1. Introduction¶
This exercise is going to give a basic introduction to Network Security.
Network Scanning is the task of finding running services and hosts on a network. In casual parlance, “what computers are connected to this router and what do they seem to do?”
Attackers commonly perform it to understand private networks they have intruded onto. Network Engineers also use it, to understand their own networks and fix problems before Attackers can use them.
This practical teaches how to perform network scanning with commonly-used tools, how to abuse services to act as private network scanners, and the mindsets to build defendable services and networks.
To begin with here’s a basic refresher on TCP networking.
1.1. Ports¶
At one time computers ran a single program at a time. That program had complete access to everything included to the computer, including any links to other computers. Any data received from a network would be passed to that single program.
Those days are long gone: modern computers are often running dozens of different programs that want internet access at the same time. This is called multitasking. Sharing one internet connection between them is a challenge: how do you decide which program should get which received data?
Port numbers are used to solve this problem. Each network packet includes a destination port number. A program that wants to communicate asks the Operating System if it can listen to a particular 16-bit numbered port. If no program is listening on that port, the Operating System allows it and starts forwarding packets with that port number to the program.
This is remarkably useful. It also presents an issue when you’re trying to secure a computer system: how do you check if all the programs listening on the network are secure enough to be allowed to do so? From the attacker’s point of view the same checks can be used to identify potentially vulnerable services to attack.
1.2. Services¶
In the decades since multitasking arrived, engineers have tried to standardise how you do particular things across a network. There is massive technical complexity in how networks function, and without some standardisation it would impossible to keep the Internet in one piece.
1.2.1. Example: opening a webpage¶
One example of this is opening http://www.york.ac.uk/ in your browser.
DNS: Your Operating System uses the DNS protocol to map the domain name www.york.ac.uk into the IP Address <IP_ADDRESS>. Lower levels of the networking stack can use this IP Address to send packets to and from the York server.
HTTP: Your web browser now asks port 80 on <IP_ADDRESS> for that webpage. The request is phrased according to the HTTP protocol, which looks like this:
GET / HTTP/1.1 Host: www.york.ac.uk […]
A webserver program on the computer at <IP_ADDRESS> is listening on port 80. This is the default port the HTTP protocol specifies. It receives that message and replies according to the HTTP protocol.:
HTTP/1.1 200 OK Date: Wed, 10 Aug 2016 11:26:18 GMT […] <!doctype html> <html lang="en"> <head> <meta charset="UTF-8"> <title>University of York</title> […]
Within a fraction of a second you’re looking at the University of York website, thanks to decades of engineers and protocol authors designing services that work well together.
Here’s some common services we’ll see later:
- Remote login: FTP (port 21), SSH (port 22), Telnet (port 23) These are protocols designed for remotely administering servers. Generally they’ll ask for a user+password combination for the server, or something to that effect like using a cryptographic key.
- Web servers: HTTP (port 80), HTTPS (port 443) These are protocols for serving webpages.
- DNS (port 53): used to resolve domain names like york.ac.uk or tumblr.com into a 32-bit or 128-bit value that network routers can use to route your packets.
- NTP (port 123): used to keep your computer’s clock syncronised with online atomic clocks.
1.3. Network Scanning¶
You’re the administrator of your personal computer. You own it and you configure it however you like. But most people know nothing about networks or computer security. Even a lot of programmers know very little about how networks work. As such your Operating System needs to keep your computer safe through secure default settings: running few or no services and having a firewall up.
But this wouldn’t work so well for a server you administer. If you need an FTP service and a HTTP service, then you have to try and manage the risks. If it’s only for people inside your company you could restrict connections to the local network - but this can be worked around by an attacker, as we’ll explore later.
You need to firewall the things you don’t want people connecting to (like a terrible chess program you wrote last week) and expose things that people need to use (like a webserver).
Let’s start performing network scanning: Exercise 1: Scanning a Public Server


"EVENING "the period following sunset with which the Jewish day began(Genesis 1:5; Mark 13:35). The Hebrews reckoned two evenings of each day, as appears from Exodus 16:12: 30:8; 12:6 (marg.); Leviticus 23:5(marg. R.V., “between the two evenings”). The “first evening” was that period when the sun was verging towards setting, and the “second evening” the moment of actual sunset. The word “evenings” in Jeremiah 5:6 should be “deserts” (marg. R.V.).
"EVENING "the period following sunset with which the Jewish day began(Genesis 1:5; Mark 13:35). The Hebrews reckoned two evenings of each day, as appears from Exodus 16:12: 30:8; 12:6 (marg.); Leviticus 23:5(marg. R.V., “between the two evenings”). The “first evening” was that period when the sun was verging towards setting, and the “second evening” the moment of actual sunset. The word “evenings” in Jeremiah 5:6 should be “deserts” (marg. R.V.).


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Company registration documents are sent to genuine customers directly by mail or fax.
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Frequently Asked Questions
FaqWe are dealing in Copy & Kraft paper corrugators having manual, semi-automatic & fully automatic plants. We are involved in supplying the Copy Papers for the office house & Kraft Papers to corrugating plants, which converts these papers into corrugated carton boxes used in all major industries for packing the products. Our clients have been nurtured by us for over one decade by building and maintaining very close rapport at all levels.
Our management’s foresightedness keeps us analyzing our working environment at every step so that we can achieve our goal as a valued player in the global pulp, paper, and paperboard industry.
Clients Reviews
What Our Clients Say About Our Works
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Had a missing color in my previous command from the color papers. Nevertheless, the quality is outstanding. Would like to make some modifications to fill the gap.


This template contains a code fragment that can be included into file templates (Templates tab) with the help of the #parse directive.


|
Bitstream Interpretation Library (BIL)
0.1
|
Abstract base class for packet processor's registers. More...
#include <packetprocessor/Register.hpp>
Public Member Functions | |
| Register (RegisterAddress::address_t address, const std::string &name, bool writable=true, bool readable=true) | |
| Constructs a Register instance. | |
| virtual | ~Register ()=0 |
| Destructs a Register instance. | |
| RegisterAddress::address_t | address () const |
| Gets register address. | |
| const std::string & | name () const |
| Gets register name. | |
| bool | writable () const |
| Tells if register is writable. | |
| bool | readable () const |
| Tells if register is readable. | |
| virtual void | write (const boost::uint32_t *words, size_t wordCount) |
| Writes data words into the register. | |
| virtual void | read (boost::uint32_t *words, size_t wordCount) const |
| Reads data words from the register. |
Abstract base class for packet processor's registers.
Each register has an address, a name, and could be written and/or read. Derived specialized registers perform some register specific actions on reads/writes, and accept only specific data.
Definition at line 25 of file Register.hpp.
| Register::Register | ( | RegisterAddress::address_t | address, |
| const std::string & | name, | ||
| bool | writable = true , |
||
| bool | readable = true |
||
| ) |
Constructs a Register instance.
Once created, name, address, writable, and readable can not be changed.
| address | Address of the register. |
| name | Name of the register (gets copied). |
| writable | Writable flag. |
| readable | Readable flag. |
Definition at line 12 of file Register.cpp.
|
pure virtual |
Destructs a Register instance.
Definition at line 22 of file Register.cpp.
| RegisterAddress::address_t Register::address | ( | ) | const |
| const std::string & Register::name | ( | ) | const |
Gets register name.
Definition at line 34 of file Register.cpp.
| bool Register::writable | ( | ) | const |
Tells if register is writable.
Definition at line 40 of file Register.cpp.
| bool Register::readable | ( | ) | const |
Tells if register is readable.
Definition at line 46 of file Register.cpp.
|
virtual |
Writes data words into the register.
Note that different Register subclasses may accept different data (e.g. only a specific number of words or some special values), and even the same register instance may accept different data over the time dependent on its internal state.
| words | Pointer to data words to write. |
| wordCount | Number of data words to write. |
| . |
Reimplemented in bil::V5FDRIRegister, bil::V5IDCODERegister, bil::V5MFWRRegister, bil::V5CMDRegister, bil::V5CRCRegister, bil::V5FARRegister, and bil::WordRegister.
Definition at line 52 of file Register.cpp.
|
virtual |
Reads data words from the register.
Note that different Register subclasses may accept different read sizes, and even the same register instance may accept different read sizes over the time dependent on its internal state.
| words | Pointer to data buffer. |
| wordCount | Number of data words to read into buffer. |
| . |
Reimplemented in bil::V5IDCODERegister, bil::V5CMDRegister, bil::V5CRCRegister, bil::V5FARRegister, and bil::WordRegister.
Definition at line 58 of file Register.cpp.


