 One such was Monroe, the robot ― except, to help him recognize his name, he was referred to as Moe. He is sufficiently mentally challenged that he needs external assistance to handle symbolic logic.
Polish notation is the prefix symbolic logic notation developed by Jan Lukasiewicz (1929). [Hence postfix expressions are referred to as being in Reverse Polish Notation ― RPN.] The notation developed by Lukasiewicz (referred to as PN below) uses upper-case letters for the logic operators and lower-case letters for logic variables (which can only be true or false). Since prefix notation is self-grouping, there is no need for precedence, associativity, or parentheses, unlike infix notation. In the following table the PN operator is shown, followed by its operation. Operators not having exactly equivalent C/C++/Java operators are shown in the truth table (using 1 for true and 0 for false). [The operator J is not found in Lukasiewicz’ original work but is included from A.N.Prior’s treatment.]
For every combination of PN operators and variables, an expression is a "well-formed formula" (WFF) if and only if it is a variable or it is a PN operator followed by the requisite number of operands (WFF instances). A combination of symbols will fail to be a "well-formed formula" if it is composed of a WFF followed by extraneous text, it uses an unrecognized character [uppercase character not in the above table or a non-alphabetic character], or it has insufficient operands for its operators. For invalid expressions, report the first error discovered in a left-toright scan of the expression. For instance, immediately report an error on an invalid character. If a valid WFF is followed by extraneous text, report that as the error, even if the extraneous text has an invalid character.
Polish notation is the prefix symbolic logic notation developed by Jan Lukasiewicz (1929). [Hence postfix expressions are referred to as being in Reverse Polish Notation ― RPN.] The notation developed by Lukasiewicz (referred to as PN below) uses upper-case letters for the logic operators and lower-case letters for logic variables (which can only be true or false). Since prefix notation is self-grouping, there is no need for precedence, associativity, or parentheses, unlike infix notation. In the following table the PN operator is shown, followed by its operation. Operators not having exactly equivalent C/C++/Java operators are shown in the truth table (using 1 for true and 0 for false). [The operator J is not found in Lukasiewicz’ original work but is included from A.N.Prior’s treatment.]
For every combination of PN operators and variables, an expression is a "well-formed formula" (WFF) if and only if it is a variable or it is a PN operator followed by the requisite number of operands (WFF instances). A combination of symbols will fail to be a "well-formed formula" if it is composed of a WFF followed by extraneous text, it uses an unrecognized character [uppercase character not in the above table or a non-alphabetic character], or it has insufficient operands for its operators. For invalid expressions, report the first error discovered in a left-toright scan of the expression. For instance, immediately report an error on an invalid character. If a valid WFF is followed by extraneous text, report that as the error, even if the extraneous text has an invalid character.
In addition, every WFF can be categorized as a tautology (true for all possible variable values), a contradiction (false for all possible variable values), or a contingent expression (true for some variable values, false for other variable values).
The simplest contingent expression is simply ‘p‘, true when p is true, false when p is false. One very simple contradiction is "KpNp", both p and not-p are true. Similarly, one very simple tautology is "ApNp", either p is true or not-p is true. For a more complex tautology, one expression of De Morgan’s Law is "EDpqANpNq".
输入:
Your program is to accept lines until it receives an empty character string. Each line will contain only alphanumeric characters (no spaces or punctuation) that are to be parsed as potential "WFFs". Each line will contain fewer than 256 characters and will use at most 10 variables. There will be at most 32 non-blank lines before the terminating blank line.
输出:
Your program is to accept lines until it receives an empty character string. Each line will contain only alphanumeric characters (no spaces or punctuation) that are to be parsed as potential "WFFs". Each line will contain fewer than 256 characters and will use at most 10 variables. There will be at most 32 non-blank lines before the terminating blank line.
样例输入:
q Cp Cpq A01 Cpqr ANpp KNpp Qad CKNppq JDpqANpNq CDpwANpNq EDpqANpNq KCDpqANpNqCANpNqDpq
样例输出:
q is valid: contingent Cp is invalid: insufficient operands Cpq is valid: contingent A01 is invalid: invalid character Cpqr is invalid: extraneous text ANpp is valid: tautology KNpp is valid: contradiction Qad is invalid: invalid character CKNppq is valid: tautology JDpqANpNq is valid: contradiction CDpwANpNq is valid: contingent EDpqANpNq is valid: tautology KCDpqANpNqCANpNqDpq is valid: tautology
/** * ID: ping128 * LANG: C++ */ #include <stdio.h> #include <iostream> #include <sstream> #include <stdlib.h> #include <string> #include <vector> #include <set> #include <queue> #include <stack> #include <list> #include <math.h> #include <algorithm> #include <map> #include <string.h> using namespace std; typedef long long LL; typedef pair<int, int>PII; typedef pair<PII, int>PII2; string s; int n; int len; map<char, int>val; int value[20]; int at; int good; // 1 good // -1 invalid character // -2 insufficient operands // -3 extraneous text int eva() { // cout << at << " " << s[at] << endl; if(good != 1) return 0; if(at >= len) { good = -2; } else if(s[at] >= 'a' && s[at] <= 'z') { return value[val[s[at++]]]; } else if(s[at] >= 'A' && s[at] <= 'Z') { at++; int x, y; switch(s[at - 1]) { case 'C': x = eva(); y = eva(); if(x && !y) return 0; else return 1; case 'N': x = eva(); return x ^ 1; case 'K': x = eva(); y = eva(); return x & y; case 'A': x = eva(); y = eva(); return x | y; case 'D': x = eva(); y = eva(); return !(x & y); case 'E': x = eva(); y = eva(); return (x == y); case 'J': x = eva(); y = eva(); return x ^ y; default: good = -1; break; } } else { good = -1; } } int main() { // freopen("g.in", "r", stdin); while(cin >> s) { good = 1; len = s.length(); n = 0; val.clear(); for(int i = 0; i < len; i++ ) { if(s[i] >= 'a' && s[i] <= 'z' && val.find(s[i]) == val.end()) { val[s[i]] = n++; } } int ct = 0, cn = 0; for(int i = 0; i < 1<<n && good == 1; i++ ) { for(int j = 0; j < n; j++ ) { if(i & (1<<j)) value[j] = 1; else value[j] = 0; } at = 0; int res = eva(); if(good == 1 && at < len) good = -3; if(res) ct++; else cn++; } cout << s << " is "; if(good == 1) { cout << "valid: "; if(ct && cn) cout << "contingent" << endl; else if(ct) cout << "tautology" << endl; else cout << "contradiction" << endl; } else if(good == -1) { cout << "invalid: invalid character" << endl; } else if(good == -2) { cout << "invalid: insufficient operands" << endl; } else { cout << "invalid: extraneous text" << endl; } } //while(1); return 0; }
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贱人。自己死了还要诅咒别人[emote]wub[/emote]
分析的很专业！
这个问题别太纠结了，不同的编译器结果不一
看来学好代数很重要啊
,,,,,,,,,,,,,,,
多谢支持！
27行 minStack.push(minValue); 去掉就对了
Thanks for taking the time to examine this, I really feel strongly about it and love studying a lot more on this topic. If possible, as you acquire experience
大哥，你复制也看看是不是这题的答案才复制吧。
mark, 备用
这个问题别太纠结了，不同的编译器结果不一样
请不要灌水，多谢！
有没有C++编译的？
还真是，i7的cpu跑了3秒
m /= i; 是一直在变的。参考这一篇关于质因数的http://www.acmerblog.com/integer-factorization-5124.html


Thank you so much for visiting my web site! This project shows off my relatively new-found skills - both in cooking and in coding! Hopefully, though, it reflects my growing love of both.
That's my wife and me in the picture, and we live in Louisville, Kentucky, where I currently work at a local high school.
However, this web site was born out of new experiences in both coding and cooking.
My passion for cooking started out simply with one of those services that sends you meal ingredients and recipes.
Up to that point, recipes had seemed so daunting, but these meals were actually kind of fun to make.
And, I found myself loving the result!
Even after cancelling this meal service (once the promos were up!) I still enjoyed trying my hand at new and exciting recipes, and this web site reflects my own desire to expand my horizons in the culinary world!
Similarly, coding always seemed so foreign and impossible to me - like some kind of magic.
I knew people made web sites and apps that did amazing things, but it just seemed like another language.
Well, I found out it was!
At a pivotal point in my life, I decided to end my career in Sterile Processing (sterilizing surgical equipment at a hospital) in order to pursue... something else.
As I was looking at what I could do, a friend told me about Code Louisville.
Why not?
But as I started learning more about this new language (or new languages), I realized I was fascinated with what I was learning.
Now, a semester into Code Louisville, I am thrilled to have designed a web site like this, and I am excited to see what's next both in cooking and coding!


|
||||||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | |||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |
java.lang.Object coins.opt.Opt
Optimization driver class. The following command line switches are supported:
Each one is done only once, and if multiple items are specified, they will be performed in the order listed above.
| Field Summary | |
AliasAnal |
fAlias
|
int |
fDbgLevel
|
java.lang.String |
fHirOpt
|
protected java.util.List |
fInlineSubps
|
java.util.List |
fKeyList
|
Flow |
flow
|
FlowRoot |
flowRoot
|
java.util.Map |
fOptionMap
|
CoinsOptions |
fOptions
|
protected java.util.List |
fReformPatternList
|
protected SubpFlow |
fSubpFlow
|
HirRoot |
hirRoot
|
IoRoot |
ioRoot
|
SymRoot |
symRoot
|
| Constructor Summary | |
Opt(FlowRoot pFlowRoot)
Opt. |
| Method Summary | |
(package private) void |
dbg(int level,
java.lang.String pHeader,
java.lang.Object pObject)
dbg. |
boolean |
doHir()
|
boolean |
doHir(java.util.List pOpts)
HIR optimizer. |
void |
releaseFlowInf(SubpDefinition pSubpDef)
Release flow information so that storage space can be reused and avoid OutofMemoryException. |
(package private) boolean |
shouldTrace(int level)
shouldTrace. |
| Methods inherited from class java.lang.Object |
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
| Field Detail |
public final FlowRoot flowRoot
public final IoRoot ioRoot
public final SymRoot symRoot
public final HirRoot hirRoot
public final Flow flow
protected SubpFlow fSubpFlow
public final AliasAnal fAlias
public final java.util.Map fOptionMap
public final CoinsOptions fOptions
public final java.lang.String fHirOpt
public final java.util.List fKeyList
public final int fDbgLevel
protected java.util.List fInlineSubps
protected java.util.List fReformPatternList
| Constructor Detail |
public Opt(FlowRoot pFlowRoot)
| Method Detail |
void dbg(int level, java.lang.String pHeader, java.lang.Object pObject)
boolean shouldTrace(int level)
public boolean doHir(java.util.List pOpts)
public boolean doHir()
public void releaseFlowInf(SubpDefinition pSubpDef)
pSubpDef
- SubpDefinition for which flow information
is to be released.
|
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| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | |||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |


