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0 Preface1 Introduction2 The evolutionary Dropback3 Conditions4 Resources5 Life & life history6 Intraspecific competition
He, F. and Gaston, K.J. 2000. Estimating species abundance from occurrence. American Naturalist 156:553-559 pdf


Hello, I'm a default panel that comes with a nodecg-react bundle!
You can play with me and directly update your stateful React graphics from here.
Timestamp!
Craftsman name Set name


I am a Software Engineering Graduate from San Jose State University,
specialized in Enterprise Software Technologies.
I am actively looking for full-time opportunities starting from July 2020.
• User Management Application: Developed FIDO2 secured web application for user management. This application is for admins to add and update users to the LDAP directory categorizing them under various organizations.
• Global Transaction View Dashboard: An online cash management portal service to provide customers,
a consolidated view of cash positions and help in making key treasury decisions.
This was developed using Angular JS, HTML, CSS, Java, Ajax, JSON, Bootstrap with Backbase Framework.
• Retail store Application: Developed an online shopping application for a retail company
which required customization of existing components of the AEM content management system using JavaScript, jQuery, HTML, CSS, Bootstrap, Java. This improved the performance of
the application by 20%.
• Personal Banking : Worked on banking application development which included modules of loans,
deposits, account summary. It was an end to end responsive application developed using Angular
JS, HTML, CSS, Java, Ajax, JSON, Bootstrap with Backbase Framework.
GPA: 3.77
GPA: 3.90


Controller loadData method requests data from OData service with ajax. Any asynchronous source could be used instead. Just return jQuery.promise from controller method. Field option itemTemplate allows to render any custom cell content, just return your markup as string, DOM Node or jQuery Element.


