About Sardonyx
Sardonyx is an online solution to transform how high school students and teachers use technology to manage workloads. Sardonyx offers a cross-platfrom experience for all its users, through its mobile and web platforms.
The web application offers a group tasklist accessible by both teachers and students. Students and teachers benefit from the transparency of the workload, organized by due dates, subjects, and task categories. Each task on the list can be given a detailed description.
The mobile application offers students and teachers a native experience in managing their online learning environment.
CAS
Sardonyx is a CAS project, which is part of the International Baccalaureate Diploma Programme. This project is planned, designed, developed, tested, and operated by two high school students. Adhering to the principles of CAS, Sardonyx is provided completely free of charge. For more information about CAS projects, visit the International Baccalaureate Organization's website.
Browser compatibility
Recently, there has been rapid development of new web technologies that facilitate the development of sophisticated web applications like Sardonyx. However, modernization comes at a cost of dropping support for legacy web browsers, like Internet Explorer. Although Sardonyx would like to support browsers used by all users, this difficult task would require an unrealistic amount of time and effort. Like many web apps have done, Sardonyx has decided to support various browsers realistically. This means that there will be varied levels of support for different browsers.
The below list shows the current level of support for popular browsers. Sardonyx recommends users to use the newest version of Google Chrome or Mozilla Firefox for the most stable experience.
- Google Chrome (newer versions) - fully supported
- Mozilla Firefox (newer versions for Windows and MacOS) - fully supported
- Mozilla Firefox (Linux) - scroll bars may not render properly but otherwise supported
- Apple Safari (newer versions) - fully supported
- Microsoft Edge - scroll bars will not render properly but otherwise supported
- Internet Explorer (version 11) - various parts of the application will not render properly, but the application is functional
Above information is as of April 2019.


I started attending College of the Sequoias, a community college in Visalia, California, in Summer of 2017 at age 17. While I had the grades to attend the colleges I was interested in, I ultimately chose community college to save money. At $400 a semester after financial aid it was the financially smart decision. I entered as a chemistry major and was sure that I wanted to work as a pharmacist. My plan was to spend two years there and transfer to a state school where I could finish my bachelor's and apply to pharmacy school.
Three semesters into my chemistry degree I began to hate it. No matter how much I studied I struggled to retain the material. I quickly lost interest and began to program more in my free time to deal with the stress. I started going home from 6-hour chemistry labs and instead of finishing my labs I would program because it was one of the few things that felt productive and did not add to the stress from school. One night after a lab I realized that I could go to school for computer science. The next day I dropped organic chemistry, changed my major to computer science, and signed up for next semester's programming course. Since then I have done extremely well in all of my classes, transferring to California State University, Fresno in Fall 2020 after earning associate degrees in mathematics and university studies in math and sciences from College of the Sequoias.
Despite changing my major almost halfway through, setting me back a year and a half, I have never once regret the decision. I now get to study something that I enjoy and actively practice in my free time. I have also met many interesting people along the way and made friends that I expect I will have for a long time.


Pokemon Sword And Shield Can Already Run On Yuzu Switch Emulator
by Francesco De MeoPokemon Sword And Shield have been released today on Nintendo Switch, and the Yuzu emulator can already run the two games.
While the new entries in the Pokemon series do run on the emulator, they are not in a playable state as of now. Still, it's really impressive how the emulator can manage to run games on day one.
CoD: Modern Warfare, The Outer Worlds, and Switch Top a Sluggish October Says NPD
Pokemon Sword and Shield are the first entries in the series to be released on a home console. The two games feature a new region, Galar, new Pokemon, plenty of new features but also some controversial aspects, like the absence of a lot of Pokemon.
Begin your adventure as a Pokemon Trainer by choosing one of three new partner Pokemon: Grookey, Scorbunny, or Sobble. Then embark on a journey in the new Galar region, where you’ll challenge the troublemakers of Team Yell, while unraveling the mystery behind the Legendary Pokémon Zacian and Zamazenta! Explore the Wild Area, a vast expanse of land where the player can freely control the camera. Team up with three other players locally or online in the new multiplayer co-op Max Raid Battles in which players will face off against gigantic and super-strong Pokémon known as Dynamax Pokemon.
Pokemon Sword and Shield are now available on Nintendo Switch worldwide.


UXPA Stretch your online research tools
Links to demos and sources
- People Don't Read Instructions - article, sources
- Loop11 demo of instructions as first demonstration task
- Flow to Recruiter after an online study
- Treejack script and randomized tasks for mobile usability sessions
- Eyes Decide remote eye tracking demo- Requires computer, Chrome & camera
- Video demonstration of Eyes Decide participant experience made for UXPA
- Chalkmark matched to eye tracking - awareness & clicks
Talk to me about me about speaking and half-day working sessions to tackle thorny UX issues with you and your design/research team.
- More at neoinsight.com
- Contact Lisa Fast
About faces - 3 reasons not to turn the camera on during moderated testing
Heard there was some discussion of this issue - here's my 2 cents...
- Date modified:


Blog
My 'Pilgrimage' to Hereford Cathedral Archive
Straight after Easter I made my own pilgrimage to Hereford – not so much to pray at the tomb, as to explore the Cathedral archives for all things Cantilupe related. As an ex-pupil of Hereford cathedral School, it felt very much like coming home!
I was met by archivist Rosalind Caird who was hugely helpful, not only in explaining the workings of the library, but also in suggesting where I might look for information.
Well, the trip was a success. I had expected to find something about Thomas de Cantilupe and his canonisation process, as it had all begun right there in the 1280s. However, I found much, much more than I had even hoped!
Perhaps one of the most exciting things I came across was MS 1443, a letter dating from 1309 written on paper (if you turned it over, the threads of were still visible where they had not been sufficiently mulched). Apart for being a novelty for me as I was looking at the oldest paper document I have yet encountered, the content of the letter was also something of a revelation.
The letter begins with Henry de Shorne’s apology for the delay in St Thomas de Cantilupe’s canonisation proceedings, but then goes on to assure the recipient John, dean of Hereford, that the six volumes of evidence copied from the original interview proceedings are to be sent directly so that work can resume on the case: six volumes!
Having worked with the manuscripts Vat. Lat. 4015 and 4016 in the flesh last Autumn, I knew that these were fair copies of the proceedings, despite the marginal notes and adjustments which had been made to the texts. The missing link was what these ‘neat versions’ had been based upon, and now I had uncovered one more link in the chain of evidence. There is still a question of what form the records of the ‘original processes’ took: they may have been on loose leaves of parchment or wax tablets. What we might consider though is that they were taken from these temporary records, placed in these six volumes, then subsequently edited to make the surviving volumes in the Vatican Library.
Other pieces of the puzzle which I came across in the archives were the letters I had read about in Bartlett’s book, appointing the bishops who were to run the enquiry, one of which even carried the now familiar notary’s mark found at the back of Vat. Lat. 4015 at the bottom. I also consulted the papal bull of Clement V which was disseminated in 1320, officially announcing the successful canonisation of the new St Thomas de Cantilupe.
I also discovered evidence for just how popular Thomas had been outside Hereford: there were a number of documents detailing the forms of indulgence offered by people seeking the saint’s aid from as far away as Norfolk where forty-five pounds, four shillings and two pence and a halfpenny had been donated to the saint. There were also forms of indulgence offered by the bishops of Worcester (MS 1420), Salisbury (MS 1429) and London (MS 1423), for penitents if they prayed for the soul Thomas de Cantilupe, even before he was officially made a saint.
All in all it was a fantastic trip, and I have found a great deal of material to help with the final notes in my edition, and the paper I will be giving on 30th January 2015 to Hereford’s branch of the Historical Association.
Şħȧřḗ ǿƞ Ŧẇīŧŧḗř Şħȧřḗ ǿƞ Ƒȧƈḗƀǿǿķ


Browser: Mozilla/5.0 (Windows NT 6.1; WOW64; rv:11.0) Gecko/20100101 Firefox/11.0
My BBLog Addon Version is: 1.6
When you look at someones weapon overview page and he's in the top of the worldwide rank with a weapon it shows "Top 0%" for it. For example the M416 from this guy.
http://battlelog.battlefield.com/bf3/soldier/Shadystuff/weapons/262928856/pc/
Shouldn't this be 1%?


KILLER: A JOURNAL OF MURDER
A film review by Vince Waldron
Copyright 1997 Vince Waldron
This worthy effort from star James Woods slipped in and out of theaters so quickly that many of you will probably assume it's a straight to video release when it finally pops up on Blockbuster's new release shelf, which should be any day now. But I suspect the film's low profile has more to do with the unfortunate timing of its release than anything else; as the third death row drama to hit the nation's screens in year when we'd already suffered through Sean Penn and Sharon Stone's long walk down the last mile, it's no surprise that this somewhat more modest movie would come and go without hope of reprieve. Which is too bad, since the movie's premature demise deprived filmgoers of a catching a great unbridled turn by James Woods on the big screen.  And though "Killer" bears perhaps too many similarities in theme and plot to the year's two previous death row dramas--Tim Robbins' "Dead Man Walking" and the one that starred Sharon Stone, the name of which eludes me right this instant--it has by far the most driving narrative of the three, as well as a central relationship that struck me as a far more plausible dynamic than that explored in either of its more high-profile precursors. But see this one for the performance of James Woods, an actor who consistently makes such interesting choices that he could well develop into one of America's most accomplished actors as he matures, assuming he doesn't accidentally become some kind of Hollywood institution along the way.
-- Please reply directly to me at:
<EMAIL_ADDRESS>Vince Waldron
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


" Need for Speed (NFS) is a racing video game franchise published by Electronic Arts and currently
developed by Criterion Games, the developers of Burnout. The series centers around illicit street
racing and in general tasks players to complete various types of races while evading the local law
enforcement in police pursuits. The series released its first title, The Need for Speed, in 1994.
The most recent game, Need for Speed Heat, was released on November 8, 2019.
The series has been overseen and had games developed by multiple notable teams over the years
including EA Black Box and Ghost Games. The franchise has been critically well received and is one
of the most successful video game franchises of all time, selling over 150 million copies of
games. Due to its strong sales, the franchise has expanded into other forms of media including a
film adaptation and licensed Hot Wheels toys. "
For reference: https://en.wikipedia.org/wiki/Need_for_Speed


