the results in an output buffer that will be written to disk at the end of the loop.
We first calculate the number of audio frames that we will be processing:
int numframes = inputaudio.size() / audioSettings.frameSize;
The processing loop is run numframes
times:
for (int i = 0; i < numframes; ++i)
{
// render a frame of spatialized audio and append to the end of outputaudio
// ...
// advance the input to the next frame
inData[0] += audioSettings.frameSize;
}
Inside the loop, rendering a frame of spatialized audio involves the following steps. First, we use the binaural effect to spatialize the input buffer, storing the results in the deinterleaved output buffer outBuffer
:
IPLBinauralEffectParams effectParams{};
effectParams.direction = IPLVector3{1.0f, 1.0f, 1.0f};
effectParams.interpolation = IPL_HRTFINTERPOLATION_NEAREST;
effectParams.spatialBlend = 1.0f;
effectParams.hrtf = hrtf;
iplBinauralEffectApply(effect, &effectParams, inBuffer, outBuffer);
All audio processing effects in Steam Audio follow a similar pattern: you populate a structure containing various parameters for the effect, then you call a function to apply the effect to an audio buffer. For the binaural effect, the parameters are:
direction
The direction vector from the listener to the source, relative to the listener’s coordinates.interpolation
How to estimate the HRTF to use when the source direction doesn’t correspond to any direction for which the HRTF data contains any measurements.IPL_HRTFINTERPOLATION_NEAREST
specifies nearest-neighbor interpolation, which just picks the closest direction for which the HRTF contains measured data. This is the most efficient option, but you can also use bilinear interpolation for smoother rendering of moving sources. In our case, since the source doesn’t move, nearest-neighbor is fine.spatialBlend
This parameter lets you blend between spatialized and unspatialized audio. If its value is 1 (as here), the output will be fully spatialized. If its value is 0, the output will be unspatialized (i.e., identical to the input). Intermediate values result in partial spatialization.hrtf
This is the HRTF to use for spatializing the input. Here, we set it to the same value we passed in theIPLBinauralEffectSettings
structure when creating the effect. Steam Audio lets you change the HRTF on the fly, and the HRTF you pass in theIPLBinauralEffectParams
structure is always the one used for rendering.
Next, we interleave the audio buffer:
iplAudioBufferInterleave(context, outBuffer, outputaudioframe.data());
At this point, outputaudioframe
contains a single frame of interleaved stereo audio. We then append this to outputaudio
, the buffer that will be written to disk:
std::copy(std::begin(outputaudioframe), std::end(outputaudioframe), std::back_inserter(outputaudio));
Once the loop finishes, we write this buffer to disk:
save_output_audio("outputaudio.raw", outputaudio);
For details on how save_output_audio