Module driver
Library for handling drivers, which are a secure way of accessing components
Functions
| changeDriver (type, addr, string) | Force the component with given address and type to use the driver found at 'path' |
| getDriver (type) | Gets an arbitrarily chosen driver of the given type |
Functions
- changeDriver (type, addr, string)
-
Force the component with given address and type to use the driver found at 'path'
Parameters:
- getDriver (type)
-
Gets an arbitrarily chosen driver of the given type
Parameters:
- type string The type of the drievr to get
Returns:
-
optional driver
The loaded driver or nil if none found


SQL Object Queries
Queries are denoted on sql object interfaces via the @SqlQuery
annotation on the query methods. The return type of the method indicates what to do with the result set. Take the following queries:
public interface SomeQueries
{
@SqlQuery("select name from something where id = :id")
String findName(@Bind("id") int id);
@SqlQuery("select name from something where id > :from and id < :to")
List<String> findNamesBetween(@Bind("from") int from, @Bind("to") int to);
@SqlQuery("select name from something")
Iterator<String> findAllNames();
}
The first method, findName
infers that you want only the first result, and it will be mapped into a String
. It will take the first element in the first row of the result set to return.
The second method, findNamesBetween
will again infer that we are looking for a String, and pull the first string from each row of the result set. Because it returns a java.util.List
it will eagerly map each row to a String and return the full result set.
The third method, findAllNames
does the same String extraction from each row, but because the method returns a java.util.Iterator
it loads results lazily, only traversing the result set as Iterator#next
or Iterator#hasNext
is called. The iterator returned is actually a ResultIterator. The underlying result set will be closed when the ResultIterator#close
method is invoked, or when the end of the result set is reached.
As with String, mappings for singular primitive types are provided out of the box by JDBI. You can plug in support for additional singular (read: single-column) data types by registering ResultColumnMapper or ResultColumnMapperFactory instances with either the DBI, the Handle, or on the sql object or individual method.
public class ValueType
{
public static ValueType valueOf(String value)
{
return value == null ? null : new ValueType(value);
}
private final String value;
private ValueType(String value)
{
this.value = value;
}
public String getValue()
{
return value;
}
}
public class ValueTypeMapper implements ResultColumnMapper<ValueType>
{
public ValueType mapColumn(ResultSet r, int columnNumber, StatementContext ctx) throws SQLException
{
return ValueType.valueOf(r.getString(columnNumber));
}
public ValueType mapColumn(ResultSet r, String columnLabel, StatementContext ctx) throws SQLException
{
return ValueType.valueOf(r.getString(columnLabel));
}
}
You can plug in support for the new mapper by registering it on the object mapper:
@RegisterColumnMapper(ValueTypeMapper.class)
public interface ValueTypeQuery
{
@SqlQuery("select value from thing where id = :id")
ValueType findById(@Bind("id") int id);
}
Alternately, we can specify a column mapper to use for a specific method:
public interface ValueTypeQuery
{
@SqlQuery("select value from thing where id = :id")
@RegisterColumnMapper(ValueTypeMapper.class)
ValueType findById(@Bind("id") int id);
}
We can also register column mappers on the DBI
or Handle
instance the sql object is attached to. The choice of where to register a mapper depends on how widely you want it to be scoped. For example, registering a mapper on the DBI
instance effectively makes it global, whereas registering it on a SQL object method limits the scope of the mapper to just that method.
For more sophisticated mappings that span multiple columns, you can register ResultSetMapper or ResultSetMapperFactory instances with either the DBI, the Handle, or on the sql object or individual method. Take for example the following result set mapper and class it maps to:
public class Something
{
private int id;
private String name;
private ValueType value;
public Something() { }
public Something(int id, String name, ValueType value)
{
this.id = id;
this.name = name;
this.value = value;
}
public int getId()
{
return id;
}
public void setId(int id)
{
this.id = id;
}
public String getName()
{
return name;
}
public void setName(String name)
{
this.name = name;
}
public ValueType getValue()
{
return value;
}
public void setValue(ValueType value)
{
this.value = value;
}
}
public class SomethingMapper implements ResultSetMapper<Something>
{
public Something map(int index, ResultSet r, StatementContext ctx) throws SQLException
{
return new Something(r.getInt("id"), r.getString("name"), ValueType.valueOf(r.getString("value"));
}
}
We can now make use of the SomethingMapper
in a couple ways. The first is to register it on the sql object itself:
@RegisterMapper(SomethingMapper.class)
public interface AnotherQuery
{
@SqlQuery("select id, name, value from something where id = :id")
Something findById(@Bind("id") int id);
}
In this case whenever a query needs to map to Something
it will find the registered mapper and try to use that.
Alternately, we can specify a result mapper to use for a specific method:
public interface YetAnotherQuery
{
@SqlQuery("select id, name, value from something where id = :id")
@RegisterMapper(SomethingMapper.class)
Something findById(@Bind("id") int id);
}
You can also register result set mappers on the DBI
or Handle
instance the sql object is attached to.
jDBI provides a bean mapper out of the box, which maps column names to bean property names. The bean mapper delegates mapping of individual columns to the column mapper registry, so make sure to register an appropriate column mapper for any custom/non-standard data types.
public interface OneMoreQuery
{
@SqlQuery("select id, name, value from something where id = :id")
@RegisterColumnMapper(ValueTypeMapper.class)
@MapResultAsBean
Something findById(@Bind("id") int id);
}
Note that the bean mapper skip properties that were not selected in the SQL query, and thus are an effective tool to retrieve a limited view of your data.


--- layout: blog title: index permalink: / ---
I am a PhD student in Information Technology at Politecnico di Milano, Dipartimento di Elettronica, Informazione e Bioingegneria (DEIB), under the supervision of prof. Paolo Cremonesi. My research interests include sequential recommender systems, deep-learning and large-scale machine learning.
I'm currently a Music Science intern at Pandora Media Inc. (Oakland, CA, USA). I'm working with Tao Ye, Erik Schmidt and Puya-Hossein Vahabi on Deep Learning algorithms for sequence classification.
Jun 2016I was a Research Intern at Telefonica I+D (Barcelona, Spain). I worked with Alexandros Karatzoglou and Balázs Hidasi (Gravity R&D) on Recurrent Neural Networks for Session Based Recommendation.
Sept 2015 - May 2016I started my PhD in Information Technology at the Polictecnico di Milano, Milan, Italy. I'll be working on Sequential Recommender Systems.
Sep 2013 - OngoingI received the M.Sc. with laude in Computer science at Politecnico di Milano (Double degree with the Univirsitat Politecnica de Catalunya, Barcelona, Spain). Thesis: E-Tourism Recommender Systems - Advisor: Prof. Paolo Cremonesi
Sep 2013I received the M.Sc. in Computer science at Univirsitat Politecnica de Catalunya, Barcelona, Spain. Thesis: Methods for frequent pattern mining in data streams within the MOA system - Advisor: Prof. Ricard Gavaldá
Sep 2013I received the B.Sc. in Computer science at Politecnico di Milano. Thesis: Integration and evaluation of featuretracking algorithms within a visual SLAM system - Advisor: Prof. Matteo Matteucci
Sep 2010That is an excerpt of my recent pubblications. The full list is available on here.
In ConferencesMassimo Quadrana, Alexandros Karatzoglou, Balázs Hidasi and Paolo Cremonesi. 2017. Personalizing Session-based Recommendation with Hierarchical Recurrent Neural Networks. ACM Recsys 2017 [To appear] PDF CODE
Leonardo Cella, Stefano Cereda, Massimo Quadrana and Paolo Cremonesi. 2017. Deriving Item Features Relevance from Past User Interactions. ACM UMAP 2017 [To appear] PDF
Balázs Hidasi, Massimo Quadrana, Alexandros Karatzoglou and Domonkos Tikk. 2016. Parallel Recurrent Neural Network Architectures for Feature-rich Session-based Recommendations. ACM Recsys 2016 PDF
Roberto Turrin, Massimo Quadrana, Roberto Pagano, Paolo Cremonesi and Andrea Condorelli. “30Music listening and playlists dataset” ACM Recsys 2015 PDF
Yashar Deldjoo, Mehdi Elahi, Massimo Quadrana, Paolo Cremonesi, and Franca Garzotto. 2015. Toward Effective Movie Recommendations Based on Mise-en-Scène Film Styles. In Proceedings of the 11th Biannual Conference on Italian SIGCHI Chapter (CHItaly 2015). ACM, New York, NY, USA, 162-165 PDF
Paolo Cremonesi and Massimo Quadrana. 2014. Cross-domain Recommendations without Overlapping Data: Myth or Reality?. In Proceedings of the 8th ACM Conference on Recommender systems (RecSys '14). ACM, New York, NY, USA, 297-300. PDF
Paolo Cremonesi, Franca Garzotto, Roberto Pagano, and Massimo Quadrana. 2014. Recommending without short head. In Proceedings of the companion publication of the 23rd international conference on World wide web companion (WWW Companion '14). International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, Switzerland, 245-246. PDF
Paolo Cremonesi, Franca Garzotto, and Massimo Quadrana. 2013. Evaluating top-n recommendations "when the best are gone". In Proceedings of the 7th ACM conference on Recommender systems (RecSys '13). ACM, New York, NY, USA, 339-342. PDF
In JournalsMassimo Quadrana, Paolo Cremonesi and Dietmar Jannach. Sequence-aware Recommender Systems ACM Computing Surveys (preprint). PDF
Massimo Quadrana, Albert Bifet, and Ricard Gavaldà. An efficient closed frequent itemset miner for the MOA stream mining system. AI Communications28.1 (2015): 143-158. PDF
In WorkshopsAndrea Condorelli, Paolo Cremonesi, Roberto Pagano, Massimo Quadrana and Roberto Turrin. “Large Scale Music Recommendation”. 3rd Workshop on Large-Scale Recommender Systems, ACM Recsys 2015 PDF


Vectors.normalize(v); Vectors.normalize(v,eps=100*Modelica.Constants.eps);
The function call
"Vectors.normalize(v)
" returns the
unit vector "v/length(v)
" of vector
v. If length(v) is close to zero (more precisely, if length(v) <
eps), v is returned in order to avoid a division by zero. For many
applications this is useful, because often the unit vector
e = v/length(v)
is used to compute a vector x*e, where the scalar
x is in the order of length(v), i.e.,
x*e is small, when length(v) is
small and then it is fine to replace e by
v to avoid a division by zero.
Since the function is implemented in one statement, it is usually inlined and therefore symbolic processing is possible.
normalize({1,2,3}); // = {0.267, 0.534, 0.802} normalize({0,0,0}); // = {0,0,0}
function normalize extends Modelica.Icons.Function; input Complex v[:] "Vector"; input Real eps = 100 * Modelica.Constants.eps "if |v| < eps then result = v"; output Complex result[size(v, 1)] "Input vector v normalized to length=1"; end normalize;


https://github.com/jaceklaskowski
https://bit.ly/mastering-apache-spark
Ranked #89 in Spark contributors
http://stackoverflow.com/users/1305344/jacek-laskowski
https://twitter.com/jaceklaskowski
Developing hands-on experience in
Spark SQL
to exploit massive datasets at rest
for advanced analytics and data-oriented decision making
Mastering Apache Spark notebook featured in the Big Data course at Coursera!


