Preparation time
less than 30 mins
Cooking time
10 to 30 mins
Serves
Serves 2
For the smoked pigeon, remove the legs and breasts from the pigeons. Take the thigh bone out of the legs and discard. Season the pigeon legs with salt and freshly ground black pepper.
Heat a tablespoon of olive oil in a small frying pan until hot, then add the boned pigeon legs and cook for 1-2 minutes on each side. Leave to rest in a warm place.
Meanwhile, place some smoking chips in the bottom half of a metal steamer and place over a medium-high heat. When the chips begin to smoke, place the pigeon breasts into the top half of the steamer, cover with a lid and smoke for four minutes. (CAUTION: this cooking method generates a lot of smoke. Cook in a well-ventilated area.)
Heat the remaining tablespoon of olive oil in the pan used to cook the legs. Take the pigeon breasts out of the steamer, season with salt and freshly ground black pepper and place in the pan. Cook for one minute, then add the butter and turn over. Cook for a further minute, or until cooked to your liking. Set aside to rest in a warm place.
For the beetroot purée, place a pan over a medium heat, add the butter, beetroot, port and vinegar and cook for 3-4 minutes. Season with salt and freshly ground black pepper, then remove from the heat and allow to cool slightly.
Transfer the beetroot mixture to a blender and blend to a purée. Pass through a sieve into a bowl and keep warm.
For the salad, place the beetroot, apple, walnuts and onion into a bowl and stir well.
Place the honey, walnut oil, vinegar, salt and freshly ground black pepper into a small bowl and whisk together. Pour over the salad and stir to coat.
To serve, arrange the salad around the edge of each of two serving plates and top with the baby red chard leaves. Spoon the beetroot purée in the centre of each plate and place the pigeon on top.


Write a script that finds the maximal increasing sequence in an array. Example: {3, 2, 3, 4, 2, 2, 4} à {2, 3, 4}.
== use spaces to separate the elements of the array
== Example: 1 5 -6 7 5 ==


--- title: "3rd & 4th Grade Summer Camps" layout: "default" published: true ---
Back to main Summer Camp page
Click the panels below for information about each camp.
Do you want to be a great gardener? Discover what it takes to grow fabulous plants as you learn tips and tricks from the professionals who work at RBG. Discover cool plants, learn about composting, investigate plant pests, and help professional horticulturists care for the Garden. End the week by planting a patio container garden container to take home.
Click Here for Camp Overview
{% include register-button.html %}Red Butte Garden
Garden Members: $124 Public: $155
801-581-8454
Partner Camp with Tanner Dance
Hear ye, hear ye! Join Tanner Dance and Red Butte Garden on a mythical medieval adventure! Spend your mornings at RBG learning about the fun, magical, mystical, and unusual traditions from the medieval era as you explore the different plants people used in their everyday lives. In the afternoon travel to Tanner Dance where you'll continue your mythical medieval explorations as you interpret myths, legends and more through dance and other art forms. End the week with a special performance at Tanner Dance to showcase all you've learned. Transportation between sites provided.
Drop off at Red Butte Garden by 9AM and pick up at Tanner Dance by 4PM
Click Here for Camp Overview
{% include register-button.html %}Red Butte Garden / Tanner Dance: 1721 Campus Center Drive, SLC, UT 84112
Garden Members: $252 Public: $315
801-581-8454
Partner Camp with Wasatch Community Gardens (WCG)
Get ready for an all-day adventure! Spend your mornings at RBG learning skills like map-reading, orienteering, and navigation before searching the Garden for hidden treasure. In the afternoon you’ll travel to the WCG Grateful Tomato Garden to learn to grow healthy, organic food that is good for both your body and the environment as you explore composting, organic gardening, and more. Transportation between sites provided.
Drop off at Red Butte Garden by 9AM and pick up at WCG Grateful Tomato Garden by 4PM
Click Here for Camp Overview
{% include register-button.html %}Red Butte Garden / Wasatch Community Gardens Grateful Tomato Garden: 600 East 769 South, SLC, UT
Garden Members: $252 Public: $315
801-581-8454
Partner Camp with Discovery Gateway Children's Museum
Explore S.T.E.A.M. (science, technology, engineering, art, and math) with Discovery Gateway Children’s Museum and Red Butte Garden! Spend your mornings at RBG investigating STEAM in the natural world as you create parachutes for action figures, craft rafts out of recyclable materials, cook edible popcorn art, and more. In the afternoon, you’ll travel to Discovery Gateway to continue your STEAM explorations with experiments in the STEAM Lab, art in The Learning Center, and excursions throughout the museum. Don’t miss out on this all-day adventure! Transportation provided.
Drop off at Red Butte Garden by 9AM and pick up at Discovery Gateway by 4PM
Click Here for Camp Overview.
{% include register-button.html %}Red Butte Garden / Disovery Gateway Children's Museum: 444 West 100 South, SLC, UT 84101
Members: $252 Public: $315
801-581-8454
Food lovers, unite! Get ready to explore the Garden with your taste buds as you practice essential kitchen skills like chopping, mixing, blending, and more. In addition to making daily snacks like Thai spring rolls, quesadillas, curry butter, and hummus, you’ll also harvest fresh ingredients from the Garden, decorate your own chef apron, and compile a camp cookbook.
Click Here for Camp Overview
Registration for this camp is closed.
{% include register-button.html %}Red Butte Garden
Garden Members: $124 Public: $155
801-581-8454
Did you know plants have “magical” properties? Explore the plant kingdom and discover the unique uses of several common (and not-so-common!) plants. From healing to textiles, you’ll investigate how people use plants around the world, and then create your own bevy of botanical potions, lotions, and charms like organic lip balm, insect repellant, beeswax candles, and more.