is implemented, see the complete code listing below.
Cleanup¶
Finally, before our program exits, we clean up all the objects we created using the Steam Audio API:
iplAudioBufferFree(context, &outBuffer);
iplBinauralEffectRelease(&effect);
iplHRTFRelease(&hrtf);
iplContextRelease(&context);
And that completes this example program for spatializing an audio file using Steam Audio! For a full listing of this example program, see below.
Full program listing¶
#include <algorithm>
#include <fstream>
#include <iterator>
#include <vector>
#include <phonon.h>
std::vector<float> load_input_audio(const std::string filename)
{
std::ifstream file(filename.c_str(), std::ios::binary);
file.seekg(0, std::ios::end);
auto filesize = file.tellg();
auto numsamples = static_cast<int>(filesize / sizeof(float));
std::vector<float> inputaudio(numsamples);
file.seekg(0, std::ios::beg);
file.read(reinterpret_cast<char*>(inputaudio.data()), filesize);
return inputaudio;
}
void save_output_audio(const std::string filename, std::vector<float> outputaudio)
{
std::ofstream file(filename.c_str(), std::ios::binary);
file.write(reinterpret_cast<char*>(outputaudio.data()), outputaudio.size() * sizeof(float));
}
int main(int argc, char** argv)
{
auto inputaudio = load_input_audio("inputaudio.raw");
IPLContextSettings contextSettings{};
contextSettings.version = STEAMAUDIO_VERSION;
IPLContext context{};
iplContextCreate(&contextSettings, &context);
auto const samplingrate = 44100;
auto const framesize = 1024;
IPLAudioSettings audioSettings{ samplingrate, framesize };
IPLHRTFSettings hrtfSettings;
hrtfSettings.type = IPL_HRTFTYPE_DEFAULT;
IPLHRTF hrtf{};
iplHRTFCreate(context, &audioSettings, &hrtfSettings, &hrtf);
IPLBinauralEffectSettings effectSettings;
effectSettings.hrtf = hrtf;
IPLBinauralEffect effect{};
iplBinauralEffectCreate(context, &audioSettings, &effectSettings, &effect);
std::vector<float> outputaudioframe(2 * framesize);
std::vector<float> outputaudio;
auto numframes = static_cast<int>(inputaudio.size() / framesize);
float* inData[] = { inputaudio.data() };
IPLAudioBuffer inBuffer{ 1, audioSettings.frameSize, inData };
IPLAudioBuffer outBuffer;
iplAudioBufferAllocate(context, 2, audioSettings.frameSize, &outBuffer);
for (auto i = 0; i < numframes; ++i)
{
IPLBinauralEffectParams params;
params.direction = IPLVector3{ 1.0f, 1.0f, 1.0f };
params.interpolation = IPL_HRTFINTERPOLATION_NEAREST;
params.spatialBlend = 1.0f;
params.hrtf = hrtf;
iplBinauralEffectApply(effect, ¶ms, &inBuffer, &outBuffer);
iplAudioBufferInterleave(context, &outBuffer, outputaudioframe.data());
std::copy(std::begin(outputaudioframe), std::end(outputaudioframe), std::back_inserter(outputaudio));
inData[0] += audioSettings.frameSize;
}
iplAudioBufferFree(context, &outBuffer);
iplBinauralEffectRelease(&effect);
iplHRTFRelease(&hrtf);
iplContextRelease(&context);
save_output_audio("outputaudio.raw", outputaudio);
return 0;
}