| yj_trans {scales} | R Documentation |
The Yeo-Johnson transformation is a flexible transformation that is similiar
to Box-Cox, boxcox_trans()
, but does not require input values to be greater
than zero.
yj_trans(p)
p |
Transformation exponent, λ. |
The transformation takes one of four forms depending on the values of y
and λ.
y ≥ 0 and λ != 0 : y^(λ) = ((y + 1)^λ - 1)/λ
y ≥ 0 and λ = 0: y^(λ) = ln(y + 1)
y < 0 and λ != 2: y^(λ) = -((-y + 1)^(2 - λ) - 1)/(2 - λ)
y < 0 and λ = 2: y^(λ) = -ln(-y + 1)
Yeo, I., & Johnson, R. (2000). A New Family of Power Transformations to Improve Normality or Symmetry. Biometrika, 87(4), 954-959. http://www.jstor.org/stable/2673623
plot(yj_trans(-1), xlim = c(-10, 10)) plot(yj_trans(0), xlim = c(-10, 10)) plot(yj_trans(1), xlim = c(-10, 10)) plot(yj_trans(2), xlim = c(-10, 10))


[
Home
]
The Oddjob User Guide
Version: @version@
Author: Rob Gordon
Contents
Getting Started
Requirements
Installing on Windows
Installing on Linux
Running Oddjob
First Clicks
Starting Oddjob
Loading The Examples
Job State
Resetting and Rerunning A Job
Oddjob Explorer
The Panels
The File Menu
The Edit Menu
The Job Menu
The Examples
Hello World
Execute a Batch File
Conditional Paths
A Scheduler Example
Sharing Jobs on A Network
Your First Oddjob File
Creating a Configuration File
Adding a Job
Running Your Job
Children of Oddjob
Job Hierarchies
A Sequential Example
Nesting Oddjob
Runtime Configuration
Introduction
Defining Properties
Job Identity
Job Properties
Variables
Name
Script Configuration
Oddjobs Within Oddjobs
Behind the GUI
The Basics
Jobs that Run Jobs
Property Elements
Runtime Property Elements
Advanced Property Access
Properties of Oddjob within Oddjobs
Scheduling In Oddjob
Introduction
The Timer Job
Retries
Holidays
Time Zone Scheduling And Daylight Saving Time
The Trigger Schedule Instruction
Scheduling with Dependencies
Oddjob Schedules
Introduction
Recurring Intervals
Constraining Schedules
Refining Schedules
Fixed Intervals
Schedule Lists
Holiday Schedules
State and Flow
Overview
If and Trigger
Wait and Join
State Conditions
Events
Distributing Work
Introduction
A Simple Example
Distributed Scheduling
Sharing Jobs on a Network
Introduction
Working With Remote Jobs
Server Drag And Drop
Connecting Multiple Servers
The Server Side
Saving Oddjob
Introduction
The Persister Property
Persistence and Identity
Removing Persisted State
Archiving
Command Line Oddjob
Command Line Help
Stopping Oddjob
In the Linux background
As a Windows Service
When Things Go Wrong
Oddments
Tasks
Bean Bus
Web
(c) R Gordon Ltd 2005 - Present


--- layout: post title: Warhorse date: '2014-09-16T07:30:36+02:00' categories: - Storyboards - Atoleiros Battle - Horses picture_url: https://film-Storyboards.tumblr.com/post/97644623433 --- 14th century Castilian knight


Here we are again, in the fifth post of Machine Learning tutorial series. Today I will talk about two common problems you may face in Machine Learning: Underfitting and Overfitting. Wait! There is something wrong, isn’t it? - You may wonder…
Of course I remember promising you in the previous post, that today I will dig deeper into Linear Regression, and together we will do some coding. Actually, I intended to name today’s post “Implementing Linear Regression” or something, but I soon realized that it would be inappropriate. Good news is today’s post will mainly focus on implementation of Linear Regression, and what we can do to improve the quality of the Model. By doing that, I am actually leading you to go into some concept which is more general, and can be applied not only in Linear Regression, but every spot which Machine Learning takes place.
That is enough of talking. First, let’s get our hands dirty. If you went through my previous post, you would now have everything set up. But if you didn’t, you might want to take a look at it here: Setting Up Python Environment.
Open Terminal and go into Python console mode:
Now let’s import sklearn module for Linear Regression. sklearn is a shortname of scikit-learn, a great Python library for Machine Learning.
We are not using only LinearRegression. We will work with arrays, so here I also imported numpy for dealing with arrays. We will also draw some graphs to visualize the data, so that’s why I imported pyplot, a great module for graph drawing.
Remember the data I used in the previous post on Linear Regression? I will show it right below for you:
| X | y |
|---|---|
| 1 | 7 |
| 2 | 8 |
| 3 | 7 |
| 4 | 13 |
| 5 | 16 |
| 6 | 15 |
| 7 | 19 |
| 8 | 23 |
| 9 | 18 |
| 10 | 21 |
Now let’s use that to prepare our training data:
Nearly every Machine Learning library requires data to be formatted in the way which each row is one training example (or testing example), and each column represents one feature’s data. So we have to reshape our data accordingly.
Now let’s plot our training data. You will receive the same figure with the one in the previous post:
Next, let’s initialize the Linear Regression model:
Then we will train our Model, using the training data above. You can do that by simply calling the fit function, which takes feature matrix \(X\) and label vector \(y\) as parameters:
Our training data is quite simple, so the learning process finished so fast as if it never happened. All the change during training (like weights and bias), was stored in the model object. Let’s see what we got:
Obviously, you can get more information through other attributes of model object, but now we will only focus on coef_, which stores the weight parameter, and intercept_, which stores the bias parameter.
Next, let’s compute the prediction vector a, using the obtained weight and bias:
Now let’s draw all \(X\), \(y\) and \(a\) on the same plot. Here we got a straight line, which fit the data better than what we did before (which is easy to understand, since we only went through 4 iterations).
So simple, right? Just a few lines of code, we have just prepared our training data, trained our Model, and visualized the result we got! Yeah, scikit-learn helps us do all the heavy things. In later posts, you will see that it can even handle more complicated jobs.
Obviously, we can see that the straight line above fits pretty well, but not good enough. And we need a more suitable approach. But first, let’s evaluate how well the Model is performing numerically, by computing the accuracy over the training data:
We cannot always evaluate something just by seeing it, right? We need something which is more concrete, yeah, a number. By looking at numbers, we will have a better look, and easily compare different things. scikit-learn provides us the score function, whose parameters are similar to the fit function.
And you can see that, our Model now has the accuracy of 85% over the training data. Commonly, we demand a higher accuracy, let’s say 90% or 95%. So by looking at the current accuracy, we can tell that our Model is not performing as we are expecting. So let’s think about an improvement. But how can we do that?
Remember I told you about Features in the first Post? Features are something we use to distinguish one object from others. So obviously, if we have more Features, then we will likely have a better fit model, since it can receive more necessary information for training. But how we can acquire more Features?
The easiest way to add more Features, is to computing polynomial features from the provided features. It means that if we have \(X\), then we can use \(X^2\), \(X^3\), etc as additional features. So let’s use this approach and see if we can improve the current Model. First, we have to modify our \(X\) matrix by adding \(X^2\):
Similar to previous step, let’s train our new Model, then compute the prediction vector \(a\):
Mathematically, we will now have \(a=\theta_0 + \theta_1X + \theta_2X^2\). Note that now we have more complicated matrix X, so we will have to use the dot function. An error will occur if we just use the multiply operator like above. I also created a new \(x\) variable, which ranges from 1 to 10, but with 0.1 step. Use the new \(x\) to compute \(a\) will result in a smoother graph of \(a\), since \(a\) is no longer a straight line anymore.
Now let’s plot things out and see what we got with new feature matrix:
As you can see, now we obtain a curved line, which seems to fit our training data much better. To be more concrete, let’s use the score function:
You see that? Now we got a new accuracy of 87%, which is a huge improvement right? At this point, you may think that we can improve it a lot more by continuing to add more polynomial features to it. Well, don’t guess. Let’s just do it. This time we will add up to degree 9.
Now we just obtained a new curve which fit our training data perfectly. Let’s use the score function again to get an exact number:
Wow, let’s see what we have here, an accuracy of 100%. This is real magic, you may think.
But that is just where the tragic begins…
Now let imagine our data has total 15 examples, and I just showed you the first 10. I will reveal the last 5 examples like below:
| X | y |
|---|---|
| 11 | 24 |
| 12 | 23 |
| 13 | 22 |
| 14 | 26 |
| 15 | 22 |
So actually our data will look like this:
Let’s see what happens if we use the Model obtained from degree 9 polynomial features:
Do you see what I am seeing? What a tragic! It doesn’t seem to fit the new data at all! We don’t even feel the need of computing the accuracy on the new data!
As I told you before, in the first post, that we only provided a fixed set of training data, and the Model will have to deal with new data which it has never seen before. New data, which may vary in unpredictable way in real life, penalized our trained Model this time! In Machine Learning term, we call it OVERFITTING problem (or High Variance). Overfitting, as the name is self-explained itself, means that the Model fits the data very well when we prodived a set of data containing a lot of features. We can see that the Model tends to memorize the data, rather than to learn from it, which makes it unable to predict the new data.
In contrast, what will happen if we use just one feature like we did in the beginning (or we can say that we provided a set of data which is poorly informative)? You have already seen that it resulted in a very low accuracy, which is not what we expected, either. We call this problem UNDERFITTING (or High Bias).
Overfitting & Underfitting, in both cases, are something that we try to avoid. And you will mostly face these problems all the time you work with Machine Learning. Of course, there are many ways to deal with them, but I will leave all the details for a future post. This time I will tell you the simplest way, which can be seen as a “must-do” in the very first step of any Machine Learning problem.
The first thing to do to prevent the problems above, is always splitting the dataset into training data and testing data. Never just count on the accuracy on training data! Why? Because even though we obtained a high accuracy, it does not mean that our Model is doing a good job. Conversely, we need to watch out for Overfitting problem. By splitting our dataset into two seperate parts, we will use one part for training, and the other for evaluating the trained Model. Because we evaluate the performance on a separate data, we can know if our Model can work well with new data that it has never seen. And we can somehow tell whether our Model has Overfitting problem or not.
Underfitting, on the other hand, can easily be discovered just by looking at the accuracy over the training data, because if our Model has Underfitting problem, then it will perform poorly on both dataset.
Finally, we will pick the Model which has the highest accuracy on the testing data.
With the approach I have shown you, let’s decide which Model to choose among three models above. We will use first ten examples as training data, and the last five examples for testing.
As you can see, both by visualizing and by looking at the accuracy. Our first model is too simple, which didn’t fit our data well. This is an example of Underfitting problem. In contrast, our third model is way too complicated, which performed very well on training data, but failed to fit the testing data. This is what we called Overfitting problem.
The second model may not fit as well as the third model, but it is the one that actually learned, which results in good performance over the testing data. And we can somehow say that, it will also predict well with any other data which it has never seen during training.
So today, through implementing Linear Regression, I led you through the most common problems you may face when working with Machine Learning, which are Underfitting and Overfitting. I also showed you the easiest way to avoid those problems, which is always splitting the dataset into two parts: one for training purpose, and one for testing.
Hope you find today’s post helpful and you now put a further step into Machine Learning world, I think. There is no stopping as you has gone this far. In the next post, I will continue with Logistic Regression, which is an extremely important algorithm that you must understand, because it is the key which leads you to the most powerful learning technique nowadays: Neural Network. So stay updated, and I will be with you soon. See you!


