Using the Flip Grip is the best way to play retro and pinball Switch games
Source: iMore
One of the things I love about the Nintendo Switch, aside from its amazing portability, is the number of retro games you can play on the console. There are hundreds if not thousands of NeoGeos, pinball games, and classic collections for anyone who loves old-school gaming. However, since many retro games were designed for a cabinet console with a vertical screen, it can be odd playing these games on the Switch. Enter the Fangamer Flip Grip. This unique accessory allows you to slide your Joy-Cons onto a vertically-oriented Switch screen to better play your favorite classics.
I spent several hours testing the Flip Grip to see how well it worked. I paid particular attention to how well the accessory was made, the price, and the number of games that can be used with it. Here's my review of the Fangamer Flip Grip for Nintendo Switch.
Cabinet window
Fangamer Flip Grip
A great accessory for retro games
Bottom line: This Switch accessory is the best way to enjoy games that work in vertical mode. It's sturdy, looks good, and doesn't cost very much. You'll just need to make sure that the game you want to use it with works in the proper orientation.
Pros
- Sturdy build
- Easy to use
- Lightweight
- Looks good
Cons
- Only works with the Switch in one direction
- Games must orient in the right direction
Fangamer Flip Grip What I like
Source: iMore
Sturdy build A well-made accessory
Despite only costing $12, the Flip Grip is made of super sturdy materials and doesn't bend when pressed on. There are felt pads on the inside of the device to prevent your console from getting scuffed up when slid in and out. What's more, it looks great. A fun logo is etched into the backside and all the divots and recesses look beautiful without any sharp edges.
It's a lightweight accessory that doesn't get in the way of my hands.
When I'm playing one of my favorite vertically-oriented games, I love the way the Switch looks and feels. It doesn't look strange and if I'm not paying attention, I forget that Flip Grip is even there. The accessory doesn't weigh much and it doesn't get in the way of my hands when I'm playing. What's more, the casing is designed to allow your Switch to vent like it needs to. There are openings along the sides of the Flip Grip that allow heat to escape, leaving your Switch running smoothly.
Easy to use Just slip the Switch in
This device is really straightforward. You simply slide your Switch Screen into the middle opening and then slide your Joy-Cons onto either side. When in place, your Switch screen and Joy-Cons remain in place and don't slide around. The Joy-Cons remove from the accessory they same way they detach from your Switch; you press the release button down and then slide them off.
Removing the Switch is a little strange, but is doable. There's a tab on the back with an orange square. You need to either slide your finger or a something small, like a credit card, below the tab to release your Switch screen. After that, the Switch comes out no problem.
Fangamer Flip Grip What I don't like
Source: iMore
Not as versatile Rigid Switch direction limits compatible games
While there are several Flip Grip compatible Switch games, there are also many that don't work with it. This is due to the fact that you can only insert the Switch screen into the Flip Grip in one direction with the cartridge slot up top. Some of the games that offer vertical mode only flip the screen in one vertical direction, which could lead to the game showing up upside down when using the Flip Grip.
The Switch only inserts in one direction, so the Flip Grip only works with some vertical-oriented games.
Unfortunately, while most publishers are good about telling you if a game supports vertical mode, they don't tend to tell you which direction the vertical mode comes in at. Some games allow every orientation possible, but others are limited to one direction. For this reason, it can be a gamble figuring out which games are compatible with the Flip Grip.
On a side note, in order to change the screen orientation of a game, you typically have to go into the settings menu and make the change. However, some games, like Star Wars Pinball, allow you to rotate the screen orientation simply by pressing a button on the screen.
One other thing about the design that's a little odd is that this orientation makes it so you cannot access the power or volume buttons while your Switch is in the Flip Grip. However, you can still resort to pressing the Home button and then entering Sleep Mode or changing the volume that way.
Fangamer Flip Grip Should you buy it?
Source: iMore
While the Flip Grip doesn't work with every retro game, it makes gaming sessions a lot more convenient for the titles it does work with. Price-wise, $12 isn't much to pay for the convenience of vertical gaming and the accessory itself is built very sturdy and feels good in your hands. If you have a specific game in mind that you know works with the Flip Grip, you should really consider getting this accessory.
4 out of 5
The Fangamer Flip Grip is a well-built and well-priced accessory that can help you play your favorite retro games more comfortably.
Cabinet window
Fangamer Flip Grip
A great accessory for retro games
This Switch accessory is the best way to enjoy games that work in vertical mode. It's sturdy, looks good, and doesn't cost very much. You'll just need to make sure that the game you want to use it with works in the proper orientation.
We may earn a commission for purchases using our links. Learn more.


Student Profile Name Points Name Points ID Name Points Short Description Add New Achievement AchievementID:


What are bookmarklets and what are they good for?
Bookmarklets are "Bookmarks" (Chrome, Firefox) or "Favorites" (Internet Explorer/Edge).
In contrast to regular bookmarks they will not lead you directly to another page. Instead they will execute a script on the current website.
A bookmarklet can do everything in a page that a developer can accomplish with JavaScript. It will be executed within your session as if yourself were doing it. So when you're logged in to a system, the script is as well.
So bookmarklets can actually be dangerous.
Never use a bookmarklet that you did not create by yourself (or if you have the slightest doubt in the person recommending a bookmarklet to you).
A bookmark that instead of an URL (starting with http://...) contains a Script starting with "javascript:"
For example: javascript:alert(document.title);
A bookmarklet can utilize everything on the page by using JavaScript.
Advanced bookmarklets may download code and run little applications on your website.
Enable your boomark bar. In Chrome it's Ctrl+Shift.
First: have a javascript snippet at hand. It must be executable on the browser console without errors and show the desired results.
Remove all comments from the code if you have any. Take care that every line is closed with a semicolon.
Second: open bookmarklet generator
Copy your JavaScript snippet into the text box, also give your Bookmarklet a name (this will show in your bookmarks)
A button to your right shows your Bookmarklet name. Press it and it will execute on the generator's page. Drag & Drop it to your Bookmark bar.


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Incredible Ingredients
Gingko Biloba
Gingko Biloba Increases moisture retention in the upper layers of the skin, and soothes irritation. A natural anti-biotic and cleanser rich in bioflavinoids. The active ingredients of ginkgo leaf improves circulation and vasodilatation. Ginkgo is also an antioxidant and is useful against free radicals, substances that damage cellular health and accelerate aging from both internal and environmental toxins. One of the best herbs for the circulatory system, it serves as a cardiac tonic by increasing the strength of the arterial walls. Gingko leaf is one of the best herbs available for promoting blood flow and oxygenation throughout the entire body.
Wild Yam - Rhizoma Dioscorea Villosae
Also called Mexican Wild Yam or Colic Root, this valuable plant has become very well known in the last few years for its remarkable abilities to stimulate and balance human hormone production.  Wild Yam is now used in many hormone-balancing formulas for women.
Damiana Leaf - Folium Turneraceae
The most amazing use of this herb from the sub tropics is for individuals with chronic loss of sexual power. Damiana has a reputation as a powerful woman's herb with it's progesteronic action which was recently confirmed (Zava 1998), a powerful aphrodisiac with the properties to act through sympathetic nervous system stimulation, to tonify the central nervous system, urinary, reproductive, digestive and respiratory systems. Damiana is also useful in the treatment of Vaginal dryness.
Suma Root - Radix Pfaffiae Paniculatae
Suma root is the perfect ideal of balance and endurance. Suma contains an amazing 152 identified compounds containing incredible nutritive value with 19 amino acids and many minerals and vitamins. Suma is an excellent natural neuroendocrine restorative, metabolic regulator, antioxidant and immune enhancer. Suma has the ability to go to the root of endocrine imbalance, immune deficiency/dysregulation and reduce toxicity from the body. Suma root is a true adaptogen with the ability to provide nourishment, restore the endocrine and immune systems and also to increase estrogens and androgens.
Vitamin A, C & E Complex Hyaluronic Acid Aloe Vera Gel
This category provides the nutrients for healthy regeneration of tissue and additionally acts as powerful antioxidants to ensure maximum circulation throughout the body. These ingredients help to restore and maintain tissue integrity.

Unique to Exotic Pleasure™ "Feminine Gel" is our innovative transdermal delivery system. By combining our botanical extracts, and therapeutic nutrients with natural skin permeation enhancers such as olive squalane, the ability of these important ingredients to cross the lipid barrier of the epidermis is greatly enhanced to provide powerful sexual sensations in a woman's body.
Discerning consumers are aware that many creams and 'natural' products on the market contain ingredients that have well-recognized health hazards associated with their use; Exotic PleasureTM “Feminine Gel” is a sexual enhancement Gel that does not contain harsh chemicals, artificial fragrances or colour, or synthetic preservatives.
- Developed for those who are chemically sensitive and/or health conscious
- Contains no petrochemical by-products, artificial colors, flavors or fragrances
- No propylene glycol and metha or proply paraben
- Natural formula promotes balance and wellness
- Preferred by women over other leading brands
- Edible non-toxic
- Protects and rejuvenates delicate tissue
- Includes organic herbal ingredients that balance and enhance women's sexual sensations
- Formula caters to women's varying needs
- Light formula developed to moisturize & rejuvinate the skin for long-lasting pleasure
Women who use Exotic Pleasure™ "Feminine Gel" will feel more vibrant and aroused with this Natural Aphrodisiac and Sexual Enhancer. Exotic Pleasure™ "Feminine Gel" increases a Woman's Sexual Energy, Stamina and Endurance.
How To Apply Exotic Pleasure™ "Feminine Gel"
Apply a small amount (2 to 3 pumps) to the mucus membrane, beneath the clitoris, and thoroughly rub it in for 3-4 minutes to help absorption. The clitoral hood is partially keratinized and absorbs less of the beneficial ingredients than the non-keratinized underside of the clitoris.
Precautions:
Do not use if you have a known hypersensitivity (allergy) to any of the natural ingredients
Do not use if you are pregnant or breast-feeding
Do not use if you are presently trying to conceive
Do not use if you have any type to vaginal infection or irritation such as a yeast infection
The Gel does NOT provide protection from Sexually Transmitted Diseases (STDSs), it is NOT a contraceptive and does not provide protection against becoming pregnant
Keep out of reach of Children
If irritation occurs remove all gel immediately by washing the area straight away.
Women with known valvar and vaginal hypersensitivity (Vulvar Vestibulitis, Vulvodynia, Lichen Sclerosis), vaginal pain or recurrent infections should apply the Gel only on “outer genital surface “ rather than on the thin tissue at eh opening of the vagina. These include the hair-bearing area above the clitoral hood, and the outer vaginal lips (labia majora)
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Region selection & copying
- Multi-select: Hold down Shift while selecting regions
- Select next: Tab
- Select All: Ctrl + A
- Cut: Ctrl + X
- Copy: Ctrl + C
- Paste: Ctrl + V
Region Manipulation with Arrow Keys
ObjDec allows you to fine tune the bounding boxes using the arrow keys in a few different ways. While a region is selected:
- Move region: Hold Ctrl + Direction
- Shrink region:: Hold Ctrl + Alt + Direction
- Expand region: Hold Ctrl + Shift + Direction
Styling and Visual manipulations
Tuning visual display of regions:
- Toggle region background: Ctrl + B