The life of a worker in the vineyard of God is the life of one who is selfless; full of love and the fear of God. The life of a worker does not belong to himself but rather to Christ (Gal. 2:20) as Christ is the sole runner of the life. The worker then is entitled more with doing the Father's business rather than his affairs.
The life of a worker in the vineyard entails one dying to self and being alive in Christ Jesus; it entails one allowing the will of God to consume his own will so that he does the Father's desires.
The vineyard worker can be likened to a soldier in service who has the obligation of obeying his commander, he does not take or handle the things of the master just like mere civilians, the vineyard worker in like manner must not mingle with the affairs of this world if he is to diligently serve his master(2 Timothy2:4 ).
The vineyard worker as a song minister; the music minister who is also a soldier of Christ does not sing to receive praises from his congregation, he has the duty of ministering in songs to bring down the glory and presence of God to heal, save, e.t.c (1 Sam 16:14-23)which implies that frequent, fervent and intentional tarrying in God's presence is very important.
The vineyard worker is also an evangelist (Prov 11:30); in every way and means by which he can proclaim the love of God and the grace of God that brings salvation(Titus 2:11), he should do it knowing that the vineyard workers do not work with the times and seasons of this as the Son of Man would come to take his children into his glory at anytime which is unknown(Jn 9:4).


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The World's Most Expensive Typos | Weird News
Sep 22, 2009 . The Worlds Most Expensive Typos . 2006, a missed comma may have become the most costly piece of punctuation in Canadian history.
http://wtf.thebizzare.com/offbeat/people-and-society/the-worlds-most-expensive-typos/Mariner 1 - Wikipedia, the free encyclopedia
2.1 "The most expensive hyphen in history". 2.1.1 Ambiguity of error location. 2.2 Other punctuation; 2.3 Overbar transcription error. 3 References; 4 External .
http://en.wikipedia.org/wiki/Mariner_1Typos. Big Deal or Not? - Goodreads
When there are a lot of typos (one a page or more), it is a huge distraction, and makes me think I am reading something like fanfiction rather than a published .
http://www.goodreads.com/topic/show/854440-typos-big-deal-or-not
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The location is shown in red.
The deisgn concept of Control and Freedom was inspired from the movie Akira. The movie is set in a post apocalyptic city where the government controls everything including the people and the people try to get out of the control through seeking freedom.
The image shown on the right is from the very last scene of the movie where one of the main character turns into a monster. I depicted the monster's undefined and random shape into how the roof is designed. You can see how the roof is made up of many rectangular blocks which goes with my concept of control and the randomness of the position of the blocks represent freedom. The color of the roof is also representing the concept of freedom where each block has its own shade of red.
The building consists of first floor and the second floor and it's divided into three part. The middle part is the theater and the two part on the side are the residence. The left side is the living room and kitchen space with big open space where the owner could invite friends to come over. The right side is the bedroom; 2 bedrooms on the second floor and one master bedroom on the first floor. Furthermore, inbetween the theater and the residence, there are gardens that has bamboo trees which fucntion as a place to relax and also as a spatial divider to control the space.
The bridge is used to connect the two residence together into one and connects the living room space with the bedroom space. This also goes with the concept of freedom where the resident can freely move around and without interacting with the people in the theater.


You are viewing an historical archive of past issues. Please report new issues to the appropriate project issue tracker on GitHub.
Issues
| # | Summary | Product | Status | Kind | When |
|---|---|---|---|---|---|
| 1837 |
Lazy-Initialization Refactoring Causing |
Command-T | closed | bug | Created , updated |
| 1836 |
versions >= 1.1b give 'NoMethodError' |
Command-T | closed | bug | Created , updated |
| 1833 |
Cache file listing to disk |
Command-T | open | feature request | Created , updated |
| 1832 |
Vertical Split seems to work, but Horizontal doesn't |
Command-T | closed | support ticket | Created , updated |
| 1831 |
Fails to see some folders within the current working directory |
Command-T | closed | bug | Created , updated |
| 1830 |
Duplicate tag failure also in the latest 1.2.1 Versioin |
Command-T | closed | bug | Created , updated |
| 1829 |
ignore whitespace in queries |
Command-T | open | feature request | Created , updated |
| 1828 |
Think about alternative data structures for greater speed |
Command-T | open | feature request | Created , updated |
| 1827 |
Update to nginx 1.0.6 |
wincent.com | closed | feature request | Created , updated |
| 1826 |
Segmentation fault when using command-t |
Command-T | closed | bug | Created , updated |


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This is a list item in an unordered list


GAIT BASED AUTHENTICATION SYSTEM
About Project
This project aims to authenticate people based on gait - the way of one’s walking,
we initially propose to collect data from android operated smartphone through accelerometer and upon analyzation we can identify the person through his/her way of walking.
Please help us populate data by installing app below, your help is much needed and appreciated. All volunteers will be included in project contributors list.
Already done setup and logged data - Upload it here
Accelerometer Apps
Consider the following link to download and install apps for data logging on your devices, please put down phone in your pocket at appropriate position whenever you're walking naturally - it may be jogging, normal walk, taking stairs.
Remember! More data we've more the perfect precision will be.
How to log data from accelerometer using android app listed above
- Open App Accelerometer Log
- Accelerometer data will start displaying in form of graph
- Put your phone instantaneously in pocket in position described :
- Start walking naturally - slow, normal or fast
- After finishing walk touch options and tap Stop
- After finishing walk touch options and tap Save Data
- Name the file in conventions mentioned below
- Note : This app dosen't works in background, so you've to keep app open during capture, thanks for your understanding.
How to name your accelerometer log files
- Your Name initials - vkku for Vivek, vtsl for Vatsal and alike.
- Name your pace - factor of : Fast, Normal, Slow
- Name your walking surface - factor of : Smooth, Rough
- Optional(Recommended) : give a initial of place you recorded data at - example : MDA, MIT etc.
Now, your file name should look like : vkkuNormalSmooth
or vtslFastRoughMDA
(Recommended).
Done Logging help us by submitting : Upload Here