Click Here for Camp Overview
{% include register-button.html %}Red Butte Garden
Garden Members: $100 Public: $125
801-581-8454
The natural world is filled with magic, and you’ll learn all about it this week at camp. Each day you’ll explore a new mythological creature and discover the magical role it plays in helping the environment. Later, use what you’ve learned to create the perfect home for these magical creatures by combining basic garden design principles with small-scale plants and handcrafted accessories. At the end of the week you’ll have your own tiny world terrarium to take home.
Click Here for Camp Overview
{% include register-button.html %}Red Butte Garden
Garden Members: $100 Public: $125
801-581-8454
Partner Camp with Wasatch Community Gardens (WCG)
Explore the magic of nature with WCG and Red Butte Garden! Spend your mornings at Red Butte Garden discovering the mythical origins of everyday things found in the natural world like honeybees, and birds as you create pinecone weather stations, concoct organic weed killer, build DIY birdfeeders, and more. In the afternoon, travel to the WCG Gratefull Tomato Garden to explore the “supernatural” properties of plants as you create botanical potions and perform “magical” plant experiments. Transportation between sites provided.
Drop off at Red Butte Garden by 9AM and pick up at WCG by 4PM
Click Here for Camp Overview
{% include register-button.html %}Red Butte Garden / Wasatch Community Gardens Grateful Tomato Garden: 600 East 800 South, SLC, UT
Garden Members: $252 Public: $315
801-581-8454
Partner Camp with Play-Well TEKnologies
Bug lovers unite! Discover the Garden’s tiniest inhabitants as you explore the world of bugs. In the morning build and program robots in this introductory Robotics class using the LEGO® WeDo® system. Learn basic programming skills, simple engineering concepts, and the names of robot components all while working in small groups to complete projects using tilt and motion sensors. In the afternoons, you’ll learn about life cycles, social insects, water bugs, and more as you hike the Garden trails, create bug-related crafts, and conduct cool experiments. By the end of the week, you’ll be an amateur bug expert!
Click Here for Camp Overview
{% include register-button.html %}Red Butte Garden
Garden Members: $252 Public: $315
801-581-8454
Discover the crazy adaptations of the plant kingdom! Each day we will explore the Garden to learn about tough environments and how plants have adapted to survive them. Through investigations, crafts and games we will discover how plants protect themselves from danger, adapt to extreme environments, reproduce and more!
Click Here for Camp Overview
{% include register-button.html %}Red Butte Garden
Garden Members: $100 Public: $125
801-581-8454
What do rubber, chocolate, and charcoal all have in common? They all come from trees! Investigate the largest residents of Red Butte Garden as you explore the world of trees using STEAM (science, technology, engineering, art, and math). By week’s end you’ll find trees are not only beautiful, but they’re also quite useful and fun!
Click Here for Camp Overview
{% include register-button.html %}Red Butte Garden
Garden Members: $100 Public: $125
801-581-8454
Become a culinary scientist this week at camp as you discover the science of cooking. Spend the week practicing essential kitchen skills like chopping, mixing, and blending, as you learn why onions make you cry, how bread rises, why fruit browns, and more. You’ll also harvest fresh ingredients from the Garden, decorate a chef’s apron, and compile a camp cookbook.
Click Here for Camp Overview
{% include register-button.html %}Red Butte Garden
Garden Members: $124 Public: $155
801-581-8454
Although the Garden is located in Utah, it’s home to plants from around the world. Go on a globetrotting adventure this week at camp as you explore the traditions, games, foods, and plants of different countries from around the world. With stops in India, Japan, Kenya and more, you’re in for a world of fun! .
Click Here for Camp Overview
{% include register-button.html %}Red Butte Garden
Garden Members: $124 Public: $155
801-581-8454
Partner Camp with Kidding Around Yoga with Sarah Bly
Shinrin-yoku means “to take in the forest atmosphere” or “forest bathing.” Come join us on this nature-loving adventure in the Garden! We will be taught classes from Kidding Around Yoga with Sarah Bly. Sarah is a certified kids yoga teacher and will be doing all kinds of breathing techniques, yoga poses, games and more. The rest of camp you will explore the Garden through hikes, crafts, games, and more. By the end of the week, not only will you have a greater appreciation of nature and the world around us, but you’ll also finish camp more relaxed and at one with the universe. Namaste.
Click Here for Camp Overview
{% include register-button.html %}Red Butte Garden
Garden Members: $168 Public: $210
801-581-8454 (RBG) or Sarah Bly (instructor)


Contents
This is a design document detailing the implementation of a Ganeti monitoring agent report system, that can be queried by a monitoring system to calculate health information for a Ganeti cluster.
There is currently no monitoring support in Ganeti. While we don’t want to build something like Nagios or Pacemaker as part of Ganeti, it would be useful if such tools could easily extract information from a Ganeti machine in order to take actions (example actions include logging an outage for future reporting or alerting a person or system about it).
Each Ganeti node should export a status page that can be queried by a monitoring system. Such status page will be exported on a network port and will be encoded in JSON (simple text) over HTTP.
The choice of JSON is obvious as we already depend on it in Ganeti and thus we don’t need to add extra libraries to use it, as opposed to what would happen for XML or some other markup format.
The report will be available from all nodes, and be concerned for all node-local resources. This allows more real-time information to be available, at the cost of querying all nodes.