Twin brothers Kent & Kevin Smotherman founded TwinFeats Software in 1983 with a simple premise:
Free software for a free world. From the beginnings on the Commodore 64 to today's Android apps, that
vision has never wavered.
Kent Smotherman has 38 years of professional development experience using over a
dozen programming languages. He enjoys teaching web development, chess, and
petting the family cat, Solo (obviously), though he is not a Bond villain.
Kevin Smotherman has 30 years of professional development experience. His current
role is as Lead/Advisory Software Development Engineer at CSG International. His
favorite day of the year is Groundhog Day. No one knows why.
Services
TwinFeats Software creates innovative, custom web applications which will meet and exceed expectations.
We will work with you to fully understand your needs, but more than that - your data. Web applications are
all about data, and without deeply understanding the needs of your data, your web application will suffer.
Web application? Huh?
Web sites are static. The content they display doesn't change dynamically. A web applications is a web site
powered by a database. Facebook is a great example of a web application - the data shown varies by user and
can change every single second.
Applications
We've written so many applications in the last 35+ years that we have literally lost count! Applications
like our FreedomWare line of games for the Commodore 64 are long gone, as are our productivity suite for
GEOS on the C64 and Amiga.
Ever use WebEx, GotoMeeting, or Zoom teleconferencing software? We wrote our own back in the 90s for not
just conferencing, but online learning. Our Android applications range from custom games to productivity
tools. Web sites and web applications? We've done 'em by the dozens.
As we like to say, if we can't do it at TwinFeats Software, its a hardware problem!


| 1 WEEK | 1 MONTH | 3 MONTHS | ||
|---|---|---|---|---|
| Admiral Markets | 1.1180 Bearish | 1.1180 Bullish | 1.1180 Sideways | |
| DeltaStock.com | 1.1180 Bearish | 1.1180 Bullish | 1.1180 Sideways | Author's comment |
| Danske Bank A/S | 1.1180 Bearish | 1.1180 Bullish | 1.1180 Sideways | Author's comment |
| Growth Aces | 1.1180 Bearish | 1.1180 Bullish | 1.1180 Sideways | |
| Mind Vision Traders | 1.1180 Bearish | 1.1180 Bullish | 1.1180 Sideways | Author's comment |
| Admiral Markets | 1.1180 Bearish | 1.1180 Bullish | 1.1180 Sideways |
If the NFP is a strong figure today, then the dollar’s run to higher ground will resume and the Euro will head back to 1.2300 and today’s session low at 1.2287
After a steep fall, the Euro will attempt to recover losses against the Dollar. However, this will be limited to 1.10 and we would need to close above this level for the sentiment to be more bullish.
Despite a near term rebound, the bears are back I control and this should continue to put pressure on the supports over the coming weeks.


This page demonstrates the interaction with running scroll animation, especially if there are multiple ones.
Each scroll animation belongs to a namespace
. The default namespace value is default. Whenever a
new scroll animation for the same namespace is started, all running ones will be stopped. This is allows to prevent
two scrolling animations acting on the same scrollView DOM element, where each interferes with the other ones.
All scroll animations on the same scrollView should thus share the same namespace.
default
customSpace
This containers scroll animation is started at the same time like the second one. As both are in the same namespace, this one's will be stopped at the moment the second one starts scrolling.
default
This containers scroll animation is started at the same time like the first one. Both are in the same namespace, but this one's is started second and thus actually takes place.
customSpace
This containers scroll animation uses a custom namespace customSpace
.
It is therefore capable to scroll independently of the status of the default
namespace
animations.


P2-E001¶
You are encouraged to familiarise yourself with a few topics prior to the exercise. The background section will provide you with some basic information and pointers. The exercise itself is designed to give you some practice in working with these concepts to give you a more intuitive grasp of them. The teacher will be able to supervise your during the exercise and explain the underlying theory if uncertainties remained during your preparation for the exercise.
Background¶
The following are topics you should try to familiarise yourself with prior to the exercise. Note that the serve as pointers and checks for your studies rather than as full instructional materials.
Topic 1: First, (re-)familiarise yourself with the normal distribution. Are you able to make sense of the figure, equation and labels below?
Topic 2: Next, you need to know about z-scoes. They are a standardised distance from a specific point to the mean, i.e. the distance expressed in standard deviations. In other words, the signal (distance) is put in relation to dispersion. \(\sigma\) is the standard deviation, \(\mu\) is the mean.
Topic 3: Familiarise yourself with central limit theorem and its implications.
Consider the following scenario. Variable x is distributed as shown below. Its mean (\(\mu\) ) is 3. You take n (5) random samples (S) from a population x.
For each of the samples, you can calculate the sample means.
Note that we have a mean of 2.5 twice. One time for sample 1 and one time for sample 2. If you plotted the frequency of the sample means, it would look like this:
If you increased the number of samples you take from the original set, you would see observe that the frequency is highest where \(\mu\) is located. The mean of all sample means (\(\mu_{\bar{x}}\) ) would shift closer and closer towards the population mean (\(\mu\) ).
Note also that the distribution of sample means is beginning to resemble a normal distribution in disregard of the original distribution of x. There is another very important effect of increasing the sample size. The dispersion of the sampling distribution decreases. We can thus summarise:
As sample size increases, the sample means of a random variable approaches a normal distribution even when the original variables are not normally distributed.
\(\mu \approx \mu_{\bar{x}}\)
The dispersion of the sampling distribution (\(\sigma_{\bar{x}}\) ) decreases as sample size increases. It can be computed as \(\sigma_{\bar{x}} = \frac {\sigma}{\sqrt {n}}\) . Since \(\sigma\) is usually not known, the sample standard deviation s is often used as an approximation for \(\sigma\) , giving us an approximation for the dispersion of the sampling distribution (\(\hat{\sigma}_{\bar{x}}\) ).
Topic 4: Finally, familiarise yourself with the concepts of standard error, confidence intervals, the t-distribution and how it is related to the normal distribution.
Exercise¶
Information¶
Learning goals |
|
|---|---|
Skills |
|
What to Submit
Submit your script(s) and 1-2 sentence answers to the questions for the tasks of this exercise. You may include the answers as comments in your script(s). Make sure to also comment each of your calculations (using the terminology you were introduced to). The script should be named [your surname]_e101.[ext], where [ext] is the file extension. In case of Python, this would be .py, for Matlab it is .m, etc.
Temperatures in the Atacama¶
The Atacama desert is the driest place on Earth, and it can also get a little warm. Through careful experimentation, namely GCM (General Circulation Model) simulations, you were able to construct two models in form of PDF’s (probability density functions) for summer temperatures in this desert. The simulations represent two very possible regional climate scenarios. Model 1 has a mean of 24.2°C and standard deviation of 6.1°C. Model 2 has a mean of 25.9°C and standard deviation of 5.5°C. A study suggested that mean summer temperatures above 23°C are related to increased health risks in the local population. The budget of the local goverment is large enough to keep the population healthy if the mean summer temperatures for the 30 year planning period do not exceed 24.5°C.
What is the probability of the local government running out of money within the given period and for each of the two regional climate scenarios?
Sampling Strategy - Reversing the Problem¶
You’re planning a field campaign to the Alps to collect samples of carbonates to reconstruct mean annual temperatures (MAT) for the Middle Miocene, and through some groundbreaking research, those carbonates are now able to adequately represent palaeo-MAT. You have limited time and resources, but still want to end up with a large enough sample size to be able to conduct decent research. More specifically, you want to have a sample number that allows you to be 95% confident that your true Middle Miocene MAT lies within 0.5°C of your sample mean. Temperatures constructed from similar samples of a pilot study have a standard deviation of 1.1°C.
How many samples are you planning to take?
Warning
Late submissions won’t be accepted!