The Newest Testament: The Teachings and Story of Donald J. Trump
by Ruben BollingTom the Dancing Bug, IN WHICH is presented the Good News about Donald J. Trump, in the Newest, Best, Third Testament
PLEASE DO join Tom the Dancing Bug's INNER HIVE! Be the first kid on your block to see each week's comic, get extra comics, sneak peeks, insider scoops, and other stuff! JOIN TODAY!
FOLLOW @RubenBolling on the Twitters and a Face Book and perhaps some Insta-grams


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A starter for a project with AngularJS (1.x), Bootstrap (4.x) and Font Awesome (4.x) powered by Webpack (3.x).
Main call to action Secondary action
# clone the repo
$ git clone https://github.com/ducrot/starter-webpack-angularjs.git my-app
# change directory to your app
$ cd my-app
# install the dependencies with npm
$ npm install
# start the server
$ npm start
Go to http://localhost:8080 in your browser!


Deep Sea Wonders
Casey Tan Jie Wei is a Singaporean painter whose works cantered on visual metaphor and narrative. Many of his artworks draw inspiration from song lyrics, movies, books and other genre of art, often finding connections between these sources of inspiration to the life experienced. Sometimes bringing drama and fantasy to the ordinary.'
Casey graduated with a Diploma in Fine Arts in 2016, NAFA, where he was also a Ngee Ann Kongsi Scholarship recipient. Prior to NAFA, Casey studied Digital Animations at Institute of Technical Education (2011-2012), where he started painting during this period of time after being inspired by the works of old masters and occasionally the visual aesthetic of eastern and western animations. In his recent practice, he tries to avoid operating by intuition, instead taking time to understand and explore different ways of interpreting a particular scenario. He believes not all art have to be decorations, but one that can aspire to hold narrative.


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|
JANA2
|
Public Types | |
| enum | AffinityStrategy { None, MemoryBound, ComputeBound } |
| enum | LocalityStrategy { Global, SocketLocal, NumaDomainLocal, CoreLocal, CpuLocal } |
Friends | |
| std::ostream & | operator<< (std::ostream &os, const JProcessorMapping &m) |
| std::ostream & | operator<< (std::ostream &os, const AffinityStrategy &s) |
| std::ostream & | operator<< (std::ostream &os, const LocalityStrategy &s) |