KNN
introml.analyticsdojo.com
21. KNN¶
#Let's get rid of some imports
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
To evaluate how well our supervised models generalize, we can split our data into a training and a test set:
from sklearn.datasets import load_iris
#Iris is available from the sklearn package
iris = load_iris()
X, y = iris.data, iris.target
Thinking about how machine learning is normally performed, the idea of a train/test split makes sense. Real world systems train on the data they have, and as other data comes in (from customers, sensors, or other sources) the classifier that was trained must predict on fundamentally new data. We can simulate this during training using a train/test split - the test data is a simulation of “future data” which will come into the system during production.
Specifically for iris, the 150 labels in iris are sorted, which means that if we split the data using a proportional split, this will result in fudamentally altered class distributions. For instance, if we’d perform a common 2/3 training data and 1/3 test data split, our training dataset will only consists of flower classes 0 and 1 (Setosa and Versicolor), and our test set will only contain samples with class label 2 (Virginica flowers).
Under the assumption that all samples are independent of each other (in contrast time series data), we want to randomly shuffle the dataset before we split the dataset as illustrated above.
y
array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])
21.1. Shuffling Dataset¶
Now we need to split the data into training and testing.
Luckily, this is a common pattern in machine learning and scikit-learn has a pre-built function to split data into training and testing sets for you.
Here, we use 50% of the data as training, and 50% testing.
80% and 20% is another common split, but there are no hard and fast rules.
The most important thing is to fairly evaluate your system on data it has not seen during training!
#Import Module
from sklearn.model_selection import train_test_split
train_X, test_X, train_y, test_y = train_test_split(X, y,
train_size=0.5,
test_size=0.5,
random_state=123)
print("Labels for training and testing data")
print(train_y)
print(test_y)
Labels for training and testing data
[1 1 0 2 2 0 0 1 1 2 0 0 1 0 1 2 0 2 0 0 1 0 0 1 2 1 1 1 0 0 1 2 0 0 1 1 1
2 1 1 1 2 0 0 1 2 2 2 2 0 1 0 1 1 0 1 2 1 2 2 0 1 0 2 2 1 1 2 2 1 0 1 1 2
2]
[1 2 2 1 0 2 1 0 0 1 2 0 1 2 2 2 0 0 1 0 0 2 0 2 0 0 0 2 2 0 2 2 0 0 1 1 2
0 0 1 1 0 2 2 2 2 2 1 0 0 2 0 0 1 1 1 1 2 1 2 0 2 1 0 0 2 1 2 2 0 1 1 2 0
2]
21.2. Stratified Splitting¶
Especially for relatively small datasets, it’s better to stratify the split.
Stratification means that we maintain the original class proportion of the dataset in the test and training sets.
For example, after we randomly split the dataset as shown in the previous code example, we have the following class proportions in percent:
print('All:', np.bincount(y) / float(len(y)) * 100.0)
print('Training:', np.bincount(train_y) / float(len(train_y)) * 100.0)
print('Test:', np.bincount(test_y) / float(len(test_y)) * 100.0)
All: [ 33.33333333 33.33333333 33.33333333]
Training: [ 30.66666667 40. 29.33333333]
Test: [ 36. 26.66666667 37.33333333]
So, in order to stratify the split, we can pass the label array as an additional option to the train_test_split
function:
train_X, test_X, train_y, test_y = train_test_split(X, y,
train_size=0.5,
test_size=0.5,
random_state=123,
stratify=y)
print('All:', np.bincount(y) / float(len(y)) * 100.0)
print('Training:', np.bincount(train_y) / float(len(train_y)) * 100.0)
print('Test:', np.bincount(test_y) / float(len(test_y)) * 100.0)
All: [ 33.33333333 33.33333333 33.33333333]
Training: [ 33.33333333 33.33333333 33.33333333]
Test: [ 33.33333333 33.33333333 33.33333333]
21.3. Prediction using Simple Nearest Neighbor Classifier¶
By evaluating our classifier performance on data that has been seen during training, we could get false confidence in the predictive power of our model.
In the worst case, it may simply memorize the training samples but completely fails classifying new, similar samples – we really don’t want to put such a system into production!
Instead of using the same dataset for training and testing (this is called “resubstitution evaluation”), it is much much better to use a train/test split in order to estimate how well your trained model is doing on new data.
from sklearn.neighbors import KNeighborsClassifier
#This creates a model object.
classifier = KNeighborsClassifier()
#This fits the model object to the data.
classifier.fit(train_X, train_y)
#This creates the prediction.
pred_y = classifier.predict(test_X)
21.4. Scoring¶
We can manually calculate the accuracy as we have done before.
metrics.accuracy_score
is passed the target value and the predicted value.Model objects also built in scoring functions.Can also us a
classifier.score
component built into the model.
from sklearn import metrics
#This calculates the accuracy.
print("Manual Accuracy:", np.sum(pred_y == test_y) / float(len(test_y)))
print("Metrics score: ", metrics.accuracy_score(test_y, pred_y) )
print("Classifier score: ", classifier.score(test_X, test_y) )
Manual Accuracy: 0.96
Metrics score: 0.96
Classifier score: 0.96
21.5. Cross Validation¶
Teh
cv
parameter is the number of folds.Scoring type depends on whether classification, clustering, regression.
from sklearn.model_selection import cross_val_score
cv1 = cross_val_score(classifier, train_X, train_y, cv=2, scoring='accuracy')
cv10 = cross_val_score(classifier, train_X, train_y, cv=10, scoring='accuracy')
print("Two-fold cross validation:", cv1)
print("Ten-fold cross validation:", cv10)
Two-fold cross validation: [ 0.97435897 0.97222222]
Ten-fold cross validation: [ 1. 0.88888889 0.88888889 1. 1. 1. 1.
1. 1. 1. ]
21.6. Visualizing Errors¶
We can also visualize the correct and failed predictions.
print('Samples correctly classified:')
correct_idx = np.where(pred_y == test_y)[0]
print(correct_idx)
print('\nSamples incorrectly classified:')
incorrect_idx = np.where(pred_y != test_y)[0]
print(incorrect_idx)
Samples correctly classified:
[ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 45 46 47 48 50 51
52 53 54 55 56 57 58 59 61 62 63 64 65 66 67 68 69 70 71 72 73 74]
Samples incorrectly classified:
[44 49 60]
# Plot two dimensions
colors = ["darkblue", "darkgreen", "gray"]
for n, color in enumerate(colors):
idx = np.where(test_y == n)[0]
plt.scatter(test_X[idx, 1], test_X[idx, 2], color=color, label="Class %s" % str(n))
plt.scatter(test_X[incorrect_idx, 1], test_X[incorrect_idx, 2], color="darkred")
plt.xlabel('sepal width [cm]')
plt.ylabel('petal length [cm]')
plt.legend(loc=3)
plt.title("Iris Classification results")
plt.show()
We can see that the errors occur in the area where green (class 1) and gray (class 2) overlap. This gives us insight about what features to add - any feature which helps separate class 1 and class 2 should improve classifier performance.
21.7. Nearest Neighbor Classification¶
Documentation and Function.
Various ways of tuning the model to take into account K neighbors or weighted values.
Generally, larger values of k reduce the effect of noise on the classification.
Weighted models can also be used, where closer neighbors are weighted more highly.
21.8. Example¶
Example below: The test sample (green circle) should be classified either to the first class of blue squares or to the second class of red triangles. If k = 3 (solid line circle) it is assigned to the second class because there are 2 triangles and only 1 square inside the inner circle. If k = 5 (dashed line circle) it is assigned to the first class (3 squares vs. 2 triangles inside the outer circle). By Antti Ajanki AnAj (Own work) [GFDL (http://www.gnu.org/copyleft/fdl.html), CC-BY-SA-3.0 (http://creativecommons.org/licenses/by-sa/3.0/) or CC BY-SA 2.5-2.0-1.0 (https://creativecommons.org/licenses/by-sa/2.5-2.0-1.0)], via Wikimedia Commons
21.9. Hyperparameter Tuning¶
Hyperprameters are the various settings you can provide to an algorithm.
For example, you could detrmine the number of neighors or the weighting method used.
classifier = KNeighborsClassifier(n_neighbors=10)
#This fits the model object to the data.
classifier.fit(train_X, train_y)
#This creates the prediction.
pred_y = classifier.predict(test_X)
print("Metrics score: ", metrics.accuracy_score(test_y, pred_y) )
Metrics score: 0.946666666667