1. Start niffing
2. Select Routing > OSPF
3. If no auth, can attack immediately.
3a. Select auth type "AUTH_NONE" in connection tab
3b. Change auth ID to 1 (reflex index of MD5 secret, usually 1, but can try others if 1 fails)
4. If auth:
4a. Select "cracking"
4b. Select wordlist
4c. Select "Crack PW"
4d. Select auth type "AUTH_CRYPT" and enter password in connection tab
5. Click "Hello" to start neighbor process
6. Wait for FULL status


@article{Misra21,
author={Misra, Sudip and Bishoyi, Pradyumna Kumar and Sarkar, Subhadeep},
journal = {IEEE Systems Journal},
title={i-MAC: In-Body Sensor MAC in Wireless Body Area Networks for Healthcare IoT},
url = {https://ieeexplore.ieee.org/abstract/document/9195889},
volume = {15},
number = {3},
year={2021},
pages={4413--4420},
doi={10.1109/JSYST.2020.3020306}
}


A list of wallets that support the Tezos protocol for transactions, delegations or other operations. Please help us to keep the list updated and let us know if a wallet is missing.
With AirGap your old smartphone is your new ‘hardware wallet’. Securely store your XTZ with the unique two device approach where one device is completely offline, delegate your Tezzies from cold storage and earn rewards. AirGap's focus is on accessible security, providing best in class security while maintaing a great user experience.
Offline device, highest security, XTZ delegation, coin agnostic, secure storage, social recovery, hot wallet option


Easy Event Notification Using Multiple Inheritance
Here. I had occasion to re-read my very first article today. I couldn't help myself, so I dropped in some annotations based on my experience since I wrote it. Bottom line: the ideas are still solid, but the writing needs work...
While I thought that this 6/95 article was lost in the mists of time, Betsy Hardinger, the copy editor on the WinForms book, faxed me a paper copy (she's been around a while, obviously : ). When the fax came in, I thought I'd have to transcribe the piece (I only type ~65wpm).
Luckily, Office 11 comes with MS Office Document Imaging, which has built-in OCR. After processing the document, I was able to select columns of text and past them into Word. I had to do considerable clean-up, but it was about 10x faster than typing it all by hand. I do so love MS software.


Customer Info Entry Form
Form Number:
Date:
Full Name:
Phone Number
Email
Street Address
City
State
Zip
Service Selection
Audio Test
Battery Repair
Data Transfer
Did not Charge
GPU Stress Test
HD Test
Installation Fee
Liquid Damage Repair
Microphone Test
New HD (Mechanical)
New OS Install
New SSD Drive
No Power Adapter
Not Repairable
OS Reinstall/Upgrade
Other
Parts placed on order
Port Test
Power Adapter
Purchased as Part(s)
Screen Replacement
Traded Computers
Tune Up
Virus Cleanup
Webcam Test
Wifi Test
What you've selected
Notes
Paid with
Deposit
Balance Due
Final Payment
Picked up by


Every week the course became more hard and difficulties become worse as you learn more and more things in a short amount of time and needs to do lots of tasks as we usually use all the resources you have just learnt.
I got myself spent hours trying to understand why my code was not working and also ticket all
the problem solving suggestions given by the learning programme such as:
Pseudocode, trying something, reading error messages, console.logging, googling to
finally figure out that I had a spelling mistake.
I also became really frustrated when I started a task where I needed to work with strings, and array built-in methods and I realise I was totally unable to go for it.  The time box suggested four hours but I spend the whole day.
Complete the sprint 4, especially would be impossible without using the problem solving techniques such as Pseudocoding, that involves taking a little time before you get into writing the code to break down your plan into small steps. This can be useful for helping you break down a complicated problem, but can also be used if one developer needs to leave instructions for another to complete the code later.
I could also use other techniques such as trying something, reading error messages, console.logging, googling, asking coaches for help and also improving your process with reflection.
I made some notes about it and I would like to share with you as I believe it could be used to any circumstances in our lives.
A common misconception about coders is that their job is to write code, but it fails to capture the essence of what a coder does, which is to solve problems. When confronted with a problem, most coders’ first instinct is to immediately start writing code, but diving into code without a plan is a trap.
Taking the time to understand the problem and structure an effective approach may seem like a time sink, but not only will it lead you to a better solution, it will also save you time in the long run.
The first thing you need to do is to understand the problem. This is crucial. If you don’t understand the parameters of the problem then you can’t possibly expect to develop an effective solution. Can you imagine when you get to the end of the code and realize that it doesn’t address the problem?
Only after that you can start working through potential solutions and write out pseudocode to describe it. Don't forget to anticipate possible issues. If you identify these cases ahead of time then you can plan for them in your solution.
Here are some useful steps to be followed when you need to solve a problem.
Ask yourself:
Going through some sample inputs and coming up with more examples sure helps you a lot to understand the problem well, and also it gives you a clear way to how many cases your code should handle, and what all can be the possible output or output range.
If you can’t solve the problem fully at once, try writing code for a simpler sub part, as you solve the problem, gradually you keep figuring out the problem in more detail, and you start getting new ideas.
Now, this is the most important part! Once you are done, look back whether the code can be
improved, is there any other way to solve the given problem?
You can ask yourself:


| 066:004 Khan | : |
If you two (wives of the Prophet SAW, namely 'Aishah and Hafsah radhiallahu'anhu) turn in repentance to Allah, (it will be better for you), your hearts are indeed so inclined (to oppose what the Prophet SAW likes), but if you help one another against him (Muhammad SAW), then verily, Allah is his Maula (Lord, or Master, or Protector, etc.), and Jibrael (Gabriel), and the righteous among the believers, and furthermore, the angels are his helpers. |
| 066:004 Maulana | : |
If you both turn to Allah, then indeed your hearts are inclined (to this); and if you back up one another against him, then surely Allah is his Patron, and Gabriel and the righteous believers, and the angels after that are the aiders. |
| 066:004 Pickthal | : |
If ye twain turn unto Allah repentant, (ye have cause to do so) for your hearts desired (the ban); and if ye aid one another against him (Muhammad) then lo! Allah, even He, is his Protecting Friend, and Gabriel and the righteous among the believers; and furthermore the angels are his helpers. |
| 066:004 Rashad | : |
If the two of you repent to GOD, then your hearts have listened.  Also, the angels are his helpers. |
| 066:004 Sarwar | : |
Would that you two (wives of the Prophet) had turned to God in repentance. Your hearts have sinned. If you conspire with each other against him, know that God is his Guardian. Gabriel, the righteous (ones) among the believers and the angels will all support him. |
| 066:004 Shakir | : |
If you both turn to Allah, then indeed your hearts are already inclined (to this); and if you back up each other against him, then surely Allah it is Who is his Guardian, and Jibreel and -the believers that do good, and the angels after that are the aiders. |
| 066:004 Sherali | : |
Now if you two turn unto ALLAH repentant - and your hearts are already so inclined - it will be better for you. But if you back up one another against him, surely, ALLAH is his helper and Gabriel and the righteous among the believers, and all the angels besides are his helpers. |
| 066:004 Yusufali | : |
If ye two turn in repentance to Him, your hearts are indeed so inclined; But if ye back up each other against him, truly Allah is his Protector, and Gabriel, and (every) righteous one among those who believe,- and furthermore, the angels - will back (him) up. |
Concepts/Themes Covered: أركان الإسلام > (محمد (صلى الله عليه وسلم > أخلاقه وصفاته Pillars of Islam> The Blessed Muhammad(PBUH)> His Manners and Attributes أركان الإسلام > (محمد (صلى الله عليه وسلم > أزواجه وبناته Pillars of Islam> The Blessed Muhammad(PBUH)> His Wives and Daughters أركان الإسلام > (محمد (صلى الله عليه وسلم > مآثره وخصائصه لله Pillars of Islam> The Blessed Muhammad(PBUH)> Memorable Qualities given to Him by Allah الإيمان > الملائكة > من ورد اسمه منهم > جبريل Faith> The Angels>Those Whose Names have been Stated> Gabriel
This tool uses eight English parallel translations for the Holy Quran imported from Mr. Clay Smith. You can also visit Qurany Concepts for browsing the Quran themes or topics. If you want to search the Holy Quran for keywords, please visit Qurany Keywords.


I made a small tweak to the blog template so hopefully this works. More tweaks to come tomorrow. Arrays and hashes can both be useful tools in Ruby because instead of storing and assigning one variable at a time, A+H's let you store multiple variables at once. The difference is in how these values are stored.
An array stores variables in an ordered fashion and saves each variable as a number (starting with 0) based on the order you store them in the array. Because of this an array is great for storing an ordered list of items.
A hash on the other hand pairs the variables with a key and a value. Basically, each variable (value) can be named (key) so you can easily call upon that variable without having to know which place in the order it lands. I'm still in the very early stages of using both arrays and hashes, but they seem to be so similar that once you get the hang of using one, the other should be extremely easy to pickup.
Nick Siefken


Victory for Brazil tribe as Portugal hotel group cancels resort
A Brazilian tribe that has been fighting for 15 years to preserve land has won a victory when public pressure here made Portuguese hotel group Vila Gale cancel plans to build a 500-room luxury resort on the Bahia coast.
Indigenous group Tupinambá de Olivença, numbering just under 5,000 people, has been fighting for the land to be designated as a reserve since 2003.
Vila Galé has consistently denied that there were any traces of an indigenous population on the territory in question, but locals say they use the land for gathering food.


Syncer
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Reverse sync, i.e., Source ← Garage
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About
Syncer
Version 1.0.2
Developed by
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Licensed under MIT license.