| Key | Summary | T | Created | Updated | Due | Assignee | Reporter | P | Status | Resolution |
|---|---|---|---|---|---|---|---|---|---|---|
| AAA-123 | Summary description | 01/01/2021 | 01/01/2021 | FirstName LastName | FirstName LastName | In Progress | Done | |||
| AAA-123 | Summary description Summary description Summary description Summary description Summary description Summary description Summary description Summary description Summary description Summary description Summary description Summary description | 01/01/2021 | 01/01/2021 | FirstName LastName | FirstName LastName | In Progress | Done | |||
| AAA-123 | Summary description | 01/01/2021 | 01/01/2021 | FirstName LastName | FirstName LastName | In Progress | Done |


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---
layout: workshops
title: DEVit Workshops - Thessaloniki 21 May 2017
ogTitle: DEVit Workshops - Thessaloniki 21 May 2017
ogDescription: Find out the latest trends and techniques for Frontend, Backend, DevOps and Mobile! Meet all the cool people! We are 360° and proud of it!
permalink: /legacy/workshops/2017/
custom_js:
- workshop.bundle.js
---
{% assign workshops=site.data.workshops.2017 %}
- DEVit 2017
- Workshops
An all day workshop is an exclusive, low participant count, class. You will get 8 hours of intense, focused training on the state of the art technologies of the web today.
- All day Workshop Ticket price: €250
- Limited to 25 seats / workshop.
- Schedule: 11:00 AM - 8:00 PM. Registration will start at 10:00 AM.
- Coffee and drinks during the day, lunch will be all provided.
- Please note that a computer will not be provided, so please bring your own laptop to the workshop.
{% for workshop in workshops.allDay %}
{% assign speaker = site.data.speakers.2017.speakers | where:"url", workshop.speaker | first %}
{% assign speakerName = speaker.first_name | append: ' ' | append: speaker.last_name %}
{% assign speakerImg = speaker.first_name | append: '_' | append: speaker.last_name %}
| Instructor |
{{ speakerName }} |
| Skill Level |
{{ workshop.skill }} |
| Prerequisites |
{{ workshop.prerequisites }} |
| Description |
{{ workshop.description }} |
|
More info |
{% endfor %}
The Open Workshops are multi-stage classes that will cover hot web development topics by industry veterans. We gave each workshop ample time to get the message through, 2.5 hours of pure hands-on experience.
- Open Workshops Ticket price: €60€ - (€100 bundle with Speaker day)
- Schedule: 11:00 AM - 8:00 PM. Registration will start at 10:00 AM.
- Coffee and drinks during the day, lunch will be all provided.
- Please note that a computer will not be provided, so please bring your own laptop to the workshop.
{% for workshop in workshops.open %}
{% if workshop.url != "" %}
{% assign speaker = site.data.speakers.2017.speakers | where:"url", workshop.speaker | first %}
{% assign speakerName = speaker.first_name | append: ' ' | append: speaker.last_name %}
{% assign speakerImg = speaker.first_name | append: '_' | append: speaker.last_name %}
| Instructor |
{{ speakerName }} |
| Skill Level |
{{ workshop.skill }} |
| Prerequisites |
{{ workshop.prerequisites }} |
| Description |
{{ workshop.description }} |
|
More info |
{% endif %}
{% endfor %}


The Trends in International Math and Science Study (TIMSS) is a series of international assessments of the mathematics and science knowledge of students around the world. It is administered by both the International Association for the Evaluation of Educational Achievement (IEA) and Boston College (BC) who first conducted the assessments in 1995 and re-administered them every 4 years after that (1999, 2003, 2007, 2011, and 2015). Different versions of the assessments are given to 4th graders and 8th graders of participating countries. Each question in the assessment test the students’ grasp of different cognitive and content domains. Every assessment contains both multiple-choice and free-response style questions.
The following visualization is my attempt at comparing question performance among the diferent participating countries on the 8th grade math assesment. The information shown is typically displayed in tabular format such that a table represents a country and each row represents a question or vice versa. The visualization is split into two panels:
While there are a total of 57 different countries that participated in TIMSS during 2015, I chose to visualize only the 34 countries that also participated in the 2011 assesment. There are a total of 7306 different datapoints on display at any given time. It would be difficult to display such a large amount of information on a static chart. While a lot of information can actually be discovered by simply looking at the color, shape, and positional encodings in the chart, even more information can be gained by hovering over a datapoint and seeing how a question compares internationally.
There is a lot of information here. Please take your time and enjoy exploring through the data.


14. Miscellaneous Information
This section contains information not directly related to
Cayenne development, but still important in the context of this
User Guide.
14.1 Cayenne and WebObjects
For those who have never heard what WebObjects is, here is a little
introduction. WebObjects is one of the first and one of the best
web application development frameworks. It was created by NeXT (now
Apple) in the mid 90's and from the very beginning captured the essense
of web application development needs. There are two areas of WebObjects
strength: presentation layer and database access layer.
It hasn't been surpassed in both of these areas even now.
Surprisingly, main reason for that is its relative obscurity.
Mainstream Java world went the hard way and invented a lot of
different solutions to solve 2 basic issues mentioned above: presentation
(servlets, JSP) and database access (JDBC). "Standard" Java technologies
can get the job done. The only thing they lack is convenience and
elegance of WebObjects. In short, there is a hard way to do things and there
is a WebObjects way.
To learn more about WebObjects, go to
webobjects.com.
So, how is Cayenne related to that? Cayenne's goal was to fill one of the
gaps that mainstream Java world has. Namely object-oriented way to access databases.
We used patterns found in WebObjects data layer (EOF) and combined it with
the latest Java technologies to create an opensource database solution
for any kind of Java applications.
14.1.1 Terms Translation
Some design patterns used in Cayenne may be familiar to the EOF folk.
This section is intended to make Cayenne understanding
easier if you have WebObjects/EOF background. Below Cayenne
terms are defined in WebObjects terms whenever it is possible.
DataNode : EOAdaptor
DataDomain : EOObjectStoreCoordinator
DataContext : EOEditingContext
DataObject : EOEnterpriseObject
DataMap : EOModel
ObjEntity + DbEntity : EOEntity
ObjRelationship + DbRelationship : EORelationship
ObjAttribute + DbAttribute : EOAttribute
SelectQuery : EOFetchSpecification
InsertQuery, DeleteQuery, UpdateQuery :
no WebObjects analogue, closest thing is
com.webobjects.eoaccess.EODatabaseOperation
Expression : EOQualifier
Ordering : EOSortOrdering
|