This is really just a silly use of decorators to do something vaguely like Ruby's or Smalltalk's blocks:
def collect(sequence): """ Like Ruby's .collect:: >>> @collect([1, 2, 3]) ... def result(item): ... return -item >>> result [-1, -2, -3] """ def decorator(func): return [func(item) for item in sequence] return decorator
Someone must have done this before. But then decorator abuse is still young, so maybe not. I wrote up a few examples: foreach, inject, and file_do. Also a decorator set, which lets you add methods to classes and objects, like:
>>> class color: ... >>> @set(color) ... filter(self, r, g, b): ... return self.__class__(self.r*r, self.g*g, self.b*b)
And then your color class gets a new method filter. It can also add methods to objects, but you have to do @set(color_instance, make_method=True). Hmm... but now that I think that it would be neater if it looked at the name of the first argument to the function, and turned it into a classmethod staticmethod (or did nothing and left it a normal method); or if attaching it to an instance it would bind the class or instance to the first argument. So, magic_set is in there too.
They are in http://svn.colorstudy.com/home/ianb/ruby_blocks.py


Timothy Hayes
Professional Experience
Illinois Institute of Technology, Chicago, IL
Master of Science, Information Architecture Candidate/Researcher
2009 to Present
- Coursework: Web Development, Information Architecture, Knowledge Management, Human Computer Interaction, Instructional Design, Information Storage & Retrieval, Rich Internet Applications, Content Management
- Masters Project: Agile Product Development Strategies: Threading content and code through the design, development and deployment processes.
Hayes Media, Palatine, IL
Consultant
2008 to Present
- Services include program management, web development, print production, content strategy/CMS integration, information architecture, usability studies, workflow reengineering, and editorial publishing systems.
Quarasan, Chicago, IL
Quarasan is a content developer and producer of educational materials. Clients include all major K-12 content creators. Products included print and digital deliverables such as, database publishing, custom editions, ebooks, and online materials.
VP, Product Delivery & Technology
2003-2008
- Led a team of 17, across three functional areas: project management, production and technology.
- Developed cross-functional project management team to drive high-volume products from development through product delivery.
- Developed full life cycle project management processes responsive to rapid startup, prototyping and delivery.
- Expanded technology team to initiate strategic technology projects, provide desktop support, and expand user application training.
- Deployed integrated publishing workflow, reducing product development cycle times.
- Developed outsourcing model that produced significant cost-savings. Introduced quality assurance processes into production management to provide for rapid startup and quality delivery of multiple overlapping high-volume projects.
Director of Product Delivery & Technology
1999 – 2003
- Directed a 25-person team, performing the functions of project management, production, design, and image acquisition.
- Responsible for the integration of work flows across functional areas.
- Worked with content team to develop and deliver educational products across a spectrum of content disciplines.
Senior Project Manager
1998 – 1999
- Promoted to senior project manager to lead Quarasan’s project management responsibilities for large-scale programs.
- Developed database systems, project management, content and design processes.
Project Manager
1996 – 1998
- Responsible for the management of single-product projects. Areas of focus included budget forecasting, resourcing, client communication, team leadership and weekly prioritization of team tasks.
Education/Certifications
- M.S., Information Architecture, Illinois Institute of Technology, GPA 4.0
- B.A., Journalism & Photography, Columbia College, Chicago
- Project Management Certification, University of Chicago (56 Qualifying Hours for PMP)
- Project Management Institute, PMP Certification Exam - October 26th, 2010
Associations
- Project Management Institute
- Information Architecture Institute
- Society for Technical Communications
Software Skills
Microsoft Word, Excel, Powerpoint, Project, Visio; Apple Pages, Numbers, Keynote. Filemaker; Omni Graffle, Adobe Creative Suite (InDesign, Dreamweaver, Photoshop, Illustrator, Flex/Flash); XML, XHTML, CSS, Javascript, JQuery, WordPress, Drupal, iPhone/iPad SDK


{{ val['first_name'] }} said this location was {{ q1(val['cat1_score']) }} to others they visited.
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Comments: {{ val['comments'] }}
Uh Oh!
It looks like we don't have any reviews yet for this place so we can't give you many details. We would
love it if you left the first review so you can help others when they are trying to find more information!


{%extends "base.html"%} {%block contentfull%}
| # | Dataset name | status | row level annotation | To Annotate | Class Statistics | See annotation |
|---|---|---|---|---|---|---|
| {{annotation.id}} | {{annotation.name}} | {{annotation.status}} | {{annotation.name}} | To annotate | To show stats | See annotation results |


I am a Flutter expert with years of experience. I can build
cross-platform high-quality Mobile apps for you. I have built
multiple Mobile apps that live on app stores and are used by
millions of users. I am happy to liaise with you on every stage of
your app development project, straight from Idea discussion,
prototypes, coding, testing, and deploying your app on the app
store. I am an efficient coder and a good communicator and take
full ownership of my work. Your app will work seamlessly on both
Android and iOS. I will also fix any bugs for free of cost till
one month of deployment on the app store. I am sure that you will
be fully satisfied with my professionalism and craftsmanship.
Technical Skills
* Flutter, React Native, Redux, REST API
* Expert in writing well-documented clean Javascript Code
* Strong experience in building React Native and Flutter and
for building cross-platform apps Android / iOS / Desktop / Web
* Hands-on Flutter BloC, Provider architecture
*
Worked on Dart Dev Tools like FlutterOctopus, Google Fonts, etc.,
* Worked with Third Party API Google, NPL, Twilio, Facebook
App Events, Branch, OneSignal, SendGrid, Google Cloud, Fabric
Crashlytics, Swagger
* Utilized Firebase DB, GraphQL, Apollo
Server, Apollo Client
* Worked with Payment Gateway, Google
Pay, Apple Pay, Braintree, Stripe
* Worked on multiple IoT
based projects
* Troubleshoots business and production
issues
* Grip on using Android Studio, XCode, WebStorm,
Sublime, Visual Studio
* Integrate analytics, push
notifications, and various other libraries to understand user
behavior and iterated on it.
* Wrote unit and integration
tests using Jest and Enzyme to ensure functionality and code
quality
* Integrated CI/CD using Fastlane, Bitrise, and Code
Magic
* Familiar with Mobile application design development
tools like Zeplin, adobe XD, sketch.
I have developed several Flutter mobile apps and React.js web applications.
Full Stack Engineer
I have developed Web applications using HTML, CSS, and jQuery.
An application that allows you to get to know the various
options around you .. restaurants, cafes or other stores It is
an application that specializes in order processing, not
delivery now, and gives you multiple options, including cash on
delivery
Application features: Ease of use, automatic
linking to your direct location to discover options nearby,
restaurants, cafes, juices, and other stores, and clear and
divided menus that facilitate your choices. A simplified and
straightforward way to communicate with the service provider,
with a separate page for each branch, with the branch’s contact
number added
Feature of app:
Browse nearby restaurants and search for food by cuisine,
restaurant name, dish or meal. Choose from a variety of food to
order Pick your food order from any menu and add it to your cart
with a few taps. Order food to be delivered by delivery people
as soon as possible. Schedule your food order in advance for a
delivery person to pick it up later.
Sailmate is a Finnish navigation app for leisure boaters with the most reliable nautical charts on the market, provided by the Finnish Transport Agency. Sailmate offers unmatched usability and a great user experience for leisure boaters.
View iOS appHome Workouts provides daily workout routines for all your main fitness. In just a few minutes a day, you can keep fitness at home without having to go to the gym. No equipment or coach needed, all exercises can be performed with just your body weight.
View Project


Rob [Redacted]
This is a paragraph summarizing the skillset of the person whose portfolio this is.
You can talk about your skills, what kind of jobs you enjoy the most.

Web developer
Get in touch:


RENAME(1) User Commands RENAME(1) NAME rename - rename files SYNOPSIS rename [options] expression replacement file... DESCRIPTION rename will rename the specified files by replacing the first occurrence of expression in their name by replacement. OPTIONS -v, --verbose Give visual feedback which files where renamed, if any. -V, --version Display version information and exit. -h, --help Display help text and exit. EXAMPLES Given the files foo1, ..., foo9, foo10, ..., foo278, the commands rename foo foo0 foo? rename foo foo0 foo?? will turn them into foo001, ..., foo009, foo010, ..., foo278. And rename .htm .html *.htm will fix the extension of your html files. WARNING The renaming has no safeguards. If the user has permission to rewrite file names, the command will perform the action without any questions. For example, the result can be quite drastic when the command is run as root in the /lib directory. Always make a backup before running the com‐ mand, unless you truly know what you are doing. SEE ALSO mmv(1), mv(1) AVAILABILITY The rename command is part of the util-linux package and is available from ftp://ftp.kernel.org/pub/linux/utils/util-linux/. util-linux June 2011 RENAME(1)


Patient fIrst name is required.
Patient Last name is required.
Patient Age is required.
Patient Date of birth is required.
Patient gender is required.
Patient Phone number is required.