{% extends 'base.html' %} {% block content %} Contacts Search {{ request.user.username }}'s Address Book Logout Create New Contact Order by First Name Order by Last Name {% for instance in object_list %}
* {{ instance.first_Name }} {{ instance.last_Name }}


List of Java third-party libraries. This list contains popular open java projects with the projects that I have favored and selected. Some listings have short descriptions and latest versions. Some projects are rated with an A- and above. These are stop projects that are universally used and are constantly updated. They aren't necessarily perfect libraries, even Java is not perfect but these A rated libraries should be familiar to most Java developers.
More opinionated listing based on Awesome Java List: github.com/akullpp/awesome-java and Java Source Net Open Projects
Apache Wicket - Component based Java web framework. Focus on specialized widgets. Competes with JSF. Latest 7.6.0, supported, Rating A
Spring Core and Spring MVC - Dependency Injection, MVC framework, many other projects. 4.3.8 GA release. Use spring core for any project, one of the popular Java frameworks. Spring MVC loosely competes with Struts. Also used for REST APIs. Spring MVC REST Controllers can be used with Angular 1 and Angular 2 front-ends. Rating A+
Spring Boot - Easy Setup Spring Web Project/Server, embedded server, 1.5.3 GA, Rating A
Java Server Faces Open Implementation of JSF standard - JSF 2.3 latest.
PrimeFaces - PrimeFaces 6.0.18, UI components for JSF, combine with impl above, competes with Wicket. Rating A
Apache Struts - Java Open MVC Framework, Apache Struts <IP_ADDRESS>. Loosely competes with Spring MVC, developers have been favoring the simplicity of Spring MVC, Struts still viable
Play Framework - Java and Scala Framework
Hibernate ORM - Object Relation Mapping Frameworking, also has JPA implementation, 5.2.10, Rating A+
Apache Lucene 6.5.1 - Java indexing and search
Apache Derby - Java based relational database, embeddable database, <IP_ADDRESS> Release, Rating A+
Camel EIP - Enterprise Integration Patterns, not necessarily ESB, 2.18.3 Release. Also support for micro services creation, data aggregation layer. Rating A
Apache CXF - REST and SOAP services, release 3.1.11
Apache Axis 2 - SOAP Services implementation, implements JAX-WS
HttpClient - HTTP Client implementation, connect to HTTP servers, version 4.5.3
Commons Bean Utils - Services for collection of beans, used by many libraries, Version 1.9.3
Commons Codec - common encoders and decoders including base64 encoding. Version 1.10
Commons Collections - Data Structures and other artifacts, latest version 4.1
Commons IO - Steam and other file io operations
Commons Lang - Manipulate core libraries, version 3.5
Google Guava - Google collections api, Guava 21.0, Rating A
Joda Time, TIME api - version 2.9.9, Rating A-
ANTLR - parser generator, used internally by many projects, latest version 4.7
Dependency Injection Framework from Google, Version 4.1
Jackson JSON Library, parser/generator for JSON, Latest 2.8.6
Quartz Java Job Scheduler - Latest Quartz 2.2.3
JUnit - JUnit 4.12, rating A+
Mockito - Mocking framework for java, 2.8.25 (License, MIT), Rating A
Cucumber-JVM is a pure Java implementation of Cucumber
Selenium - 3.4.0 - automating browser functions, Rating A+
Apache JMeter - Load Testing, version 3.2
Apache POI - Java API for Microsoft Documents, 3.16
iText - Dynamic PDF Library, 5.5.11, license, AGPL license
JasperReports Library - Report Engine in Java with templating, v6.3.1
JFreeChart, Generate a variety of charts types in Java, Latest 1.0.19
Functional Java - Functional Java Programming, version 4.7
Scala Language - Functional and Object Oriented Programming, version 2.12.2, Rating A
Clojure Language - Dynamic programming language
Rhino is an open-source implementation of JavaScript - Latest qualified version 1.7R5
Drools - Open Rules Engine in Java, 2016-10-25: Drools 6.5.0.Final released
Heaton Research - Encog for Java, Neural Network and Machine Learning Libraries, latest version 3.4
Apache Hadoop - Version 2.8.0, platform for distributed processing
Weka - Machine Learning in Java, latest 3.8
Simple POM Xml for Spring WebApps
Dependency Tree with Spring Boot - From command mvn dependency:tree
Simple POM Xml with Spring Boot - Simple Pom XML with just Spring Boot
Dependency Tree with Spring Boot - From command mvn dependency:tree
Twitter - twitter, berlinbrowndev2, or email berlin dot brown, google's email system
Updated on 5/2/2017, latest GA versions as of this date. Copyright Berlin Brown 2017.


Overall, I really enjoyed the Fireside Chat. It seems that Shereef is a very conscious being, and I am sure the entire DBC program will reflect his POV.
Git is in fact a version control program. The main idea is there is a master/original copy of the project. And new edition to the project will be added to a branched out copy, then push back to the master copy after all the changes are made and tested. All of the changes will be store in a log for future referencing. This is very helpful for collaboration because each "push" has some detail information about what was changed and who made the changes along with some messages the author put up. This requires the author follow the general messaging scheme to ease the referencing in the future.


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{include file="public/left" /}
{volist name="new" id="new"}
{$new.title}
{$new.desc}
{$new.thumb}
继续阅读
{/volist}
{$artlist}
{include file="public/foot" /}


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| Base | 0% | 0% | 0% |
| Bite | 0% | 0% | 0% |
| Crush | 0% | 0% | 0% |
| Pierce | 0% | 0% | 0% |
| Slash | 0% | 0% | 0% |
| Burn | 0% | 0% | 0% |
| Cold | 0% | 0% | 0% |
| Acid | 0% | 0% | 0% |
| Infection | 0% | 0% | 0% |
| Internal | 0% | 0% | 0% |
| Poison | 0% | 0% | 0% |
| Water | 0% | 0% | 0% |
Wurm Toolbox 2.1.0
Made by Warlander - 2017-2018
Official Wurm Online forum profile
GitHub profile
Wurm Online is a game produced by Code Club AB. Wurm Online and Code Club AB do not endorse and are not responsible or liable for any content, products, services or information available on warlander.github.io/Wurm-Toolbox
warlander.github.io/Wurm-Toolbox is not connected to Wurm Online nor is it the official website. Please visit the Wurm Online Official Site at www.wurmonline.com


Data Acquisition and Processing
More information on our data acquisition and processing methods for this project coming soon.
ESRI On-line Map Services
The resulting maps of this project are available for download via the standard ESRI ArcServer interface. Each service provides exactly one map. They are available here:


About
Keep focused by breaking up tasks into manageable intervals
Earn Pomos for finishing tasks. Can YOU catch them all?
Work
20 min
20 min
5 min
10 min
Break
Break
Work
2


Enjoy this DOS Gorilla game I played back when I was a child while I learn to build games of my own.
Gorillas, also known under the source code's file name Gorilla.Bas, is a video game first distributed with MS-DOS 5 and published in 1991 by IBM corporation. It is a turn-based artillery game. The game consists of two gorillas throwing explosive bananas at each other above a city skyline. The players can adjust the angle and velocity of each throw, as well as the gravitational pull of the planet. Written in QBasic, it is one of the programs included as a demonstration of that programming language.
Enjoy this fun Jazz Jackrabbit game I played back when I was a child while I learn to build games of my own.
Jazz Jackrabbit is a platform game developed and published by Epic MegaGames. It was released in 1994 for PCs operating DOS, with subsequent Macintosh and Microsoft Windows releases in 1995 and 1996. It was one of the first games to bring the side-scrolling platformer style - common on gaming consoles - to a PC audience.


This task has a dependency on Task 1.1.4 and all of it's prerequisites.
One of the coolest things about PWAs on Windows 10 is the technical advantages you get over just running in the browser:
In order to utilize a Windows UWP API, you will want to implement a pattern called "feature detection". This allows you to write JavaScript on your page that will only be executed in the context where the APIs are available. In this case, it will only execute while inside a Windows PWA.
Feature detection can be as simple as looking for the Windows object as below:
if(window.Windows){
/*execute code that calls WIndows APIs */
}
Keep in mind that not all Windows APIs are available on all Windows 10 devices. Think about the fact that a Windows Phone has a "dialer" for making phone calls, so it has unique APIs that are not available on other device types. Another example is the "Surface Dial" that can be paired to many Windows devices, but not the Xbox, so the "radial dial" APIs are not present on the Xbox. For more specific feature detection, you will want to feature detect the API family as well like below:
if(window.Windows && Windows.UI.Core.SystemNavigationManager){
/*execute code that calls WIndows APIs */
}
Start out by adding some code that adds a message to the tile in the start menu. This code can be run from any page, but you will likely want to place it on index.cshtml, so that it runs as soon as the app is opened.
Open Shipping\index.cshtml
in Visual Studio and add a script tag at the bottom of the page.
Add the following feature detection inside the script tag:
if(window.Windows && Windows.UI.Notifications){
/*execute code that calls WIndows APIs */
}
Inside the feature detection, add the following code to load the live tile:
var tileContent = new Windows.Data.Xml.Dom.XmlDocument();
var tile = tileContent.createElement("tile");
tileContent.appendChild(tile);
var visual = tileContent.createElement("visual");
tile.appendChild(visual);
var bindingMedium = tileContent.createElement("binding");
bindingMedium.setAttribute("template", "TileMedium");
visual.appendChild(bindingMedium);
var peekImage = tileContent.createElement("image");
peekImage.setAttribute("placement", "peek");
peekImage.setAttribute("src", "https://unsplash.it/150/150/?random");
peekImage.setAttribute("alt", "Welcome to Knowsie!");
bindingMedium.appendChild(peekImage);
var text = tileContent.createElement("text");
text.setAttribute("hint-wrap", "true");
text.innerText = "Demo Message";
bindingMedium.appendChild(text);
//fire the notification
var notifications = Windows.UI.Notifications;
var tileNotification = new notifications.TileNotification(tileContent);
notifications.TileUpdateManager.createTileUpdaterForApplication().update(tileNotification);
To test the changes you have just made, follow the instructions in section 1.1.3 for deploying your web app, then re-deploy your changes. One of the great things about a PWA is that you don't need to redeploy your app to see the changes, you just need to deploy to the web server. You should be able to run your app again and see your live tile code take effect (pin your app's tile to the start menu to see). If you don't see it immediately, it may be because of the great caching you have in your app. If this happens, you can force a cache clear by uninstalling your PWA, then installing it again from the script you downloaded from PWABuilder.com.
NOTE if your Visual Studio session is still debugging your Azure instance, you may need to halt your debugger before you test locally again.