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CD audiobooks
Toto: the dog-gone amazing story of the Wizard of Oz, by Michael Morpurgo, read by Christopher Ragland
“‘I was there,’ Papa Toto said, and those magic words sent shivers down my spine. It was going to be the Wizard story. ‘Dorothy and me were both there.’ We were all silent, snuggled up together, waiting, waiting.
“Then Papa Toto began….”
World’s Greatest Liar, by Barry Hutchison, read by Joe Coen
“Dylan Malone, aka Beaky, has a habit for telling porkies - every time he opens his mouth, out pops a whopper. But then his long-suffering sister shoves him into the truth-telling machine at Madame Shirley’s Marvellous Emporium of Peculiarities. Now Beaky can’t tell a lie - not even a teeny-weeny one - and a truth-telling Beaky is even worse.”
Harper and the Night Forest, by written and read by Cerrie Burnell
“Harper and her friends get to visit the mysterious Night Forest, and it is rumoured that a magical Ice Raven lives amongst the ebony trees. Will the children try to capture it? Or give it freedom?”
Miraculous Miranda, by Siobhán Parkinson, read by Caroline Lennon
“Miranda has a big imagination, and always wins Word of the Day at school. When her sister Gemma is taken into hospital, Miranda escapes into her own fantasy land, Magnanimous. With giraffe police, ham sandwich trees and a Crystal-Clear Glass Hospital for Getting-Better Children, Magnanimous grows and grows.
 Can Miranda write a miracle for her sister?”
A Home for Molly, by Holly Webb, read by Victoria Fox
“On holiday at the seaside, Anya is excited when she meets a friendly family with children her own age - playing with them and their gorgeous puppy, Molly, is so much fun!
“But when she returns to the beach the next day, she discovers the pup all on its own. Anya sets out to look for her owners, but when she eventually tracks down the family, they’re very surprised. Molly isn’t their dog - they thought she belonged to Anya! With her holiday drawing to a close, can Anya find Molly’s real owners?”
Goth Girl and the Sinister Symphony, by Chris Riddell, read by Helen Keeley
“Lord Goth is throwing a music festival at Ghastly-Gorm Hall, with performances from the finest composers in the land. Ada can’t wait, but it’s quite distracting when her grandmother is trying to find her father a fashionable new wife, there’s a faun living in her wardrobe and Maltravers is up to his old tricks. Ada must make sure everything goes to plan, and luckily help is at hand from a very interesting house guest.”
A Twist of Tales, by Julia Donaldson, read by Helen Keeley
“In this fantastical collection of stories, a king hides a terrible secret under his crown, a marvellous dream inspires an epic journey and a clever girl outwits the king!
“This collection of three lovely tales from Julia Donaldson are an ideal first step for children embarking on a lifelong journey through the wonders of stories.”
Freddie Mole, Lion Tamer, by Alexander McCall Smith, read by Dan Bottomley
“Freddie is an ordinary boy who joins the circus one day. He can’t believe his luck as he is asked to understudy some of the acts. But is he brave enough to go into the lions’ cage?”
Fairy Tales, by Hans Christian Andersen, read by Angèle Masters
“A magical storyteller’s timeless tales. Here are 16 of Andersen’s classic stories translated in the 19th century. Included are such well-known tales as ‘Thumbelina’, ‘The Little Mermaid’ and ‘The Brave Tin Soldier.’”
Montmorency and the Assassins, by Eleanor Updale, read by John Sessions
“The 19th century draws to an agonised end. A terrorist plot threatens Europe and Montmorency and his friends must fight it undercover. But with loyalties divided and convictions tested, how can they know who to trust?”
OverDrive
The Eagle of the Ninth, by Rosemary Sutcliff, read by various
“Around 117 AD the Roman Ninth Legion was ordered to put down an uprising among the Caledonian tribes. Four thousand men vanished without a trace into the swirling mists of the north of Roman Britain. Many years after their disappearance, a young centurion, Marcus Aquila, sets out to discover the truth about what happened to the legion and to its First Cohort Commander—his father.”
A Living Nightmare, by Darren Shan, read by Ralph Lister
“Darren Shan and his best friend, Steve, get tickets to the Cirque du Freak, a wonderfully gothic freak show featuring weird, frightening half-human-half-animals who interact terrifyingly with the audience. In the midst of the excitement, true terror raises its head when Steve recognizes that one of the performers — Mr. Crepsley — is a vampire!
“Steve confronts the vampire after the show finishes — but his motives are surprising! In the shadows of a crumbling theater, a horrified Darren eavesdrops on his friend and the vampire, and is witness to a monstrous, disturbing plea. As if by destiny, Darren is pulled to Mr. Crepsley and what follows is his horrifying descent into the dark and bloody world of vampires.”
BorrowBox
Midnight for Charlie Bone, by Jenny Nimmo, read by Simon Russell Beale
“Since his father died, Charlie Bone has lived with his mother and her mother in the house of his other grandmother, Grandma Bone. Looking at a picture of a couple with a baby and a cat, he suddenly discovers he can hear their voices.
“Although he tries to hide his new gift, Grandma Bone and her scary sisters soon find out and send him to Bloor’s Academy. Charlie quickly finds life at Bloor’s pretty tough, with its strict rules and the malevolent head boy, Manfred, set against him.
“When Charlie discovers that the child in the photograph is being held, hypnotised, against her will, he and his new friends with ‘gifts’ try to awaken her. But can they overcome Manfred’s sinister hypnotic gifts?”
Charlie Bone and the Time Twister, by Jenny Nimmo, read by Joe Coen
“January 1916. On the coldest night in memory, Henry Yewbeam’s cousin Zeke tricks him into using the Time Twister, a beautiful marble full of shining colours that draws him into the future.
“January 2002. Charlie meets Henry reappearing at Bloor’s in the 21st century, and realises Henry’s cousin is none other than old Ezekiel Bloor. Charlie and Fidelio are joined by Cook and the Flames in their attempts to hide Henry from Ezekiel’s spies. But Charlie must soon enter the portrait of Skarpo the sorceror and retrieve the wand of a Welsh wizard.”


Header for site
The most important part of site is the header
more
hello world
hello world
hello world
hello world


"" "
These WiFi configuration pages can be disabled for security on a shared network. If a password is supplied then they can be unlocked. Warning: if no password is supplied then it will not be possible to unlock these pages via this interface.
A restart is necessary for some changes to take effect.
Erases the device configuration stored in the flash memory and restarts the device. " "This might be useful to clear stored passwords and private configuration information." "


Finding Failure - Git Bisect
Originally posted at Hashrocket.com
Any sufficiently large development team will assuredly run into a situation where a failure occurs in a codebase that isn't exposed by the test suite. Depending on the teams' commitment to test coverage this situation might be all too common. When this happens it helps to have something to reach for, something to help you narrow down that sea of incoming code into a manageable chunk to be debugged. With only a few team members this is something that you'd likely do without thinking.
"Oh, it's broken? Must have been that commit from yesterday." -Joe Developer
What happens when Joe's team is too large for him to review every commit? Well, that's what I'd like to talk about. There is a tool for that:
Git bisect!
Git bisect is a tool that performs a binary search (more on this later) across a range of commits to help you find where an error was introduced. Unfortunately, its use is not as wide spread as it should be. Personally, I found it quite intimidating at first, though now I find myself utilizing it everytime an issue like this arises.
Let's get started
If you're anything like me you want to get started with git bisect straight away. With that in mind, we'll cover basic functionality first then move into what is actually happening later.
The first command is git bisect start
, which begins the bisect. With the bisect begun, you'll now need to mark a bad commit. You'll do that with:
git bisect bad
That will mark HEAD as a bad commit. Alternatively, you could pass the sha
of a specific commit like so:
git bisect bad <sha>
Then, unsurprisingly, you'll want to set a good commit. This will be the last known good commit. These two commands will set the outer bounds of the binary search. After the git bisect good <sha>
command, git will split the revisions and load the first guess.
At this point you'll need to find a way to determine if that commit is broken or not. Usually this can be accomplished by running a test or loading up your develpment environment. When you know the state of the commit run git bisect good
or git bisect bad
to load the next revision. Eventually git will tell you which commit the error was introduced.
Ta da! Git bisect in a nutshell.
 A binary search is also know as a half-interval search, and in my opinion, better describes what it does. I'm going to run through an example to illustrate how bisect works, but if you'd like to get a better understanding of binary search look here.
Let's assume that we have 5 commits. They'll look something like:
We notice that we have a failure at commit five. We start git bisect off with the commands above:
git bisect start
git bisect good <c1-sha>
git bisect bad
Git will do somethings under the hood (more on that later), and mark the commits like so:
After that last command git will transition your current working branch to the next revision up for testing:
We run our test to find that c3 is good and we tell git that it's good by running git bisect good
again:
And again git will take current working branch to the next revision up for testing:
We run our spec and discover that c4 is the commit the error was introduced in.
In this example, with five commits, we were able to find the commit that introduced the error without having to test each individual commit. We were able to omit testing of commit two. Over a larger range of commits this saving will be, obviously, greater. (1 + log2 N) [3]
A little deeper, still
I run Hashrocket's dotmatrix on my development machine. It sets a prompt that has information relevant to git when in a git directory. I noticed, during a debugging session, that the prompt was aware of when a bisect was happening. Naturally, this sparked my curiosity so I delved a little deeper to find out what git was doing under the surface.
So what is git doing to facilitate the bisect? Let's explore with an actual example of git bisect.
If you'd like to follow along you can pull down this repo.
Let's assume we've been developing a calculator program that adds two numbers together. Everything is looking great, until we notice that our Rspec suite no longer passes. In our haste to create the world's best calculator in Ruby we've pushed bad code up to master. We know to use bisect in this scenario so we start it up.
We run git bisect start
and to know what files git is creating we follow that with tail -n +1 -- .git/BISECT_*
to see the following output:
We know that master is bad so let's mark it git bisect bad
and again follow up with our tail command:
We can now see that git has logged the commit we've marked as bad. We remember explicitly that rspec ran green on our initial commit so we let git know that by typing git bisect good HEAD~4
Git tells us the commit that we've jumped to for test, and how many more steps we have to go.
We can see that git uses .git/BISECT_EXPECTED_REV
to set which revision is currently under test. Running our test suite verifies that this revision is good. So we tell git git bisect good
. Git again tells which revision we're on, and how many we have left.
Our test suite fails on this commit. So we mark it as bad with git bisect bad
To which Git tells us that this was the commit that introduced the error and prints the log for that commit.
In this example we could have automated the whole thing with
git bisect run rspec calculator.rb
Which would have run the test suite against each revision and marked good or bad based on the exit code of the command.
Conclusions
We've learned that Git's bisect command is a powerful way to diagnose problems as they arise in a code base. Though it can appear intimidating at first the command is actually quite simple. So next time you run into an error remember to reach for this, it may save you some time. :)
TL;DR
git bisect start
git bisect bad
git bisect good HEAD~4
git bisect run rspec <test_file>
References:
- Linux Bisect Man Page
- Git Bisect - Thoughtbot Article
- Good Stack Overflow Answer
- Fighting regressions with git bisect