The INSERT keyword can be used to insert new documents into a collection. On a single server, an insert operation is executed transactionally in an all-or-nothing fashion.
If the RocksDB engine is used and intermediate commits are enabled, a query may execute intermediate transaction commits in case the running transaction (AQL query) hits the specified size thresholds. In this case, the query's operations carried out so far will be committed and not rolled back in case of a later abort/rollback. That behavior can be controlled by adjusting the intermediate commit settings for the RocksDB engine.
For sharded collections, the entire query and/or insert operation may not be transactional, especially if it involves different shards and/or database servers.
Each INSERT operation is restricted to a single collection, and the collection name must not be dynamic. Only a single INSERT statement per collection is allowed per AQL query, and it cannot be followed by read or write operations that access the same collection, by traversal operations, or AQL functions that can read documents.
The syntax for an insert operation is:
INSERT document INTO collection [ OPTIONS options ]
Note: The IN keyword is allowed in place of INTO and has the same meaning.
collection must contain the name of the collection into which the documents should be inserted. document is the document to be inserted, and it may or may not contain a _key attribute. If no _key attribute is provided, ArangoDB will auto-generate a value for _key value. Inserting a document will also auto-generate a document revision number for the document.
FOR i IN 1..100
INSERT { value: i } INTO numbers
An insert operation can also be performed without a FOR loop to insert a single document:
INSERT { value: 1 } INTO numbers
When inserting into an edge collection, it is mandatory to specify the attributes _from and _to in document:
FOR u IN users
FOR p IN products
FILTER u._key == p.recommendedBy
INSERT { _from: u._id, _to: p._id } INTO recommendations
The OPTIONS keyword followed by an object with query options can optionally be provided in an INSERT operation.
It can be used to suppress query errors that may occur when violating unique key constraints:
FOR i IN 1..1000
INSERT {
_key: CONCAT('test', i),
name: "test",
foobar: true
} INTO users OPTIONS { ignoreErrors: true }
To make sure data are durable when an insert query returns, there is the waitForSync query option:
FOR i IN 1..1000
INSERT {
_key: CONCAT('test', i),
name: "test",
foobar: true
} INTO users OPTIONS { waitForSync: true }
If you want to replace existing documents with documents having the same key there is the overwrite query option. This will let you safely replace the documents instead of raising an "unique constraint violated error":
FOR i IN 1..1000
INSERT {
_key: CONCAT('test', i),
name: "test",
foobar: true
} INTO users OPTIONS { overwrite: true }
The inserted documents can also be returned by the query. In this case, the INSERT
statement can be a RETURN
statement (intermediate LET
statements are allowed, too).
To refer to the inserted documents, the INSERT
statement introduces a pseudo-value
named NEW
.
The documents contained in NEW
will contain all attributes, even those auto-generated by
the database (e.g. _id
, _key
, _rev
).
INSERT document INTO collection RETURN NEW
Following is an example using a variable named inserted
to return the inserted
documents. For each inserted document, the document key is returned:
FOR i IN 1..100
INSERT { value: i }
INTO users
LET inserted = NEW
RETURN inserted._key


Module build
source code
CSB build related tools and programs.
When executed as a program, this module will run the CSB Build Console
and build the source tree it belongs to. The source tree is added at the
beginning of sys.path to make sure that all subsequent imports
from the Test and Doc consoles will import the right thing (think of
multiple CSB packages installed on the same server).
Here is how to build, test and package the whole project:
$ hg clone https://hg.codeplex.com/csb CSB
$ CSB/csb/build.py -o <output directory>
The Console can also be imported and instantiated as a regular Python
class. In this case the Console again builds the source tree it is part
of, but sys.path will remain intact. Therefore, the Console will assume
that all modules currently in memory, as well as those that can be
subsequently imported by the Console itself, belong to the same CSB
package.
Note:
The CSB build services no longer support the option to build
external source trees.
See Also:
[CSB 0000038]
|
|
PARENT = ' /home/travis/build/csb-toolbox/CSB/csb '
|
|
|
ROOT = ' csb '
|
|
|
SOURCETREE = ' /home/travis/build/csb-toolbox/CSB '
|
|
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__package__ = ' csb '
|