Account ID
From Date
To Date
From Balance
To Balance
No statements yet!
Statements
Account ID
Statement ID
Amount
Date


--- title: Images (Aspect Ratios) section: Components ---
Sometimes you can't control the size of an image, you can constrain your images with aspect-ratios. The base class
aspect-ratio
maintains a 1 to 1 ratio relative to its container's width.Use
aspect-ratio-3-to-2
,aspect-ratio-4-to-3
,aspect-ratio-8-to-5
, oraspect-ratio-16-to-9
to maintain the specific ratio relative to its container or create your own by settingpadding-bottom
to the ratio you want e.g. 2 to 1.aspect-ratio-2-to-1 { padding-bottom: 50% }
.
Use
aspect-ratio-item
if you want to keep the content's original size and crop the visible area.
aspect-ratio-item-fluid
sets the max-width to be 100%, similar toimg-fluid
. If the image is narrower than its container, it won't expand to fill the remaining space. This generally should be used to keep wide images viewable inside theaspect-ratio
container.
aspect-ratio-item-vertical-fluid
sets the max-height to be 100%. If the image is shorter than its container, it won't expand to fill the remaining space. This generally should be used to keep tall images viewable inside theaspect-ratio
container.
aspect-ratio-item-flush
forces the width to be 100.6%. This utility should generally be used for images that are too narrow when usingaspect-ratio-item-fluid
and you still want to fill the remaining space.
aspect-ratio-item-vertical-flush
forces the height to be 100.6%. This utility should generally be used for images that are too short when usingaspect-ratio-item-flush
and you still want to fill the remaining space.


| Interface | Description |
|---|---|
| Configuration |
The main Configuration interface.
|
| ConfigurationBuilder |
Definition of an interface for objects that can create a configuration.
|
| ConfigurationComparator |
Comparator for configurations interface.
|
| FileConfiguration |
A persistent configuration loaded and saved to a file.
|
| FileOptionsProvider |
Some FileSystems allow options to be passed on File operations.
|
| FileSystemBased |
Interface used to install or locate the FileSystem
|
| PropertiesConfiguration.IOFactory |
Definition of an interface that allows customization of read and write
operations.
|
| Class | Description |
|---|---|
| AbstractConfiguration |
Abstract configuration class.
|
| AbstractFileConfiguration |
Partial implementation of the
FileConfiguration interface. |
| AbstractHierarchicalFileConfiguration |
Base class for implementing file based hierarchical configurations.
|
| BaseConfiguration |
Basic configuration class.
|
| BaseConfigurationXMLReader |
A specialized SAX2 XML parser that processes configuration objects.
|
| CombinedConfiguration |
A hierarchical composite configuration class.
|
| CompositeConfiguration |
CompositeConfiguration allows you to add multiple Configuration
objects to an aggregated configuration. |
| ConfigurationConverter |
Configuration converter.
|
| ConfigurationFactory | Deprecated
Use
DefaultConfigurationBuilder instead; this class
provides the same features as ConfigurationFactory plus some more; it can
also process the same configuration definition files. |
| ConfigurationFactory.AdditionalConfigurationData |
A simple data class that holds all information about a configuration
from the
<additional> section. |
| ConfigurationFactory.ConfigurationBuilder |
An internally used helper class for constructing the composite
configuration object.
|
| ConfigurationKey | Deprecated
Use
DefaultConfigurationKey
instead. |
| ConfigurationMap |
The
ConfigurationMap wraps a
configuration-collection
Configuration
instance to provide a Map interface. |
| ConfigurationUtils |
Miscellaneous utility methods for configurations.
|
| ConfigurationXMLReader |
A base class for "faked"
XMLReader classes
that transform a configuration object in a set of SAX parsing events. |
| DatabaseConfiguration |
Configuration stored in a database.
|
| DataConfiguration |
Decorator providing additional getters for any Configuration.
|
| DefaultConfigurationBuilder |
A factory class that creates a composite configuration from an XML based
configuration definition file.
|
| DefaultConfigurationBuilder.ConfigurationDeclaration |
A specialized
BeanDeclaration implementation that
represents the declaration of a configuration source. |
| DefaultConfigurationBuilder.ConfigurationProvider |
A base class for creating and initializing configuration sources.
|
| DefaultConfigurationBuilder.FileConfigurationProvider |
A specialized provider implementation that deals with file based
configurations.
|
| DefaultConfigurationBuilder.XMLConfigurationProvider |
A specialized configuration provider for XML configurations.
|
| DefaultFileSystem |
FileSystem that uses java.io.File or HttpClient
|
| DynamicCombinedConfiguration |
DynamicCombinedConfiguration allows a set of CombinedConfigurations to be used.
|
| EnvironmentConfiguration |
A Configuration implementation that reads the platform specific
environment variables using the map returned by
System.getenv() . |
| FileSystem |
Abstract layer to allow various types of file systems.
|
| HierarchicalConfiguration |
A specialized configuration class that extends its base class by the
ability of keeping more structure in the stored properties.
|
| HierarchicalConfiguration.BuilderVisitor |
A specialized visitor base class that can be used for storing the tree of
configuration nodes.
|
| HierarchicalConfiguration.Node |
A data class for storing (hierarchical) property information.
|
| HierarchicalConfiguration.NodeVisitor |
Definition of a visitor class for traversing a node and all of its
children.
|
| HierarchicalConfigurationXMLReader |
A specialized SAX2 XML parser that "parses" hierarchical
configuration objects.
|
| HierarchicalINIConfiguration |
A specialized hierarchical configuration implementation for parsing ini
files.
|
| HierarchicalReloadableConfiguration |
A base class for hierarchical configurations with specific reloading
requirements.
|
| HierarchicalXMLConfiguration | Deprecated
This class is deprecated.
|
| INIConfiguration | Deprecated
This class has been replaced by HierarchicalINIConfiguration,
which provides a superset of the functionality offered by this class.
|
| JNDIConfiguration |
This Configuration class allows you to interface with a JNDI datasource.
|
| Lock |
A simple class acting as lock.
|
| MapConfiguration |
A Map based Configuration.
|
| MultiFileHierarchicalConfiguration |
This class provides access to multiple configuration files that reside in a location that
can be specified by a pattern allowing applications to be multi-tenant.
|
| PatternSubtreeConfigurationWrapper |
Wraps a HierarchicalConfiguration and allows subtrees to be access via a configured path with
replaceable tokens derived from the ConfigurationInterpolator.
|
| PropertiesConfiguration |
This is the "classic" Properties loader which loads the values from
a single or multiple files (which can be chained with "include =".
|
| PropertiesConfiguration.DefaultIOFactory |
A default implementation of the
IOFactory interface. |
| PropertiesConfiguration.PropertiesReader |
This class is used to read properties lines.
|
| PropertiesConfiguration.PropertiesWriter |
This class is used to write properties lines.
|
| PropertiesConfigurationLayout |
A helper class used by
PropertiesConfiguration to keep
the layout of a properties file. |
| PropertyConverter |
A utility class to convert the configuration properties into any type.
|
| StrictConfigurationComparator |
Strict comparator for configurations.
|
| SubnodeConfiguration |
A specialized hierarchical configuration class that wraps a single node of
its parent configuration.
|
| SubsetConfiguration |
A subset of another configuration.
|
| SystemConfiguration |
A configuration based on the system properties.
|
| VFSFileSystem |
FileSystem that uses Commons VFS
|
| XMLConfiguration |
A specialized hierarchical configuration class that is able to parse XML
documents.
|
| XMLPropertiesConfiguration |
This configuration implements the XML properties format introduced in Java
5.0, see http://java.sun.com/j2se/1.5.0/docs/api/java/util/Properties.html.
|
| Exception | Description |
|---|---|
| ConfigurationException |
Any exception that occurs while initializing a Configuration
object.
|
| ConfigurationRuntimeException |
A configuration related runtime exception.
|
| ConversionException |
Exception thrown when a property is incompatible with the type requested.
|
The Configuration main package. It contains the definition of the
Configuration
interface and frequently used implementations
like PropertiesConfiguration
(dealing with .properties
files) or XMLConfiguration
that can load XML documents.
$Id: package.html<PHONE_NUMBER>-09-02 20:42:10Z oheger $
Copyright © 2001–2013 The Apache Software Foundation. All rights reserved.


Society must impart values in children — Mike Oquaye
The Speaker of Parliament, Professor Mike Oquaye, has said society has a right and an inherent duty to impart good ideas and values in children while they enjoy their childhood.
Describing childhood as a period of innocence, liberty and emptiness, he said society must ensure that the rights of children were not abused.
Prof. Oquaye said this when he inaugurated the first Children’s Parliament in Accra yesterday as part of this year’s World Children’s Day celebration.
The Children’s Parliament, organised by the Ministry of Gender, Children and Social Protection, in collaboration with the United Nations Children’s Fund (UNICEF), also formed part of activities marking the 30th anniversary of the enactment of the UN Convention on the Rights of the Child (UNCRC).
Prof. Oquaye said as children were left to enjoy their childhood in innocence, they were also ready to learn and acquire ideas, beliefs and values from society which could be good or bad.
Appeal
Prof. Oquaye also appealed to traditional leaders, as the custodians of customs, traditions and values, to support in the development of children.
He said the formation of the Children’s Parliament was going to provide an opportunity for all to reaffirm their commitment to the rights of children.
The Speaker further expressed the readiness of Parliament to give children the needed support and mentorship as they deliberated on issues affecting their well-being in society.
The wife of the Vice-President, Mrs Samira Bawumia, said when the abuse of children in society stopped, children could enjoy their childhood and realise their full potential.
She said no form of violence against children was justifiable.
According to her, all forms of violence and child abuse were preventable and, therefore, called on society to be concious of the way they treated children.
The Minister of Local Government and Rural Development, Hajia Alima Mahama, said it was time to take bold actions to ensure that no child was left behind in the development process of the country.
The Minister of Gender, Children and Social Protection, Mrs Cynthia Morrison, also said the ministry had, over the years, identified the inclusion of children’s views as critical to the developmental process of the country.
Commendation
The UNICEF Regional Director for West and Central Africa, Mrs Marie-Pierre Poirier, commended the Gender Ministry for its efforts to ensure that children’s rights were protected in the country.
UNCRC
The UNCRC is a legally binding international agreement which deals with the civil, political, economic, social and cultural rights of every child, regardless of his or her race, religion or abilities.
The treaty was adopted by UN Member States on November 20, 1989.
Ghana signed the UNCRC on January 29, 1990, and on February 5, 1990, it became the first country in the world to ratify it.
The UNCRC consists of 54 articles that set out children’s rights and how governments should work together to make them beneficial to all children.
Under the terms of the convention, governments are required to meet children’s basic needs and also help them reach their full potential.
Central to this is the acknowledgment that every child has basic fundamental rights.