-
Emma Watson UN speech
www.youtube.com
Emma Watson's moving speech about gender equality and the he for she campaign To join heforshe: http://www.heforshe.org/
-
Mo’ne Davis Dominates at Little League World Series
www.nytimes.com
With Mo’ne Davis, a 13-year-old girl, pitching a shutout in the Little League World Series, the baseball world witnessed history.
-
Companies With Women On The Board Crush Companies That Are Only Men
www.businessinsider.com
Especially in Asian countries.
-
Exposing Hidden Bias at Google - NYTimes.com
www.nytimes.com
Google, like many tech companies, is a man’s world.
-
Why Disruptors Are Always White Guys
nymag.com
We don't think of women and minorities when we think of bosses and entrepreneurs.
-
The Secret Goldman Sachs Tapes
www.bloombergview.com
The public doesn't seem to understand just how dysfunctional our financial regulatory system is, Michael Lewis writes. That may change today with a ...
-
Wheels of Change: How The Bicycle Empowered Women
www.brainpickings.org
A visual history of the steed upon which women rode into a new world. As much as I love bike culture and everything bikes stand for, I, l
-
5 Tips for Scaling NoSQL Databases: Don’t Trust Assumptions—Test, Test, ...
highscalability.com
Alex Bordei, product manager for Bigstep’s Full Metal Cloud, in Scaling NoSQL databa...
-
Ello | waxpancake
ello.co
Ello is a beautiful, simple, and ad-free social network. The Ello interface supports posting and private messaging. Ello allows users to share ...
-
Miss a Payment? Good Luck Moving That Car - NYTimes.com
dealbook.nytimes.com
The thermometer showed a 103.5-degree fever, and her 10-year-old’s asthma was flaring up. Mary Bolender, who lives in Las Vegas, needed to get her ...
-
The Power of Diverse Leadership (SSIR)
www.ssireview.org
One of the most powerful strategies funders can pursue is to invest in the leadership of those most affected by problems in their communities.
-
Emma Watson at HeForShe 2014
www.youtube.com
More information on: https://www.facebook.com/emmazingbr You can check the official video here: https://www.youtube.com/watch?v=Q0Dg226G2Z8 And ...
-
Register To Vote
nationalvoterregistrationday.org
The online voter registration form is safe, secure, and easy to use. Sensitive information is held on a secure server and then discarded.
-
Emma Watson HeForShe Speech at the United Nations | UN Women 2014
www.youtube.com
UN Women Global Goodwill Ambassador, Emma Watson, delivers her moving remarks during the HeForShe Special Event at United Nations Headquarters in New ...
-
Even in Palin country, raising the minimum wage is wildly popular
www.motherjones.com
With Congress paralyzed, 5 more states will let voters settle the issue.
-
Facebook updates its algorithm (yet) again. Here's what to expect.
gigaom.com
Facebook has tweaked its algorithm to surface more timely stories. Is the company trying to redeem itself from the Ferguson-Ice Bucket feed problem?
-
New Law Emphasizes Diversity in Health Care Workforce | California ...
cpehn.org
OAKLAND, Calif. – On September 18th, Governor Jerry Brown helped California take an important step in identifying shortages in the health care ...
-
Asset Limits Are a Barrier to Economic Security and Mobility
www.americanprogress.org
It is long past due to reform asset limits for safety net programs that work at odds with the goals of family economic security and mobility.
-
A Long, Ugly Year of Depression That's Finally Fading - Rand's Blog
moz.com
Yesterday morning I woke up early to speak at the Business of Software conference in Boston. It was my first time there, and it’s an exceptional ...
-
You Can’t Feed a Family With G.D.P.
www.nytimes.com
The census numbers on what American families made last year are as mediocre as they are predictable.
-
Missouri Enacts 72-Hour Wait for Abortion
www.nytimes.com
The Republican-controlled legislature enacted one of the most stringent waiting periods for women seeking abortions, overriding a veto by the state’s ...
-
Grants Management Associate | Women's Foundation of California
www.womensfoundca.org
Reports to: Director of Programs Classification: Non-exempt, temporary Schedule: Part-time (25–30 hours/week) Location: San Francisco POSITION ...
-
Changing (feat. Paloma Faith) - Single by Sigma
itunes.apple.com
Preview and download Changing (feat. Paloma Faith) - Single on iTunes. See ratings and read customer reviews.
-
Putting women and girls at the center of development
www.sciencemag.org
The development field needs to be more serious about gender inequities and women’s empowerment. By ignoring gender inequities, many development ...
-
Startup Weekend Guayaquil 2014
www.eventbrite.com
Para más detalles acerca del evento, visita guayaquil.startupweekend.org ¡Gracias! Startup Weekend Crew Acerca de Startup Weekend: Startup Weekend ...
-
Using Gambling to Entice Low-Income Families to Save
www.nytimes.com
A growing number of credit unions and nonprofit groups are using lotteries to encourage low-income families to save.
-
Surina Khan | Women's Foundation of California
www.womensfoundca.org
<EMAIL_ADDRESS>SF office:<PHONE_NUMBER> LA office:<PHONE_NUMBER> @surinakhan
-
How to Close the Startup Gender Gap
online.wsj.com
An expert panel looks at how to build women's presence in the entrepreneurship world.


Before Change
return gateway
gateways = hass.data.get(MYSENSORS_GATEWAYS)
if gateways is not None:
_LOGGER.error(
"%s already exists in %s, will not setup %s component",
MYSENSORS_GATEWAYS, hass.data, DOMAIN)
return False
// Setup all devices from config
gateways = []
conf_gateways = config[DOMAIN][CONF_GATEWAYS]
for index, gway in enumerate(conf_gateways):
device = gway[CONF_DEVICE]
persistence_file = gway.get(
CONF_PERSISTENCE_FILE,
hass.config.path("mysensors{}.pickle".format(index + 1)))
baud_rate = gway.get(CONF_BAUD_RATE)
tcp_port = gway.get(CONF_TCP_PORT)
in_prefix = gway.get(CONF_TOPIC_IN_PREFIX)
out_prefix = gway.get(CONF_TOPIC_OUT_PREFIX)
ready_gateway = setup_gateway(
device, persistence_file, baud_rate, tcp_port, in_prefix,
out_prefix)
if ready_gateway is not None:
gateways.append(ready_gateway)
if not gateways:
_LOGGER.error(
"No devices could be setup as gateways, check your configuration")
return False
hass.data[MYSENSORS_GATEWAYS] = gateways
for component in ["sensor", "switch", "light", "binary_sensor", "climate",
"cover"]:
discovery.load_platform(hass, component, DOMAIN, {}, config)
discovery.load_platform(
hass, "device_tracker", DOMAIN, {}, config)
discovery.load_platform(
After Change
device, persistence_file, baud_rate, tcp_port, in_prefix,
out_prefix)
if ready_gateway is not None:
gateways[id(ready_gateway)] = ready_gateway
if not gateways:
_LOGGER.error(


A server error occurred. Please contact an administrator.
Invalid username or password.
Your account is currently locked. If you are unsure why or for how long, please contact a
moderator.
Login failed for unknown reason, please contact an administrator.