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Preparation time
less than 30 mins
Cooking time
10 to 30 mins
Serves
Serves 2
Flaky poached smoked haddock in white sauce flavoured with a spoonful of mustard, served with poached egg and spinach.
Pour the milk into a large frying pan or sauté pan, add the sliced onion and bay leaf and bring to a simmer.
Carefully place the haddock, skin-side down into the liquid and simmer for two minutes then turn over and simmer for another two minutes.
Meanwhile, add the white wine vinegar and a pinch of salt to a small saucepan of water and bring it to a rapid boil. Whisk the water to create a vortex then crack an egg into the water and cook for three minutes.
Remove and place straight into iced water, then repeat with the second egg.
Lift the haddock out of the milk and set aside on a plate, cover and keep warm in a low oven.
Strain the milk into a jug through a sieve to remove the onion, then wipe out the pan and return it to the heat.
Melt 25g/1oz of the butter in the pan then add the flour and cook out for 2-3 minutes. Pour in the reserved milk a little at a time, whisking as it’s added to remove any lumps. Cook for 3-4 minutes until you have a smooth shiny sauce.
Add the mustard and check the seasoning and texture. Add a little more milk if necessary to make it thick enough to coat the back of a spoon. Add a squeeze of lemon juice.
Heat a frying pan until hot, add the last of the butter and the spinach and cook until wilted down and hot through. Drain on kitchen paper then place onto the serving plates.
Drop the poached egg back into the pan of simmering water for 20-30 seconds to heat through.
Place the cooked haddock on top of the spinach followed by the poached egg, then finish with the mustard sauce over the top.


|
VItA
1.1
Virtual Iterative Angiogenesis library
|
#include <AbstractVascularElement.h>
Public Types | |
| enum | TERMINAL_TYPE { COMMON, RESERVED } |
| enum | BRANCHING_MODE { NO_BRANCHING, RIGID_PARENT, DEFORMABLE_PARENT, DISTAL_BRANCHING, ONLY_AT_PARENT_HOTSPOTS } |
| enum | VESSEL_FUNCTION { DISTRIBUTION, PERFORATOR, TRANSPORT } |
Public Member Functions | |
| AbstractVascularElement () | |
| virtual | ~AbstractVascularElement () |
| void | setBranchingMode (BRANCHING_MODE mode) |
| virtual void | getBranchingPoints (vector< point > *branchingPoints, point xNew)=0 |
| vector< SingleVessel * > & | getVessels () |
| virtual AbstractVascularElement * | getParent ()=0 |
| virtual SingleVessel * | getParentVesselTo (point xp)=0 |
| virtual vector< AbstractVascularElement * > & | getChildren ()=0 |
| void | addChild (AbstractVascularElement *newChild) |
| void | removeChildren () |
| virtual vector< SingleVessel * > * | getChildrenVesselTo (point xd)=0 |
| virtual vector< SingleVessel * > * | getVesselsConnectedTo (point p)=0 |
| virtual long long int | getTerminals ()=0 |
| virtual long long int | getTerminals (TERMINAL_TYPE type)=0 |
| virtual double | getVolume ()=0 |
| virtual void | updatePressure ()=0 |
| virtual double | getDistalRadius ()=0 |
| virtual double | getProximalPressure ()=0 |
| virtual void | saveVesselData (ofstream *treeFile)=0 |
| virtual void | saveVesselConnectivity (ofstream *treeFile)=0 |
Public Attributes | |
| TERMINAL_TYPE | terminalType |
| double | qReservedFraction |
| int | stage |
| AbstractVascularElement * | parent |
| vector< AbstractVascularElement * > | children |
| BRANCHING_MODE | branchingMode |
| VESSEL_FUNCTION | vesselFunction |
Protected Attributes | |
| vector< SingleVessel * > | vessels |
Abstract vascular element that can be composed by a set of vessels.
Indicates how and where this vessel can branch when it is parent.
Indicates the behavior of the terminal for flow distribution.
Indicates the vessel function that constraints where it can bifurcates. Perforators may bifurcates while they are partially outside the domain, but its partition point must be inside the domain. Transport vessels bifurcates outside the domain and distribution vessels bifurcates inside the domain.
| AbstractVascularElement::AbstractVascularElement | ( | ) |
Common constructor.
|
virtual |
Destructor constructor.
| void AbstractVascularElement::addChild | ( | AbstractVascularElement * | newChild | ) |
Adds a new child to the children vector.
| newChild | New child to be inserted in the structure. |
|
pure virtual |
Fill branchingPoints
with the potential points for branching in the current vessel, accorading to the branchingMode
.
| branchingPoints | Vector filled with the possible branching points of this vessel. |
Implemented in SingleVessel.
|
pure virtual |
Returns all the children vessels to this vascular element.
Implemented in SingleVessel, and MultiSegmentVessel.
|
pure virtual |
Returns all SingleVessel that are children of this vascular element.
| xd | Point of children vessels attachment. |
Implemented in SingleVessel, and MultiSegmentVessel.
|
pure virtual |
Returns the radius at the distal point of the vessel.
Implemented in SingleVessel, and MultiSegmentVessel.
|
pure virtual |
Returns its parental AbstractVascularElement.
Implemented in SingleVessel, and MultiSegmentVessel.
|
pure virtual |
Returns the SingleVessel that is parent for this vascular element.
| xp | Point of parent vessel attachment. |
Implemented in SingleVessel, and MultiSegmentVessel.
|
pure virtual |
Returns the pressure at the proximal point of the vessel.
Implemented in SingleVessel, and MultiSegmentVessel.
|
pure virtual |
Returns all the inner terminals in the current vascular element.
Implemented in SingleVessel, and MultiSegmentVessel.
| vector< SingleVessel * > & AbstractVascularElement::getVessels | ( | ) |
Returns all vessels in the vascular element.
|
pure virtual |
Returns all vessels in this vascular structure connected to point p
.
| p | Point at which all target vessels are connected to. |
p
. Implemented in SingleVessel, and MultiSegmentVessel.
|
pure virtual |
Returns the functional cost contribution of this vascular element. If any constraint –e.g. geometric constrains– are violated the function must return INFINITY.
Implemented in SingleVessel, and MultiSegmentVessel.
| void AbstractVascularElement::removeChildren | ( | ) |
Remove all children.
|
pure virtual |
Writes connectivity of this vessel at the file associated to treeFile
stream.
| treeFile | Output file stream. |
Implemented in SingleVessel, and MultiSegmentVessel.
|
pure virtual |
Writes the vessel data at the file associated to treeFile
stream.
| treeFile | Output file stream. |
Implemented in SingleVessel, and MultiSegmentVessel.
| void AbstractVascularElement::setBranchingMode | ( | BRANCHING_MODE | mode | ) |
Sets the branching mode for the current vessel.
| mode | Branching mode. |
|
pure virtual |
Updates vascular element pressure from children to parent direction.
Implemented in SingleVessel, and MultiSegmentVessel.
| BRANCHING_MODE AbstractVascularElement::branchingMode |
Indicates the hotspots where the vessel can bifurcate.
| vector<AbstractVascularElement *> AbstractVascularElement::children |
The children vascular structures.
| AbstractVascularElement* AbstractVascularElement::parent |
Parent vascular structure.
| double AbstractVascularElement::qReservedFraction |
Fraction of the qReserved assigned to this vessel.
| int AbstractVascularElement::stage |
Parent vascular structure.
| TERMINAL_TYPE AbstractVascularElement::terminalType |
Indicates the behavior of the terminal for flow distribution.
| VESSEL_FUNCTION AbstractVascularElement::vesselFunction |
Type of vessel function for the current vessel.
|
protected |
Vessels that constitute the current vascular element.


Czech Defence Ministry to Sign $635Mln Deal With US to Purchase 12 Helicopters
PRAGUE (Sputnik) - The Czech Defence Ministry has decided to purchase eight UH-1Y Venom utility helicopters and four AH-1Z Viper attack helicopters from the United States worth a total of 14.6 billion Czech crowns ($635 million), Defence Minister Lubomir Metnar said on Tuesday.
"This amount, apart from the helicopters, also covers weapons systems, ammunition, a training simulator and the cost of initial training for pilots and technicians. I hope that we will conclude the agreement with the US government by the end of this year and that our army will receive these helicopters in 2023", Metnar told reporters.
According to the minister, the Czech military wants to replace 17 Russian Mi-24/35 attack helicopters, since it no longer wants to be dependent on the supply of spare parts from Russia.
In August, Lubomir Metnar said Czech Republic was going to buy a mixed fleet of US helicopters from the Bell Helicopter Company.


kotcity4
/
kotcity.data
/
Corners
/
includes
includes
fun
includes
(
block
:
BlockCoordinate
)
:
Boolean


| Code | Result | |
|---|---|---|
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concat
this is a definition of the concat command.
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indexOf
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join
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lastIndexOf
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pop
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push
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reverse
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shift
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slice
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sort
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splice
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toString
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unShift
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valueOf
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INTERACTIVEs
Home
Minesweepers
Calculator
Click the Dot
Flappy Block
Traveling block
8D block Travel
A lot of squares


Initiate
Build Button events


Page Not Found Sorry, but the page you were trying to view does not exist — perhaps you can try searching for it below. Thanks for reading! If you found this post valuable, please help share it with others Twitter Facebook Google+


The maintenance and administration of UseGalaxy.eu is done primarily by the
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groups across Europe and with the US Galaxy team.
We make a best effort to provide high uptime. Please see our status page for more information.
The Freiburg Team tries to maintain a balance between service uptime and
the health of the administrators behind the service.
Sometimes we are able to achieve this, sometimes due to factors outside
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At Event Trucker, we provide a platform for you to track your events/occasions. Events being important we help you track your events through a countdown from current time to the time of your event.
All you need to do is to create an account to access the event platform. later you will enter a short description of the event and set the time to begin your countdown.
Most of what we value is your feedback after using our platform. You can input your suggestions for better improvement or for appreciating our work to help you track your events. You can do this through filling the contact form or through social-media pages below this page..