Type Server
object
--+
|
Server
I handle requests for an OpenID server.
Some types of requests (those which are not checkid
requests) may be handed to my handleRequest
method, and I will take care
of it and return a response.
For your convenience, I also provide an interface to Decoder.decode
and SigningEncoder.encode
through my methods decodeRequest
and encodeResponse
.
All my state is encapsulated in an OpenIDStore
, which means I'm not generally
pickleable but I am easy to reconstruct.
Example:
oserver = Server(FileOpenIDStore(data_path), "http://example.com/op")
request = oserver.decodeRequest(query)
if request.mode in ['checkid_immediate', 'checkid_setup']:
if self.isAuthorized(request.identity, request.trust_root):
response = request.answer(True)
elif request.immediate:
response = request.answer(False)
else:
self.showDecidePage(request)
return
else:
response = oserver.handleRequest(request)
webresponse = oserver.encode(response)
| Method Summary |
| |
__init__ (self,
store,
op_endpoint)
A new Server . |
OpenIDRequest
|
decodeRequest (self,
query)
Transform query parameters into an OpenIDRequest . |
WebResponse
|
encodeResponse (self,
response)
Encode a response to a WebResponse , signing it first if
appropriate. |
OpenIDResponse
|
handleRequest (self,
request)
Handle a request. |
OpenIDResponse
|
openid_associate (self,
request)
Handle and respond to associate requests. |
OpenIDResponse
|
openid_check_authentication (self,
request)
Handle and respond to check_authentication requests. |
Inherited from object :
__delattr__ ,
__getattribute__ ,
__hash__ ,
__new__ ,
__reduce__ ,
__reduce_ex__ ,
__repr__ ,
__setattr__ ,
__str__
|
decodeRequest(self,
query)
Transform query parameters into an OpenIDRequest .
If the query does not seem to be an OpenID request at all, I return
None .
-
- Parameters:
query -
The query parameters as a dictionary with each key mapping to
one value.
(type=dict)
- Returns:
-
OpenIDRequest
- Raises:
ProtocolError -
When the query does not seem to be a valid OpenID request.
See Also: Decoder.decode
|
encodeResponse(self,
response)
Encode a response to a WebResponse , signing it first if
appropriate.
-
- Returns:
-
WebResponse
- Raises:
EncodingError -
When I can't figure out how to encode this message.
AlreadySigned -
When this response is already signed.
See Also: SigningEncoder.encode
|
handleRequest(self,
request)
Handle a request.
Give me a request, I will give you a response. Unless it's a type of
request I cannot handle myself, in which case I will raise
NotImplementedError . In that case, you can handle it
yourself, or add a method to me for handling that request type.
-
- Returns:
-
OpenIDResponse
- Raises:
NotImplementedError -
When I do not have a handler defined for that type of
request.
|
openid_associate(self,
request)
Handle and respond to associate requests.
-
- Returns:
-
OpenIDResponse
|
openid_check_authentication(self,
request)
Handle and respond to check_authentication
requests.
-
- Returns:
-
OpenIDResponse
|
| Instance Variable Details |
decoder
I'm using this to decode things.
-
- Type:
-
Decoder
|
encoder
I'm using this to encode things.
-
- Type:
-
Encoder
|
op_endpoint
My URL.
-
- Type:
-
str
|
signatory
I'm using this for associate requests and to sign things.
-
- Type:
-
Signatory
|


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The Chef's KitchenOct 12th
Photo credit: ChalonHandmade / Foter / CC BY
I can't imagine what DBC would be like without this unique culture. I feel like so much of my educational value is coming from the effects of its unique way of thinking and teaching. It is like stepping into a completely different environment, where testing, experimenting, and failing is encouraged rather than a static absorption of material delivered from an instructor or book. I can already feel the shifting in attitude from a top-down approach in education that is all too prevalent to one that encourages giving back to this community. This is a culture designed for rapid growth, development, and change not only for the program as a whole, but for the individual. I have high hopes for this new shift in educational paradigm. As I continue my journey through DBC, it is constantly exceeding my expectations.
I think the challenge here is about being able to adapt. We need to get out of this mindset of being simply consumers in this environment. We need to change the way we approach learning to be able to fully utilize this environment provided for us. The key here is that being a part of this community asks for much more involvement and giving back from the individual. I like being given this opportunity, because staying outside and being at the table is boring.
Out of all of this, I'm hoping to not only become a world class developer, but to achieve something even deeper and long-lasting for my personal growth. I hope to learn from these new ideas, from DBC's experience and my cohort as well as within myself, to become a world class learner. Suriving the bootcamp to me is just the first step in a lifelong journey towards constant learning and improving. I hope this doesn't stop with web development - I want to come out of this as a better person.