The Lazy Person's Guide to Saving the World
The Sustainable Development Goals are important, world-changing
objectives that will require cooperation among governments, international
organizations and world leaders. It seems impossible that the average
person can make an impact. Should you just give up?
No! Change starts with you. Seriously. Every human on earth - even the
most indifferent, laziest person among us - is part of the solution.
Fortunately, there are some easy things we can adopt into our routines,
that, if we all do it, will make a big difference.
Have a look at just a few of many things you can do to make an impact!
Things you can do from your couch
Level 1
Sofa Superstar
-
Save electricity by plugging appliances into a power strip
and turning them off completely when not in use, including your computer.
-
Stop paper bank statements and pay your bills online of via mobile.
-
Speak up! Ask your local and national authorities to engage in initiatives
that don't harm people or the planet. You can also voice your support for the
Paris Agreement and ask your country to ratify it or sign it if it hasn't yet.
-
Turn off the lights. Your TV or computer screen provides a cosy glow,
so turn off other lights if you don't need them.
-
Do a bit of online research and buy only from companies that you know
have sustainable practices and don't harm the enviroment.
-
Report online bullies. If you notice harassment on a message board or
in a chat room, flag that person.
-
In addition to the above, offset your remaining carbon emissions!
You can calculate your carbon footprint and purchase climate credits from
Climate Neutral Now. In this way, you help reduce global emissions faster!
Things you can do from home
Level 2
Household Hero
-
Air dry. Let your hair and clothes dry naturally instead of running a machine.
If you do wash your clothes, make sure the load is full.
-
Take short showers. Bathtubs require gallons more water than a 5-10 minute shower.
-
Eat less meat, poultry, and fish. More resources are used to provide meat
than plants.
-
Compost - composting food scraps can reduce climate impact while
also recycling nutrients.
-
Recycling paper, plastic, glass, and aluminium keeps landfills from growing.
-
Buy minimally packaged goods.
-
Plug air leaks in windows and doors to increase energy efficiency.
-
Adjust your thermostat, lower in the winter, higher in summer.
-
Replace old appliances with energy efficient models and light bulbs.
Things you can do in your neighbourhood
Level 3
Neighbourhood Nice Guy
-
Shop local. Supporting neighbourhood businesses keeps people employed
and helps prevent trucks from driving far distances.
-
Let your favourite businesses know that ocean-friendly seafood
is on your shopping list.
-
Shop only for sustainable seafood. There are now many apps that will
tell you what is safe to consume.
-
Bike, walk, or take public transport. Save the car trips for when
you've got a big group.
-
Use a refillable water bottle and coffee cup. Cut down on waste and
maybe even save money at the coffee shop.
-
Bring your own bag when you shop. Pass on the plastic bag and start
carrying your own reusable totes.
-
Take fewer napkins. You don't need a handful of napkins to eat your
takeout. Take just what you need.
-
Shop vintage. Brand-new isn't necessarily the best. See what you can
repurpose from second-hand shops.
-
Donate what you don't use. Local charities will give your gently used
clothes, books, and furniture a new life.
-
Take advantage of your right to elect the leaders in your country
and local community.
Things you could do at work
Level 4
Exceptional Employee
-
Give fruits or snacks that you don't want to someone who needs
and is asking for help.
-
Does everyone at work have access to healthcare? Find out what
your rights are to work. Fight against inequality.
-
Mentor young people. It's a thoughtful, inspiring, and a powerful
way to guide someone towards a better future.
-
Voice your support for equal pay for equal work.
-
Lend your voice to talk about the lack of toilets in many communities
around the world!
-
Make sure your company uses energy efficient heating and cooling technology.
-
Raise your voice against any type of discrimination in your office. Everyone
is equal regardless of their gender, race, sexual orientation, social background,
and physical abilites.
-
Bike, walk, or take public transportation to work. Save the car trips
for when you've got a big group.
-
Organize a No Impact Week at work. Learn to live more sustainably
for at least a week: Be the Change.
https://www.un.org/sustainabledevelopment/takeaction/


public GameModule
@NotNull Timer getTimer()
class Timer
for this game.void setTimer(@NotNull Timer timer)
Set remaining class Timer
for this game.
class Timer
void setCanJoinAfterStart(boolean p)
After the game starts, you decide to allow or deny players from joining this game. (Defaults to false)
void setCanRespawn(boolean p)
Whether or not to let players respawn upon death. (Defaults to false)
void setKeepInventory(boolean keep, boolean drop)
Decide if players' items should be kept upon death.
void setRespawnTimer(@NotNull Timer timer)
The amount of class Timer
players have to wait before they respawn.
class Timer
void setLastManStanding(boolean enable)
Toggle LastManStanding mode.
In this mode, the player (or its team) wins if they have survived and the others are dead.
enable
- true to enable LMS. (Defaults to false)void setGameMode(@NotNull org.bukkit.GameMode mode)
Set default GameMode.
void setPVP(boolean pvp)
Control the ability of players to attack each other.
void setMoneyReward(@NotNull org.bukkit.entity.Player player, double amount)
void setItemReward(@NotNull org.bukkit.entity.Player player, @NotNull org.bukkit.loot.LootTable lootTable)
Set lootTable the Player will be rewarded at the end.
Use ItemModule.getLootTable
to get a loot table.
ItemModule.getLootTable
void broadcast(@NotNull java.lang.String message)
Broadcast message
to everyone in this game.
message
void finishGame(@NotNull org.bukkit.scoreboard.Team winner, @NotNull Timer timer)
Finish game and mark winner team.
winner
- The winning team.timer
- Amount of time to celebrate.void finishGame(@NotNull org.bukkit.entity.Player winner, @NotNull Timer timer)
Finish game and mark a winner.
winner
- The winner.timer
- Amount of time to celebrate.void drawGame(@NotNull Timer timer)
Finish game and mark it as draw.
timer
- Amount of time to celebrate.


{% extends "template.html" %} {% block content %}
To start the game, click above on 'Verbum Disco game'.
The word and translated word in .txt file have to be in a one line separated by semi-column and 1 space.
Each 2-tuple of words have to be in new line. At least 5 2-tuples of words should be added.
The set of the uploaded words will be stored.


model_share.tamoc_nc_file¶
-
model_share.
tamoc_nc_file
(fname, title, summary, source)[source]¶ Write the header meta data to an netCDF file for a TAMOC output
The TAMOC suite stores its output by detaul in a netCDF dataset file. This function writes the standard TAMOC metadata to the header of the netCDF file.
- Parameters
- fnamestr
File name of the file to write
- title: str
String stating the TAMOC module where the data originated and the type of data contained.
- summarystr
String summarizing what is contained in the dataset or information needed to interpret the dataset
- sourcestr
String describing the source of the data in the dataset or of related datasets
- Returns
- ncnetCDF4.Dataset object
The netCDF4.Dataset object containing the open netCDF4 file where the data should be stored.


SSI from Current US Army Reserve Units
63rd Infantry Division
Wikipedia Article
76th Infantry Division
Wikipedia Article
75th Innovation Command
Wikipedia Article
78th Infantry Division
Wikipedia Article
80th Training Command
Wikipedia Article
81st Readiness Division
Wikipedia Article
84th Training Command
Wikipedia Article
95th Training Division
Wikipedia Article
96th Sustainment Brigade
Wikipedia Article
98th Training Division
Wikipedia Article
100th Training Division
Wikipedia Article
104th Training Division
Wikipedia Article
108th Training Command
Wikipedia Article