---
layout: post
title: 'How to Win the Cyber War We''re Loosing: A Rebuttal'
date: '2012-07-12T09:07:00.001+09:00'
author: Joshua James
tags:
- Discussion
modified_time: '2012-07-12T09:07:26.972+09:00'
blogger_id: tag:blogger.com,1999:blog-2701259639305045003.post-3819920414754985613
---
http://www.cyberdialogue.ca/wp-content/uploads/2011/03/Mike-McConnell-How-to-Win-the-Cyberwar-Were-Losing.pdf
http://www.tandfonline.com/doi/abs/10.1080/01495939308402915


Sorry, this application requires Javascript to run. For now.
We recommend
Firefox
or
Chrome
.
A
UA 571-C
REMOTE SENTRY WEAPON SYSTEM
A
Rounds
Remaining
500
CRITICAL
TIME AT 100%
(secs)
0.00
Temp
R(M)


|
dpmeans()
|
DP-Means Clustering |
|
gmm()
|
Finite Gaussian Mixture Model |
|
gmm03F()
|
Ensemble of Gaussian Mixtures with Random Projection |
|
gmm11R()
|
Regularized GMM by Ruan et al. (2011) |
|
gmm16G()
|
Weighted GMM by Gebru et al. (2016) |
|
kmeans()
|
K-Means Clustering |
|
kmeans18B()
|
K-Means Clustering with Lightweight Coreset |
|
kmeanspp()
|
K-Means++ Clustering |
|
sc05Z()
|
Spectral Clustering by Zelnik-Manor and Perona (2005) |
|
sc09G()
|
Spectral Clustering by Gu and Wang (2009) |
|
sc10Z()
|
Spectral Clustering by Zhang et al. (2010) |
|
sc11Y()
|
Spectral Clustering by Yang et al. (2011) |
|
sc12L()
|
Spectral Clustering by Li and Guo (2012) |
|
scNJW()
|
Spectral Clustering by Ng, Jordan, and Weiss (2002) |
|
scSM()
|
Spectral Clustering by Shi and Malik (2000) |
|
scUL()
|
Spectral Clustering with Unnormalized Laplacian |


Unistring Test
Dump grapheme cluster info
Dump word boundary info
(
Combine adjacent characters belongs to the same script
)
Dump sentence boundary info
Dump


German: Native speaker
English: Business fluent
French: Basic communication skills
Latin: Reading skills (german degree "Großes Latinum", a certificate for advanced qualification)
German: Native speaker
English: Business fluent
French: Basic communication skills
Latin: Reading skills (german degree "Großes Latinum", a certificate for advanced qualification)


How to Build an E-commerce App using Node.js ?
React, Angular, and Vue –– all these frontend frameworks are good for building eCommerce applications. But what about Backend? Node.js is one such framework that allows you to build scalable eCommerce backends. This article provides you all the necessary information you need to use Nodejs in eCommerce web application development.
Whether it be React.js, Vue.js, and Angular.js, we love them all! ❤
However, building web applications involves a lot more complex processing at the server-side, let alone the frontend of the application. Here’s why we thought to share our knowledge regarding building eCommerce applications.
Although our article on building a scalable web application covers a lot about modeling complex interfaces, today we will take a step forward to understand how to build an eCommerce app with Nodejs.
Let’s begin with the top reason to consider Node.js as a technology to develop complex and scalable eCommerce applications. Thereafter, we’ll walk you through the options you should have while using node in eCommerce web applications.
Table of Content
- The Truth about eCommerce, no one tells you!
- Building a Nodejs eCommerce website: what are your options?
- Building a Nodejs eCommerce app with MEAN/ MERN stack
- Building a Nodejs eCommerce app with Microservices architecture
The Truth about eCommerce, no one tells you!
The best eCommerce backends in the world are designed for scalability, for they are always in charge of generating, maintaining and delivering the information to the frontend displays of a user.
The backend of your eCommerce application, however, can be either homogeneous or heterogeneous and comprises of 2 parts: the database (where the data or information is stored) and a server (which connects the data with the browser).
The Problem
Building a scalable, complex eCommerce interface is hard!
When your eCommerce business critically depends on elements such as payment gateways, logistics, suppliers, shipping etc, it induces more complexity for you to maintain high performance and availability 24×7.
Integrating most of these elements can slow down crucial activities, limiting your website’s performance. Behind the scenes (preferable at the backend), there will be hundreds or thousands of requests that are instantly pinged around the world to carry out such kinds of complex operations.
There’s a lot more going on in those few seconds; there’s a lot more that can go wrong with your website in those few seconds.
Note that, the backend of your application is one of the most critical part of any website. In most complex websites, the frontend depends gravely on the backend.
“The frontend of a website depends gravely on the backend.”
So, if the backend of a website fails due to complexity, the frontend will fall apart. As simple as that!
The Real Problem: slow backend load time
At times, a lot of developers focus on improving the frontend performance of an eCommerce to rectify these things. The real bottleneck, however, remains the slow backend load time.
So, if you have a slow backend, the only thing that your users will see on your site is a white screen (which is due to the backend tasks are not completed). The heavier your load, larger would be the backend load time. Also at some point, it may get skyrocketed to a certain extent that the application becomes unstable.
Furthermore, a *study done by Moz *shows that a slow backend load time (which is also referred to as Time to First Byte) can have a serious impact on your Search engine rankings.
 A good back end load time to aim for is 200ms or less.
Nodejs to the Rescue
Building an eCommerce app with Nodejs makes a lot more sense because it ensures the balance between frontend and backend load time due to its asynchronous nature.
Here’s a graph which illustrates the importance of Asynchronous programming in improving the backend load time of an eCommerce website:
Notice how using Asynchronous communication in web apps saves you alot on backend load time.
In web applications such as eCommerce where you have huge catalog of products , going asynchronous makes a lot more sense. The good news is that Node.js allows you to use Asynchronous programming and make things easy for you.
What’s more, Node gives you an ability to handle multiple concurrent users at a time.
The reason being it be single-threaded and its event-driven architecture.
This is unlikely the case with other web technologies, which creates an additional thread for each request. This eats up the whole memory until the processing gets over.
Node.js help developers to make the best use of event loop and callbacks for I/O operations. The event loop is the classic Node.js’s way of breaking the long-running request into smaller chunks.
What heartbeat is to heart, event-loop is to Nodejs.
Node.js will keep on checking a work queue every few milliseconds, which is a great way to queue and process new tasks simultaneously at the same time. Here’s why it can be possible for your backend to serve hundreds of thousands or even a million concurrent users.
Node.js can also improve the website loading time in eCommerce.
According to a Google Study, “40% of people abandon a website that takes more than 3 seconds to load and a 1-second delay in page response can result in a 7% reduction in conversions.”
Put another way, every time your website gets slow — you might be losing out on tons of conversions.
After all, it’s been said clearly,
When your site is eCommerce, EVERY SINGLE SECOND MATTERS. Period.
PayPal has an interesting story of how they migrated from Java to Node.js which provided the site an 100% increase in business availability, even with huge load. After using Node.js, in its backend, PayPal experienced a 35% decrease in the average response time for the page which made the startup time 200ms faster.
Here’s a more statistical yet graphical representation of the results achieved by Paypal through their migration to Nodejs:
Another way of improving the startup time of your Node.js app is by turning on Gzip compression. It greatly decreases the bundled size of your app hence contributing to the increase in speed.
Building a Nodejs eCommerce website: what are your options?
Now that we have covered what goes behind the screens while developing a node eCommerce website, let’s throw some light on the options you may have with Node.js.
Building a Nodejs eCommerce app with MEAN/ MERN stack The MEAN stack works brilliantly for building an eCommerce web app. We’ll briefly explain what the MEAN stack is all about. Take a look!
MEAN and MERN stack with Nodejs: The definition
MongoDB: A NoSQL database that provides data storage and NodeJS allows server running continuously.
Express.js: A back-end web framework that enables you to create web applications by providing a simpler interface.
Angular.js/React.js: A front-end web framework that simplifies web development by providing automatic view/model synchronization. Unlike Angular, React is not a framework, it is a library.
Node.js: A JavaScript runtime built on Chrome’s V8 JS engine. It uses an event-driven and non-blocking I/O model, which makes it lightweight and efficient.
The MEAN/ MERN stack Architecture for eCommerce
MEAN and MERN stack offers you free and open-source javascript framework.
If you are just starting out to build a Node.js eCommerce website for some relevant users (say up to 10k users), either MEAN or MERN stack would be a good choice.
Here’s what goes behind the scenes while building a Nodejs eCommerce website with MEAN stack:
- Initially, it begins with Angular.js where all the requests emerging from the client-side are processed.
- Once the initial phase is over, the request enters to Node which is in control of the server-side.
- The request enters to Express.js where it requests for database access.
- MongoDB collects the entire data once the request is accepted and relays it to Express.js
- Express relays the response to Node which sends it to Angular.
An ideal eCommerce app is when it can respond quickly to usage spikes. When building an ecommerce site with MEAN/ MERN stack, you will be able to serve an abundance of at least 10k users, which is an ideal number for your business at the stage of inception. Valued for its scalability, flexibility, and consistency, the MEAN stack is a strong choice for developing eCommerce apps with Node because of its ability to provide concurrency and stability at the same time.
If you are curious to know about building an eCommerce web app with Reactjs (or with MERN stack), we have already covered it before. You can even go-through this amazing blog on building an eCommerce PWA with React, thank us later.
Building a NodeJS eCommerce App with Microservice Architecture
Although MEAN/MERN stacks are the best for building monolithic eCommerce applications when your business is at an early stage. There might be some situations when your eCommerce web app requires to handle millions of users.
Nodejs based Microservices Architecture to the rescue
Nodejs and Microservices are a match made in heaven! Nodejs truly complements microservices as it supports several microservice frameworks such as Seneca.js.
Instead of going for monolithic applications that require heavy maintenance, you can simply deploy a microservices-based eCommerce app that simply breaks down several functionalities into a small suite of services integrating via APIs. This kind of approach empowers multiple services (like payments and products) of your eCommerce business and let them update and scale separately.
For instance, you might deploy a product metadata service that retrieves (or periodically cache) metadata for a particular product. Moreover, you also can have a product pricing service that retrieves the product’s price for a given customer.
Aesthetically, you can have several microservices that are exposed to clients via REST APIs. Your web application can communicate with these REST APIs through an API gateway.
The following diagram shows an example of eCommerce-oriented backend microservices architecture:
Cadenza, a subscription-based eCommerce platform, migrated its Nodejs app from MEAN stack to a micro-service based architecture. While they were using MEAN stack, they faced a hard time managing 125 different APIs in different categories such as product management, user management, payment management, etc. Even worse, problems like repetitive application crashes and huge-costs were taking a toll on their eCommerce business.
By adopting the microservices architecture pattern, they got an opportunity to code services independent of programming language and database. It simply eliminates any long-term commitment to the technology stack. What’s more, it helped them to cut down huge costs on operations.
However, Candanza is not only the eCommerce business that moved to the microservice architecture, but there are also a lot more examples. eBay, the renowned eCommerce marketplace, employed microservices architecture and scaled beyond 1 billion of users. There’s no way an eCommerce business can be scaled to such an extent with a basic architecture.
Conclusion
We possess expertise in building Node.js applications using MEAN stack. We find MEAN stack to be more convenient and easiest to learn and implement. However, we also recommend Microservices to our clients who already have built monoliths and need more scalability. Microservice architecture puts our developer in the state of a 100% control over the individual services, which is necessary when you want to build scalable kinds of stuff. With this article, we stressed the reasons why building an eCommerce web app with Nodejs is the need of the hour and what are the ways of building it.