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SCJP Reading Notes - 6
| |Summary
It is reading notes about the book – SCJP.
Inner Class
inner class is part of outer class, can access outer class’s private(or any other) members (one of key benefits, special relationship with outer class object)
eg. class MyOuter { class MyInner {} }
Instantiate inner class:
a. inside outer class: MyInner inner = new MyInner();
b. outside outer class: MyOuter.MyInner inner = MyOuter.new MyInner();
method local inner class: class defined inside method; it cannot access method local variable, unless it’s marked as final, the only modifiers can apply to are abstract and final(unlike regular inner class, the modifiers are public, private, protected, final, abstract, etc.)
Anonymous Inner class
eg:
Popcorn p = new Popcorn() { public void pop() { System.out.println("anoymous popcorn"); } };
Static Nested Class: is not inner class, is a static member of the enclosing class, like static method, it cannot access instance variables and nonstatic members of outer class eg:
class BigOuter { static vlass Nested {} }
Thread
Define Thread
a. Extend java.lang.Thread (limitation: can’t extend anything else)
b. Implement Runnable interface (benefit: can extend any class)
eg: class MyRunnable implements Runnable { public void run() {System.out.println("implement job running in My Runnable");} }
Instantiate thread
a. Thread t = new Thread();
b. need two classes
i. Thread class: thread-specific code ii. Runnable implementation class: job that should be run by a thread code eg: i. instantiate runnable class MyRunnable r = new MyRunnable(); ii. still need an instance of java.lang.Thread Thread t = new Thread(r); //pass Runnable to thread
It’s legal the Thread itself implements Runnable
eg:
Thread t = new Thread(new MyThread());
A thread is dead after run() method completes, the isAlive() is the best way to determine that; debug: getState()
Starting thread: t.start(), only start once
process: start –> go to runnable pool –> jvm select –> running
which thread is running? Thread.currentThread().getName()
Nothing is guaranteed, except: each thread will start, and each thread will run to completion
which thread run first?
There’s the Thread Scheduler, which is part of JVM, could schedule thread to run based on thread priority(1-10, bigger, higher), but not guaranteed. eg: t.setPriority(8)
yield(): make currently running thread head back and make other with same priority get their turn
join(): join at the end of the current running thread
Thread problem: two different threads can access to a single instance of class?(multiple threads access to the same resource)
eg.
Two people each have checkbook, access to a single checking account
Check the balance
if have enough money, make the withdrawal
It’s race condition, may produce corrupted data.
Way to Protect: automic operation
i. make variable private ii. Synchronize the code that modifies the variable
Thread-safe class: class synchronized to protect its data
wait(), notify(), notifyAll() are all instance methods of Object
wait(): make thread go to waiting pool, if is current, gives up lock immediately
notify(): notify certain thread in the waiting pool to get ready to get the lock(lock may be not available immediately)


Dear {Name},
We have received the request to reset your password. You can change your password using this link below:
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Thank you, Softeq NetKit Team


Primary attainment and RAISE online statistics team
Email<EMAIL_ADDRESS>Telephone: Ayo Babatunde 020 7340 8473
National Statistics
Revised information on the 2014 national curriculum assessment at key stage 2, including breakdowns by pupil characteristics.
PDF , 1.66MB , 32 pages
MS Excel Spreadsheet , 1.25MB
MS Excel Spreadsheet , 5.04MB
MS Excel Spreadsheet , 2.43MB
MS Excel Spreadsheet , 342KB
ZIP , 228KB
PDF , 1.07MB , 32 pages
PDF , 778KB , 18 pages
Plain text , 2KB
This statistical first release ( SFR ) provides revised results for pupils in schools at national, regional and local authority level. Information on attainment is also broken down by:
It includes results from the key stage 2 tests in:
and on key stage 2 teacher assessments in:
This release also includes figures on expected progress between key stage 1 and key stage 2.
The revised figures are based on data checked by schools prior to publication in the primary school performance tables.
Figures combine this revised data with the information gathered through the school census in January 2014. This SFR also updates provisional figures released in August in SFR 30/2014.
Read the statistical working paper: ‘ Measuring disadvantaged pupils’ attainment gaps over time ’ For information about our proposed methodology for measuring attainment gaps for disadvantaged pupils.
Email<EMAIL_ADDRESS>Telephone: Ayo Babatunde 020 7340 8473
Published: 11 December 2014
Updated: 20 May 2015
From: Department for Education
Part of: Statistics: key stage 2