In this notebook, some template code has already been provided for you, and you will need to implement additional functionality to successfully complete this project. You will not need to modify the included code beyond what is requested. Sections that begin with '(IMPLEMENTATION)' in the header indicate that the following block of code will require additional functionality which you must provide. Instructions will be provided for each section, and the specifics of the implementation are marked in the code block with a 'TODO' statement. Please be sure to read the instructions carefully!
Note: Once you have completed all of the code implementations, you need to finalize your work by exporting the iPython Notebook as an HTML document. Before exporting the notebook to html, all of the code cells need to have been run so that reviewers can see the final implementation and output. You can then export the notebook by using the menu above and navigating to \n", "File -> Download as -> HTML (.html). Include the finished document along with this notebook as your submission.
In addition to implementing code, there will be questions that you must answer which relate to the project and your implementation. Each section where you will answer a question is preceded by a 'Question X' header. Carefully read each question and provide thorough answers in the following text boxes that begin with 'Answer:'. Your project submission will be evaluated based on your answers to each of the questions and the implementation you provide.
Note: Code and Markdown cells can be executed using the Shift + Enter keyboard shortcut. Markdown cells can be edited by double-clicking the cell to enter edit mode.
The rubric contains optional "Stand Out Suggestions" for enhancing the project beyond the minimum requirements. If you decide to pursue the "Stand Out Suggestions", you should include the code in this IPython notebook.
In this notebook, you will make the first steps towards developing an algorithm that could be used as part of a mobile or web app. At the end of this project, your code will accept any user-supplied image as input. If a dog is detected in the image, it will provide an estimate of the dog's breed. If a human is detected, it will provide an estimate of the dog breed that is most resembling. The image below displays potential sample output of your finished project (... but we expect that each student's algorithm will behave differently!).
In this real-world setting, you will need to piece together a series of models to perform different tasks; for instance, the algorithm that detects humans in an image will be different from the CNN that infers dog breed. There are many points of possible failure, and no perfect algorithm exists. Your imperfect solution will nonetheless create a fun user experience!
We break the notebook into separate steps. Feel free to use the links below to navigate the notebook.
In the code cell below, we import a dataset of dog images. We populate a few variables through the use of the load_files
function from the scikit-learn library:
train_files
, valid_files
, test_files
- numpy arrays containing file paths to imagestrain_targets
, valid_targets
, test_targets
- numpy arrays containing onehot-encoded classification labels dog_names
- list of string-valued dog breed names for translating labelsfrom sklearn.datasets import load_files
from keras.utils import np_utils
import numpy as np
from glob import glob
# define function to load train, test, and validation datasets
def load_dataset(path):
data = load_files(path)
dog_files = np.array(data['filenames'])
dog_targets = np_utils.to_categorical(np.array(data['target']), 133)
return dog_files, dog_targets
# load train, test, and validation datasets
train_files, train_targets = load_dataset('dogImages/train')
valid_files, valid_targets = load_dataset('dogImages/valid')
test_files, test_targets = load_dataset('dogImages/test')
# load list of dog names
dog_names = [item[20:-1] for item in sorted(glob("dogImages/train/*/"))]
# print statistics about the dataset
print('There are %d total dog categories.' % len(dog_names))
print('There are %s total dog images.\n' % len(np.hstack([train_files, valid_files, test_files])))
print('There are %d training dog images.' % len(train_files))
print('There are %d validation dog images.' % len(valid_files))
print('There are %d test dog images.'% len(test_files))
In the code cell below, we import a dataset of human images, where the file paths are stored in the numpy array human_files
.
import random
random.seed(8675309)
# load filenames in shuffled human dataset
human_files = np.array(glob("lfw/*/*"))
random.shuffle(human_files)
# print statistics about the dataset
print('There are %d total human images.' % len(human_files))
We use OpenCV's implementation of Haar feature-based cascade classifiers to detect human faces in images. OpenCV provides many pre-trained face detectors, stored as XML files on github. We have downloaded one of these detectors and stored it in the haarcascades
directory.
In the next code cell, we demonstrate how to use this detector to find human faces in a sample image.
import cv2
import matplotlib.pyplot as plt
%matplotlib inline
# extract pre-trained face detector
face_cascade = cv2.CascadeClassifier('haarcascades/haarcascade_frontalface_alt.xml')
# load color (BGR) image
img = cv2.imread(human_files[3])
# convert BGR image to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# find faces in image
faces = face_cascade.detectMultiScale(gray)
# print number of faces detected in the image
print('Number of faces detected:', len(faces))
# get bounding box for each detected face
for (x,y,w,h) in faces:
# add bounding box to color image
cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)
# convert BGR image to RGB for plotting
cv_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
# display the image, along with bounding box
plt.imshow(cv_rgb)
plt.show()
Before using any of the face detectors, it is standard procedure to convert the images to grayscale. The detectMultiScale
function executes the classifier stored in face_cascade
and takes the grayscale image as a parameter.
In the above code, faces
is a numpy array of detected faces, where each row corresponds to a detected face. Each detected face is a 1D array with four entries that specifies the bounding box of the detected face. The first two entries in the array (extracted in the above code as x
and y
) specify the horizontal and vertical positions of the top left corner of the bounding box. The last two entries in the array (extracted here as w
and h
) specify the width and height of the box.
We can use this procedure to write a function that returns True
if a human face is detected in an image and False
otherwise. This function, aptly named face_detector
, takes a string-valued file path to an image as input and appears in the code block below.
# returns "True" if face is detected in image stored at img_path
def face_detector(img_path):
img = cv2.imread(img_path)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray)
return len(faces) > 0
Question 1: Use the code cell below to test the performance of the face_detector
function.
human_files
have a detected human face? dog_files
have a detected human face?  You will see that our algorithm falls short of this goal, but still gives acceptable performance. We extract the file paths for the first 100 images from each of the datasets and store them in the numpy arrays human_files_short
and dog_files_short
.
Answer: Being applied to the first 100 images, the face_detector
function detects human faces in 98.7% of human images and 11% of dog images.
human_files_short = human_files[:100]
dog_files_short = train_files[:100]
# Do NOT modify the code above this line.
## DONE: Test the performance of the face_detector algorithm
## on the images in human_files_short and dog_files_short.
n_human_faces = 1000
human_files_short_faces = [item for item in human_files[:n_human_faces] if face_detector(item) == True]
print("Percentage of detected human faces in human images: {0:.1f}%"\
.format(100*len(human_files_short_faces)/n_human_faces))
dog_files_short_faces = [item for item in dog_files_short if face_detector(item) == True]
print("Percentage of detected human faces in dog images: {0:.0f}%"\
.format(100*len(dog_files_short_faces)/len(dog_files_short)))
Question 2: This algorithmic choice necessitates that we communicate to the user that we accept human images only when they provide a clear view of a face (otherwise, we risk having unneccessarily frustrated users!). In your opinion, is this a reasonable expectation to pose on the user? If not, can you think of a way to detect humans in images that does not necessitate an image with a clearly presented face?
Answer: Naively, 99% is very high accuracy rate. To understand whether the users would complain about 1% non-detections, I have visually inspected some positive and negative detections:
fig = plt.figure(figsize=(20,20))
print(len(human_files_short_faces))
for i in range(len(human_files[:100])):
ax = fig.add_subplot(10, 10, i + 1, xticks=[], yticks=[])
img_face = human_files_short_faces[i]
img = cv2.imread(img_face)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray)
for (x,y,w,h) in faces:
# add bounding box to color image
cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)
#print('Number of faces detected:', len(faces))
cv_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
ax.set_title("{}".format(len(faces)),
color=("red" if len(faces) > 1 else "blue"))
ax.imshow(cv_rgb)
n_human_badface = 3000
human_files_badfaces = [item for item in human_files[:n_human_badface] if face_detector(item) == False]
len(human_files_badfaces)
fig = plt.figure(figsize=(20,8))
for i in range(len(human_files_badfaces)):
ax = fig.add_subplot(4, 10, i + 1, xticks=[], yticks=[])
img_face = human_files_badfaces[i]
img = cv2.imread(img_face)
cv_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
ax.set_title("{}".format(i+1), color='green')
ax.imshow(cv_rgb)
As we see, the 37 negative detections can be visually classified on several groups:
faces that are looking away from the camera (images 1, 6, 8, 9, 13, 16, 17, 18, 21, 25, 28, 29, 36) or significantly inclined faces (3, 20, 22, 27, 31, 34) - such negative detections are expected because we are using a frontal face detection algorithm:
face_cascade = cv2.CascadeClassifier('haarcascades/haarcascade_frontalface_alt.xml')
Therefore, if the algorithm fails to recognize the person's face, I suggest asking a more detailed photo avoiding the above categories.
Another important question is whether the Viola-Jones face detection works reasonably well. To answer this question, we should compare it with different face detection algorithms. Hopefully, it has been done for Labeled Faces in the Wild (LFW) dataset, see the draft of new LFW survey paper and the description of evaluation criteria. Therefore, I conclude that the Viola-Jones face detection works reasonably well. In an optional task below, I consider its improvement which gives even better accuracy.
We suggest the face detector from OpenCV as a potential way to detect human images in your algorithm, but you are free to explore other approaches, especially approaches that make use of deep learning :). Please use the code cell below to design and test your own face detection algorithm. If you decide to pursue this optional task, report performance on each of the datasets.
I start considering alternative algorithms in haarcascades
. Notably, their default frontalface algorithm haarcascade_frontalface_default.xml
algorithm gives even better performance:
## (Optional) DONE: Report the performance of another
## face detection algorithm on the LFW dataset
### Feel free to use as many code cells as needed.
face_cascade2 = cv2.CascadeClassifier('haarcascades/haarcascade_frontalface_default.xml')
def face_detector2(img_path):
img = cv2.imread(img_path)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces2 = face_cascade2.detectMultiScale(gray)
return len(faces2) > 0
n_human_faces = 1000
human_files_short_faces_2 = [item for item in human_files[:n_human_faces] if face_detector2(item) == True]
print("Percentage of detected human faces in human images: {0:.1f}%"\
.format(100*len(human_files_short_faces_2)/len(human_files[:n_human_faces])))
n_dog_faces = 100
dog_files_faces_2 = [item for item in train_files[:n_dog_faces] if face_detector2(item) == True]
print("Percentage of detected human faces in dog images: {0:.0f}%"\
.format(100*len(dog_files_faces_2)/len(train_files[:n_dog_faces])))
Despite its remarkable true positive rate (99.9% vs. 98.7% for the initial algorithm), the false negative rate is also inappropriately large, $\approx 5.3$ times higher than in our original haarcascade_frontalface_alt.xml
algorithm (11%). This rate seems to be too high for a working algorithm; that's why we will not use face_detector2
.
Let us combine haarcascades/haarcascade_frontalface_alt.xml
with two other algorithms:
haarcascades/haarcascade_profileface.xml
- that may help us to identify non-frontal faces:face_cascade_profile = cv2.CascadeClassifier('haarcascades/haarcascade_profileface.xml')
#def face_detector_profile(img_path):
# img = cv2.imread(img_path)
# gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# faces = face_cascade.detectMultiScale(gray)
# faces_profile = face_cascade_profile.detectMultiScale(gray)
# return (len(faces) + len(faces_profile)) > 0
#print(">>> frontalface + profileface:")
#n_human_faces = 1000
#human_files_faces_profile = [item for item in human_files[:n_human_faces] if face_detector_profile(item) == True]
#print("Percentage of detected human faces in human images: {0:.2f}%"\
# .format(100*len(human_files_faces_profile)/len(human_files[:n_human_faces])))
#n_dog_faces = 100
#dog_files_faces_profile = [item for item in train_files[:n_dog_faces] if face_detector_profile(item) == True]
#print("Percentage of detected human faces in dog images: {0:.0f}%"\
# .format(100*len(dog_files_faces_profile)/len(train_files[:n_dog_faces])))
haarcascades_cuda/haarcascade_eye_tree_eyeglasses.xml
- that may help to identify glasses:face_cascade_new = cv2.CascadeClassifier('haarcascades_cuda/haarcascade_eye_tree_eyeglasses.xml')
def face_detector_all(img_path):
img = cv2.imread(img_path)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray)
faces_profile = face_cascade_profile.detectMultiScale(gray)
faces_profile_new = face_cascade_new.detectMultiScale(gray)
return (len(faces) + len(faces_profile) + len(faces_profile_new)) > 0
print(">>> frontalface + profileface + tree_eyeglasses:")
n_human_faces = 1000
human_files_faces_all = [item for item in human_files[:n_human_faces] if face_detector_all(item) == True]
print("Percentage of detected human faces in human images: {0:.2f}%"\
.format(100*len(human_files_faces_all)/len(human_files[:n_human_faces])))
n_dog_faces = 100
dog_files_faces_all = [item for item in train_files[:n_dog_faces] if face_detector_all(item) == True]
print("Percentage of detected human faces in dog images: {0:.0f}%"\
.format(100*len(dog_files_faces_all)/len(train_files[:n_dog_faces])))
As we see, the combination of haarcascades/haarcascade_frontalface_alt.xml
with haarcascades/haarcascade_profileface.xml
and haarcascades_cuda/haarcascade_eye_tree_eyeglasses.xml
have much better performance on identification of human faces (99.5% vs 98.7%), i.e., the false positive rate drops from 1.3% to 0.5%. On the other hand, the false negative rate (based on dog images sample) increases insignificantly: from 11% to 15%. As a result, the combination of glasses detection with frontal and profile face detection provides better performance in human image classification based on LFW dataset.
Finally, define the final instructions given to a user in case of misclassification of a human dataset:
n_human_badface = 3000
human_files_badfaces_all = [item for item in human_files[:n_human_badface] if face_detector_all(item) == False]
len(human_files_badfaces_all)
Then, show the misclassified images again from the first n_human_badface marking blue those classified with the face_detector_profile
algorithm:
fig = plt.figure(figsize=(20,8))
for i in range(len(human_files_badfaces)):
ax = fig.add_subplot(4, 10, i + 1, xticks=[], yticks=[])
img_face = human_files_badfaces[i]
img = cv2.imread(img_face)
cv_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
ax.set_title("{}".format(i+1),
color=("red" if human_files_badfaces[i] in human_files_badfaces_all else "blue"))
ax.imshow(cv_rgb)
As we see, new 18 negative detections can be visually classified on the same groups as above:
Therefore, if the algorithm fails to recognize the person's face, I suggest asking a more detailed photo avoiding the above categories.
In this section, we use a pre-trained ResNet-50 model to detect dogs in images. Our first line of code downloads the ResNet-50 model, along with weights that have been trained on ImageNet, a very large, very popular dataset used for image classification and other vision tasks. ImageNet contains over 10 million URLs, each linking to an image containing an object from one of 1000 categories. Given an image, this pre-trained ResNet-50 model returns a prediction (derived from the available categories in ImageNet) for the object that is contained in the image.
from keras.applications.resnet50 import ResNet50
# define ResNet50 model
ResNet50_model = ResNet50(weights='imagenet')
When using TensorFlow as backend, Keras CNNs require a 4D array (which we'll also refer to as a 4D tensor) as input, with shape
$$ (\text{nb_samples}, \text{rows}, \text{columns}, \text{channels}), $$where nb_samples
corresponds to the total number of images (or samples), and rows
, columns
, and channels
correspond to the number of rows, columns, and channels for each image, respectively.
The path_to_tensor
function below takes a string-valued file path to a color image as input and returns a 4D tensor suitable for supplying to a Keras CNN. The function first loads the image and resizes it to a square image that is $224 \times 224$ pixels. Next, the image is converted to an array, which is then resized to a 4D tensor. In this case, since we are working with color images, each image has three channels. Likewise, since we are processing a single image (or sample), the returned tensor will always have shape
The paths_to_tensor
function takes a numpy array of string-valued image paths as input and returns a 4D tensor with shape
Here, nb_samples
is the number of samples, or number of images, in the supplied array of image paths. It is best to think of nb_samples
as the number of 3D tensors (where each 3D tensor corresponds to a different image) in your dataset!
from keras.preprocessing import image
from tqdm import tqdm
def path_to_tensor(img_path):
# loads RGB image as PIL.Image.Image type
img = image.load_img(img_path, target_size=(224, 224))
# convert PIL.Image.Image type to 3D tensor with shape (224, 224, 3)
x = image.img_to_array(img)
# convert 3D tensor to 4D tensor with shape (1, 224, 224, 3) and return 4D tensor
return np.expand_dims(x, axis=0)
def paths_to_tensor(img_paths):
list_of_tensors = [path_to_tensor(img_path) for img_path in tqdm(img_paths)]
return np.vstack(list_of_tensors)
Getting the 4D tensor ready for ResNet-50, and for any other pre-trained model in Keras, requires some additional processing. First, the RGB image is converted to BGR by reordering the channels. All pre-trained models have the additional normalization step that the mean pixel (expressed in RGB as $[103.939, 116.779, 123.68]$ and calculated from all pixels in all images in ImageNet) must be subtracted from every pixel in each image. This is implemented in the imported function preprocess_input
. If you're curious, you can check the code for preprocess_input
here.
Now that we have a way to format our image for supplying to ResNet-50, we are now ready to use the model to extract the predictions. This is accomplished with the predict
method, which returns an array whose $i$-th entry is the model's predicted probability that the image belongs to the $i$-th ImageNet category. This is implemented in the ResNet50_predict_labels
function below.
By taking the argmax of the predicted probability vector, we obtain an integer corresponding to the model's predicted object class, which we can identify with an object category through the use of this dictionary.
from keras.applications.resnet50 import preprocess_input, decode_predictions
def ResNet50_predict_labels(img_path):
# returns prediction vector for image located at img_path
img = preprocess_input(path_to_tensor(img_path))
return np.argmax(ResNet50_model.predict(img))
While looking at the dictionary, you will notice that the categories corresponding to dogs appear in an uninterrupted sequence and correspond to dictionary keys 151-268, inclusive, to include all categories from 'Chihuahua'