For those who did not know what Desmos is, check out here. A lot of people use desmos to draw interesting objects, and I am just one of them. This page will be continuously updated as I draw more.
Alhamdulillah is a phrase meaning "all praise is due to Allah". This phrase is called Tahmid. It is frequently used by Muslim to praise Allah for what has happened to them.
A logo of my high school, SMAN 1 Yogyakarta, which is also known as SMA Teladan. Many said that it is one of the best schools in Indonesia. Not quite difficult to draw this logo since I am not giving any color.
Garuda Pancasila is the national emblem of Indonesia. I admit that there was a huge difficulty in drawing it. I was drawing everyday and it took me about 2 weeks to complete it.
I felt challenged when Ardy Seto asked me to draw Violet (the main character of Violet Evergarden). I decided to accept it since I knew that Violet's hair would be interesting to draw. However, the main difficulty was about giving it color. It took me quite times to think.


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Personality
In fiction and journalism people use the terms "charming personality", "charismatic personality", "electrifying personality" "a milk-toast personality" , and so on. These phrases/terms, however, do not use the scientific principles of vocabulary. The term personality has been derived from Latin persona, a mask worn by actors in classical dramas. Behaviorist psychologists feel that if a person has some kind of impact, makes a certain impression on others, it must be because of his actions. The simplest possible way to explain the meaning of personality, therefore, is "the pattern of responses" which characterizes the individual. Contrary to the general idea that good personality refers to good physical features or trendy looking individual, the fact is the personality includes many other more important behavioral and mental features. Think of a boy who is not handsome and attractive apparently, not even tall or robust, yet he has a commanding verbal influence which makes him a successful army officer. Think of Mahatma Gandhi for example. Most people did not find him to be attractive or impressive as far as his physical make - up was concerned. Short, meek looking, bard, dhoti - clad old looking man moved the whole India behind him. Even today he is known his 'Personality' which had its impact, and force with an everlasting impression on the people around him that made him the man of world - fame.  Personality is the core potential that determines one's success in life and career. If you feel depressed or disheartened because you think that you are born with weak personality - stop thinking negatively. Personality make - up of yours is not only (totally) determined by heredity. There is another important factor that determines (influences) your personality - environment. It is the environment that can be manipulated to get the desirable positive effects in shaping the personality. Well known psychologist Watson (a behaviorist) emphasized the importance of environment in shaping personality of the child so much that he declared, " give me a dozen mentally healthy infects, well formed, and my own specified world to bring them up in and I will guarantee to make any one at random and train him to be any type of specialist I desire - doctor, lawyer, artist, merchant ..., even beggar man or thief!" This is obviously an extreme view that some behaviorists have taken to focus on the role of environment in personality development. The fact remains that enriching the environment of the individual does show significant improvement in developing your personality. At the same time, however, do not get blindly influenced by some non-standard, cheap publicity stunts of some people who sell their "personality-development" courses just to make quick money.  Improving your personality requires a strong will and determination to achieve healthy growth. Any one of you who aspires for self-improvement should make an objective evaluation about himself. Then he must look into his positive potentials and also (confess) weaknesses. On this basis of this information, an objective self-evaluation may be carried. At the same time it is also essential to counteract all negative and pessimistic thoughts.  Brooding, self-debasement, feeling of inferiority, hate and jealousy are all negative and damaging emotions. One must face and tactfully weaken these negative forces to emerge as successful. You should not be fatalistic in your approach to life. Never feel you are at mercy of others. So what if you have failed repeatedly? With all your intelligence, wisdom, and vigor make your positive potentials to grow. Reveal yourself with truth and you will find that there is a storehouse of virtues within you. You dont have to expect chances of miracles, just decide to move in a determined and positive direction, and move. Social Development is an important aspect of ideal personality development. Closed room with open books, may help you to fetch higher marks in certain exam, however, for a full and complete growth of personality, one must be able to expose himself to the social realities of life. Interaction with people around you may give you pain or pleasure but it certainly adds to your personality growth if taken from healthy frame of mind. A school or university is just not a cramming machine. You may study at home too. The value of lectures over reading is that you get a sense of participation with other like you. Such a situation is also suitable for development of the personality. The class - room situation encourages debags and discussions and thus broaden the horizons of mind. There is always a need to socialize - mix with fellow colleagues to know them, feel them and understand them to grow socially (socially educated). There is a natural (social) need to know if other are like you or different. Some people are more social than others. Sociability is an art which should be cultivated to grow into effective personality. Extrovert is a person whose basic orientation is towards external world.  The psychological research shows that their (extreme extraverts) academic performance tends to be lower than that of introverts, because (mostly) concentration is not one of their strong points. Introvert is a person who may be described at the other continuum of the bipolar scale, as hesitant, reflective, withdrawn .., reserved etc. None of you fall on the extreme of the bipolar scale. Infact, most of the people have various degrees of the two types combined.  In reality most of us are ambivert - the mixture of two. The degree of the two sets of characteristics varies from person to person and that is what makes you different from others. In case you find that you are basically an introvert but in peace and comfortable with yourself, fine! Don't push yourself out and out to be like extreme extroverts. You will have your own merits of uniqueness. Making fun, giggling, over-laughing or teasing, talking more are not the goals to be achieved by modeling others or practice. By asking you to socialize with people I don't mean to suggest that you become a joker or chirping bird. The whole essence of social development as a part of personality improvement reminds us of the fact that we are social by nature. Total isolation from others is against the basic principles of social living - of which you and me are the part. Emotions, empathy, co-operation, competition, group working, group cohesiveness, being one like the others and yet unique in the whole world should be the outcome of healthy personality development. Have positive attitude towards people in general. It is wrong to think of people as always striving to side on one another's back. The natural urge is towards co-operation. Such attitudes always help you to cultivate stronger and healthy personality. Another interesting area of personality research has highlighted two types of personality - Type A and type B personalities. The individuals who are like Type A personality, are action - emotion oriented that can be observed in any person who is aggressively involved in a chronic, incessant struggle to achieve more and more in less and less time, and if required to do so, against the opposing efforts of other things or other persons. It may also be seen as a temperament characterized by excessive drive and competitiveness, unrealistic sense of time urgency, inappropriate ambition, reluctance to provide self - evaluation and a tendency to emphasize quantity of output over quality. Such people are believed to have greater chances of coronary disease. In contrast, Type B persons are characterized by a relaxed, easygoing approach to life. They focus on quality over quantity have low competitiveness and a tendency for self - reflection. A word of caution into a 'type' - don't fall into type fallacy. None of the above types are mutually exclusive or bipolar, in the sense that you need not label yourself at one or the other extreme. You may have the characteristics of both the types i.e., Introversion - Extraversion; type A - type B, and all the qualities have their virtues if used in a balanced and mature way at appropriate place.
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Principal Web Developer at LinkedIn
San Francisco Bay Area
Principal Web Developer at LinkedIn
San Francisco Bay Area
Steve Ganz is passionate about connecting people, semantic markup, sushi, and disc golf - not necessarily in that order. Currently obsessed with developing the user experience at LinkedIn, Steve is a second generation Silicon Valley geek and a veteran web professional who has been building human-computer interfaces since 1994.
HTML, XHTML, CSS, DOM, JavaScript, W3C, web standards, front-end engineering, user interface development, user-centered design, usability, accessibility, semantic markup, microformats, POSH
(Privately Held; 201-500 employees; Internet industry)
November 2006 — Present (1 year 8 months)
Building the core user experience, developing best practices for front-end architecture, leading the implementation of microformats, championing data portability enabling technologies, and identifying and evangelizing new patterns in social design.
(Public Company; 5001-10,000 employees; ebay; Internet industry)
September 2005 — November 2006 (1 year 3 months)
Joined PayPal to evangelize, teach, and implement W3C Web standards in an effort to separate the front-end architecture tier into distinct structural, presentational, and behavioral layers.
(Public Company; 1001-5000 employees; MFE; Computer & Network Security industry)
June 2003 — August 2005 (2 years 3 months)
Converted to full-time employee after being brought on for a one month contract in June 2003 and making an immediate contribution to the Web Services group in areas of usability, accessibility and presentation layer coding optimization. Leading the way towards uniform and company-wide adoption of coding conventions based on W3C Web standards.
(Privately Held; 11-50 employees; Printing industry)
June 2001 — September 2002 (1 year 4 months)
Given increased challenges and responsibilities after Printable acquired Collabria's intellectual property; led integration of complex product suites.
(Privately Held; 51-200 employees; Printing industry)
April 1998 — June 2001 (3 years 3 months)
Hired as employee number one to play a key role in the development of Collabria's award-winning flagship suite of e-commerce procurement, supply chain, and productivity applications.
(Self-Employed; Myself Only; Internet industry)
January 1995 — April 1998 (3 years 4 months)
Designed, developed, and maintained dynamic
web sites and intranets for small businesses, major corporations, and
start-up ventures.
• Sun Microsystems, 1995: Converted
thousands of pages of product data-sheets and white papers from
FrameMaker to HTML. Designed, tested, and implemented innovative
software that automatically converted FrameMaker tables to HTML tables.
• Communication Intelligence Corporation, 1996:
Developed the first external web site for CIC using an organized
layout, logical directory structures and intuitive naming conventions.
Trained in-house Webmaster to maintain and extend the website using
custom templates.
• Aspect Development, 1996: Provided early user
interface design and database programming for an online product catalog
developed exclusively for Siemens Corporation.
• Corporate Store, 1997: Designed user interface for
online print procurement and order processing system.
(Partnership; 1-10 employees; Broadcast Media industry)
1993 — 1995 (2 years)
• Produced and directed industrial
videos; handled multiple responsibilities including pre-and
post-production, filming, and editing.
• Created video
training manuals for a popular management software solution.
• Produced introduction video for an automated online
ordering system.
Theatre, Film, Television 1986 — 1990
Radio Broadcasting 1983 — 1986
social design, human-computer interaction, disc golf
Usability Professionals Association, BayCHI, Silicon Valley WebGuild, PDGA, Social Graph Foo Camp, SXSW, Twitter, DataPortability
• 2008 SXSW Interactive Festival Panelist
— "Social Networking and Your Brand"
•
2003 PDGA Volunteer of the Year


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Software Developer from California, graduated from California State University East Bay in Criminal Justice. Background in military Intelligence and law enforcement.
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Software Developer in the Midwest, graduated from the University of Arizona, with a background in electronics assembly and customer service. Pursuing the role of Full Stack JavaScript Developer through Code Fellows.
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This work is a joint project between EnsemblGenomes at the European Bioinformatics Institute and the group of Doreen Ware at the Cold Spring Harbor Laboratory, who have developed the Gramene database, a resource for plant comparative genomics based on Ensembl technology. A common set of databases are available through EnsemblGenomes and Gramene, and the two groups are collaborating on the integration of content, quality control and the development of new features.