For companies that already have a relatively mature selling process, Salesforce is one excellent tool that helps them manage their business. However, Salesforce’s products have not been serving people who own local business, which provide goods or services to a local population. They have a very different way to manage their data. My design product, Salesforce for local business, is to serve owners of local business.
*This is my personal project. I am not affiliate with Salesforce.
Goal and Target User
Goal
1) Finding out what are pain points of current local business running system
2)Solving the pain points.
Target users
People who own local business.
User Research Process
Before anything, collecting data is the starting point for any user-centered design. I defined local business to be business that do not belong to big chain store, those who serve a local population.
I conducted user research at University Way NE, Seattle, WA 98105, or, as most people call it, the Ave. I chose to conduct user research on the ave, because it is right next to University of Washington, Seattle campus. UW students and faculty members make the Ave rather crowded and popular, which enriches the data for my user research. Also, based on data provided by Yelp, about 70% of all stores on the ave are local business (i.e, stores like Starbucks, 7–11, American Apparel not counted as local business). It makes the Ave a great location for my user research.
Before I went on to the Ave, I decided the best way to get data would be interviewing the business owners and field study (observation). Based on Yelp data, about 80% of all local business stores here are food and dining services (i.e., restaurants, dessert stores, cafe). So I picked several food service local business owners to interview. I went on the Ave, stayed there from 2 to 5 p.m on weekdays, because that’s when most of the stores are not busy. In the end, I successfully conducted 4 interview sessions with 2 restaurants owners , 1 bubble store owner and 1 cup cake store owner.
After finishing my very first interview with a restaurant owner, I got to know that they a product called Clover. According to the owner, that is now what most people use in local business. So I Googled clover, did some competitive analysis, before I move on to the second interview.
By the end of the four interviews, I realized the information my interviewees can provide start to get more and more repetitive, and that is when I know that I can stop on user research and start analyse my data.
During the interview process, I was so tempted to conduct more field study, where I observe how owners run their business. However, their actual business data are too sensitive for them to show me. I ended up conducting one field study at the bubble tea store, while it was crowded and busy, and I got the chance to see how customers and cashiers interact with Clover and data involved.
Pain points
I collected and analysed data from interviews, field study and competitive analysis. I then generated these five pain points:
1. Anyone can access and edit data
Authorization is a big issue in the current local business managing system, AKA Clove. A restaurant owner reported in interview that anyone who log into the system can input, edit or delete any order. He described a former employee of his delete some of the orders and stole the money, and he can still edit data on Clove after he’s fired. Clover, however, never reported any of it.
“We fired him because he was deleting orders and taking the money. Now he is not working for us anymore and yet he can still log into the system to change whatever he wants. And Clove never report it!”
2. Useless categorization and visualization
According to the owner of bubble tea store and the owner of cupcake store, data in Clove system is hard to manage. All the data appear to be categorized by time. However, the owners would love to see the data categorized by type of drink/cupcake or others. Visualized chart, on the scale of time/date is not so helpful.
3. No customize categorization or sub-categorization
Unable to customize categorization and sub-categorization is extremely inconvenient. For local business owner, the items they sell are customizable. For example, bubble tea customers can add extra servings of tapioca or herbal jelly, restaurant customers might order dish with extra meat. These data are important to owners, and yet the current system, Clove, fail to create sub-categorization for them.
4. Have to manually input tips
Restaurant owners have to manually input the amount of tip per order every night. It takes quite long time.
“It is tedious to input manually tips at the end of the day.”
However, I believe, instead of being a design problem, this is a bank policy issue. Bank does not charge tips from credit/debit card unless seeing signature of customers. Therefore, these number have to be provided to the bank afterwards.
5. Data don’t sync between two stores
Local business, though only serves local residents, still has the chance of expanding the business to other region. The bubble tea owner reported that syncing.
“I have another bubble tea shop at Renton, and the data just don’t sync. ”
The owner now pays for two different account of clover. He has to check and analyze data from one store, then do exactly the same thing to the other. It takes time and extra calculation to add everything together using a calculator.
User Needs
I generated user needs based on users’ pain points. Also, I did not treat the manually-tips-inputing as a design problem, since it’s a problem because of bank policy.
Different Authorisation level
One way to solve the data access/editing problem is to have different authorization of one account. For example, the owner has the highest authorisation level, where he/she gets to see all the on-time sales data, shifts, and orders, and authorizing employees to edit sales data.
Customised data management
Local Business owners need to customize data based on different type of items they sell. Customizable data categorization and sub-categorization is important.
Able to sync among multiple (usually 2) stores
Data from more than one store should be able to sync and analyzed together.
Able to manage data and also people
This user need is based on my field study. I observe that these local business often run on owners assigning shifts and employees dropping and picking up shifts. It is a critical process of running the business, so I take it as a user need as well.
Sample Data
The data below came from interview + field study with at the bubble tea store. The owner did not show me the actual data. He only listed the types of data for me, because the actual number is a sensitive subject. I then stayed in the store for another half an hour after the interview to conduct field study, observing and documenting the data involved.
From the sample data, I was able to brainstorm and define key path.
Key Path
Brainstorm
I then started brainstorming the features and tools local business owners need based on the key path above. I wrote down one idea on one post-it and combined them with key path.
Sketches
I chose to design on web app platform, because when owners are running the store, it is unlikely for them to check the data online. They prefer to check everything at the end of the day, where they can take close look to the data. My interviewees reported so, that they do it on their own computer.
The design above solves the design pain points listed. Authorization are customizable by store owners. When employees try to edit sales data, they need to request the action and owners can either approve, disapprove or message the employees.
Data management and categorization/sub-categorization are all customizable, where owners get to view the data on scale of time, date, type, add-on, and more.
Owner can only register for one account, and manage multiple stores, where data are generated together. Besides manage data, owners can manage people by assigning shifts to employees, and employees can drop and pick up shift through the product under owner’s aware.
Prototype
I made some high fidelity prototypes here to deliver the user interface design idea.
Constraints
Clover is a pos machine that takes in data from each transaction. So far, there is no way to connect Salesforce products with Clover. In other words, data that are in Clover system cannot be transferred into Salesforce system. Therefore, my design is based on an assumption that Salesforce products are able to access those transaction data in Clover.
Also, ideally, this should be a pretty big product. As a college student, I am not able to get all the data I need. I only reached the data I could access, tried my best to design it out. I did not conduct any usability test either, because it is difficult for me to recruit participants.