to 'Mexican hairless'
. Thus, in order to check to see if an image is predicted to contain a dog by the pre-trained ResNet-50 model, we need only check if the ResNet50_predict_labels
function above returns a value between 151 and 268 (inclusive).
We use these ideas to complete the dog_detector
function below, which returns True
if a dog is detected in an image (and False
if not).
### returns "True" if a dog is detected in the image stored at img_path
def dog_detector(img_path):
prediction = ResNet50_predict_labels(img_path)
return ((prediction <= 268) & (prediction >= 151))
Question 3: Use the code cell below to test the performance of your dog_detector
function.
human_files_short
have a detected dog? dog_files_short
have a detected dog?Answer: Being applied to the first 100 images, the dog_detector
function detects dogs in 2% of human images and 100% of dog images.
### DONE: Test the performance of the dog_detector function
### on the images in human_files_short and dog_files_short.
human_files_short_dogs = [item for item in human_files_short if dog_detector(item) == True]
print("Percentage of detected dogs in human images: {0:.0f}%"\
.format(100*len(human_files_short_dogs)/len(human_files_short)))
dog_files_short_dogs = [item for item in dog_files_short if dog_detector(item) == True]
print("Percentage of detected dogs in dog images: {0:.0f}%"\
.format(100*len(dog_files_short_dogs)/len(dog_files_short)))
Now that we have functions for detecting humans and dogs in images, we need a way to predict breed from images. In this step, you will create a CNN that classifies dog breeds. You must create your CNN from scratch (so, you can't use transfer learning yet!), and you must attain a test accuracy of at least 1%. In Step 5 of this notebook, you will have the opportunity to use transfer learning to create a CNN that attains greatly improved accuracy.
Be careful with adding too many trainable layers! More parameters means longer training, which means you are more likely to need a GPU to accelerate the training process. Thankfully, Keras provides a handy estimate of the time that each epoch is likely to take; you can extrapolate this estimate to figure out how long it will take for your algorithm to train.
We mention that the task of assigning breed to dogs from images is considered exceptionally challenging. To see why, consider that even a human would have great difficulty in distinguishing between a Brittany and a Welsh Springer Spaniel.
| Brittany | Welsh Springer Spaniel |
|---|---|
It is not difficult to find other dog breed pairs with minimal inter-class variation (for instance, Curly-Coated Retrievers and American Water Spaniels).
| Curly-Coated Retriever | American Water Spaniel |
|---|---|
Likewise, recall that labradors come in yellow, chocolate, and black. Your vision-based algorithm will have to conquer this high intra-class variation to determine how to classify all of these different shades as the same breed.
| Yellow Labrador | Chocolate Labrador | Black Labrador |
|---|---|---|
We also mention that random chance presents an exceptionally low bar: setting aside the fact that the classes are slightly imabalanced, a random guess will provide a correct answer roughly 1 in 133 times, which corresponds to an accuracy of less than 1%.
Remember that the practice is far ahead of the theory in deep learning. Experiment with many different architectures, and trust your intuition. And, of course, have fun!
We rescale the images by dividing every pixel in every image by 255.
from PIL import ImageFile
ImageFile.LOAD_TRUNCATED_IMAGES = True
# pre-process the data for Keras
train_tensors = paths_to_tensor(train_files).astype('float32')/255
valid_tensors = paths_to_tensor(valid_files).astype('float32')/255
test_tensors = paths_to_tensor(test_files).astype('float32')/255
Create a CNN to classify dog breed. At the end of your code cell block, summarize the layers of your model by executing the line:
model.summary()
We have imported some Python modules to get you started, but feel free to import as many modules as you need. If you end up getting stuck, here's a hint that specifies a model that trains relatively fast on CPU and attains >1% test accuracy in 5 epochs:
Question 4: Outline the steps you took to get to your final CNN architecture and your reasoning at each step. If you chose to use the hinted architecture above, describe why you think that CNN architecture should work well for the image classification task.
Answer: CNNs have very intuitive structure. They consist of a sequence of convolutional layers and pooling layers. Each convolutional layer multiplicatively increases the size of the third dimension, and each pooling layer multiplicatively decreases the size of the first two dimensions. After the last convolutional layer, the corresponding pooling layer should decrease the size of the first two dimensions to 1x1 (e.g., with the help of global average pooling), so we end with 1D array of features which is finally used for classification with the 'standard' neural network.
Important tips for building CNNs:
to avoid the vanishing gradient problem, it is highly recommended to use the relu
activation function) in all intermediate layers. In the final layer, the softmax activation function should be used instead to provide the multi-dimensional output. In our case of 133 different breeds, the final layer can be written as
model.add(Dense(133, activation='relu'));
to avoid the problem with overfitting, it is recommended to add intermediate dropout layer(s);
padding = 'same'
.I finish with two different CNNs:
Default architecture (matches the sample CNN in the image above):
model.add(Conv2D(16, (2, 2), input_shape=(224, 224, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(32, (2, 2), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(64, (2, 2), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(GlobalAveragePooling2D())
model.add(Dense(133, activation='softmax'))
Improved architecture (adding more internal layers, including Dropout to reduce overfitting), with an initial number of filters n_filter
set as a free parameter:
model.add(Conv2D(n_filters, kernel_size=(2, 2), padding='same', activation='relu', strides=(1, 1), \
input_shape=(224, 224, 3)))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.2))
model.add(Conv2D(2*n_filters, kernel_size=(2, 2), padding='same', activation='relu', strides=(1, 1)))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.2))
model.add(Conv2D(4*n_filters, kernel_size=(2, 2), padding='same', activation='relu', strides=(1, 1)))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.2))
model.add(Conv2D(8*n_filters, kernel_size=(2, 2), padding='same', activation='relu', strides=(1, 1)))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.2))
model.add(Conv2D(16*n_filters, kernel_size=(2, 2), padding='same', activation='relu', strides=(1, 1)))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(GlobalAveragePooling2D())
model.add(Dense(133, activation='softmax'))
I run models for a different number of epochs and the initial number of filters. The obtained test score, the total running time and the final convergence of the run (i.e., whether the validation score improves during the last epoch(s): if the fit is not converged, it is desirable to execute the training further by adjusting initial_epoch
) is summarized in the table below:
| Model | n_filters | n_epochs | Running time, s | score, % | Converged |
|---|---|---|---|---|---|
| default | 16 | 5 | 983 | 2.99 | NO |
| default | 16 | 10 | 1985 | 4.43 | NO |
| default | 16 | 20 | 3910 | 4.67 | NO |
| default | 16 | 40 | 7874 | 7.66 | YES |
| improved | 4 | 5 | 422 | 2.39 | NO |
| improved | 4 | 10 | 832 | 3.11 | NO |
| improved | 4 | 20 | 1668 | 4.78 | NO |
| improved | 4 | 40 | 3381 | 6.22 | YES |
| improved | 8 | 5 | 673 | 3.11 | NO |
| improved | 8 | 10 | 1417 | 4.07 | NO |
| improved | 8 | 20 | 2867 | 6.58 | NO |
| improved | 8 | 40 | 5881 | 11.72 | YES |
from keras.layers import Conv2D, MaxPooling2D, GlobalAveragePooling2D
from keras.layers import Dropout, Flatten, Dense
from keras.models import Sequential
model = Sequential()
### DONE: Define your architecture.
# Default architecture (matches the sample CNN above)
#model.add(Conv2D(16, (2, 2), input_shape=(224, 224, 3), activation='relu'))
#model.add(MaxPooling2D(pool_size=(2, 2)))
#model.add(Conv2D(32, (2, 2), activation='relu'))
#model.add(MaxPooling2D(pool_size=(2, 2)))
#model.add(Conv2D(64, (2, 2), activation='relu'))
#model.add(MaxPooling2D(pool_size=(2, 2)))
#model.add(GlobalAveragePooling2D())
#model.add(Dense(133, activation='softmax'))
# Improved architecture:
n_filters = 8 # initial number of filters
model.add(Conv2D(n_filters, kernel_size=(2, 2), padding='same', activation='relu', strides=(1, 1), \
input_shape=(224, 224, 3)))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.2))
model.add(Conv2D(2*n_filters, kernel_size=(2, 2), padding='same', activation='relu', strides=(1, 1)))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.2))
model.add(Conv2D(4*n_filters, kernel_size=(2, 2), padding='same', activation='relu', strides=(1, 1)))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.2))
model.add(Conv2D(8*n_filters, kernel_size=(2, 2), padding='same', activation='relu', strides=(1, 1)))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.2))
model.add(Conv2D(16*n_filters, kernel_size=(2, 2), padding='same', activation='relu', strides=(1, 1)))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(GlobalAveragePooling2D())
model.add(Dense(133, activation='softmax'))
model.summary()
model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])
Train your model in the code cell below. Use model checkpointing to save the model that attains the best validation loss.
You are welcome to augment the training data, but this is not a requirement.
from keras.callbacks import ModelCheckpoint
### DONE: specify the number of epochs that you would like to use to train the model.
epochs = 40
### Do NOT modify the code below this line.
checkpointer = ModelCheckpoint(filepath='saved_models/weights.best.from_scratch.hdf5',
verbose=1, save_best_only=True)
model.fit(train_tensors, train_targets,
validation_data=(valid_tensors, valid_targets),
initial_epoch=0,
epochs=epochs, batch_size=20, callbacks=[checkpointer], verbose=1)
model.load_weights('saved_models/weights.best.from_scratch.hdf5')
Try out your model on the test dataset of dog images. Ensure that your test accuracy is greater than 1%.
# get index of predicted dog breed for each image in test set
dog_breed_predictions = [np.argmax(model.predict(np.expand_dims(tensor, axis=0))) for tensor in test_tensors]
# report test accuracy
test_accuracy = 100*np.sum(np.array(dog_breed_predictions)==np.argmax(test_targets, axis=1))/len(dog_breed_predictions)
print('Test accuracy: %.4f%%' % test_accuracy)
#model.save('default_model.h5')
#model.save('improved_model_filt_08.h5')
bottleneck_features = np.load('bottleneck_features/DogVGG16Data.npz')
train_VGG16 = bottleneck_features['train']
valid_VGG16 = bottleneck_features['valid']
test_VGG16 = bottleneck_features['test']
The model uses the the pre-trained VGG-16 model as a fixed feature extractor, where the last convolutional output of VGG-16 is fed as input to our model. We only add a global average pooling layer and a fully connected layer, where the latter contains one node for each dog category and is equipped with a softmax.
VGG16_model = Sequential()
VGG16_model.add(GlobalAveragePooling2D(input_shape=train_VGG16.shape[1:]))
VGG16_model.add(Dense(133, activation='softmax'))
VGG16_model.summary()
VGG16_model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'])
checkpointer = ModelCheckpoint(filepath='saved_models/weights.best.VGG16.hdf5',
verbose=1, save_best_only=True)
VGG16_model.fit(train_VGG16, train_targets,
validation_data=(valid_VGG16, valid_targets),
epochs=20, batch_size=20, callbacks=[checkpointer], verbose=1)
VGG16_model.load_weights('saved_models/weights.best.VGG16.hdf5')
Now, we can use the CNN to test how well it identifies breed within our test dataset of dog images. We print the test accuracy below.
# get index of predicted dog breed for each image in test set
VGG16_predictions = [np.argmax(VGG16_model.predict(np.expand_dims(feature, axis=0))) for feature in test_VGG16]
# report test accuracy
test_accuracy = 100*np.sum(np.array(VGG16_predictions)==np.argmax(test_targets, axis=1))/len(VGG16_predictions)
print('Test accuracy: %.4f%%' % test_accuracy)
from extract_bottleneck_features import *
def VGG16_predict_breed(img_path):
# extract bottleneck features
bottleneck_feature = extract_VGG16(path_to_tensor(img_path))
# obtain predicted vector
predicted_vector = VGG16_model.predict(bottleneck_feature)
# return dog breed that is predicted by the model
return dog_names[np.argmax(predicted_vector)]
You will now use transfer learning to create a CNN that can identify dog breed from images. Your CNN must attain at least 60% accuracy on the test set.
In Step 4, we used transfer learning to create a CNN using VGG-16 bottleneck features. In this section, you must use the bottleneck features from a different pre-trained model. To make things easier for you, we have pre-computed the features for all of the networks that are currently available in Keras:
The files are encoded as such:
Dog{network}Data.npz
where {network}
, in the above filename, can be one of VGG19
, Resnet50
, InceptionV3
, or Xception
. Pick one of the above architectures, download the corresponding bottleneck features, and store the downloaded file in the bottleneck_features/
folder in the repository.
In the code block below, extract the bottleneck features corresponding to the train, test, and validation sets by running the following:
bottleneck_features = np.load('bottleneck_features/Dog{network}Data.npz')
train_{network} = bottleneck_features['train']
valid_{network} = bottleneck_features['valid']
test_{network} = bottleneck_features['test']
### DONE: Obtain bottleneck features from another pre-trained CNN.
#bottleneck = 'VGG19'
#bottleneck = 'Resnet50'
bottleneck = 'InceptionV3' # our best model with ~80% accuracy
#bottleneck = 'Xception' # does not work (ends with MemoryError) !!!
bottleneck_features = np.load('bottleneck_features/Dog{}Data.npz'.format(bottleneck))
train_bottleneck = bottleneck_features['train']
valid_bottleneck = bottleneck_features['valid']
test_bottleneck = bottleneck_features['test']
Create a CNN to classify dog breed. At the end of your code cell block, summarize the layers of your model by executing the line:
<your model's name>.summary()
Question 5: Outline the steps you took to get to your final CNN architecture and your reasoning at each step. Describe why you think the architecture is suitable for the current problem.
Answer: Finally, I have found that the InceptionV3
architecture produces the best result in dog breed classification. According to the original paper, this improvement can be done by further increase of the architecture complexity and by taking the number of trained parameters reasonably small "by suitably
factorized convolutions and aggressive regularization".
### DONE: Define your architecture.
bottleneck_model = Sequential()
bottleneck_model.add(GlobalAveragePooling2D(input_shape=train_bottleneck.shape[1:]))
bottleneck_model.add(Dense(133, activation='softmax'))
bottleneck_model.summary()
### DONE: Compile the model.
bottleneck_model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'])
Train your model in the code cell below. Use model checkpointing to save the model that attains the best validation loss.
You are welcome to augment the training data, but this is not a requirement.
### DONE: Train the model.
checkpointer = ModelCheckpoint(filepath='saved_models/weights.best.{}.hdf5'.format(bottleneck),
verbose=1, save_best_only=True)
bottleneck_model.fit(train_bottleneck, train_targets,
validation_data=(valid_bottleneck, valid_targets),
epochs=5, batch_size=20, callbacks=[checkpointer], verbose=0)
### DONE: Load the model weights with the best validation loss.
bottleneck_model.load_weights('saved_models/weights.best.{}.hdf5'.format(bottleneck))
Try out your model on the test dataset of dog images. Ensure that your test accuracy is greater than 60%.
### DONE: Calculate classification accuracy on the test dataset.
# get index of predicted dog breed for each image in test set
bottleneck_predictions = [np.argmax(bottleneck_model.predict(np.expand_dims(feature, axis=0))) \
for feature in test_bottleneck]
# report test accuracy
test_accuracy = 100*np.sum(np.array(bottleneck_predictions) == \
np.argmax(test_targets, axis=1))/len(bottleneck_predictions)
print('Test accuracy: %.4f%%' % test_accuracy)
Write a function that takes an image path as input and returns the dog breed (Affenpinscher
, Afghan_hound
, etc) that is predicted by your model.
Similar to the analogous function in Step 5, your function should have three steps:
dog_names
array defined in Step 0 of this notebook to return the corresponding breed.The functions to extract the bottleneck features can be found in extract_bottleneck_features.py
, and they have been imported in an earlier code cell. To obtain the bottleneck features corresponding to your chosen CNN architecture, you need to use the function
extract_{network}
where {network}
, in the above filename, should be one of VGG19
, Resnet50
, InceptionV3
, or Xception
.
### DONE: Write a function that takes a path to an image as input
### and returns the dog breed that is predicted by the model.
def bottleneck_predict_breed(img_path):
# extract bottleneck features
bottleneck_feature = extract_InceptionV3(path_to_tensor(img_path))
# obtain predicted vector
predicted_vector = bottleneck_model.predict(bottleneck_feature)
# return dog breed that is predicted by the model
return dog_names[np.argmax(predicted_vector)]
bottleneck_predict_breed("./dima_n.jpg")
Write an algorithm that accepts a file path to an image and first determines whether the image contains a human, dog, or neither. Then,
You are welcome to write your own functions for detecting humans and dogs in images, but feel free to use the face_detector
and dog_detector
functions developed above. You are required to use your CNN from Step 5 to predict dog breed.
Some sample output for our algorithm is provided below, but feel free to design your own user experience!
### DONE: Write your algorithm.
### Feel free to use as many code cells as needed.
%matplotlib inline
def dog_breed_predictor(img_path):
print(">>> Hi, I'm a dog breed predictor.")
print(">>> This is your photo:")
img = cv2.imread(img_path)
cv_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
plt.imshow(cv_rgb)
plt.show()
if face_detector_all(img_path):
print(">> I think you are a human, and the following dog breed may look quite similar to you:")
elif dog_detector(img_path):
# print(">> I think you are not human. If you are, please provide your other photo.")
print(">> I think you are dog, and your breed is:")
else:
print(">> I think you are neither human nor dog, sorry\n \
However, the following dog breed may look quite similar to you:")
bottleneck_feature = extract_InceptionV3(path_to_tensor(img_path))
predicted_vector = bottleneck_model.predict(bottleneck_feature)
predicted_breed = dog_names[np.argmax(predicted_vector)]
print(">>> ", predicted_breed,":")
s = random.choice([item for i,item in enumerate(train_files) if str(predicted_breed) in item])
sample_img = cv2.imread(s)
sample_cv_rgb = cv2.cvtColor(sample_img, cv2.COLOR_BGR2RGB)
plt.imshow(sample_cv_rgb)
plt.show()
In this section, you will take your new algorithm for a spin! What kind of dog does the algorithm think that you look like? If you have a dog, does it predict your dog's breed accurately? If you have a cat, does it mistakenly think that your cat is a dog?
Test your algorithm at least six images on your computer. Feel free to use any images you like. Use at least two human and two dog images.
Question 6: Is the output better than you expected :) ? Or worse :( ? Provide at least three possible points of improvement for your algorithm.
Answer: As we see below, the algorithm works reasonably well, similar to expectations:
## DONE: Execute your algorithm from Step 6 on
## at least 6 images on your computer.
## Feel free to use as many code cells as needed.
print('*'*60)
print("***** My photo #1:")
dog_breed_predictor("./dima_n.jpg")
print('*'*60)
print("***** Boxer dog:")
dog_breed_predictor("./boxer.jpg")
print('*'*60)
print("***** Kitten:")
dog_breed_predictor("./kitten.jpg")
print('*'*60)
print("***** My photo #2:")
dog_breed_predictor("./dima_m.jpg")
print('*'*60)
print("***** Homeless dog:")
dog_breed_predictor("./homeless_dog.jpg")
print('*'*60)
print("***** Cosmic web:")
dog_breed_predictor("./dm.jpg")