library(torch)
In this article we describe various ways of creating torch
tensors in R.
You can create tensors from R objects using the torch_tensor
function. The torch_tensor
function takes an R vector, matrix or array and creates an equivalent torch_tensor
.
You can see a few examples below:
torch_tensor(c(1,2,3)) # conform to row-major indexing used in torch torch_tensor(matrix(1:10, ncol = 5, nrow = 2, byrow = TRUE)) torch_tensor(array(runif(12), dim = c(2, 2, 3)))
By default, we will create tensors in the cpu
device, converting their R datatype to the corresponding torch dtype
.
Note currently, only numeric and boolean types are supported.
You can always modify dtype
and device
when converting an R object to a torch tensor. For example:
torch_tensor(1, dtype = torch_long()) torch_tensor(1, device = "cpu", dtype = torch_float64())
Other options available when creating a tensor are:
requires_grad
: boolean indicating if you want autograd
to record operations on them for automatic differentiation.pin_memory
: – If set, the tensor returned would be allocated in pinned memory. Works only for CPU tensors.These options are available for all functions that can be used to create new tensors, including the factory functions listed in the next section.
You can also use the torch_*
functions listed below to create torch tensors using some algorithm.
For example, the torch_randn
function will create tensors using the normal distribution with mean 0 and standard deviation 1. You can use the ...
argument to pass the size of the dimensions. For example, the code below will create a normally distributed tensor with shape 5x3.
x <- torch_randn(5, 3) x
Another example is torch_ones
, which creates a tensor filled with ones.
x <- torch_ones(2, 4, dtype = torch_int64(), device = "cpu") x
Here is the full list of functions that can be used to bulk-create tensors in torch:
torch_arange
: Returns a tensor with a sequence of integers,torch_empty
: Returns a tensor with uninitialized values,torch_eye
: Returns an identity matrix,torch_full
: Returns a tensor filled with a single value,torch_linspace
: Returns a tensor with values linearly spaced in some interval,torch_logspace
: Returns a tensor with values logarithmically spaced in some interval,torch_ones
: Returns a tensor filled with all ones,torch_rand
: Returns a tensor filled with values drawn from a uniform distribution on [0, 1).torch_randint
: Returns a tensor with integers randomly drawn from an interval,torch_randn
: Returns a tensor filled with values drawn from a unit normal distribution,torch_randperm
: Returns a tensor filled with a random permutation of integers in some interval,torch_zeros
: Returns a tensor filled with all zeros.Once a tensor exists you can convert between dtype
s and move to a different device with to
method. For example:
x <- torch_tensor(1) y <- x$to(dtype = torch_int32()) x y
You can also copy a tensor to the GPU using:
x <- torch_tensor(1) y <- x$cuda())


Unforyunately I was absent on this day. It was election in our place and I must vote. It takes about 5-6 hours from my place to fablab so no way to reach fablab after giving the vote.
On this day the they discussrd about project. The found the problems of fablab so that the solutions for each problem can be taken as project.
We spent these days for doing a project. Since the time is less Luciano asked us to do projects in group. So me and Sibu are doing the project togethr. The project is making a Soldering station. We selected this idea because there is a damaged soldering iron of "Weller" in the fab lab. The only part which is in condition is its tip. So we decided to make a soldering station using the iron of Weller.
I am documenting the project seperately , Here is the link.
Project
Thursday(day 14) is the last day with Luciano at fablab kochi.Since our laser cutter is not working properly, tomorrow we all will go to Fablab Trivandrum for learning about Laser cutter. After that Luciano will be staying there in trivandrum and Fransisco will be our trainer for the next week.
Off to Trivandrum for the Laser Cutter
Since the laser cutter in our lab is not working , we went to trivandrum fablab which is about to travel 5-6 Hour. We Luciano went there yesterday itself and we left Kochi at 5AM morning. Around 10 AM we reached Fablab, Trivandrum. Both Fransisco and Luciano was there.
The laser cutter there is of Trotec.Laser cutter is a machine which can cut small wood, acrylic, fibers, and even metel (some moachines with higher power).There is no Fabmodule for laser cutter there right now so we used the deafault windows software provided by Trotec. We all had some confusion with using it. Not confusion actually, many of the options are not working properly in that software, but we fix it finally. Fab modules are necessary for laser cutter also. Heard it is available in the new web version of fab module. Anyway it was running in windows OS there
We hav been designing all the things in rhino. So here also we started with Rhino.
The steps are
We will design somthing in Rhino
Will give print option
Select printer as Trotec
A new window by trotec software will come
Select the options (cut or engrave)
Finally the cut
By default, the trotec software has some clor code. That means if the drawing are in black (it can be changed by the user) it will cut the thing (what we want to cut), and if it is in red , it will engrave. So choose the color color of the design according to the operaton we want to do.
The cut and the engrave is differntiated according to the speed. If the laser is going very slowly, then it will cut the thing. But if it is goingso fast, then there is no enough time and power to cut the thing, so it engraves the material. So also by adjusting the speed, we can choose the option as cut or engrave. This is what we did. It was not working properly with the color options (some problem, don't know why) . So we choosed the operation by adjusting the speed.
Following are the steps what i did. Sorry that I couldn't take the screenshots.
The design
Downloaded a pic from the Internet.
Using gimp made it to .png and grayscale mode. Then i imported the pic in to Rhino usith the command "Pictureframe". Resized it. Gave print option and selected trotec as printer. So the trotec window came out. Clicked on the "Set" button to place our design on the required portion of the material. So the position of the material where to be cut/ engrave is fixed
I am engraving this pic in a wood piece. So i choose the material as wood. There is a small button in the "Material settings". This is to set the power and speed. Clicked on it. Now another window will come. Set the process corresponding to BLACK color as engrave. Gave the power as 65 and speed as 100. Then clicked OK.
Next is to set the Z axis of the laser cutter. There are buttons on the laser cutter for adjusting Z axis. There is a metal tool which is to be hung on the head of laser cuttrer. Then slowly pove the plattform (where the wood to be cut is placed) upwards by UP button. When it touches the hanging tool, the tool will falll down. Now this is the required Z axis. So Z axis is set
Next is to give print. The machine will start printing.
For cutting it out we have to do the same procedure like we did in PCB milling. We should try with another cut out file. Althogh there is option to do both steps in a single step with two different colours, it was not working properly.This is why we tried seperately with seperate files.
Repeat all the procedure. Hide the image in rhino and shoe only the cut out border. Give print, select trotec ,set material as wood. Next comes the change. Select the process for black color as cut. give speed as 1.5 and power as 90. Give print
Now the laser cutter will start cutting.
Here the final product