This simulation tries to aid our understanding of how non-lethal agonistic behaviour evolves among males competing for mates.
What do King cobras and black mambas have in common?
The inspiration for this simulation came first from a National Geographic video that showed two male King Cobras performing an elaborate wrestling match to pin each other down, yet they made no attempt to bite one another. Male black mambas have a similar ritual when competing for mates.
The second inspiration was from watching male wildebeests competing for female mates in a South African game park. Despite witnessing several fights, none of them resulted in any harm to the protagonists.
Why should such non-lethal behaviour evolve? It isn't obvious that extremely homicidal agonistic behaviour when competing for mates should be an evolutionary disadvantage. On the contrary, permanently eliminating competitors might on the face of it even seem advantageous.
Yet, by tinkering with the various parameters in this agent-based simulation, you can see that there are many situations where there is selective pressure against lethal behaviour. There are also scenarios in which non-lethal individuals often don't become dominant within the population, and the population goes extinct. Yet there are other scenarios where the proportion of homicidal males is unaffected, or where they are reduced to a stable minority.
How the simulation works
When the Simulate button is clicked, the simulation executes as follows
- A population of individuals, or agents, is initialised. The male-female split is about 50-50. All individuals (male and female) have a set of genes which indicate how lethal they are.
- The simulation then consists of multiple iterations, where each iteration could be thought of as a mating season. On every iteration, all the males and females are randomly shuffled.
 The number of mating seasons that individuals live is determined by an option that you can change.
- In each iteration, males are paired to fight with each other. The male that wins the fight will kill the losing male with probability proportional to the number of its lethal genes that are switched on.
- Also, there is a revenge factor (which you can switch off). This is the probability that a homicidal male will also be mortally wounded by its adversary. I introduced this because of the possibility that a murderous individual might be injured by a dying angry opponent. E.g. if a black mamba decides to bite his adversary, this would signal the probability that the adversary bites back before dying. Whether or not this is a realistic parameter, I have no idea. The higher it is though, the greater the probability that non-lethal behaviour evolves, or if it doesn't that the population goes extinct. Setting the revenge factor to zero is however more interesting, because even here non-lethal behaviour evolves in many scenarios.
- A winning --and surviving-- male mates with one other female in that iteration (or mating season), if the monogamous option is chosen. If the polygamous option is chosen, the number of mates the male has is equal to the proportion of females to males in the population, but at least one. Each female only mates once in a mating season.
- The females then give birth. The number of babies per female can also be changed by a user-specified option.
- A newly born individual's genes are a random combination of the male and female parents. The more peaceful the parents, the more peaceful the child is likely to be.
There are two sets of output from the simulation: a graph and a table. The graph shows the change in population (red) after each iteration, or mating season, and the change in the number of peaceful individuals (yellow). A peaceful individual is defined as one who has fewer lethal genes than the proportion specified by the Proportion homicidal parameter (under Advanced parameters).
The second set of outputs is a table, which produces interesting demographics for every iteration.
Play around with the advanced parameters to see the way lethal versus non-lethal agonistic behaviour is affected.
One interesting observation is that the same set of parameters can lead to wildly different outcomes on repeated runs of the simulation. Even in this very simple model, there is much unpredictability.
Parameters
- Population
- Initial number of individuals (or agents) in the simulation
- Mating seasons
- Number of iterations to run the simulation. Each iteration corresponds to a mating season in which males compete with each other, and the winners mate with females, producing offspring.
- Update delay
- Minimum time delay of user interface updates between each iteration. Default is 100 milliseconds. Set to 0 if you want a minimal delay between iterations.
Advanced parameters
- Maximum population
- The simulation stops if the population exceeds this value. If this is set too high, the simulation can become very slow if the population explodes.
- Number of genes
- Number of genes that every individual has that determine how lethal males are if they win a fight. Genes are either on or off. The likelihood of a winning male killing a losing male is proportional to the number of his genes that are switched on divided by his total number of genes.
- Proportional homicidal
- This is the proportion of genes that are likely to be switched on, i.e. to make the agent more likely to be homicidal. For each gene of a newly born agent, a random number is generated from 0 to 1. If it is less than the value of this parameter, it is set to zero (non-homicidal), else one. To simply model the effects of the birth rate and death rate from old age, set this parameter to zero.
- Revenge factor
- The revenge factor tries to model the risk of a homicidal male who wins a fight being killed by the losing adversary. The thinking behind it is that a male who is homicidal is more likely to be mortally wounded by his adversary. By default it is set to zero. Setting it 0.2, or even 0.1, confers a great selection advantage to being non-homicidal.
- Discrete Weibull
- This distribution is a stochastic, more sophisticated, way of modelling the number of seasons an agent lives than the Fixed number of mating seasons parameter.  The Β parameter determines the shape of the distribution. The higher the value of Β the narrower the distribution.
- Fixed number of mating seasons
- If this parameter is selected every agent lives through exactly the specified number of mating seasons, unless he is killed by another male.
- Binomial distribution
- This is a stochastic, more sophisticated, way of modelling the number children that a pair of mating agents will have, than the Fixed number of children parameter. Set n to the maximum number of children that can be produced in a single mating, and p to the probability that any single child will be born live. The average number of live children that will be born is p * n.
- Fixed number of children
- If this parameter is selected, a pair of mating agents will produce exactly the specified number of children.
- Mating strategy
- If monogamous is selected (the default) then each male that wins a fight will mate with exactly one female. If polygamous is selected then each male that wins a fight will mate with a number of females proportional to the ratio of females to males.
- Random number generation
- By default the simulation uses the Javascript random number generator. If you need a replicable high-quality, generator select the Mersenne Twister option, which also allows you to change the generator's seed.
Some preliminary results
I have not methodically analysed the results. In the experiments described here the update delay was set to zero.
- With the default parameters widely different outcomes occur on different runs. Nevertheless, on balance there appears to be selective pressure against being homicidal.
- The revenge factor selects against being homicidal. Even setting it to 0.1 (i.e. 10%) with all other parameters left at their defaults drives down the proportion of homicidal genes, usually very quickly, in most runs.
- Tinkering with other parameters can result in vastly different results. For example, switching the mating strategy to polygamous does not appear to cause any selective pressure against being homicidal. Though it's unclear to me how realistic the polygamous strategy is.
Finally, a word of caution. This simulation tool is a highly simplified account of reality. All it can hope to achieve is to inspire some thinking about the evolution of non-lethal agonistic behaviour. Be weary about drawing firm conclusions from it.


A local service is a daemon that listens to incoming connections on a Unix domain socket. Clients of the service are programs connecting to this socket: the daemon performs operations on their behalf.
The service is called local because it is not accessible to clients from the network.
A widely known example of a local service is the syslogd daemon. On most implementations, it listens to the /dev/log socket. Its clients connect to it and send their logs via the socket. The openlog() function is just a wrapper arround the connect() system call, the syslog() function a wrapper around write(), and so on.
The most important benefit of a local service is that it permits controlled privilege gains without using setuid programs. The daemon is run as user S; a client running as user C and connecting to the daemon asks it to perform operations: those will be done as user S.
Standard Unix permissions on the listening socket can be used to implement some basic access control: to restrict access to clients belonging to group G, change the socket to user S and group G, and give it 0420 permissions. This is functionally equivalent to the basic access control for setuid programs: a program having user S, group G and permissions 4750 will be executable by group G and run with S rights.
But modern systems implement the getpeereid() system call or library function. This function allows the server to know the client's credentials: so fine-grained access control is possible. On those systems, local services can do as much authentication as setuid programs, in a much more controlled environment.
The most obvious difference between a local service and a network service is that a local service does not serve network clients. But local services have another nice perk: while network services usually only provide you with a single channel (a TCP or UDP socket) of communication between the client and the server, forcing you to multiplex your data into that channel, local services allow you to have as many communication channels as you want.
(The SCTP transport layer provides a way for network services to use several communication channels. Unfortunately, it is not widely deployed yet, and a lot of network services still depend on TCP.)
The fd-passing mechanism is Unix domain socket black magic that allows one peer of the socket to send open file descriptors to the other peer. So, if the server opens a pipe and sends one end of this pipe to a client via this mechanism, there is effectively a socket and a pipe between the client and the server.
The UCSPI protocol is an easy way of abstracting clients and servers from the network. A server written as a UCSPI server, just as it can be run under inetd or s6-tcpserver, can be run under s6-ipcserver: choose a socket location and you have a local service.
Fine-grained access control can be added by inserting s6-ipcserver-access in your server command line after s6-ipcserver.
A client written as an UCSPI client, i.e. assuming it has descriptor 6 (resp. 7) open and reading from (resp. writing to) the server socket, can be run under s6-ipcclient.
skarnet.org libraries often use a separate process to handle asynchronicity and background work in a way that's invisible to the user. Among them are:
Those processes are usually spawned from a client, via the corresponding *_startf*() library call. But they can also be spawned from a s6-ipcserver program in a local service configuration. In both cases, they need an additional control channel to be passed from the server to the client: the main socket is used for synchronous commands from the client to the server and their answers, whereas the additional channel, which is now implemented as a socket as well (but created by the server on-demand and not bound to a local path), is used for asynchronous notifications from the server to the client. The fd-passing mechanism is used to transfer the additional channel from the server to the client.


DEFAULT PASSWORDS
https://cirt.net/passwords
Username enumeration
- brute force if login gives verbose error messages
- user registration function. If user found app will not allow registration
- password change and forgotten password functions (if it returns different error messages)
- examine subtle differences in login responses for existing and non-existent user, use Burp Comparer
- examine web app reponse times
- Informative parameters in server's response
- User accounts can be associated to directories. There can be different server responses when accessing such directories (403 vs. 404)
Passwords policy
- Review site for password policy
- If not publicly specified, try to guess via registration
- Try to change passwords within your account
Brute force with Burp list + cewl (john mutated) list
Examine failed attempts error messages
Vulnerable transmission of credentials
- Check login forms which are loaded without SSL
- Check for unencrypted credentials transmission in both requests and responses - GET, POST, Cookies
- Check for encrypted or obfuscated credentials and find out if they can be decrypted or deobfuscated
Password change functionality
- Discover password change functionality by enumerating logged user interface or by burp Discover content
- Try invalid inputs and discover possibility of brute-forcing user enumeration
- If change password script doesn't have "username" input try to add it and use same parameter name as in login form
Forgotten password functionality
- Discover password remind functionality by enumerating logged user interface or by burp Discover content
- Check for insecure challenges/hints. Enumerate them with user list and choose weakest ones.
- Brute force challenges/hints
- Check if recovery URL string can be send to different email (hidden fields, additional email parameter)
- Check for recovery URL strings predictability. Can user reset another user's password?
Remember me functionality
- Login with "remember me" and check if cookie is created
- Examine this cookie - can be brute-forced? Predicted? Decoded? Reverse-engineered?
- Examine if there are "remember me" variables (or other user identifiers) in other local stores
- IE UserData
- SilverLight isolated storage
- Flash local shared object
- Attempt to modify cookie or other data for impersonatin other user
Impersonation functionality
- Discover impersonation function (if not referenced directly, use Discover content)
- Manipulate parameters to function such that you impersonate user. Search for paramterers where you use username
- If more than 1 valid password found for user then second one can be universal impersonation password for admin user
Incomplete validation of credentials
- Test if validation checks just first N characters
- Test if validation doesn't check "caseness" (change one letter case)
- Test if validation removes special characters
Nonunique usernames
- Try to register 2 same usernames with different passwords
- Does application block 2nd registration? If yes we can have usernames enumeration vulnerability.
- If registration succeeds then try 2 same usernames and 2 same passwords. If second registration fails then we can enumerate passwords for given user.
Insecure distribution of credentials
- Does app distribute new credentials via email?
- Can activation URL be predicted? Register few new accounts and check few activation URLs.
- Can same activation URL be used few times? Can activation URL unlock account?
Alternative weaker login channels
- Identify any of the following
- mobile websites
- alternative websites hosted on different IPs
- partner websites
- development websites
- Check following functionalities within alternative channels
- Login
- Logout
- Register
- Password reset
- Password change