The ff-select attribute allows an end user to select and edit a row.
In this example, if isImportant is truthy
In this example, if the url contains the hash #red, the class red will be added to the div. Notice this example has no Firebase emulator because changing the hash has no effect on firebase or the view-model.
_selected will add the class selected when the list item is selected. _not-selected will add the class not-selected when the list item is not selected.
See ff-select attribute


Add Point Markers
If your monitoring site is a relatively small location we recommend mapping using point markers.


Running on an ESP32 microcontroller
System Info: Chip Revision: ? Sdk Version: ? Cpu Frequency (MHz): ? Flash Chip Speed (MHz): ? Flash Chip Size (kB): ? Heap Size (kB): ? Free Heap (kB): ? Sketch Size (kB): ? Free Sketch Space (kB): ? Sketch MD5 Checksum: ???????????????????????????????? ChipID (MAC Address): ? Software Version: ?
DTV-Online brings all the fun in a wide set of innovative products. Established in the 1970s we have designed and manufactured famous devices such as
and many more.
Grabenweg 4 3683 Yspertal Phone: +43 699 1968 1006 Email<EMAIL_ADDRESS>Web: dtv-online.net


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U.S. Department of Veterans Affairs | eScreening Health Assessment (Ver 1.0)
Welcome Last Name, First Name | Logout
* This wireframe provide the workflow, the structure, and the interaction between some specific pages and sections in the eScreening project and it's covered all the interaction between the application features and behaviours.
* Some of the interaction between pages may require using a specific rules and steps in order to demonstrate.
* This wireframe still in progress.
IMPORTANT NOTE - Click to Expand
v
Welcome to VA San Diego Healthcare System!
We thank you for your service to our country and look forward to assisting in your enrollment for VA healthcare and transition from active duty to Veteran status.
The following eScreening Questionnaire will help your VA healthcare team to assess your needs and provide the best health and wellness options available to you. Please take your time and answer all questions as completely as possible. If you have any questions, please ask for assistance.
Start Assessment


Europe police agency hits Islamic State servers in blow to jihadist publicity
by ReutersTHE HAGUE — Belgian prosecutors have knocked out several internet servers used by Islamic State, shutting a large number of accounts and websites run by its news arm, in an operation led by Europe’s police agency, the Belga press agency reported on Monday.
Europol, the European policy agency, said it would release details of the initiative at a news conference later on Monday.
“We were able to shut down a large number of accounts and a series of websites,” Belga quoted prosecution spokesman Eric Van Der Sypt as saying.
Europol said in a statement it has been working with nine of the largest Internet platforms to counter Islamic State propaganda operations, including with Google, Twitter, Instagram and Telegram.
Europol said on its website it had examined “propaganda videos, publications and social media accounts supporting terrorism and violent extremism” over the course of two days last week.
“Telegram was the online service provider” where most offending material was found, Europol said.
“As a result, a significant portion of key actors within the IS network on Telegram was pushed away from the platform.”
It praised Telegram’s assistance and said the company was helping Europol to “root out … malicious content.” (Reporting by Toby Sterling, Editing by William Maclean)


Kanaf:
Shh! Please be still. Over there is the old town, which is the only entrance to the Steel Citadel. It is used as a prison camp for Native captives.
Some time ago, when the Steel Citadel's defenses were temporarily weakened, we attacked the town in an attempt to rescue the captives. However, the enemy was still too strong, and many of us were killed or wounded.
And our comrades in the town still thirst for their freedom!
Infiltrate the town.
Ask him for new information about the town.
Quest.


2019-01-01
My blog - you cannot hack it!
Ho, ho, ho, this is my own blog. I somehow managed to set it up. Will be posting lots of great new stuff in this upcoming year, I promise.
 That's why it says "The unhackable blog" in the title, right?