The monitoring agent system will report on the following basic information:
The report of the will be in JSON format, and it will present an array of report objects. Each report object will be produced by a specific data collector. Each report object includes some mandatory fields, to be provided by all the data collectors:
Here follows a minimal example of a report:
[
{
"name" : "TheCollectorIdentifier",
"version" : "1.2",
"format_version" : 1,
"timestamp" :<PHONE_NUMBER>000000000,
"category" : null,
"kind" : 0,
"data" : { "plugin_specific_data" : "go_here" }
},
{
"name" : "AnotherDataCollector",
"version" : "B",
"format_version" : 7,
"timestamp" :<PHONE_NUMBER>123854000,
"category" : "storage",
"kind" : 1,
"data" : { "status" : { "code" : 1,
"message" : "Error on disk 2"
},
"plugin_specific" : "data",
"some_late_data" : { "timestamp" :<PHONE_NUMBER>123942720,
...
}
}
}
]
These collectors only provide data about some component of the system, without giving any interpretation over their meaning.
The value of the kind field of the report will be 0.
These collectors will provide information about the status of some component of ganeti, or managed by ganeti.
The value of their kind field will be 1.
The rationale behind this kind of collectors is that there are some situations where exporting data about the underlying subsystems would expose potential issues. But if Ganeti itself is able (and going) to fix the problem, conflicts might arise between Ganeti and something/somebody else trying to fix the same problem. Also, some external monitoring systems might not be aware of the internals of a particular subsystem (e.g.: DRBD) and might only exploit the high level response of its data collector, alerting an administrator if anything is wrong. Still, completely hiding the underlying data is not a good idea, as they might still be of use in some cases. So status reporting plugins will provide two output modes: one just exporting a high level information about the status, and one also exporting all the data they gathered. The default output mode will be the status-only one. Through a command line parameter (for stand-alone data collectors) or through the HTTP request to the monitoring agent (when collectors are executed as part of it) the verbose output mode providing all the data can be selected.
When exporting just the status each status reporting collector will provide, in its data section, at least the following field:
summarizes the status of the component being monitored and consists of two subfields:
It assumes a numeric value, encoded in such a way to allow using a bitset to easily distinguish which states are currently present in the whole cluster. If the bitwise OR of all the status fields is 0, the cluster is completely healty. The status codes are as follows:
A message to better explain the reason of the status. The exact format of the message string is data collector dependent.
The field is mandatory, but the content can be an empty string if the code is 0 (working as intended) or 1 (being fixed automatically).
If the status code is 2, the message should specify what has gone wrong. If the status code is 4, the message shoud explain why it was not possible to determine a proper status.
The data section will also contain all the fields describing the gathered data, according to a collector-specific format.
At the moment each node knows which instances are running on it, which instances it is primary for, but not the cause why an instance might not be running. On the other hand we don’t want to distribute full instance “admin” status information to all nodes, because of the performance impact this would have.
As such we propose that:
Monitoring and auditing systems can then use the reason to understand the cause of an instance status, and they can use the timestamp to understand the freshness of their data even in the absence of an atomic cross-node reporting: for example if they see an instance “up” on a node after seeing it running on a previous one, they can compare these values to understand which data is freshest, and repoll the “older” node. Of course if they keep seeing this status this represents an error (either an instance continuously “flapping” between nodes, or an instance is constantly up on more than one), which should be reported and acted upon.
The instance status will be on each node, for the instances it is primary for, and its data section of the report will contain a list of instances, named instances, with at least the following fields for each instance:
Each hypervisor should provide its own instance status data collector, possibly with the addition of more, specific, fields. The category field of all of them will be instance. The kind field will be 1.
Note that as soon as a node knows it’s not the primary anymore for an instance it will stop reporting status for it: this means the instance will either disappear, if it has been deleted, or appear on another node, if it’s been moved.
The code of the status field of the report of the Instance status data collector will be:
The storage collectors will be a series of data collectors that will gather data about storage for the current node. The collection will be performed at different granularity and abstraction levels, from the physical disks, to partitions, logical volumes and to the specific storage types used by Ganeti itself (drbd, rbd, plain, file).
The name of each of these collector will reflect what storage type each of them refers to.
The category field of these collector will be storage.
The kind field will depend on the specific collector.
Each storage collector’s data section will provide collector-specific fields.
The various storage collectors will provide keys to join the data they provide, in order to allow the user to get a better understanding of the system. E.g.: through device names, or instance names.
This storage data collector will gather information about the status of the disks installed in the system, as listed in the /proc/diskstats file. This means that not only physical hard drives, but also ramdisks and loopback devices will be listed.
Its kind in the report will be 0 (Performance reporting collectors).
Its category field in the report will contain the value storage.
When executed in verbose mode, the data section of the report of this collector will be a list of items, each representing one disk, each providing the following fields:
This data collector will gather information about the attributes of logical volumes present in the system.
Its kind in the report will be 0 (Performance reporting collectors).
Its category field in the report will contain the value storage.
The data section of the report of this collector will be a list of items, each representing one logical volume and providing the following fields:
This data collector will run only on nodes where DRBD is actually present and it will gather information about DRBD devices.
Its kind in the report will be 1 (Status reporting collectors).
Its category field in the report will contain the value storage.
When executed in verbose mode, the data section of the report of this collector will provide the following fields:
Information about the DRBD version number, given by a combination of any (but at least one) of the following fields:
A list of structures, each describing a DRBD device (a minor) and containing the following fields:
The performance indicators. This field will contain the following sub-fields:
(Optional) The status of the synchronization of the disk. This is present only if the disk is being synchronized, and includes the following fields:
Ganeti will report what information it has about its own daemons. This should allow identifying possible problems with the Ganeti system itself: for example memory leaks, crashes and high resource utilization should be evident by analyzing this information.
The kind field will be 1 (Status reporting collectors).
Each daemon will have its own data collector, and each of them will have a category field valued daemon.