SublimeVideo Playlist jQuery Responsive Resize
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Three arrested as gardaí seize two firearms in Limerick
by By Digital Desk staffGardaí have seized two firearms in a search in Limerick.
Gardaí in Mayorstone carried out the search of a house under warrant in Ballynanty in the north side of the city.
The search was carried out last night, at around 7.30pm, by Detectives of Mayorstone Garda Station assisted by the Divisional Drug unit members and Regional Armed Support Unit.
Two men, in their 50s and 20s, and a teenager were arrested at the scene.
They were taken to Henry Street Garda station and Roxboro Road Garda station where they are all detained under Section 30 Offences Against the State Act, 1939.
Gardaí have said investigations ongoing.


礦ision Build Log
Tool Versions:
IDE-Version: μVision V<IP_ADDRESS> Copyright (C) 2020 ARM Ltd and ARM Germany GmbH. All rights reserved. License Information: 123 123, 123, LIC=UJ5FJ-U6IZJ-HWY2Q-N8BDV-ZHPEB-9M8EE Tool Versions: Toolchain: PK51 Prof. Developers Kit Version: <IP_ADDRESS> Toolchain Path: C:\Keil_v5\MDK\C51\BIN C Compiler: C51.exe V<IP_ADDRESS> Assembler: A51.exe V<IP_ADDRESS> Linker/Locator: BL51.exe V<IP_ADDRESS> Library Manager: LIB51.exe V<IP_ADDRESS> Hex Converter: OH51.exe V<IP_ADDRESS> CPU DLL: S8051.DLL V<IP_ADDRESS> Dialog DLL: DP51.DLL V<IP_ADDRESS>Project:
D:\后来\单片机\物联网\基于涂鸦智能的燃气报警装置V1.0\User\Project.uvproj Project File Date: 02/28/2021Output:
Rebuild target 'Target 1' compiling main.c... compiling Delay.C... compiling Uart3.c... compiling Key.C... compiling MQ-5.c... compiling mcu_api.c... ..\SDK\mcu_api.c(575): warning C280: 'len': unreferenced local variable ..\SDK\mcu_api.c(587): warning C280: 'len': unreferenced local variable ..\SDK\mcu_api.c(599): warning C280: 'len': unreferenced local variable compiling protocol.c... compiling system.c... linking...  "..\Output\Project" - 0 Error(s), 20 Warning(s). Build Time Elapsed: 00:00:01