Two woodcutters—a father and son—were trapped in the forest when a sudden blizzard arose. They took shelter in an abandoned cabin, huddling in their clothes to sleep next to a meager fire. In the middle of the night, the son awoke when the door banged open and an ethereally beautiful woman came in from the blizzard. The woman crept over the father and blew her breath on him, then sucked up his living essence. As she turned to do the same to the son, she paused. Captivated by his youth and beauty, the Yuki Onna said she would let him live, but only on the condition that he never speak of this night. The following winter, the young man was standing in his doorway when a beautiful woman traveler came walking by. The man offered her refuge from the elements, and the woman accepted. They quickly fell in love, and the woman never made it to her destination. She stayed on, married the young man, and they lived happily for years. They even had several children. One night, when the kids where happily playing, the man looked up at his wife and a memory surfaced that he hadn’t thought about in years. When his wife asked him what was the matter, he told her of his encounter with the snow spirit years ago, wondering if it had all been a dream. The smile fell from his wife’s face, as she revealed herself to be that very same Yuki Onna. She was livid that her husband had broken her promise, and would have killed him there were it not for the children. As it was she left instantly, leaving the husband behind with regret and sorrow.


DEMO Site Title
About
Feed
A short description..
My FIRST Issue
Friday, May 11, 2018
Why are we here?
Discuss


Imperial Mountains [as71], mountain, in Imperia, wilderness
Last updated Turn #109
Imperial Mountains [as71], mountain, in Imperia, wilderness
------------------------------------------------------------------------
20: Sir Charles [j5r], Imperial Empire, with 48 nazgul, arrived from the
20: east, accompanied by:
20: Phantom III [d6x], Imperial Empire
21: Sir Charles [j5r], Imperial Empire, with 48 nazgul, went west,
21: accompanied by:
21: Phantom III [d6x], Imperial Empire
30: It has stopped raining.
Routes leaving Imperial Mountains:
North, mountain, to Imperial Mountains [ar71], 10 days
East, plain, to Buffalo Plains [as72], 7 days
South, plain, to Buffalo Plains [at71], 7 days
West, mountain, to Imperial Mountains [as70], 10 days
Inner locations:
Graveyard [ac9f], graveyard, 1 day
Seen here:
Stack of nazgul [hb6n], nazgul, number: 4


My Team
Nathan
Manager
ID: 12
Email:
<EMAIL_ADDRESS>Office Number: 12
bob
Intern
ID: 13
Email:
<EMAIL_ADDRESS>School: carleton
Steven
Engineer
ID: 19
Email:
<EMAIL_ADDRESS>Github User Name:
StevySteve
Kevin
Engineer
ID: 13
Email:
17
Github User Name:
kevin
Ted
Engineer
ID: 13
Email:
<EMAIL_ADDRESS>Github User Name:
TeddyBear


v3.5.2 (FCGI); September 20, 2016
Minor improvements.
Highlights of this release include the following:
1. Updates to the HttpClient and HttpsClient classes
2. Added 'GetCert' method to the TCPSecureSocket and HttpsClient classes
Please refer to the project website for additional information about the language. The Programmer’s Guide is located the “doc” directory. Additional examples are available on the Rosetta Code website. Please submit bugs to<EMAIL_ADDRESS>bug reports are greatly appreciated!!
In order to compile programs outside of the “bin” directory the “OBJECK_LIB_PATH” environment variable must be set. When this variable is set all library files must be in the directory specified.
To manually setting the environment paths in Windows:
1. set OBJECK_LIB_PATH=C:\Documents and Settings\Administrator\objeck-lang\bin
Comping a simple web program:
· Ensure you’ve download the compiler.
· obc -src ..\examples\hello_fcgi.obs -lib fcgi.obl,collect.obl -tar web -dest ..\hello_fcgi.obw
Steps:
· Install the following IIS modules:
a. CGI
b. ISAPI Extensions
c. ISAPI Filters
d. IIS Management Console
· Copy the Objeck “deploy_fcgi” directory to IIS “wwwroot”
· Copy *.obw to wwwroot\deploy_fcgi
· Copy or create wwwroot\index.objk
· Navigate to the “default website” and create a module mapping
a. Adjust the “Request Restrictions”
b. Mapping: Invoke handler file, check “file”
c. Verbs: All verbs
d. Access: None
· Next, go to top home node and select FastCGI Settings
· Select “obr_fcgi.exe” and add environment variables (see below)
· Set FCGI_CONFIG_PATH property to the location of the web app *.obw
· Restart IIS
Steps:
· Install Apache and FastCGI:
· sudo apt-get install libapache2-mod-fastcgi apache2-MPM-worker libfcgi-dev uuid-dev
· Add the entry below to the: “/etc/apache2/mods-enabled/fastcgi.conf” file:
|
<IfModule mod_fastcgi.c> AddHandler objeck-lang .objk FastCgiServer /var/www/html/deploy_fcgi/bin/obr_fcgi -initial-env FCGI_CONFIG_PATH=/var/www/html/deploy_fcgi/hello.obw -idle-timeout 60 -processes 1 ScriptAlias /objk "/var/www/html/deploy_fcgi/bin/obr_fcgi" </IfModule> |
Few tips to make FastCGI easier to work with:
· Use the “Sytem.IO.File.Logger” class for debugging
· If you’d like your web app to connect to a database using ODBC setup a 32-bit ODBC data source as admin.
For OS X (10.9 or greater), you’ll need to install OpenSSL in order to use encryption APIs as well as ODBC to enable database support. Please refer to the following link for OpenSSL directions and this link for about ODBC support.
The Objeck source repository is located here and the source can be fetch using the following command GIT command:
· mkdir objeck
· cd objeck
· git init
· git clone https://github.com/objeck/objeck-lang.git
· On Windows, the ODBC driver must be a 32-bit driver for compatibility.
· Some Redhat based Linux distributions may report errors about heap buffer execution. This is triggered by the JIT compiler’s execution of generated machine code. To resolve this issue please consult the Fedora error console.
· The XML parser class does not support DTDs or DOCTYPE tags
· By design, the regular expression class performs greedy pattern matching.
· All unit tests executed successfully but I'm sure there are some bugs. Send an e-mail we’ll get them sorted.