When executed in verbose mode, their data section will include at least:
Any other daemon-specific information can be included as well in the data section.
Each hypervisor has a view of system resources that sometimes is different than the one the OS sees (for example in Xen the Node OS, running as Dom0, has access to only part of those resources). In this section we’ll report all information we can in a “non hypervisor specific” way. Each hypervisor can then add extra specific information that is not generic enough be abstracted.
The kind field will be 0 (Performance reporting collectors).
Each of the hypervisor data collectory will be of category: hypervisor.
Since Ganeti assumes it’s running on Linux, it’s useful to export some basic information as seen by the host system.
The category field of the report will be null.
The kind field will be 0 (Performance reporting collectors).
The data section will include:
Note that we won’t go into any hardware specific details (e.g. querying a node RAID is outside the scope of this, and can be implemented as a plugin) but we can easily just report the information above, since it’s standard enough across all systems.
This data collector will export CPU load statistics as seen by the host system. Apart from using the data from an external monitoring system we can also use the data to improve instance allocation and/or the Ganeti cluster balance. To compute the CPU load average we will use a number of values collected inside a time window. The collection process will be done by an independent thread (see Mode of Operation).
This report is a subset of the previous report (Node OS resources report) and they might eventually get merged, once reporting for the other fields (memory, filesystem, NICs) gets implemented too.
Specifically:
The category field of the report will be null.
The kind field will be 0 (Performance reporting collectors).
The data section will include:
The CPU load report function will get N values, collected by the CPU load collection function and calculate the above averages. Please see the section Mode of Operation for more information one how the two functions of the data collector interact.
The queries to the monitoring agent will be HTTP GET requests on port 1815. The answer will be encoded in JSON format and will depend on the specific accessed resource.
If a request is sent to a non-existing resource, a 404 error will be returned by the HTTP server.
The following paragraphs will present the existing resources supported by the current protocol version, that is version 1.
The root resource. It will return the list of the supported protocol version numbers.
Currently, this will include only version 1.
Not an actual resource per-se, it is the root of all the resources of protocol version 1.
If requested through GET, the null JSON value will be returned.
Returns a list of tuples (kind, category, name) showing all the collectors available in the system.
A list of the reports of all the data collectors, as a JSON list.
Status reporting collectors will provide their output in non-verbose format. The verbose format can be requested by adding the parameter verbose=1 to the request.
Returns the report of the collector [collector_name] that belongs to the specified [category].
The category has to be written in lowercase.
If a collector does not belong to any category, default will have to be used as the value for [category].
Status reporting collectors will provide their output in non-verbose format. The verbose format can be requested by adding the parameter verbose=1 to the request.
As for the instance status Ganeti has now only partial information about its instance disks: in particular each node is unaware of the disk to instance mapping, that exists only on the master.
For this design doc we plan to fix this by changing all RPCs that create a backend storage or that put an already existing one in use and passing the relevant instance to the node. The node can then export these to the status reporting tool.
While we haven’t implemented these RPC changes yet, we’ll use Confd to fetch this information in the data collectors.
The monitoring system will be equipped with a plugin system that can export specific local information through it.
The plugin system is expected to be used by local installations to export any installation specific information that they want to be monitored, about either hardware or software on their systems.
The plugin system will be in the form of either scripts or binaries whose output will be inserted in the report.
Eventually support for other kinds of plugins might be added as well, such as plain text files which will be inserted into the report, or local unix or network sockets from which the information has to be read. This should allow most flexibility for implementing an efficient system, while being able to keep it as simple as possible.
In order to ease testing as well as to make it simple to reuse this subsystem it will be possible to run just the “data collectors” on each node without passing through the agent daemon.
If a data collector is run independently, it should print on stdout its report, according to the format corresponding to a single data collector report object, as described in the previous paragraphs.
In order to be able to report information fast the monitoring agent daemon will keep an in-memory or on-disk cache of the status, which will be returned when queries are made. The status system will then periodically check resources to make sure the status is up to date.
Different parts of the report will be queried at different speeds. These will depend on: - how often they vary (or we expect them to vary) - how fast they are to query - how important their freshness is
Of course the last parameter is installation specific, and while we’ll try to have defaults, it will be configurable. The first two instead we can use adaptively to query a certain resource faster or slower depending on those two parameters.
When run as stand-alone binaries, the data collector will not using any caching system, and just fetch and return the data immediately.
Since some performance collectors have to operate on a number of values collected in previous times, we need a mechanism independent of the data collector which will trigger the collection of those values and also store them, so that they are available for calculation by the data collectors.
To collect data periodically, a thread will be created by the monitoring agent which will run the collection function of every data collector that provides one. The values returned by the collection function of the data collector will be saved in an appropriate map, associating each value to the corresponding collector, using the collector’s name as the key of the map. This map will be stored in mond’s memory.
The collectors are divided in two categories:
For example: the collection function of the CPU load collector will collect a CPU load value and save it in the map mentioned above. The collection function will be called by the collector thread every t milliseconds. When the report function of the collector is called, it will process the last N values of the map and calculate the corresponding average.
The status daemon will be implemented as a standalone Haskell daemon. In the future it should be easy to merge multiple daemons into one with multiple entry points, should we find out it saves resources and doesn’t impact functionality.
The libekg library should be looked at for easily providing metrics in json format.
We will implement the agent system in this order:
As a future step it can be useful to “centralize” all this reporting data on a single place. This for example can be just the master node, or all the master candidates. We will evaluate doing this after the first node-local version has been developed and tested.
Another possible change is replacing the “read-only” RPCs with queries to the agent system, thus having only one way of collecting information from the nodes from a monitoring system and for Ganeti itself.