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--- title: Exploring Process Mining in R author: Gertjan Verhoeven date: '2019-06-20' summary: In this post, we'll explore the BupaR suite of Process Mining packages created by Gert Janssenswillen of Hasselt University. slug: exploring-process-mining draft: FALSE categories: - Data science tags: - process mining - R baseurl: "https://gsverhoeven.github.io" header: image: "headers/ceiling-clean-clinic-247786.jpg" preview: FALSE ---
In this post, we'll explore the BupaR suite of Process Mining packages created by Gert Janssenswillen from Hasselt University.
We start with exploring the patients
dataset contained in the eventdataR
package. According to the documentation, this is an "Artifical eventlog about patients".
After installing all required packages, we can load the whole "bupaverse" by loading the bupaR
package.
library(ggplot2)
library(bupaR)
## Warning in library(package, lib.loc = lib.loc, character.only = TRUE,
## logical.return = TRUE, : there is no package called 'xesreadR'
## Warning in library(package, lib.loc = lib.loc, character.only = TRUE,
## logical.return = TRUE, : there is no package called 'processmonitR'
## Warning in library(package, lib.loc = lib.loc, character.only = TRUE,
## logical.return = TRUE, : there is no package called 'petrinetR'
library(processmapR)
Now, our dataset is already in eventlog
format, but typically this not the case. Here's how to turn a data.frame into an object of class eventlog
:
patients <- eventdataR::patients
df <- eventlog(patients,
case_id = "patient",
activity_id = "handling",
activity_instance_id = "handling_id",
lifecycle_id = "registration_type",
timestamp = "time",
resource_id = "employee")
## Warning: The `add` argument of `group_by()` is deprecated as of dplyr 1.0.0.
## Please use the `.add` argument instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.
Let's check it out.
summary(df)
## Number of events: 5442
## Number of cases: 500
## Number of traces: 7
## Number of distinct activities: 7
## Average trace length: 10.884
##
## Start eventlog: 2017-01-02 11:41:53
## End eventlog: 2018-05-05 07:16:02
## handling patient employee handling_id
## Blood test : 474 Length:5442 r1:1000 Length:5442
## Check-out : 984 Class :character r2:1000 Class :character
## Discuss Results : 990 Mode :character r3: 474 Mode :character
## MRI SCAN : 472 r4: 472
## Registration :1000 r5: 522
## Triage and Assessment:1000 r6: 990
## X-Ray : 522 r7: 984
## registration_type time .order
## complete:2721 Min. :2017-01-02 11:41:53 Min. : 1
## start :2721 1st Qu.:2017-05-06 17:15:18 1st Qu.:1361
## Median :2017-09-08 04:16:50 Median :2722
## Mean :2017-09-02 20:52:34 Mean :2722
## 3rd Qu.:2017-12-22 15:44:11 3rd Qu.:4082
## Max. :2018-05-05 07:16:02 Max. :5442
##
So we learn that there are 500 "cases", i.e. patients. There are 7 different activities.
Let's check out the data for a single patient:
df %>% filter(patient == 1) %>%
arrange(handling_id) #%>%
## Log of 12 events consisting of:
## 1 trace
## 1 case
## 6 instances of 6 activities
## 6 resources
## Events occurred from 2017-01-02 11:41:53 until 2017-01-09 19:45:45
##
## Variables were mapped as follows:
## Case identifier: patient
## Activity identifier: handling
## Resource identifier: employee
## Activity instance identifier: handling_id
## Timestamp: time
## Lifecycle transition: registration_type
##
## # A tibble: 12 x 7
## handling patient employee handling_id registration_ty… time
## <fct> <chr> <fct> <chr> <fct> <dttm>
## 1 Registr… 1 r1 1 start 2017-01-02 11:41:53
## 2 Registr… 1 r1 1 complete 2017-01-02 12:40:20
## 3 Blood t… 1 r3 1001 start 2017-01-05 08:59:04
## 4 Blood t… 1 r3 1001 complete 2017-01-05 14:34:27
## 5 MRI SCAN 1 r4 1238 start 2017-01-05 21:37:12
## 6 MRI SCAN 1 r4 1238 complete 2017-01-06 01:54:23
## 7 Discuss… 1 r6 1735 start 2017-01-07 07:57:49
## 8 Discuss… 1 r6 1735 complete 2017-01-07 10:18:08
## 9 Check-o… 1 r7 2230 start 2017-01-09 17:09:43
## 10 Check-o… 1 r7 2230 complete 2017-01-09 19:45:45
## 11 Triage … 1 r2 501 start 2017-01-02 12:40:20
## 12 Triage … 1 r2 501 complete 2017-01-02 22:32:25
## # … with 1 more variable: .order <int>
# select(handling, handling_id, registration_type) # does not work
We learn that each "handling" has a separate start and complete timestamp.
The summary info of the event log also counts so-called "traces". A trace is defined a unique sequence of events in the event log. Apparently, there are only seven different traces (possible sequences). Let's visualize them.
To visualize all traces, we set coverage
to 1.0.
df %>% processmapR::trace_explorer(type = "frequent", coverage = 1.0)
## Warning: `rename_()` is deprecated as of dplyr 0.7.0.
## Please use `rename()` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.
So there are a few traces (0.6%) that do not end with a check-out. Ignoring these rare cases, we find that there are two types of cases:
A really powerful visualization in process mining comes in the form of a "dotted chart". The dotted chart function produces a ggplot
graph, which is nice, because so we can actually tweak the graph as we can with regular ggplot objects.
It has two nice use cases. The first is when we plot actual time on the x-axis, and sort the cases by starting date.
df %>% dotted_chart(x = "absolute", sort = "start") + ggtitle("All cases") +
theme_gray()
## Joining, by = "patient"
The slope of this graphs learns us the rate of new cases, and if this changes over time. Here it appears constant, with 500 cases divided over five quarter years.
The second is to align all cases relative to the first event, and sort on duration of the whole sequence of events.
df %>% dotted_chart(x = "relative", sort = "duration") + ggtitle("All cases") +
theme_gray()
## Joining, by = "patient"
A nice pattern emerges, where all cases start with registration, then quickly proceed to triage and assessment, after that, a time varying period of 1-10 days follows where either the blood test + MRI scan, or the X-ray is performed, followed by discussing the results. Finally, check out occurs.
To conclude, the process mining approach to analyze time series event data appears highly promising. The dotted chart is a great addition to my data visualization repertoire, and the process mining folks appear to have at lot more goodies, such as Trace Alignment.


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{{ govukTable({
firstCellIsHeader: false,
head: [
{
text: "Question"
},
{
text: "Answer"
},
{
text: "Change answer"
}
],
rows: [
[
{
text: "Where is your business based?"
},
{
text: data.answers['business-location'] | getAnswerAsString('business-location')
},
{
html: 'Change where your business is based'
}
] if data.answers['business-location'] | length,
[
{
text: "What size was your business as of 28 February?"
},
{
text: data.answers['business-size'] | getAnswerAsString('business-size')
},
{
html: 'Change what size your business was as of 28 February'
}
] if data.answers['business-size'] | length,
[
{
text: "What is your turnover per year?"
},
{
text: data.answers['annual-turnover'] | getAnswerAsString('annual-turnover')
},
{
html: 'Change the value of your turnover per year'
}
] if data.answers['annual-turnover'] | length,
[
{
text: "Are you an employer with a PAYE scheme?"
},
{
text: data.answers['paye-scheme'] | getAnswerAsString('paye-scheme')
},
{
html: 'Change whether your are an employer with a PAYE scheme'
}
] if data.answers['paye-scheme'] | length,
[
{
text: "Are you self-employed?"
},
{
text: data.answers['self-employed'] | getAnswerAsString('self-employed')
},
{
html: 'Change whether you are self-employed'
}
] if data.answers['self-employed'] | length,
[
{
text: "What is the rateable value of your business' non-domestic property?"
},
{
text: data.answers['non-domestic-property'] | getAnswerAsString('non-domestic-property')
},
{
html: 'Change whether your business has a non-domestic property with a rateable value'
}
] if data.answers['non-domestic-property'] | length,
[
{
text: "Is your business in any of the following sectors:"
},
{
text: data.answers['business-sector'] | getAnswerAsString('business-sector')
},
{
html: 'Change whether your business is in a sector'
}
] if data.answers['business-sector'] | length,
[
{
text: "Is your business in receipt of small business rate relief or rural rate relief as of the 11 March 2020?"
},
{
text: data.answers['rate-relief'] | getAnswerAsString('rate-relief')
},
{
html: 'Change whether your business is in receipt of small business rate relief or rural rate relief as of the 11 March 2020'
}
] if data.answers['rate-relief'] | length,
[
{
text: "Are you due to pay a self-assessment payment on account by 31 July 2020?"
},
{
text: data.answers['self-assessment-payment'] | getAnswerAsString('self-assessment-payment')
},
{
html: 'Change whether you are due to pay a self-assessment payment on account by 31 July 2020'
}
] if data.answers['self-assessment-payment'] | length
]
}) }}


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CrimPapers
Articles published on 24 December 2018
On 24 December 2018, 6 new articles were published in 5 journals.
Measuring the Evolution of Radical Right-Wing Posting Behaviors Online by Ryan Scrivens, Garth Davies, Richard Frank
Book Review: Protect, Serve, and Deport: the Rise of Policing As Immigration Enforcement by Yalidy Matos
Aggravating Effects of Alcohol Outlet Types on Street Robbery and Aggravated Assault in New York City by Shun Q. Feng, Eric L. Piza, Leslie W. Kennedy, Joel M. Caplan
Eu’s Emerging Strategic Cyber Culture(s)
Swat Unit Proactive Search Warrant Deployments: A Mixed Effects Model Exploration
Male Partner Contraceptive Interference: Associations With Destructive Conflict and Women’s Relational Power by Jennifer Katz, Jillian Larose