I am really enjoying the writing style of S.I.Y.and it's sense of playfulness. Self-help books tend to be very earnest, a bit too much for me. I like that Mr Meng regards this process as a scientific, measurable and demonstrative. Maybe I should have
been an engineer too.
I think the analogy of gym training, with a desired result of producing better functioning equipment (brain and emotional rather than muscular) that anyone can benefit from, and taylor to themselves is
very powerful. It is obvious immediately that this sort of mental fitness and empathetic mindset would be invaluble in the field we are all looking to be a part of, not to mention life in general.
I have been trying out the ‘easy way’ two minute session with mixed results. I enjoy it and find it usuful to have a litle micro break but instead of Mr M’s commonly reported issue of vearing off track with thoughts of worthlessness, ‘I’m crap at this’, ‘this won’t work for me” etc. I find myself cogratulating myself; ‘hey I’m meditating, this is great’, ‘ not thinking about anything ‘, ‘mind as clear as an azure sky of deepest summer’ ‘I’m think ..... oh bollocks ! ‘ . Start again .. breathe in.


The following are a series of suggested principles intended to help guide state agencies. Your feedback will help us improve them!
Submit Feedback1. Appreciate that the data we collect require a considerable investment of resources and have value beyond the purpose for which they are collected.
2. Perform continuous data quality improvement to ensure the value of data is protected and maximized
3. Ensure standardized data governance in order to protect data and improve its quality and utility.
4. Practice effective data stewardship and governance by maintaining modern data security practices, protecting individual privacy, and maintaining promised confidentiality.
5. Employ ethical standards in the use of data to avoid excessive intrusion into the lives of Connecticut residents
6. Promote a culture of continuous and collaborative learning with data and about data.
7. Create, acquire, use, and disseminate data deliberately and thoughtfully, considering quality, consistency, privacy, value, reuse, and interoperability from the start.
8. Coordinate and prioritize data needs and uses, utilize data from multiple sources, and acquire new data only when necessary.
9. Manage a data asset one time and use it for multiple purposes, to the extent possible within legal and regulatory constraints
10. Improve data sharing and access with ongoing input from users and other stakeholders.
11. Standardize common data elements, such as addresses, to ensure data are easily shared and analyzed.
12. Create clear and predictable pathways for data sharing which are necessary for effective data use and sharing.


<tomcat-users> <user name="tomcat" password="tomcat" roles="tomcat" /> <user name="role1" password="tomcat" roles="role1" /> <user name="both" password="tomcat" roles="tomcat,role1" /> <user name="john" password="jjj" roles="employee" /> <user name="mary" password="mmm" roles="employee" /> <user name="bob" password="bbb" roles="employee, supervisor" /> </tomcat-users>
The following is the web.xml snippet showing the security constraint that allows only a supervisor to send a POST request:
<security-constraint> <web-resource-collection> <web-resource-name>declarativetest</web-resource-name> <url-pattern>/servlet/DeclarativeSecureServlet</url-pattern> <http-method>POST</http-method> </web-resource-collection> <auth-constraint> <role-name>supervisor</role-name> </auth-constraint> <user-data-constraint> <transport-guarantee>NONE</transport-guarantee> </user-data-constraint> </security-constraint>To make a POST request, submit the form present on this page.