{% extends 'crumbless.html' %} {% load bs4 %} {% block content_outer %}
Welcome to the HAWC portal. Here you're able to create new assessments or work on existing assessments.
{% include "common/inline_filter_form.html" %} {% include "assessment/fragments/assessment_list_team.html" %} {% include "includes/paginator.html" with plural_object_name="assessments" %} {% endblock content_outer %} {% block extrajs %} {% include "includes/hero_access.html" %} {% endblock extrajs %}


Gateway
BOT
15-Mar-19 09:33 AM
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[telegram] <feng>
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[telegram] <Vollka_Racho>
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[telegram] <feng>
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https://aws.amazon.com/pt/partners/find/partnerdetails/?n=Komodo%20Platform&id=0010L00001sCXagQAG
[telegram] <feng> 0.0003btc
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(edited)
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[telegram] <prblocknet> look at it go
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[telegram] <feng>
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[telegram] <moonwolftokens> omg its on amazon
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[telegram] <yukbong> Amazon.


Getting started¶
Here is a tutorial to explain how to create and use distributions, channels, functions and so on.
Content
Distributions¶
Creating distributions¶
The vector of probabilities is structed as a std::vector<long double>
.
There are 3 options available to instantiate a Distribution
object:
Read the vector of probabilities from a file
The vector of probabilities can be read from a file in the following format:
n p1 p2 ... pn
where
n
is the number of elements in the distribution andpi
is the probability of the ith element occurs.Suppose you have a file called
prior_distribution.txt
containing the vector of probabilities. To create a distribution, you have to type:Distribution dist("prior_distribution.txt");
Use an existing vector of probabilities
A distribution can be created using a vector stored in a variable. The type of the vector must be
std::vector<long double>
.To create a distribution from a vector of probabilities
v
you have to type:std::vector<long double> v({0.25, 0.25, 0.5}); Distribution dist(v);
Create a random or uniform distribution
To create an uniform or a random distribution, you have to give the number of elements as the first parameter and
"uniform"
or"random"
as the second parameter.For example, to create two uniform distributions with 5 and 10 elements respectively, you have to type:
Distribution dist(5, "uniform"); Distribution dist(10, "random");
See also
For more details, see Distribution constructors.
Using distributions¶
Suppose you have created a new distribution using one of the constructors
above and stored it in the variable dist
.
Accessing the elements¶
To get the probability of each element, you have to access the attribute
prob
. The elements in prob
are indexed starting at position 0.
For example, to get the fourth element of the distribution, you use the index 3:
long double prob_fourth_element = dist.prob[3];
Getting the number of elements¶
The number of elements is stored in the attribute num_el
:
int number_elements = dist.num_el;
Printing¶
To print the distribution on the screen, you can use the method toString()
.
The method has one parameter: the precision of the probabilities, and it
returns a std::string
.
For example, if you want to get a string with precision of 4, you have to type:
std::string str_dist = dist.toString(4);
std::cout << str_dist << std::endl;
The result is the following:
0.2500 0.2500 0.5000
For more details, see the documentation of Distribution::toString()
.
You can also print the distribution right into a file using
Distribution::printToFile()
.
For more details about the class, see the documentation here.
Channels¶
Creating channels¶
An informational channel can be represented as a matrix \(n \times y\), where
\(n\) is the number of secrets (the rows), \(y\) is the number of possible
outputs (the columns) and each value stored at matrix[x][y]
is the
conditional probability \(p(y|x)\). Before creating a channel, you must have
already created an object Distribution
that contains the prior
distribution on the set of secrets.
Note
Before creating a channel, you have to create a Distribution
that corresponds to the prior distribution on the set of secrets.
There are 3 options available to instantiate a Channel
object:
Read the channel from a file
You can read the channel from a file in the following format:
n y p11 p12 ... p1y p21 p22 ... p2y ... pn1 pn2 ... pny
where
n
is the number of secrets,y
is the number of outputs and each two values must be separated by a space.Suppose you have a file called
channel.txt
. To create a channel you have to type:Distribution dist("prior_distribution.txt"); Channel channel(dist, "channel.txt");
Use an existing matrix of numbers
The type of the matrix must be
std::vector<std::vector<long double>>
. To create a channel from a matrixM
you have to type:Distribution prior("prior_distribution.txt"); std::vector<std::vector<long double>> M({{0.25, 0.25, 0.50}, {0.30, 0.30, 0.40}, {1.00, 0.00, 0.00}}); Channel channel(prior, M);
Generate a random channel
To create a random channel you have to give the prior distribution as the first parameter and the number of outputs as the second parameter.
For example, to create a channel with 3 outputs, you have to type:
Distribution prior("prior_distribution.txt"); Channel channel(prior, 3);
See also
For more details, see Channel constructors.
Using channels¶
Suppose you have created a channel using one of the constructors above and stored
it in the variable channel
.
Acessing the prior distribution¶
You can use the attribute Channel::prior
. You can get, for example,
the number of secrets:
int number_secrets = channel.prior.num_el;
Acessing the channel matrix¶
To get a conditional probability from the channel matrix, you have to access the
attribute Channel::matrix
. Both rows and columns are indexed
starting at the position 0.
For example, to get the probability \(p(y_3|x_2)\), you have to type:
long double p = channel.matrix[2][1];
Getting the number of outputs from the channel¶
The number of outputs is stored in the attribute Channel::num_out
:
int number_outputs = channel.num_out;
Printing¶
To print the channel on the screen, you can use Channel::toString()
.
The method has one parameter: the precision of the probabilities, and it
returns a std::string
.
For example, if you want to get a string with precision of 4, you have to type:
std::string str_channel = channel.toString(4);
std::cout << str_channel << std::endl;
The result is the following:
0.2500 0.2500 0.5000
1.0000 0.0000 0.0000
0.9000 0.0100 0.0900
For more details, see the documentation of Channel::toString()
.
You can also print the channel right into a file using
Channel::printToFile()
.
For more details about the class, see the documentation here.
Hyper-distributions¶
A hyper-distribution is a distribution on distributions on a set of secrets. Each output of an informational channel is a possible “world”, and each possible world is a new distribution on the set of secrets. We call the possible worlds the inner distributions. Each possible world has a probability to occur, and we call the distribution on the possible worlds as the outer distribution.
Creating hyper-distributions¶
There are 2 options available to instantiate a Hyper
object:
Read the prior distribution and the channel from files
You can read the prior distribution on a set of secrets and the channel from files.
The prior distribution must be in the following format:
n p1 p2 ... pn
where
n
is the number of secrets andpi
is the probability of the ith secret occuring.The channel must be in the following format:
n y p11 p12 ... p1y p21 p22 ... p2y ... pn1 pn2 ... pny
where
n
is the number of secrets (the rows),y
is the number of possible outputs (the columns) and each valuepij
is the conditional probability \(p(j|i)\). Each two values must be separated by a space.Suppose you have the prior distribution in a file called
prior_distribution.txt
and a channel in a file calledchannel.txt
. To create the hyper-distribution you have to type:Hyper hyper("prior_distribution.txt", "channel.txt");
Use existing Distribution and Channel objects
As a Channel has the attribute
Channel::prior
, it is not necessary to give a prior distribution as a parameter. So to create a hyper-distribution, you can give only the channel:Distribution prior("prior_distribution.txt"); Channel channel(prior, "channel.txt"); Hyper hyper(channel);
See also
For more details, see Hyper constructors.
Using hyper-distributions¶
Suppose you have created a Hyper object and stored it in a variable hyper
.
Accessing the prior distribution and the channel
The access to the prior distribution can be made by
Hyper::prior
and the access to the channel byHyper::channel
.For example, you can access the number of secrets and the number of channel outputs:
int number_secrets = hyper.prior.num_el; int number_outputs = hyper.channel.num_out;
Accessing the joint distribution
The attribute
Hyper::joint
represents the joint distribution. It is a matrix (std::vector<std::vector<long double>>
) \(n \times y\) where \(n\) is the number of secrets and \(y\) is the number of outputs of the channel. Both rows and columns are indexed starting at position 0.For example, to get the joint probability \(p(x_2,y_1)\), you have to type:
long double joint_x2_y1 = hyper.joint[1][0]
Accessing the outer distributon
The attribute
Hyper::outer
represents the outer distribution, and it is aDistribution
object.For example, to access the probability \(p(y2)\) of the 2nd posterior occurring, you have to type:
long double prob_second_posterior = hyper.outer.prob[1];
Accessing the inner distributions
The attribute
Hyper::inners
keeps the inner distributions. It is a matrix (std::vector<std::vector<long double>>
) \(n \times y\) where \(n\) is the number of secrets and \(y\) is the number of posterior distributions. Both rows and columns are indexed starting at position 0.For example, to access the conditional probability \(p(x2|y1)\) you have to type:
long double prob_x2_y1 = hyper.inners[1][0];
Changing the prior distribution or the channel
If you have already created a hyper-distribution and need to change the prior distribution or the channel, it is possible to do it using the function
Hyper::rebuildHyper()
.To change the prior distribution or the channel, you have to type:
// Original hyper-distribution Hyper hyper("prior.txt", "channel.txt"); // Creating new prior and channel Distribution new_prior("another_prior.txt"); Channel new_channel("another_channel.txt"); // Rebuilding the hyper-distribution using the new prior hyper.rebuildHyper(new_prior); // Or rebuild using a new channel hyper.rebuildHyper(new_channel);
See also
For more details, see
Hyper::rebuildHyper()
.Printing
To print the joint, the outer, the inners or the labels, you can use
Hyper::toString()
. The first parameter must be"joint"
,"outer"
,"inners"
or"labels"
, and the second parameter the precision of probabilities. This method returns astd::string
.For example, if you want to get a string of the joint matrix with precision of 3, you have to type:
std::string joint_matrix = hyper.toString("joint", 3); std::cout << joint_matrix << std::endl;
The output will be the following:
0.062 0.062 0.125 0.250 0.000 0.000 0.000 0.500 0.000
Note
The
Hyper::labels
is a map from posterior distribution indexes (in the matrix) to a set of channel output indexes. It is used to store the relationship between posterior distributions and channel outputs.You can also print all elements from right into a file using
Hyper::printToFile()
.
For more details about the class, see the documentation here.
Gain functions¶
A gain function can be represented as a matrix \(w \times n\), where \(w\) is the number of actions that the adversary can take and \(n\) is the number of secrets. Each value \(gain(w_i,x_j)\) corresponds to the gain that the adversary achieves when taking the action \(w_i\) and the real value of the secret is \(x_j\).
Creating gain functions¶
Note
Before creating a gain function, you have to create a
Distribution
that corresponds to the prior distribution on the
set of secrets.
There are 3 options available to instantiate a Gain
object:
Read the gain function matrix from a file
You can read the gain function matrix from a file in the following format:
w n p11 p12 ... p1n p21 p22 ... p2n ... pw1 pw2 ... pwn
where
w
is the number of actions,n
is the number of secrets and each two values must be separated by a space.Suppose you have a file called
gain.txt
. To create a gain function you have to type:Distribution prior("prior_distribution.txt"); Gain gain(prior, "gain.txt");
Warning
The number of columns in the gain function matrix must be equal to the number of elements in the prior distribution.
Use an existing matrix of numbers
The type of the matrix must be
std::vector<std::<long double>>
.To crete a gain function from a matrix
M
you have to type:Distribution prior("prior_distribution.txt"); std::vector<std::vector<long double>> M({{10, 20, 30}, {-5, -6, 10}, { 0, 10, 0}}); Gain gain(prior, M);
Warning
The number of columns in matrix
M
must be equal to the number of elements in the prior distribution.Generate a random gain function
To create a random gain function, there are 4 parameters in the constructor:
prior
: Prior distribution on the set of secrets;num_act
: Number of actions that the adversary can take;MIN
: Lower bound for the gain of an action;MAX
: Upper bound for the gain of an action.
For example, to create a gain function with 7 actions which give a gain in the interval \([-5,5]\), you have to type:
Distribution prior("prior_distribution.txt"); Gain gain(prior, 7, -5, 5);
For more details see Gain constructors.
Using gain functions¶
Suppose you have created a gain function using one of the constructors above and
stored it in the variable gain
.
Accessing the prior distribution
The access to the prior distribution can be done by
Gain::prior
. You can get, for example, the number of secrets:int number_secrets = gain.prior.num_el;
Accesing the gain function matrix
To get a gain for an action and a secret, you can use
Gain::matrix
. Both rows and columns are indexed starting at position 0.For example, to get the gain that the adversary achieves by taking action \(w_4\) when the actual value of the secret is \(n_2\), you have to type:
long double gain_w4_n2 = gain.matrix[3][1];
Getting the number of actions from the gain function
The number of actions is stored in
Gain::num_act
:int number_actions = gain.num_act;
Printing
To print the gain function matrix on the screen, you can use
Gain::toString()
. The method has one parameter: the precision of the probabilities, and it returns astd::string
.For example, if you want to get a string with precision of 2, you have to type:
std::string str_gain = gain.toString(2); std::cout << str_gain << std::endl;
The output will be the following:
50.00 -6.00 10.00 -9.00 25.00 -9.00 -3.00 12.00 33.00
For more details, see
Gain::toString()
. You can also print the gain function matrix right into a file usingGain::printToFile()
.
For more details about the class, see the documentation here.