One extra feature we may need is a way to query for only sub-parts of the report (eg. instances status only). This can be done by passing arguments to the HTTP GET, which will be defined when we get to this funtionality.
Finally the autorepair system design. system (see its design) can be expanded to use the monitoring agent system as a source of information to decide which repairs it can perform.


Web User Interface
The all-in-one, user interface kit. This interface is suitable for use in many programming languages.
Fast web templates
It allows you to translate HTML code into programming languages, so you can quickly and effectively create web templates.
Depends on standards
W3 standards compliant, semantic, new and old browser support, clean structure allows for quick application.
Compatible
You can design sites that are compatible with search engines.
Semantics
cybotranik-wui provides additional information to the browser's accessibility API.
Cross-platform
It takes advantage of the additional features of the new generation of browsers, and supports browsers such as the older generation (ie ie8).
Mobile
It provides good looks on mobile devices and provides performance with a single file library.


7 June 2020
Advanced Message Queuing Protocol Features
by Q
The Advanced Message Queuing Protocol (AMQP) is an open standard protocol for passing business messages between applications or organizations.
AMQP connects across:
- Organizations
- Platforms
- Time
- Space
AMQP has following features:
- Ubiquity
- Easy to (a) use, (b) implement, and (c) extend
- Clear and unambiguous core functionality for business message routing and delivery within Internet infrastructure
- Low barrier to understand, use and implement
- Safety
- Infrastructure for a secure and trusted global transaction network
- Tamper proof business messages
- Independent durable message for connected receivers
- Resilient message delivery for technical failure
- Supports financial transactions of business requirements
- Impossible for spam injection
- Fidelity
- Well-stated message queuing and delivery semantics covering
- Well-stated ordering semantics always having a sender and an observer
- Well-stated reliable exception handling
- Unity
- Any AMQP client can initiate communication to any AMQP broker over TCP/IP
- Provides the core set of messaging patterns
- Asynchronous directed messaging
- Request/reply
- Publish/subscribe
- Store
- Forward
- Negotiable transport protocols (e.g. SCTP, UDP/Multicast) with any AMQP broker
- Supports
- Hub & Spoke messaging topology
- Hub-to-Hub messaging
- Peer-to-Peer messaging
- Interoperability
- Multiple stable and interpolating broker implementations
- Stable core (client-broker) wire protocol
- Layered architecture
- Backward compatibility
- Manageability
- Binary WIRE protocol to be ubiquitous, fast and embedded
- Scalable
- Global addressing standardizing end-to-end delivery
- Decentralized
- Supports routing and relay management
References:
Updated: 7 June 2020
tags: Network - Web


Sanchita Sharma
Murex Test Analyst at
Luxoft
I am a Software Engineer
Work Experience
I was a part of Murex Environment support Team
I worked as a Murex Tester for APG
Education Background
Dates
Education
2015-2019
BBD, Lucknow
2014-15
W.H.Smith Memorial school
2012-13
W.H.Smith Memorial school
My Hobbies
My Contact


---
layout: post
title: RetroRuby4 What's Next? Presentation
date: '2014-02-02T10:10:00.000-05:00'
author: Allison McMillan
tags:
- mentoring
- code tools
- retroruby4
- ruby
- setting goals
- becoming a developer
- learning
- projects
- getting started
modified_time: '2014-02-02T10:10:39.479-05:00'
blogger_id: tag:blogger.com,1999:blog-6054545261088815023.post-4000157731645812805
blogger_orig_url: http://allisonsherenmcmillan.blogspot.com/2014/02/retroruby4-whats-next-presentation.html
---
Here are my slides from retro yesterday! Enjoy!!


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<xsd:element name="BusinessName" type="BusinessNameType" minOccurs="0"> | NEW line(s): 71
<xsd:element name="BusinessName" type="BusinessNameType"> |
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<!-- Name --> <xsd:element name="Name" type="NameLine1Type"> <xsd:annotation> <xsd:documentation> <Description>Name</Description> </xsd:documentation> </xsd:annotation> </xsd:element> </xsd:choice> <!-- Choice between EIN, SSN or Missing EIN Reason --> |
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<!-- Were You Requested to Participate in or Cooperate with an Internation Boycott --> | NEW line(s): 710
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<Description>Were you requested to participate in or cooperate with an internation boycott</Description> | NEW line(s): 714
<Description>Were you requested to participate in or cooperate with an international boycott</Description> |


Ex-NASA Chief Dan Goldin has revealed his company KnuEdge, which develops neural chips that follow the principles of the human brain and produces voice recognition and authentication technology.
After almost 10 years of working incognito, former National Aeronautics and Space Administration head Daniel Goldin is finally ready to formally present KnuEdge to the world.
KnuEdge is a “neural technology innovation company,” an outfit that builds hardware and software based on neural technology, with a main focus on human-machine interaction. While newly revealed publicly, it has been in stealth mode for a decade now, and has already raised $100 million in funding to build its neural chips.
The company has revealed its two primary products: KnuVerse, which is a voice authentication technology, and KnuPath, its state-of-the-art neural chip. It has also unveiled Knurld.io, a software development kit with a cloud-based voice recognition and authentication service.
Apple announced that the new iOS 10 will come with Swift Playgrounds, which would teach kids to code “in a seriously fun way.”
Apple believes that coding is an essential skill, with their slogan “Everyone Can Code,” they think…well, that everyone can learn the basics of coding—and that everyone should.
Swift Playgrounds introduces kids to coding using a graphical and playful interface that challenges the user with puzzles and goals to keep it entertaining, much like a game. A special keyboard with shortcuts and features will aid in making it as easy as it is fun.