You are entering the online portfolio of amateur artist and hobbyist - John LaFosse aka "Johngo" (Johngo do this John go do that.)
Discover MoreJohn is a digital enthusiast for audio and 3d environments. Knitting and garden design are two other hobbies that he will occassionally experiment with. This static portfolio is a window into his artistic world.
Start ExploringExplore current and past audio projects.
Take a look at the hats Johngo is making with his Studio 360 knitting machine!
Subscribe to Johngo's monthly newletter
No less significant than other amateur projects, explore this section for other interests and information.
Interested in participating in any of the projects you've discovered? That's great! Give me a call or send an email and I will get back to you as soon as possible!
123-456-6789


|
F´ Flight Software - C/C++ Documentation
NASA-v1.5.0
A framework for building embedded system applications to NASA flight quality standards.
|
#include <Os/WatchdogTimer.hpp>
Public Types | |
| enum | WatchdogStatus { WATCHDOG_OK, WATCHDOG_INIT_ERROR, WATCHDOG_START_ERROR, WATCHDOG_CANCEL_ERROR } |
| typedef void(* | WatchdogCb) (void *) |
Public Member Functions | |
| WatchdogTimer () | |
| virtual | ~WatchdogTimer () |
| WatchdogStatus | startTicks (I32 delayInTicks, WatchdogCb p_callback, void *parameter) |
| parameter with which to call routine); More... | |
| WatchdogStatus | startMs (I32 delayInMs, WatchdogCb p_callback, void *parameter) |
| parameter with which to call routine); More... | |
| WatchdogStatus | restart (void) |
| restart timer with previous value More... | |
| WatchdogStatus | cancel (void) |
| cancel timer More... | |
Definition at line 7 of file WatchdogTimer.hpp.
| typedef void(* Os::WatchdogTimer::WatchdogCb) (void *) |
Definition at line 17 of file WatchdogTimer.hpp.
| Enumerator | |
|---|---|
| WATCHDOG_OK | Call was successful. |
| WATCHDOG_INIT_ERROR | Timer initialization failed. |
| WATCHDOG_START_ERROR | Timer startup failed. |
| WATCHDOG_CANCEL_ERROR | Timer cancellation failed. |
Definition at line 10 of file WatchdogTimer.hpp.
| Os::WatchdogTimer::WatchdogTimer | ( | ) |
Definition at line 5 of file WatchdogTimer.cpp.
|
virtual |
Definition at line 10 of file WatchdogTimer.cpp.
| WatchdogTimer::WatchdogStatus Os::WatchdogTimer::cancel | ( | void | ) |
cancel timer
Definition at line 25 of file WatchdogTimer.cpp.
| WatchdogTimer::WatchdogStatus Os::WatchdogTimer::restart | ( | void | ) |
restart timer with previous value
Definition at line 21 of file WatchdogTimer.cpp.
| WatchdogTimer::WatchdogStatus Os::WatchdogTimer::startMs | ( | I32 | delayInMs, |
| WatchdogCb | p_callback, | ||
| void * | parameter | ||
| ) |
parameter with which to call routine);
| delayInMs | number of ms to delay. |
| p_callback | routine to call on time-out |
Definition at line 17 of file WatchdogTimer.cpp.
| WatchdogTimer::WatchdogStatus Os::WatchdogTimer::startTicks | ( | I32 | delayInTicks, |
| WatchdogCb | p_callback, | ||
| void * | parameter | ||
| ) |
parameter with which to call routine);
| delayInTicks | number of ticks to delay. OS/timing dependent |
| p_callback | routine to call on time-out |
Definition at line 13 of file WatchdogTimer.cpp.


PERLLINUX
Section: Perl Programmers Reference Guide (1)Updated: 2009-02-12
Index Return to Main Contents
NAME
README.linux - Perl version 5 on Linux systemsDESCRIPTION
This document describes various features of Linux that will affect how Perl version 5 (hereafter just Perl) is compiled and/or runs.Experimental Support for Sun Studio Compilers for Linux OS
Sun Microsystems has released a port of their Sun Studio compiliers for Linux. As of November 2005, only an alpha version has been released. Until a release of these compilers is made, support for compiling Perl with these compiler experimental.Also, some special instructions for building Perl with Sun Studio on Linux. Following the normal "Configure", you have to run make as follows:
LDLOADLIBS=-lc make
"LDLOADLIBS" is an environment variable used by the linker to link modules "/ext" modules to glibc. Currently, that environment variable is not getting populated by a combination of "Config" entries and "ExtUtil::MakeMaker". While there may be a bug somewhere in Perl's configuration or "ExtUtil::MakeMaker" causing the problem, the most likely cause is an incomplete understanding of Sun Studio by this author. Further investigation is needed to get this working better.
AUTHOR
Steve Peters<EMAIL_ADDRESS>report any errors, updates, or suggestions to<EMAIL_ADDRESS>Index
This document was created by man2html, using the manual pages.
Time: 05:29:08 GMT, December 24, 2015


@(title: String, room: String, rooms: Iterable[String])(content: Html)(implicit request: MaybeAuthenticatedRequest)
@request.userOpt.map { u => Logged in as @u.firstname @u.lastname.take(1) }.getOrElse{ Login }