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Happy Makara Sankranthi to You and your Family
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Bolognese Sauce
Basic bolognese sauce for lasagne or pasta dishes
- ⏲ Prep time: 45 min
- 🍳 Cook time: at least 4 hours
Ingredients
- extra virgin olive oil
- 2 carrots
- 2 celery sticks
- 1 large onion
- 2 garlic cloves (optional, but not traditional)
- 200 grams pancetta (optional)
- 300 grams beef mince
- 350 grams pork mince
- 350 grams veal mince (if you cant find veal replace with pork)
- 140 grams concentrated tomato paste
- 690 grams diced tomatoes
- water
- 1 can peeled tomatoes
- 1-2 glasses wine (whatever you have open)
- 1 glass of milk (optional)
- 1 glass of beef/vegetable stock (optional)
Directions
- Finely dice the carrots, celery, onion and pancetta, if your blender can handle it you can optionally blend all this to speed it up.
- Heat up a large saucepan or dutch oven on medium low heat and put enough olive oil in to cover the bottom of the pan.
- Put in your diced veggies and pancetta and let it slowly sauté for about 10 minutes until everything is fragrant and soft.
- If you are using garlic add it here and let it sweat for a minute
- Add in your minced meat and break it up into small clumps about the size of a rice grain with a spoon, keep stiring until all your meat is lightly cooked.
- Once all your meat is cooked and looks a bit grey add your wine, add enough to fully deglaze the pan (I usually add a little bit extra for the flavor)
stir it through until it has completely evaporated. (If you are using it you can also add the stock at this point)
- Once your pan is somewhat dry you can add the tomato paste to the beef, let it cook for a minute to remove the slight metallic taste.
- Add your canned and peeled tomatoes and use your spoon to break them up, you don’t have to be too thorough since they will soften up while the sauce cooks.
- add the diced tomatoes, fill the jar your tomatos came in with water give it a shake and add that to the pot, do the same with your can of peeled tomatoes.
- lower your heat to the lowest setting and slightly cover the pan with your lid.
- let it simmer for as long as you can wait, preferably at least 4 hours, if it starts getting a bit too dry you can add a bit more water to make sure the sauce doesn’t burn.
- Once it’s done simmering for as long as you can muster and its nice and thick, the liquid should not come above your meat and should be pretty concentrated
- You can add your glass of milk, let it simmer for 10 more minutes and serve.
Contribution
- Yiusa, eth
0x68f1317c6512f0267fa711cafb6c134ae968fa80
Recipe tags:
sauce
italian
pasta