Pre-made templates are available to demonstrate advanced codes, and an additional feature allows sharing and editing projects through Mail, Messages, or Airdrop. You can also import and export between Mac toolkit and Playgrounds.
Swift Playground is now open source, with the developer preview already out and the final version that comes with the new iOS 10 free of charge.


This is my page
See my work on GitHub
The above link was created using the anchor tag in HTML. Learn more about that here.


Congolese journalist helping with Ebola crisis slain
BENI, Congo (AP) - Authorities in eastern Congo say a journalist who was working to spread awareness about the Ebola crisis has been killed.
Papy Mahamba Mumbere, who worked at a radio station in Congo’s Ituri province, also had been serving as a community health worker in the village of Lwemba.
The attack Saturday also left his wife critically injured.
Authorities say that two suspects have been apprehended, and police are investigating the exact motive for the crime.
Health workers have come under attack by distrustful communities since the Ebola outbreak began in August 2018. Some attacks are believed to be financially motivated, as perpetrators think that Ebola workers are being well compensated.
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Web app security enthusiast, CTF player, tutor, recent graduate of Rutgers University with Bachelor's degree in Computer Engineering.
Certifications
eLearnSecurity Junior Penetration Tester
Tutoring
Interested in tutoring for Python, C, or math? I offer online tutoring through Wyzant.
Hire me as a tutor, or check out my profile on Wyzant using the following link: https://is.gd/rRJ6re


Docker Enterprise
Estimated reading time: 6 minutesThe Docker Enterprise platform is the leading container platform for continuous, high-velocity innovation. Docker is the only independent container platform that enables developers to seamlessly build and share any application — from legacy to modern — and operators to securely run them anywhere - from hybrid cloud to the edge.
Docker Enterprise is a secure, scalable, and supported container platform for building and orchestrating applications across multi-tenant Linux, Windows Server 2016, and Windows Server 2019.
Docker Enterprise enables deploying highly available workloads using either the Docker Kubernetes Service or Docker Swarm. Docker Enterprise automates many of the tasks that orchestration requires, like provisioning pods, containers, and cluster resources. Self-healing components ensure that Docker Enterprise clusters remain highly available.
Role-based access control (RBAC) applies to Kubernetes and Swarm orchestrators, and communication within the cluster is secured with TLS. Docker Content Trust is enforced for images on all of the orchestrators.
Docker Enterprise includes Docker Universal Control Plane (UCP), the cluster management solution from Docker. UCP can be installed on-premises or in your public cloud of choice, and helps manage your cluster and applications through a single interface.
Docker Enterprise features
Docker Enterprise provides multi-architecture orchestration using the Docker Kubernetes Service and Docker Swarm orchestrators. Docker Enterprise enables a secure software supply chain, with policy-based image promotion, image mirroring between registries - including Docker Hub, and signing & scanning enforcement for container images.
Docker Kubernetes Service
The Docker Kubernetes Service fully supports all Docker Enterprise features, including role-based access control, LDAP/AD integration, image scanning and signing enforcement policies, and security policies.
Docker Kubernetes Services features include:
- Kubernetes orchestration full feature set
- CNCF Certified Kubernetes conformance
- Kubernetes app deployment via UCP web UI or CLI (
kubectl
) - Compose stack deployment for Swarm and Kubernetes apps (
docker stack deploy
) - Role-based access control for Kubernetes workloads
- Blue-Green deployments, for load balancing to different app versions
- Ingress Controllers with Kubernetes L7 routing
- Pod Security Policies to define a set of conditions that a pod must run with in order to be accepted into the system
- Note: Pod Security Policies are currently
Beta
status in Kubernetes 1.14
- Note: Pod Security Policies are currently
- Container Storage Interface (CSI) support
- iSCSI support for Kubernetes
- Non-disruptive Docker Enterprise platform upgrades (blue-green upgrades)
- Experimental features (planned for full GA in subsequent Docker Enterprise releases):
- Kubernetes-native ingress (Istio)
In addition, UCP integrates with Kubernetes by using admission controllers, which enable:
- Authenticating user client bundle certificates when communicating directly with the Kubernetes API server
- Authorizing requests via the UCP role-based access control model
- Assigning nodes to a namespace by injecting a
NodeSelector
automatically to workloads via admission control - Keeping all nodes in both Kubernetes and Swarm orchestrator inventories
- Fine-grained access control and privilege escalation prevention without
the
PodSecurityPolicy
admission controller - Resolving images of deployed workloads automatically, and accepting or rejecting images based on UCP’s signing-policy feature
The default Docker Enterprise installation includes both Kubernetes and Swarm components across the cluster, so every newly joined worker node is ready to schedule Kubernetes or Swarm workloads.
Orchestration platform features
- Docker Enterprise manager nodes are both Swarm managers and Kubernetes masters, to enable high availability
- Allocate worker nodes for Swarm or Kubernetes workloads (or both)
- Single pane of glass for monitoring apps
- Enhanced Swarm hostname routing mesh with Interlock 2.0
- One platform-wide management plane: secure software supply chain, secure multi-tenancy, and secure and highly available node management
Secure supply chain
- DTR support for the Docker App format, based on the CNAB specification
- Note: Docker Apps can be deployed to clusters managed by UCP, where they will be displayed as Stacks
- Image signing and scanning of Kubernetes and Swarm images and Docker Apps for validating and verifying content
- Image promotion with mirroring between registries as well as Docker Hub
- Define policies for automating image promotions across the app development lifecycle of Kubernetes and Swarm apps
Centralized cluster management
With Docker, you can join thousands of physical or virtual machines together to create a cluster, allowing you to deploy your applications at scale. Docker Enterprise extends the functionality provided by Docker Engine to make it easier to manage your cluster from a centralized place.