--- title: Getting Started layout: page pageOrder: 1 section: 'Mock Server' subsection: true sitemap: priority: 0.9 changefreq: 'monthly' lastmod: 2015-04-18T22:40:27+01:00 ---
The typical sequence for using MockServer is as follows:
MockServer is flexible and support numerous usage patterns.
{% include_subpage _includes/running_mock_server_summary.html %}To mock an HTTP / HTTPS interaction an expectation is setup specifying how a request should be matched and what action MockServer should take when the request has been matched.
When a request is matched there are three actions that can be defined as follows:
The following Java example shows the creation of an simple expectation with a respond action:
new MockServerClient("localhost", 1080)
.when(
request()
.withMethod("POST")
.withPath("/login")
.withBody("{username: 'foo', password: 'bar'}")
)
.respond(
response()
.withStatusCode(302)
.withCookie(
"sessionId", "2By8LOhBmaW5nZXJwcmludCIlMDAzMW"
)
.withHeader(
"Location", "https://www.mock-server.com"
)
);
The following Java example shows the creation of a more complex expectation with a respond action:
new MockServerClient("localhost", 1080)
.when(
request()
.withMethod("POST")
.withPath("/login")
.withQueryStringParameters(
new Parameter("returnUrl", "/account"),
new Parameter("_timestamp", "1430199066291")
)
.withCookie("sessionId", "2By8LOhBmaW5nZXJwcmludCIlMDAzMW")
.withBody(json("{username: 'foo', password: 'bar'}", MatchType.STRICT)),
Times.exactly(1),
TimeToLive.exactly(TimeUnit.MINUTES, 5l)
)
.respond(
response()
.withStatusCode(401)
.withHeaders(
new Header("Content-Type", "application/json; charset=utf-8"),
new Header("Cache-Control", "public, max-age=86400")
)
.withBody("{ message: 'incorrect username and password combination' }")
.withDelay(new Delay(TimeUnit.SECONDS, 1))
);
The following JavaScript example shows the creation of an simple expectation with a respond action:
mockServerClient("localhost", 1080).mockAnyResponse({
"httpRequest": {
"method": "POST",
"path": "/login",
"body": "{username: 'foo', password: 'bar'}"
},
"httpResponse": {
"statusCode": 302,
"headers": [
{
"name": "Location",
"values": "https://www.mock-server.com"
}
],
"cookies": [
{
"name": "sessionId",
"value": "2By8LOhBmaW5nZXJwcmludCIlMDAzMW"
}
]
}
});
The following JavaScript example shows the creation of a more complex expectation with a respond action:
mockServerClient("localhost", 1080).mockAnyResponse({
"httpRequest": {
"method": "POST",
"path": "/login",
"queryStringParameters": [
{
"name": "returnUrl",
"values": ["/account"]
},
{
"name": "_timestamp",
"values": ["1430199066291"]
}
],
"cookies": [
{
"name": "sessionId",
"values": ["2By8LOhBmaW5nZXJwcmludCIlMDAzMW"]
}
],
"body": {
"type": "JSON",
"value": JSON.stringify({ username: "foo", password: "bar" }),
"matchType": "STRICT"
}
},
"httpResponse": {
"statusCode": 401,
"headers": [
{
"name": "Content-Type",
"values": ["application/json; charset=utf-8"]
},
{
"name": "Cache-Control",
"values": ["public, max-age=86400"]
}
],
"body": JSON.stringify({ message: "incorrect username and password combination" }),
"delay": {
"timeUnit": "SECONDS",
"value": 1
}
},
"times": {
"remainingTimes": 1,
"unlimited": false
},
"timeToLive": {
"timeUnit": "MINUTES,
"timeToLive": 5,
"unlimited": false
}
});
The following Ruby example shows the creation of an simple expectation with a respond action:
client = MockServerClient.new('localhost', 1080)
expectation = expectation do |expectation|
expectation.request do |request|
request.method = 'POST'
request.path = '/login'
request.body = exact({ username: 'foo', password: 'bar' }.to_json)
end
expectation.response do |response|
response.status_code = 302
response.headers << header('Location', 'https://www.mock-server.com')
end
end
client.register(expectation)
The following Ruby example shows the creation of a more complex expectation with a respond action:
client = MockServerClient.new('localhost', 1080)
expectation = expectation do |expectation|
expectation.request do |request|
request.method = 'POST'
request.path = '/login'
request.query_string_parameters << parameter('returnUrl', '/account')
request.query_string_parameters << parameter('_timestamp', '1430199066291')
request.cookies = [cookie('sessionId', '2By8LOhBmaW5nZXJwcmludCIlMDAzMW')]
request.body = exact({ username: 'foo', password: 'bar' }.to_json)
end
expectation.response do |response|
response.status_code = 401
response.headers << header('Content-Type', 'application/json; charset=utf-8')
response.headers << header('Cache-Control', 'public, max-age=86400')
response.body = body({ message: 'incorrect username and password combination' }.to_json)
response.delay = delay_by(:SECONDS, 1)
end
expectation.times = exactly(1)
end
client.register(expectation)
For more details see the section below on creating expectations.
When a request is matched there are three actions that can occur as follows:
A response action defines a mock response containing:
Responses can be further controlled using:
For more details see the section below on creating expectations.
A forward action defines how a request should be forwarded and contains:
Only the host is mandatory as the port is defaulted to 80 and the scheme is defaulted to HTTP.
For more details see the section below on creating expectations.
A callback action defines a class that will be used to dynamically generate the response and contains:
The class must define a default constructor and implement org.mockserver.mock.action.ExpectationCallback.
For more details see the section below on creating expectations.


All About Me
Come and learn more about me!
Hi there! My name is Jenny, and I am venturing into the world of software development. I have been working in non profit for the last 8 years, and I am excited to be trying something new. Some of my hobbies include reading, photography, hiking, camping, playing or watching sports, and hanging out with friends.
Quiz Questions:
- Did I play more than one sport in high school?
- How many sports did I play?
- Do I own a dog?
- How many siblings do I have?
- Am I a fraternal twin?
- Have I traveled to every continent?
- How many have I traveled to?
- Guess my lucky number
Top Ten Places I want to Travel
- Antartica
- Portugal
- Romania
- Ecuador
- Greece
- Cambodia
- Spain
- New Zealand
- Croatia
- South Africa


hello {{user.email}},
Your account has been successfully created in the Shot in the Dark CRM by {{created_by.email}}


Your enrolment requires renewal
Renew your enrolment by {{ enrolment?.expiryDate | formatDate | default }} to maintain your access to PharmaNet.
Pending Terms of Access
You must accept the user terms of access to complete your renewal. Please use the link below to go to the Terms
of Access.
Terms of Access
Reported Absent
You are currently reported absent from
{{enrolleeAbsence.startTimestamp | formatDate}}{{enrolleeAbsence.endTimestamp ? ' - ' + (enrolleeAbsence.endTimestamp | formatDate) : ''}},
click the link below if you would like to end your absence.
Absence Management
You are now an independent PharmaNet user
Review and update your PRIME profile.
Make sure your college information is correct.
Sign the terms of access for independent users.
Share your approval notification with your PharmaNet administrator.
You have approval to access PharmaNet
Renew your enrolment by {{ enrolment?.expiryDate | formatDate }} to maintain your approval to access
PharmaNet
GPID
For your reference your GPID is:
{{ GPID }}
{{
(isInitialEnrolment)
? 'Enrolment Review'
: (withinDaysOfRenewal)
? 'Renew Enrolment'
: 'Enrolment Information'
}}
Review all the information to ensure it is accurate. Incorrect information may result in delays. If you need to edit your information, use the pencil icon to navigate to that section and make the necessary changes.
Renew Enrolment by {{ enrolment?.expiryDate | formatDate }}
Review the information below to ensure it is current and accurate. Inaccurate information may result in a
delay in your renewal for PharmaNet access. If any information below requires updating, use the pencil icon to
navigate to the appropriate editing screen.
You currently have approval to access PharmaNet. If any information below requires updating, use the pencil icon
to navigate to the appropriate section.
Your enrolment contains an error that will need to be updated before submission.
Paper Enrolment Declaration
{{ paperEnrolleeGpid }}
Personal Information from BC Services Card


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GuacaList
A Web todo-list with the delicious taste of Guacamole
Try it ! (not online yet)
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How to use it ?
How can I contribute ?
Aie Aie Caliente !
GuacaLibC
A basic C library with the delicious taste of Guacamole
Try it ! (not online yet)


This tool projects annual phenology metrics and long-term phenology trends, following methodologies described in John (2016) <https://etda.libraries.psu.edu/catalog/13521clj5135>.
| Version: | 1.2.1 |
| Depends: | R (≥ 3.4.0) |
| Imports: | dplyr, phenex, plyr, raster, stringr, rgdal, doParallel |
| Published: | 2018-03-26 |
| Author: | Christian John [aut, cre] |
| Maintainer: | Christian John <cjohn at ucdavis.edu> |
| BugReports: | https://github.com/JepsonNomad/phenomap/issues |
| License: | GPL-3 |
| URL: | https://github.com/JepsonNomad/phenomap |
| NeedsCompilation: | no |
| Materials: | NEWS |
| CRAN checks: | phenomap results |
| Reference manual: | phenomap.pdf |
| Package source: | phenomap_1.2.1.tar.gz |
| Windows binaries: | r-devel: phenomap_1.2.1.zip, r-release: phenomap_1.2.1.zip, r-oldrel: phenomap_1.2.1.zip |
| OS X binaries: | r-release: phenomap_1.2.1.tgz, r-oldrel: phenomap_1.2.1.tgz |
| Old sources: | phenomap archive |
Please use the canonical form https://CRAN.R-project.org/package=phenomap to link to this page.


|
Elektra
0.8.25
|
The gpgme
plugin is a filter plugin that enables users to encrypt values before they are persisted and to decrypt values after they have been read from a backend. The encryption and decryption is designed to work transparently.
The cryptographic operations are performed by GnuPG via the libgpgme
library.
libgpgme11
version 1.10 or laterThe plugin has been tested on Ubuntu 18.04 with libgpgme
version 1.10.
You can mount the plugin like this:
kdb mount test.ecf /t gpgme "encrypt/key=DDEBEF9EE2DC931701338212DAF635B17F230E8D"
Now you can specify a key user/t/a
and protect its content by using:
kdb set user/t/a kdb setmeta user/t/a crypt/encrypt 1 kdb set user/t/a "secret"
The value of user/t/a
(for this example: "secret") will be stored encrypted. You can still access the original value by using kdb get
:
kdb get user/t/a
The GPG recipient keys can be specified in two ways:
encrypt/key
directly.encrypt/key
.The following example illustrates how multiple GPG recipient keys can be specified:
encrypt/key/#0 encrypt/key/#1
gpgme
operates in textmode per default. In textmode the output of GPG is ASCII armored.
Textmode can be disabled by setting /gpgme/textmode
to 0
in the plugin configuration.
The encrypted values are valid PGP messages, that can be decrypted and read solely by the GnuPG binary without Elektra.