Amenities: Tv,Kitchen, Heating, Balcony.
Location: City Road el, IN 4654
Fixed 2000 per month.
Select now and proceed to book.
Amenities: WI-FI, Shower, Tv,Kitchen, Heating, Balcony, Parking
Location: City Road el, IN 454
Fixed 2500 per month.
Select now and proceed to book.
Amenities: WI-FI, Shower, Tv,Kitchen, Heating,Balcony, Parking.
Location: City Road el, IN 4654
Fixed 3000 per month.
Select now and proceed to book.
| # | Tittle | Info |
|---|---|---|
| 1 | Client Name: | ........... |
| 2 | Client Phone: | ............. |
| 3 | Check In Date: | ........... |
| 4 | Check Out Date: | ........ |
| 5 | Selected Houses Names | ........... |
| 6 | Houses Total Cost | .......... |


--- layout: pattern --- {% include header/inc-header.html logo=' ' surcharge-ds='style="position: relative;"' %}


{%extends "registration/base.html"%} {% load bootstrap4 %} {% load static %} {% block styles %} {{block.super}} {% endblock %} {% block content %}
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Which option describes your current role ? :
How likely is that you would recommend freeCodeCamp to a friend ?
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Things that should be improved in the future (check all that apply)


Tcl/Tk Keywords - C
- cache
-
InternAtom
- callback
-
CallDel, CrtChnlHdlr, CrtCloseHdlr, CrtFileHdlr, CrtTimerHdlr, DoOneEvent, DoWhenIdle, Exit, CrtCmHdlr, CrtErrHdlr, CrtGenHdlr, EventHndlr, HandleEvent, ManageGeom, QWinEvent, SetClassProcs
- cancel
-
after
- canvas
-
canvas, CanvPsY, CanvTkwin, CanvTxtInfo, CrtItemType
- cap style
-
ConfigWidg, GetCapStyl
- caret
-
SetCaret
- carriage return
-
fconfigure
- case
-
ToUpper
- case conversion
-
string
- catch
-
catch, return
- cell
-
grid
- center
-
GetJustify
- centimeters
-
GetPixels
- channel
-
close, eof, fcopy, fileevent, flush, gets, puts, read, socket, tell, ChnlStack, CrtChnlHdlr, GetOpnFl, OpenFileChnl
- channel closing
-
CrtCloseHdlr
- channel driver
-
CrtChannel, OpenFileChnl
- channel registration
-
CrtChannel
- channel type
-
CrtChannel
- checkbutton
-
checkbutton
- child
-
DetachPids
- children
-
winfo
- class
-
options, winfo, AddOption, GetOption, SetClass, SetClassProcs
- classification
-
UniCharIsAlpha
- cleanup
-
Exit
- clear
-
clipboard, selection, Clipboard, ClrSelect
- client
-
OpenTcp
- clientData
-
TraceCmd, TraceVar
- clipboard
-
clipboard, Clipboard
- clock
-
clock, CrtTimerHdlr, QWinEvent
- close
-
close
- color
-
colors, palette, photo, 3DBorder, CanvPsY, ConfigWidg, ConfigWind, GetColor, SetOptions
- color selection dialog
-
chooseColor
- colormap
-
GetClrmap, GetVisual, SetVisual, WindowId
- command
-
info, rename, trace, AppInit, CrtCommand, CrtInterp, CrtObjCmd, CrtSlave, CrtTrace, ParseCmd, RecEvalObj, RecordEval, SetResult, TraceCmd, WrongNumArgs
- command line
-
ParseArgv
- command substitution
-
subst, SubstObj
- command-line arguments
-
Tcl_Main, Tk_Main
- compare
-
expr, string
- compiler
-
tclvars
- complete command
-
CmdCmplt
- compression
-
ChnlStack
- concat
-
StringObj
- concatenate
-
concat, eval, Concat, StringObj
- condition variable
-
Thread
- conditional
-
if
- configuration option
-
SetOptions
- configuration options
-
ConfigWidg
- configure
-
ConfigWind
- connection
-
socket
- console
-
console
- containing
-
CoordToWin
- context
-
uplevel, upvar
- continue
-
continue, return, AllowExc
- conversion
-
GetInt, PrintDbl, GetDash
- conversion specifier
-
format, scan
- convert
-
Encoding, SplitList, GetPixels
- coordinates
-
CoordToWin, GetRootCrd
- copy files
-
file
- create
-
open, CrtCommand, CrtInterp, CrtObjCmd, CrtTrace, CrtWindow
- ctype
-
string
- current directory
-
filename
- cursor
-
cursors, ConfigWidg, GetCursor, SetCaret, SetOptions
- custom
-
ConfigWidg
Copyright © 1989-1994 The Regents of the University of California.
Copyright © 1990-1994 The Regents of the University of California
Copyright © 1992-1999 Karl Lehenbauer and Mark Diekhans
Copyright © 1992-1999 Karl Lehenbauer and Mark Diekhans.
Copyright © 1993-1997 Bell Labs Innovations for Lucent Technologies
Copyright © 1994 The Australian National University
Copyright © 1994-2000 Sun Microsystems, Inc.
Copyright © 1997-2000 Ajuba Solutions.
Copyright © 1997-2000 Scriptics Corporation.
Copyright © 1998 Mark Harrison.
Copyright © 1998-1999 Scriptics Corporation
Copyright © 1998-2000 Ajuba Solutions
Copyright © 1999 Scriptics Corportation
Copyright © 2000 Jeffrey Hobbs.
Copyright © 2001 ActiveState Tool Corp.
Copyright © 2001 Kevin B. Kenny.
Copyright © 2001 Vincent Darley
Copyright © 2001-2002 ActiveState Corporation
Copyright © 2001-2002 Donal K. Fellows
Copyright © 2001-2002 Kevin B. Kenny. All rights reserved.
Copyright © 2001-2004 ActiveState Corporation.
Copyright © 2006-2007 Daniel A. Steffen
Copyright © 1995-1997 Roger E. Critchlow Jr.