You can manage and monitor your container cluster using a graphical web interface.
Deploy, manage, and monitor
With Docker Enterprise, you can manage all of the infrastructure resources you have available, like nodes, volumes, and networks, from a central console.
You can also deploy and monitor your applications and services.
Built-in security and access control
Docker Enterprise has its own built-in authentication mechanism with role-based access control (RBAC), so that you can control who can access and make changes to your cluster and applications. Also, Docker Enterprise authentication integrates with LDAP services and supports SAML SCIM to proactively synchronize with authentication providers. Learn about role-based access control. You can also opt to enable PKI authentication to use client certificates, rather than username and password.
Docker Enterprise integrates with Docker Trusted Registry so that you can keep the Docker images you use for your applications behind your firewall, where they are safe and can’t be tampered with. You can also enforce security policies and only allow running applications that use Docker images you know and trust.
Windows Application Security
Windows applications typically require Active Directory authentication in order to communicate with other services on the network. Container-based applications use Group Managed Service Accounts (gMSA) to provide this authentication. Docker Swarm fully supports the use of gMSAs with Windows containers.
Docker Enterprise and the CLI
Docker Enterprise exposes the standard Docker API, so you can continue using the tools that you already know, including the Docker CLI client, to deploy and manage your applications.
For example, you can use the docker info
command to check the
status of a Swarm managed by Docker Enterprise:
docker info
Which produces output similar to the following:
Containers: 38
Running: 23
Paused: 0
Stopped: 15
Images: 17
Server Version: 17.06
...
Swarm: active
NodeID: ocpv7el0uz8g9q7dmw8ay4yps
Is Manager: true
ClusterID: tylpv1kxjtgoik2jnrg8pvkg6
Managers: 1
…
Use the Kubernetes CLI
Docker Enterprise exposes the standard Kubernetes API, so you can use kubectl to manage your Kubernetes workloads:
kubectl cluster-info
Which produces output similar to the following:
Kubernetes master is running at https://<IP_ADDRESS>:6443
KubeDNS is running at https://<IP_ADDRESS>:6443/api/v1/namespaces/kube-system/services/kube-dns:dns/proxy
To further debug and diagnose cluster problems, use 'kubectl cluster-info dump'.
Docker Context
A new Docker CLI plugin called docker context
is available with client version 19.03.0. docker context
helps manage connections to multiple environments so you do not have to remember and type out connection strings. Read more about docker context
.


The Seattle Design Festival celebrates the ways design makes life better. Community members, city officials, citizen-experts, and designers all come together at workshops, performances, tours, exhibitions, talks, films, gatherings and more to exchange ideas.
This year, the Seattle Design Festival explores how design can contribute to a more equitable society. From buildings that everyone can move through, to apps that enable participation by people in every location and language, to cities where we can all afford to live – design is the vehicle of innovation. Design for Equity challenges us to question our biases and assumptions and work toward ensuring that everyone in our society – from every background, ability, race, age, gender, location or economic status – can access the same opportunities and outcomes, both now and in the future.
View the Seattle Design Festival mobile appThere’s always room for volunteers to join our team in staffing the fifth annual Seattle Design Festival. We welcome designers and design lovers from all backgrounds and disciplines. Experience is welcome but not necessary. All who are enthusiastic about raising the bar for equity in creative practices are invited to jump in!
To get involved, please subscribe to our e-mail list and be the first to receive our open call for volunteers to help staff the 2015 Seattle Design Festival. We will open volunteer registration in early August 2015.


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| Packages that use com.avaje.ebean | |
|---|---|
| com.avaje.ebean | Core API (see Ebean and EbeanServer). |
| com.avaje.ebean.bean | Enhanced beans API and Support objects |
| com.avaje.ebean.cache | Server Cache Service |
| com.avaje.ebean.common | Common non-public interfaces and implementation. |
| com.avaje.ebean.config | Configuration settings for EbeanServer construction |
| com.avaje.ebean.config.dbplatform | Database platform specific support |
| com.avaje.ebean.event | Persist and Query Event Controllers and Listeners |
| com.avaje.ebean.text | Utility objects for CSV, JSON and XML processing. |
| com.avaje.ebean.text.csv | CSV processing objects. |
| Classes in com.avaje.ebean used by com.avaje.ebean | |
|---|---|
| AdminAutofetch
Administrative control of Autofetch during runtime. |
|
| AdminLogging
Administrative control over transaction logging at runtime. |
|
| AdminLogging.LogFileSharing
Defines if transactions share a single log file or each have their own transaction log file. |
|
| AdminLogging.LogLevel
The transaction logging level. |
|
| AdminLogging.LogLevelStmt
Statement logging level. |
|
| AdminLogging.LogLevelTxnCommit
A log level for transaction begin, commit and rollback events. |
|
| BackgroundExecutor
Background thread pool service for executing of tasks asynchronously. |
|
| BeanState
Provides access to the internal state of an entity bean. |
|
| CallableSql
For making calls to stored procedures. |
|
| EbeanServer
Provides the API for fetching and saving beans to a particular DataSource. |
|
| ExampleExpression