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Data Cleansing:
Before we are able to use the data set provided on our algorithm, we need to clean the data. The data contained several columns with a value of “#DIV!”. So while reading the data from file, I turned “#DIV!” into NA. So the total NA values include cell values with “NA” as well as cell values with “#DIV!”.
data <- read.csv(“pml-training.csv”, header = TRUE, na.string = c(“#DIV!”, “NA”, stringsAsFactors = FALSE)
Quick visual glance at the data tells us that first 6 columns are not contributing to predict “classe”. They were time variables, user name. So out of 160 variables read, I removed the first 6 variables as part of cleansing.
data <- data <- [, -(1:6)]
Another thing that stands out by visual glance at the data is that there are some columns where most values equal to “NA”. So, to take care of that, I removed any columns from our dataset that have more than 80% “NA” values.
cols <- apply (data, 2, function(x) sum(is.na(x)))
data_na <- data[, cols < (nrow(data) * .2)]
This removed another hundred columns from our data and we are looking at a reasonable 54 columns instead of original 160. But of course we can do better. Now, I looked at highly correlated predictors and eliminated anything that has a correlation greater than 90%. This really can be reduced to even 70-80% but I wanted to make sure that I don’t compromise on accuracy.
highlyCorPred <- findCorrelation(cor(data_na[, -54]), cutoff = 0.9)
data_na <- data_na [, -highlyCorPred]
This leaves me with only 41 variables to use as predictors.
At this point I thought about using PCA to further reduce the predictors but using PCA means I will lose the ability to infer. Given dataset is only 12MB, and 41 predictors is not a terribly bad number even if you have a laptop, so I will keep my 41 variables and not use PCA. A quick test on trying to use PCA tells me that 20 variables will retain 90% variability.
Now, I am ready to create my training, cross validation and test set.
inTrain <- createDataPartition (y=data_na$classe, p=0.7, list=FALSE, t=2)
training <- data_na [, inTrain]
testing <- data_na[, -inTrain]
x_validation <- data_na[-inTrain[, 2], ] #notice t = 2 in createDataPartition
Choosing algorithm:
All datasets have been prepared and data have been cleaned. Now I have to choose an algorithm.
I initially tried to use linear algorithm rather than using a tree algorithm. Very soon I learned the glm cannot be used to predict multiple classes. In other words, the problem we have is a multi class classification which glm canbe used to solve binary classification. So that goes out the door.
Then I was down to using tree algorithms. I could create my model using rpart, random forrest or bagging (tree bag).
On first run of trying to create a model using rpart, I got an error on classe variable. It has been read as “chr”. This needs to be Factor. So, I converted classe to factor.
training$classe <- as.factor (training$classe)
now run the model using rpart
modelFit <- train (classe ~ ., method = "rpart", data=training)
Looking at this tree model we can easily traverse and see that our predictors that are really contributing to predict classe variable are six. They are:
Pitch_forearm
Magnet_belt_y
Magnet_dumbbell_y
Role_forearm
Magnet_dumbbell_z
num_window
This unfortunately is not very accurate. Here are the results for rpart
> modelFit$results
cp Accuracy Kappa AccuracySD KappaSD
1 0.03473706 0.5470362 0.4265475 0.01535966 0.01887568
2 0.03867867 0.5113930 0.3726988 0.03362248 0.05275437
3 0.06602838 0.3969048 0.1859103 0.09391434 0.15495164
As you can see, Accuracy is horrible. So this is not the model, I want to use. Next I want to try bagging to see if it improves accuracy.
For this purpose I choose treebag.
> modelFit <- train (classe ~ ., method = "treebag", data=training)
> modelFit$results
parameter Accuracy Kappa AccuracySD KappaSD
1 none 0.9941846 0.9926424 0.001983685 0.002510777
This model is pretty accurate, to the point that I think this is over fitting my data. Being a novice, I am not sure if a model without over fitting can be this good.
So, I want to try other methods like gbm.
I create another model based on “gbm” and here is what I get.
gbm_modelFit <- train(classe~ ., method = "gbm", data=training, verbose = FALSE)
> gbm_modelFit$results
shrinkage interaction.depth n.minobsinnode n.trees Accuracy Kappa AccuracySD KappaSD
1 0.1 1 10 50 0.7362443 0.6652206 0.005829953 0.007295411
4 0.1 2 10 50 0.8744365 0.8409511 0.004568948 0.005805287
7 0.1 3 10 50 0.9323927 0.9143904 0.005082109 0.006464350
2 0.1 1 10 100 0.8097637 0.7590058 0.004130229 0.005157751
5 0.1 2 10 100 0.9397778 0.9237704 0.003868634 0.004899705
8 0.1 3 10 100 0.9764632 0.9702163 0.002602426 0.003298157
3 0.1 1 10 150 0.8540641 0.8152253 0.003417016 0.004296838
6 0.1 2 10 150 0.9670954 0.9583618 0.003075631 0.003896764
9 0.1 3 10 150 0.9901671 0.9875584 0.001504606 0.001906313
I run this gbm model on my x-validation set and results are pretty encouraging.
> prediction <- predict(gbm_modelFit, x_validation_set)
> confusionMatrix(prediction)
Confusion matrix of this model tells us that its pretty accurate.
Statistics by Class:
Class: A Class: B Class: C Class: D Class: E
Sensitivity 0.9970 0.9886 0.9864 0.9865 0.9945
Specificity 0.9981 0.9966 0.9975 0.9978 0.9992
Pos Pred Value 0.9952 0.9860 0.9883 0.9886 0.9963
Neg Pred Value 0.9988 0.9973 0.9971 0.9974 0.9988
Prevalence 0.2845 0.1935 0.1743 0.1638 0.1839
Detection Rate 0.2836 0.1913 0.1720 0.1616 0.1828
Detection Prevalence 0.2850 0.1941 0.1740 0.1635 0.1835
Balanced Accuracy 0.9976 0.9926 0.9919 0.9921 0.9968
> gbm_modelFit
Stochastic Gradient Boosting
27474 samples
41 predictor
5 classes: 'A', 'B', 'C', 'D', 'E'
No pre-processing
Resampling: Bootstrapped (25 reps)
Summary of sample sizes: 27474, 27474, 27474, 27474, 27474, 27474, ...
Resampling results across tuning parameters:
interaction.depth n.trees Accuracy Kappa Accuracy SD Kappa SD
1 50 0.7362443 0.6652206 0.005829953 0.007295411
1 100 0.8097637 0.7590058 0.004130229 0.005157751
1 150 0.8540641 0.8152253 0.003417016 0.004296838
2 50 0.8744365 0.8409511 0.004568948 0.005805287
2 100 0.9397778 0.9237704 0.003868634 0.004899705
2 150 0.9670954 0.9583618 0.003075631 0.003896764
3 50 0.9323927 0.9143904 0.005082109 0.006464350
3 100 0.9764632 0.9702163 0.002602426 0.003298157
3 150 0.9901671 0.9875584 0.001504606 0.001906313
Tuning parameter 'shrinkage' was held constant at a value of 0.1
Tuning parameter 'n.minobsinnode' was
held constant at a value of 10
Accuracy was used to select the optimal model using the largest value.
The final values used for the model were n.trees = 150, interaction.depth = 3, shrinkage = 0.1
and n.minobsinnode = 10.
The best tune option for this model is
>gbm_modelFit$bestTune
n.trees interaction.depth shrinkage n.minobsinnode
9 150 3 0.1 10
Another important aspect to look at is the finalModel object. This will tell us how many variables actually contributed.
>gbm_modelFit$finalModel
A gradient boosted model with multinomial loss function. 150 iterations were performed. There were 41 predictors of which 33 had non-zero influence.
Plotting the model to see the estimates of performance and tuning parameters.
As you can see, Accuracy increases as we increase number of iterations and tree depth.
At this point, I can use either gbm or treebag. I continue with treebag.
In Sample and Out of Sample Error:
To check In Sample Error, run predict and then show confusion matrix on training data.
> confusionMatrix(train_predict, training$classe)
Confusion Matrix and Statistics
Reference
Prediction A B C D E
A 7804 23 0 0 0
B 6 5274 36 5 0
C 0 9 4748 37 2
D 2 10 8 4460 19
E 0 0 0 2 5029
Overall Statistics
Accuracy : 0.9942
95% CI : (0.9932, 0.9951)
Accuracy in case of in sample is 99.42%.
To check out of sample error, I ran predict on cross validation and then call the confusion matrix to see out of sample error.
> prediction <- predict(modelFit, x_validation_set)
> confusionMatrix(prediction, x_validation_set$classe)
Confusion Matrix and Statistics
Reference
Prediction A B C D E
A 1669 8 0 0 0
B 4 1126 12 0 0
C 0 2 1012 10 0
D 1 2 2 951 6
E 0 1 0 3 1076
Overall Statistics
Accuracy : 0.9913
95% CI : (0.9886, 0.9935)
From both confusion matrices, we can see that in sample error is lower than out of sample error. Accuracy is high.
Finally I ran the model on test data created using “CreateDataPartition()” earlier and the results are very encouraging. The out of sample error is little more than train error as expected but accuracy of algorithm is still pretty high.
> confusionMatrix(test_predict, testing$classe)
Confusion Matrix and Statistics
Reference
Prediction A B C D E
A 481 3 0 0 0
B 0 343 2 0 0
C 0 0 310 5 0
D 0 1 1 277 2
E 0 1 0 1 344
Overall Statistics
Accuracy : 0.991
Variable of Importance:
To find which variables are contributing most towards prediction, I ran varImp() method on my model. Please see the result.
> varImp(modelFit)
treebag variable importance
only 20 most important variables shown (out of 41)
Overall
num_window 100.00
yaw_belt 74.09
magnet_dumbbell_y 46.49
magnet_dumbbell_z 43.54
accel_dumbbell_y 37.31
roll_forearm 34.70
magnet_belt_y 34.32
pitch_forearm 33.18
total_accel_belt 29.30
gyros_belt_z 29.02
roll_dumbbell 27.39
magnet_dumbbell_x 24.19
magnet_belt_z 22.53
magnet_forearm_z 18.86
total_accel_dumbbell 17.55
magnet_belt_x 15.46
roll_arm 14.91
magnet_arm_x 14.40
accel_forearm_x 13.14
yaw_dumbbell 12.33
We can see 20 most important variables that are contributing towards the outcome. Knowing these, we can have our users improve their exercise so they are doing it right.


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