Luci
Current Builds (1):
Some current build
[Running for: 0]
Pending Build Requests (1):
(, waiting 0)
Recent Builds:
Time
Revision
Result
Build #
Changes
Info
deadbeef
NotRun
Some current build
[]


Take-A-Break
Working for longer time while sitting can affect your back. Let's fix that 😇🦄🌈 .
Interval for Notification ⏲
1
30
60
90
120
180
Select Resting period 🛏
1
2
3
4
5
Let's Go ✈


git mergetool
Run merge conflict resolution tools to resolve merge conflicts.
More information: home page
- Launch the default merge tool to resolve conflicts:
git mergetool
- List valid merge tools:
git mergetool --tool-help
- Launch the merge tool identified by a name:
git mergetool --tool tool_name
- Don't prompt before each invocation of the merge tool:
git mergetool --no-prompt
- Explicitly use the GUI merge tool (see the `merge.guitool` config variable):
git mergetool --gui
- Explicitly use the regular merge tool (see the `merge.tool` config variable):
git mergetool --no-gui


SMD (SOD-123) 300 mA Diode 20v - DS303
Summary
Name: SMD (SOD-123) 300 mA Diode 20v
ID: DIOD-S123-X-A03D-VG
Hex ID: DS303
WebPage: https://github.com/oomlout/oomlout-OOMP/wiki/DIOD-S123-X-A03D-VG
Short URL: http://oom.lt/DS303
Revision History: https://github.com/oomlout/oomlout-OOMP/blob/master/parts/DIOD-S123-X-A03D-VG/
| Type |
Size |
Color |
Description |
Index |
DIOD Diode |
S123 SMD (SOD-123) |
X
|
A03D 300 mA |
VG 20v |
Images
About
This part is awaiting a description.
Specifications
| Info |
Value |
| Type |
Diode |
| Size |
SMD (SOD-123) |
| Description |
300 mA |
| Index |
20v |
Extra Details
Spotted a mistake, want to add more? Let us know<EMAIL_ADDRESS>All images and resources are licensed [CC BY-SA] unless otherwise stated (ie. the datasheets)


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Independent report
Statutory assessment arrangements for pupils working below the standard of national curriculum tests at key stages 1 and 2 (known as SATs).
This report sets out the recommendations of the independent Rochford Review group. It follows the publication in December 2015 of the Rochford Review’s interim recommendations .
The final report’s recommendations include:
We will consult on the report’s recommendations in early 2017. Final decisions will be made following that consultation.
In the meantime schools should continue to use the pre-key stage standards and P scales for the statutory assessment of pupils working below the standard of the national curriculum tests.
The interim pre-key stage standards for key stage 1 and key stage 2 and more information about the review group are also available.


Roslyn Code Overload
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--- layout: post date: 2011-03-21 time: "10:00:00" authors: ["Greg Wilson"] title: Videos of Autumn School Lectures tags: ["Content", "Queen Mary University London"] ---
I'm pleased to announce that video recordings of the Software Carpentry lectures I gave in London last fall are now online at http://soundsoftware.ac.uk/autumnschool2010video. Please excuse the mustache—I don't know what I was thinking... Many thanks to Chris Cannam, Ivan Damnjanovic, Luis Figueira, and of course Mark Plumbley.


In this project, you'll use generative adversarial networks to generate new images of faces.
You'll be using two datasets in this project:
Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.
If you're using FloydHub, set data_dir
to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".
data_dir = './data'
# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper
helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
show_n_images = 25
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
#pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images
.
show_n_images = 25
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

You'll build the components necessary to build a GANs by implementing the following functions below:
model_inputs
discriminator
generator
model_loss
model_opt
train
This will check to make sure you have the correct version of TensorFlow and access to a GPU
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf
# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer. You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))
# Check for a GPU
if not tf.test.gpu_device_name():
warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
Implement the model_inputs
function to create TF Placeholders for the Neural Network. It should create the following placeholders:
image_width
, image_height
, and image_channels
.z_dim
.Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)
import problem_unittests as tests
def model_inputs(image_width, image_height, image_channels, z_dim):
"""
Create the model inputs
:param image_width: The input image width
:param image_height: The input image height
:param image_channels: The number of image channels
:param z_dim: The dimension of Z
:return: Tuple of (tensor of real input images, tensor of z data, learning rate)
"""
# Tensor of real input images
input_real = tf.placeholder(
dtype=tf.float32,
shape=(None, image_width, image_height, image_channels),
name='input_real')
# Tensor of z data
input_z = tf.placeholder(
dtype=tf.float32,
shape=(None, z_dim),
name='input_z')
# Learning rate
learning_rate = tf.placeholder(dtype=tf.float32)
return input_real, input_z, learning_rate
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Implement discriminator
to create a discriminator neural network that discriminates on images
. This function should be able to reuse the variables in the neural network. Use tf.variable_scope
with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).
# 不可以指定卷积层与Batch Norm层的name，否则会报错：
# ValueError: Variable conv1/kernel already exists, disallowed.
# Did you mean to set reuse=True or reuse=tf.AUTO_REUSE in VarScope?
# 遗留问题。
def discriminator(images, reuse=False):
"""
Create the discriminator network
:param images: Tensor of input image(s)
:param reuse: Boolean if the weights should be reused
:return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
"""
with tf.variable_scope('discriminator', reuse=reuse):
# Layer 1
conv1 = tf.layers.conv2d(
inputs=images,
filters=64,
kernel_size=5,
strides=1,
padding='same')
lr1 = tf.nn.leaky_relu(features=conv1, name='lk1')
# Layer 2
conv2 = tf.layers.conv2d(
inputs=lr1,
filters=128,
kernel_size=5,
strides=2,
padding='same')
bn2 = tf.layers.batch_normalization(inputs=conv2, training=True)
lr2 = tf.nn.leaky_relu(features=bn2, name='lk2')
# Layer 3
conv3 = tf.layers.conv2d(
inputs=lr2,
filters=256,
kernel_size=5,
strides=2,
padding='same')
bn3 = tf.layers.batch_normalization(inputs=conv3, training=True)
lr3 = tf.nn.leaky_relu(features=bn3, name='lk3')
# Layer 4
flatten = tf.layers.flatten(inputs=lr3, name='flatten')
logits = tf.layers.dense(inputs=flatten, units=1)
output = tf.nn.sigmoid(logits)
return output, logits
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Implement generator
to generate an image using z
. This function should be able to reuse the variables in the neural network. Use tf.variable_scope
with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim
images.
def generator(z, out_channel_dim, is_train=True, reuse=False):
"""
Create the generator network
:param z: Input z
:param out_channel_dim: The number of channels in the output image
:param is_train: Boolean if generator is being used for training
:return: The tensor output of the generator
"""
if is_train is False:
reuse = True
with tf.variable_scope('generator', reuse=reuse):
# Layer 1
dense1 = tf.layers.dense(inputs=z, units=4*4*512, activation=None)
seeds = tf.reshape(tensor=dense1, shape=(-1, 4, 4, 512), name='seeds')
bn1 = tf.layers.batch_normalization(inputs=seeds, training=is_train)
lk1 = tf.nn.leaky_relu(bn1, name='lk1')
# [4, 4, 512]
# Layer 2
conv2 = tf.layers.conv2d_transpose(inputs=lk1, filters=256, kernel_size=4, strides=1, padding='valid')
bn2 = tf.layers.batch_normalization(inputs=conv2, training=is_train)
lk2 = tf.nn.leaky_relu(bn2, name='lk2')
# [7, 7, 256]
# Layer 3
conv3 = tf.layers.conv2d_transpose(inputs=lk2, filters=128, kernel_size=3, strides=2, padding='same')
bn3 = tf.layers.batch_normalization(inputs=conv3, training=is_train)
lk3 = tf.nn.leaky_relu(bn3, name='lk3')
# [14, 14, 128]
# Layer 4 -> [28, 28, 3]
logits = tf.layers.conv2d_transpose(inputs=lk3, filters=out_channel_dim, kernel_size=5, strides=2, padding='same')
out = tf.tanh(logits)
return out
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Implement model_loss
to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:
discriminator(images, reuse=False)
generator(z, out_channel_dim, is_train=True)
def model_loss(input_real, input_z, out_channel_dim):
"""
Get the loss for the discriminator and generator
:param input_real: Images from the real dataset
:param input_z: Z input
:param out_channel_dim: The number of channels in the output image
:return: A tuple of (discriminator loss, generator loss)
"""
# Generate fake image with generator
image_generated = generator(z= input_z, is_train=True, out_channel_dim=out_channel_dim)
# Discriminator shows up. Learn from real image..
estimation_from_real, logtis_real = discriminator(images=input_real, reuse=False)
# ..then the fake one.
estimation_from_fake, logits_fake = discriminator(images=image_generated, reuse=True)
loss_d_real = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(
logits=logtis_real, labels=tf.ones_like(estimation_from_real)))
loss_d_fake = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(
logits=logits_fake, labels=tf.zeros_like(estimation_from_fake)))
loss_d = loss_d_real + loss_d_fake
loss_g = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(
logits=logits_fake, labels=tf.ones_like(estimation_from_fake)))
return loss_d, loss_g
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Implement model_opt
to create the optimization operations for the GANs. Use tf.trainable_variables
to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).
def model_opt(d_loss, g_loss, learning_rate, beta1):
"""
Get optimization operations
:param d_loss: Discriminator loss Tensor
:param g_loss: Generator loss Tensor
:param learning_rate: Learning Rate Placeholder
:param beta1: The exponential decay rate for the 1st moment in the optimizer
:return: A tuple of (discriminator training operation, generator training operation)
"""
var_d = tf.trainable_variables(scope='discriminator')
var_g = tf.trainable_variables(scope='generator')
# Optimize
with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
train_op_d = tf.train.AdamOptimizer(learning_rate=learning_rate, beta1=beta1).minimize(d_loss, var_list=var_d)
train_op_g = tf.train.AdamOptimizer(learning_rate=learning_rate, beta1=beta1).minimize(g_loss, var_list=var_g)
return train_op_d, train_op_g
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np
def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
"""
Show example output for the generator
:param sess: TensorFlow session
:param n_images: Number of Images to display
:param input_z: Input Z Tensor
:param out_channel_dim: The number of channels in the output image
:param image_mode: The mode to use for images ("RGB" or "L")
"""
cmap = None if image_mode == 'RGB' else 'gray'
z_dim = input_z.get_shape().as_list()[-1]
example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])
samples = sess.run(
generator(input_z, out_channel_dim, False),
feed_dict={input_z: example_z})
images_grid = helper.images_square_grid(samples, image_mode)
pyplot.imshow(images_grid, cmap=cmap)
pyplot.show()
Implement train
to build and train the GANs. Use the following functions you implemented:
model_inputs(image_width, image_height, image_channels, z_dim)
model_loss(input_real, input_z, out_channel_dim)
model_opt(d_loss, g_loss, learning_rate, beta1)
Use the show_generator_output
to show generator
output while you train. Running show_generator_output
for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator
output every 100 batches.
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
"""
Train the GAN
:param epoch_count: Number of epochs
:param batch_size: Batch Size
:param z_dim: Z dimension
:param learning_rate: Learning Rate
:param beta1: The exponential decay rate for the 1st moment in the optimizer
:param get_batches: Function to get batches
:param data_shape: Shape of the data
:param data_image_mode: The image mode to use for images ("RGB" or "L")
"""
input_real, input_z, lr = model_inputs(
image_width=data_shape[1],
image_height=data_shape[2],
image_channels=data_shape[3],
z_dim=z_dim)
loss_d, loss_g = model_loss(input_real=input_real, input_z=input_z, out_channel_dim=data_shape[3])
train_op_d, train_op_g = model_opt(d_loss=loss_d, g_loss=loss_g, beta1=beta1, learning_rate=learning_rate)
steps = 0
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for epoch_i in range(epoch_count):
for batch_images in get_batches(batch_size):
batch_images *= 2
steps += 1
# Sample random noise for G
batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
# Run optimizers
_ = sess.run(train_op_d, feed_dict={input_real: batch_images, input_z: batch_z})
_ = sess.run(train_op_g, feed_dict={input_z: batch_z, input_real: batch_images})
if steps % 10 == 0:
# At the end of each epoch, get the losses and print them out
train_loss_d = loss_d.eval({input_z: batch_z, input_real: batch_images})
train_loss_g = loss_g.eval({input_z: batch_z})
print("Epoch {}/{}...".format(epoch_i+1, epoch_count),
"Discriminator Loss: {:.4f}...".format(train_loss_d),
"Generator Loss: {:.4f}".format(train_loss_g))
# Show generator output
if steps % 100 == 0:
show_generator_output(
image_mode=data_image_mode,
sess=sess,
input_z=input_z,
n_images=batch_size,
out_channel_dim=data_shape[3])
print('Epoch {} Done!'.format(epoch_i + 1))
show_generator_output(
image_mode=data_image_mode,
sess=sess,
input_z=input_z,
n_images=batch_size,
out_channel_dim=data_shape[3])
print("All done!")
Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.
batch_size = 128
z_dim = 128
learning_rate = 0.01
beta1 = 0.5
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2
mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
mnist_dataset.shape, mnist_dataset.image_mode)
Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.
batch_size = 64
z_dim = 256
learning_rate = 0.001
beta1 = 0.5
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1
celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
celeba_dataset.shape, celeba_dataset.image_mode)
When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.