{% load i18n %} {% comment %} It might not be imediately obvious where this snippet is used because it is injected into a template through crispy forms. It's "parent" html is found here: apps/userreports/templates/reportbuilder/configure_report.html {% endcomment %} {% comment %} TODO: - Move the fade stuff into an option for the `sortable` binding. - The span1's don't give the best look... - Move grip and delete icons into their own widgets/templates {% endcomment%}
| {% trans "Property" %} | {% trans "Label" %} | {% trans "Format" %} | {% trans "Format" %} | {% trans "Filter Value" %} | ||
|---|---|---|---|---|---|---|
|
{% block property_name_display %}
{% endblock %}
{% trans "Please select a property." %}
{% blocktrans %}
This property or question no longer exists in your app. You must delete this property to update this report. {% endblocktrans %} |
{% trans "Display text should not be blank." %}
|
{# there must be no whitespace between | ||||
| {% endblock %} |


Bases: object
Extend core controllers of nova OpenStack API.
Provide a way to extend existing nova OpenStack API core controllers.
Bases: object
Base class that defines the contract for extensions.
Note that you don’t have to derive from this class to have a valid extension; it is purely a convenience.
List of extensions.ControllerExtension extension objects.
Controller extensions are used to extend existing controllers.
List of extensions.ResourceExtension extension objects.
Resources define new nouns, and are accessible through URLs.
Synthesize a namespace map from extension.
Synthesize element and attribute names.
Bases: object
Load extensions from the configured extension path.
See nova/tests/api/openstack/volume/extensions/foxinsocks.py or an example extension implementation.
Returns a list of ControllerExtension objects.
Returns a list of ResourceExtension objects.
Execute an extension factory.
Loads an extension. The ‘ext_factory’ is the name of a callable that will be imported and called with one argument–the extension manager. The factory callable is expected to call the register() method at least once.
Bases: nova.api.openstack.xmlutil.TemplateBuilder
Bases: nova.api.openstack.wsgi.Resource
Bases: nova.api.openstack.xmlutil.TemplateBuilder
Bases: object
Add top level resources to the OpenStack API in nova.
Registers all standard API extensions.
Ensure errors are not passed along.


Initialize the library.
#include <ftplib.h> void FtpInit(void);
None.
Performs any required initialization for the library.


WITNESS LIST Committee of the Whole Senate Committee May 13, 2004 -10:00A CSHB 1 Providing written testimony: On: Gregg, Brock (Association of Texas Professional Educators), Austin, TX HB and other revenue proposals On: Wolters, Bill (Texas Automobile Dealers Association), Austin, TX HB1/SB 1 ON: Bresnen, Steve (Coalition to Revive Charitable Bingo), Austin, TX SB 1 ON: Colbert, Paul (Self and El Paso ISD), Houston, TX SB 1 and impact to CISD ON: Porter, Hal (Cisco ISD), Cisco, TX SB 1 and SJR 1 ON: Howard, Donna (Common Cause and Texas Education Crisis Coalition), Austin, TX SB/HB 1 ON: Reed, Cyrus (Texas Center for Policy Studies), Austin, TX


{% extends "layout.html" %} {% block page_title %} Apprenticeships {% endblock %} {% block content %} {% include "includes/phase_banner_beta.html" %} {% include "includes/secondary-nav-people.html" %} Account home Spend levy {% include "includes/account-name-number.html" %}Settings Account settings: Your team Rename your levy account Personal settings: First name Last name Email Change your password Email notifications Choose whether to receive emails when there are things that change or that you need to do Yes No Cancel {% endblock %}


current playing :
current time :
played / downloaded :
state :
duration :
loop : false
muted : false
event 5s : not triggered


I am a first year MS of Computer Science student at Georgia Tech, specializing in machine learning.
Before coming to Atlanta, I was working as a member of technical staff at RAM Laboratories in San Diego,
where we did R&D pertaining to security, advanced analytics, modeling and simulation, and more. My contributions
in particular include work on fuzzing, natural language processing, social media analysis, firmware analysis,
and machine learning security.
Before that I was a student at the University of California, Irvine, where I studied computer science.
My resume can be found
here and I can be reached at jbeel3 AT gatech DOT edu.


|
KratosMultiphysics
KRATOS Multiphysics (Kratos) is a framework for building parallel, multi-disciplinary simulation software, aiming at modularity, extensibility, and high performance. Kratos is written in C++, and counts with an extensive Python interface.
|
All the processes python should be derived from "Process". More...
Public Member Functions | |
| def | __init__ (self, Model, settings) |
| def | ExecuteInitialize (self) |
| def | ExecuteInitializeSolutionStep (self) |
| def | ExecuteFinalizeSolutionStep (self) |
| Public Member Functions inherited from KratosUnittest.TestCase | |
| def | run (self, result=None) |
| def | skipTestIfApplicationsNotAvailable (self, *application_names) |
| Skips the test if required applications are not available. More... | |
| def | assertEqualTolerance (self, first, second, tolerance, msg=None) |
| Fails if first and second have a difference greater than tolerance. More... | |
| def | assertIsClose (self, first, second, rel_tol=None, abs_tol=None, msg=None) |
| Fails if the two objects are unequal as determined by their absolute and relative difference. More... | |
| def | assertVectorAlmostEqual (self, vector1, vector2, places=7, msg=None, delta=None) |
| def | assertMatrixAlmostEqual (self, matrix1, matrix2, places=7, msg=None, delta=None) |
Public Attributes | |
| non_trivial_local_system | |
| R | |
| x0 | |
| model | |
| settings | |
| variable | |
| interval | |
| value_is_numeric | |
| is_time_function | |
| value | |
| function_string | |
| aux_function | |
| tol | |
| model_part | |
| mesh | |
| Public Attributes inherited from KratosUnittest.TestCase | |
| mismatch_idx | |
| aux_message | |
| idx_1 | |
| idx_2 | |
All the processes python should be derived from "Process".
| def check_scalar_on_nodes_process.CheckScalarOnNodesProcess.__init__ | ( | self, | |
| Model, | |||
| settings | |||
| ) |
| def check_scalar_on_nodes_process.CheckScalarOnNodesProcess.ExecuteFinalizeSolutionStep | ( | self | ) |
| def check_scalar_on_nodes_process.CheckScalarOnNodesProcess.ExecuteInitialize | ( | self | ) |
| def check_scalar_on_nodes_process.CheckScalarOnNodesProcess.ExecuteInitializeSolutionStep | ( | self | ) |
| check_scalar_on_nodes_process.CheckScalarOnNodesProcess.aux_function |
| check_scalar_on_nodes_process.CheckScalarOnNodesProcess.function_string |
| check_scalar_on_nodes_process.CheckScalarOnNodesProcess.interval |
| check_scalar_on_nodes_process.CheckScalarOnNodesProcess.is_time_function |
| check_scalar_on_nodes_process.CheckScalarOnNodesProcess.mesh |
| check_scalar_on_nodes_process.CheckScalarOnNodesProcess.model |
| check_scalar_on_nodes_process.CheckScalarOnNodesProcess.model_part |
| check_scalar_on_nodes_process.CheckScalarOnNodesProcess.non_trivial_local_system |
| check_scalar_on_nodes_process.CheckScalarOnNodesProcess.R |
| check_scalar_on_nodes_process.CheckScalarOnNodesProcess.settings |
| check_scalar_on_nodes_process.CheckScalarOnNodesProcess.tol |
| check_scalar_on_nodes_process.CheckScalarOnNodesProcess.value |
| check_scalar_on_nodes_process.CheckScalarOnNodesProcess.value_is_numeric |
| check_scalar_on_nodes_process.CheckScalarOnNodesProcess.variable |
| check_scalar_on_nodes_process.CheckScalarOnNodesProcess.x0 |


The dog has been selectively bred over millennia for various behaviors, sensory capabilities and physical attributes. Dogs are subclassified into breeds, which vary widely in shape, size and color. They perform many roles for humans, such as hunting, herding, pulling loads, protection, assisting police and the military, companionship, therapy, and aiding disabled people. This influence on human society has given them the sobriquet of "man's best friend."
The domestic dog (Canis familiaris or Canis lupus familiaris) is a domesticated form of wolf. The dog descended from an ancient, extinct wolf, with the modern grey wolf being the dog's nearest living relative. The dog was the first species to be domesticated by hunter–gatherers over 15,000 years ago, before the development of agriculture. Their long association with humans has led dogs to be uniquely attuned to human behavior, enabling an abundant cosmopolitan distribution and the ability to thrive on a starch-rich diet that would be inadequate for other canids.


Storing sensitive data can be problematic. A 24 word BIP39 mnemonic for instance, is the "keys to the kingdom". If stolen, your addresses are compromised. If you make multiple copies, this increases the risk of being stolen or misplaced. If encrypted, you face the same problem storing the encryption key. Is there a safer way?
Secret Coin uses Shamir's Secret Sharing algorithm to divide your secret into multiple (N) shares, and then you can restore the secret using a specified number of the shares (M of N). None of the shares contain identifiable information about the secret. If any share, or up to (M-1) shares are compromised, the secret is still safe.
All operations happen in your browser. You can disconnect from the network and use this web page. The source for this website is available on GitHub. It is highly recommended to download the source .zip and launch it on an off-line computer. Instructions can be found at the GitHub Repository.