- ScrollingSheet - Class in cs3500.music.view
-
A scrollable panel to display a music sheet.
- ScrollingSheet(JPanel) - Constructor for class cs3500.music.view.ScrollingSheet
-
Public constructor for a scrolling panel.
- scrollVertical(int) - Method in class cs3500.music.view.AudioVisualView
-
- scrollVertical(int) - Method in class cs3500.music.view.AView
-
- scrollVertical(int) - Method in class cs3500.music.view.GuiViewFrame
-
- scrollVertical(int) - Method in interface cs3500.music.view.IView
-
Scroll the view in the provided direction.
- scrollVertical(int) - Method in class cs3500.music.view.ScrollingSheet
-
Scrolls the vertical bar if possible.
- send(MidiMessage, long) - Method in class cs3500.music.view.MockReceiver
-
- setCurrentBeat(int) - Method in class cs3500.music.view.AudioView
-
- setCurrentBeat(int) - Method in class cs3500.music.view.AudioVisualView
-
- setCurrentBeat(int) - Method in class cs3500.music.view.GuiViewFrame
-
- setCurrentBeat(int) - Method in interface cs3500.music.view.IView
-
Set the view to a certain beat.
- setCurrentBeat(int) - Method in class cs3500.music.view.SheetPanel
-
Sets the current beat to place the cursor at.
- setCurrentBeat(int) - Method in class cs3500.music.view.TextView
-
- setKeyListener(KeyListener) - Method in class cs3500.music.view.AudioVisualView
-
- setKeyListener(KeyListener) - Method in class cs3500.music.view.AView
-
- setKeyListener(KeyListener) - Method in class cs3500.music.view.GuiViewFrame
-
- setKeyListener(KeyListener) - Method in interface cs3500.music.view.IView
-
Provide the view with a key listener to allow the program to
process key input.
- setKeyListener(KeyListener) - Method in class cs3500.music.view.TextView
-
- setKeyPressedStrategy(Map<Integer, Runnable>) - Method in class cs3500.music.control.KeyHandler
-
- setKeyPressedStrategy(Map<Integer, Runnable>) - Method in interface cs3500.music.control.KeyStrategy
-
Sets the strategy to use for pressed keys.
- setKeyReleasedStrategy(Map<Integer, Runnable>) - Method in class cs3500.music.control.KeyHandler
-
- setKeyReleasedStrategy(Map<Integer, Runnable>) - Method in interface cs3500.music.control.KeyStrategy
-
Sets the strategy to use for released keys.
- setKeyStrategy() - Method in class cs3500.music.control.SimpleController
-
Sets the key strategy to use for handling keyboard input.
- setKeyTypedStrategy(Map<Integer, Runnable>) - Method in class cs3500.music.control.KeyHandler
-
- setKeyTypedStrategy(Map<Integer, Runnable>) - Method in interface cs3500.music.control.KeyStrategy
-
Sets the strategy to use for typed keys.
- setMouseEvents(Map<Integer, MouseEventProcessor>) - Method in class cs3500.music.control.MouseHandler
-
- setMouseEvents(Map<Integer, T>) - Method in interface cs3500.music.control.MouseStrategy
-
Sets the mouse event strategy.
- setMouseListener(MouseListener) - Method in class cs3500.music.view.AudioVisualView
-
- setMouseListener(MouseListener) - Method in class cs3500.music.view.AView
-
- setMouseListener(MouseListener) - Method in interface cs3500.music.view.IView
-
Sets the view with a mouse listenr to allow the program
to process mouse input.
- setMouseStrategy() - Method in class cs3500.music.control.SimpleController
-
Sets the mouse strategy to use for handling user mouse input.
Mouse Click - activate note if practicing.
- setNotes(List<PitchSequence>) - Method in class cs3500.music.view.AudioVisualView
-
- setNotes(List<PitchSequence>) - Method in class cs3500.music.view.AView
-
- setNotes(List<PitchSequence>) - Method in class cs3500.music.view.GuiViewFrame
-
- setNotes(List<PitchSequence>) - Method in interface cs3500.music.view.IView
-
Provide the view with the notes to be displayed.
- setNotes(List<PitchSequence>) - Method in class cs3500.music.view.SheetPanel
-
Sets the notes for this panel to display.
- setNotes(List<PitchSequence>) - Method in class cs3500.music.view.TextView
-
- setOnKeys(HashSet<Pitch>) - Method in class cs3500.music.view.PianoPanel
-
Sets which keys are currently being pressed.
- setPlayingMode(PlayingMode) - Method in class cs3500.music.view.AudioVisualView
-
- setPlayingMode(PlayingMode) - Method in class cs3500.music.view.AView
-
- setPlayingMode(PlayingMode) - Method in interface cs3500.music.view.IView
-
- setRepeats(List<RepeatInstr>) - Method in class cs3500.music.view.AudioVisualView
-
- setRepeats(List<RepeatInstr>) - Method in class cs3500.music.view.AView
-
- setRepeats(List<RepeatInstr>) - Method in class cs3500.music.view.GuiViewFrame
-
- setRepeats(List<RepeatInstr>) - Method in interface cs3500.music.view.IView
-
Provide the view with the repeats to be enacted on this music.
- setRepeats(List<RepeatInstr>) - Method in class cs3500.music.view.SheetPanel
-
Sets the repeats to display.
- setTempo(int) - Method in interface cs3500.music.control.IController
-
Set the tempo to play at.
- setTempo(int) - Method in class cs3500.music.control.SimpleController
-
- setTempo(int) - Method in interface cs3500.music.util.CompositionBuilder
-
Sets the tempo of the piece.
- setTempo(int) - Method in class cs3500.music.util.SheetBuilder
-
- setTempo(int) - Method in class cs3500.music.view.AudioView
-
- setTempo(int) - Method in class cs3500.music.view.AudioVisualView
-
- setTempo(int) - Method in class cs3500.music.view.AView
-
- setTempo(int) - Method in interface cs3500.music.view.IView
-
Sets the rate of play for the given view.
- setViewNotes() - Method in interface cs3500.music.control.IController
-
Pass the sheet of notes from the model to the view.
- setViewNotes() - Method in class cs3500.music.control.SimpleController
-
- SHEET_START_X - Static variable in class cs3500.music.util.ViewConstants
-
- SHEET_START_Y - Static variable in class cs3500.music.util.ViewConstants
-
- SheetBuilder - Class in cs3500.music.util
-
Builds a sheet of music by adding notes and setting the tempo.
- SheetBuilder(IController) - Constructor for class cs3500.music.util.SheetBuilder
-
Public constructor for a SheetBuilder object.
- SheetPanel - Class in cs3500.music.view
-
Displays the sheet of music.
- SheetPanel() - Constructor for class cs3500.music.view.SheetPanel
-
Constructs a sheet panel.
- SimpleController - Class in cs3500.music.control
-
A simple controller to connect the view and the model.
- SimpleController(MusicOperations, IView...) - Constructor for class cs3500.music.control.SimpleController
-
Constructs a simple controller with a model and one or more views.
- SimpleController(MusicOperations, boolean, IView...) - Constructor for class cs3500.music.control.SimpleController
-
Constructs a simple controller with a model, one or more views, and a default playing
argument to allow the controller to immediately start playing.
- StringUtilities - Class in cs3500.music.util
-
Utility class to contain methods to modify String
s.
- StringUtilities() - Constructor for class cs3500.music.util.StringUtilities
-
- StringUtilitiesTest - Class in cs3500.music.tests
-
- StringUtilitiesTest() - Constructor for class cs3500.music.tests.StringUtilitiesTest
-


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