Query by Example expression. |
|
| Expression
An expression that is part of a WHERE or HAVING clause. |
|
| ExpressionFactory
Expression factory for creating standard expressions. |
|
| ExpressionList
List of Expressions that make up a where or having clause. |
|
| FetchConfig
Defines the configuration options for a "query fetch" or a "lazy loading fetch". |
|
| Filter
Provides support for filtering and sorting lists of entities without going back to the database. |
|
| FutureIds
FutureIds represents the result of a background query execution for the Id's. |
|
| FutureList
FutureList represents the result of a background query execution that will return a list of entities. |
|
| FutureRowCount
Represents the result of a background query execution for the total row count for a query. |
|
| InvalidValue
Invalid value returned from validation rules. |
|
| JoinConfig
Defines the configuration options for a "query join" or a "lazy loading join". |
|
| Junction
Represents a Conjunction or a Disjunction. |
|
| LikeType
Used to specify the type of like matching used. |
|
| OrderBy
Represents an Order By for a Query. |
|
| OrderBy.Property
A property and its ascending descending order. |
|
| Page
Represents a Page of results that is part of a PagingList. |
|
| PagingList
Used to page through a query result rather than fetching all the results in a single query. |
|
| Query
Object relational query for finding a List, Set, Map or single entity bean. |
|
| Query.Type
The type of query result. |
|
| QueryListener
Provides a mechanism for processing a query one bean at a time. |
|
| RawSql
Used to build object graphs based on a raw SQL statement (rather than generated by Ebean). |
|
| RawSql.ColumnMapping
Defines the column mapping for raw sql DB columns to bean properties. |
|
| RawSql.ColumnMapping.Column
A Column of the RawSql that is mapped to a bean property (or ignored). |
|
| RawSql.Sql
Represents the sql part of the query. |
|
| RawSqlBuilder
Builds RawSql instances from a SQL string and column mappings. |
|
| SqlFutureList
The SqlFutureList represents the result of a background SQL query execution. |
|
| SqlQuery
Query object for performing native SQL queries that return SqlRow's. |
|
| SqlQueryListener
Provides a mechanism for processing a SqlQuery one SqlRow at a time. |
|
| SqlRow
Used to return raw SQL query results. |
|
| SqlUpdate
A SqlUpdate for executing insert update or delete statements. |
|
| Transaction
The Transaction object. |
|
| TxCallable
Execute a TxCallable in a Transaction scope. |
|
| TxIsolation
The Transaction Isolation levels. |
|
| TxRunnable
Execute a TxRunnable in a Transaction scope. |
|
| TxScope
Holds the definition of how a transactional method should run. |
|
| TxType
Used to define the transactional scope for executing a method. |
|
| Update
An Insert Update or Delete statement. |
|
| ValuePair
Holds two values as the result of a difference comparison. |
| Classes in com.avaje.ebean used by com.avaje.ebean.bean | |
|---|---|
| ExpressionList
List of Expressions that make up a where or having clause. |
| Classes in com.avaje.ebean used by com.avaje.ebean.cache | |
|---|---|
| EbeanServer
Provides the API for fetching and saving beans to a particular DataSource. |
| Classes in com.avaje.ebean used by com.avaje.ebean.common | |
|---|---|
| EbeanServer
Provides the API for fetching and saving beans to a particular DataSource. |
|
| ExpressionList
List of Expressions that make up a where or having clause. |
| Classes in com.avaje.ebean used by com.avaje.ebean.config | |
|---|---|
| AdminLogging.LogFileSharing
Defines if transactions share a single log file or each have their own transaction log file. |
|
| AdminLogging.LogLevel
The transaction logging level. |
|
| AdminLogging.LogLevelStmt
Statement logging level. |
|
| AdminLogging.LogLevelTxnCommit
A log level for transaction begin, commit and rollback events. |
| Classes in com.avaje.ebean used by com.avaje.ebean.config.dbplatform | |
|---|---|
| BackgroundExecutor
Background thread pool service for executing of tasks asynchronously. |
| Classes in com.avaje.ebean used by com.avaje.ebean.event | |
|---|---|
| EbeanServer
Provides the API for fetching and saving beans to a particular DataSource. |
|
| Query
Object relational query for finding a List, Set, Map or single entity bean. |
|
| Transaction
The Transaction object. |
| Classes in com.avaje.ebean used by com.avaje.ebean.text | |
|---|---|
| Query
Object relational query for finding a List, Set, Map or single entity bean. |
| Classes in com.avaje.ebean used by com.avaje.ebean.text.csv | |
|---|---|
| EbeanServer
Provides the API for fetching and saving beans to a particular DataSource. |
|
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Gradient Descent
Contents
Gradient
Gradient descent method is based on gradient
gradient always point to the asent direction
Gradient Descent
f is object function, and this is unconstrained
f = (@(X) (exp(X(1,:)-1) + exp(1-X(2,:)) + (X(1,:) - X(2,:)).^2));
%f = (@(X) (sin(0.5*X(1,:).^2 - 0.25 * X(2,:).^2 + 3) .* cos(2*X(1,:) + 1 - exp(X(2,:))) ))
Fail situation
Rosenbrock function Gradient descent/ascent algorithm zig-zags, because the gradient is nearly orthogonal to the direction of the local minimum in these regions. It's hard to convergence
%f = (@(X) (1-X(1,:)).^2 + 100 * (X(2,:) - X(1,:).^2).^2); [X, Y] = meshgrid([-2:0.1:2]); XX = [reshape(X, 1, numel(X)); reshape(Y, 1, numel(Y))]; %surf(X, Y, reshape(f(XX), length(X), length(X))) contour(X, Y, reshape(f(XX), length(X), length(X)), 50) hold on;
plot gradient of function
for i=1:5:length(XX) tmp = XX(:,i); g = gradient_of_function(f, tmp); %plot([tmp(1),tmp(1)+g(1)*0.02],[tmp(1),tmp(2)+g(1)*0.02]); quiver(tmp(1),tmp(2),g(1)*0.02,g(2)*0.02) end %quiver(X,Y,DX,DY) %wolfe(f, [1;1], 1) x0 = [-1; -1]; [x, v, h] = gradient(f, x0) [x, v] = fminunc(f, x0) for i=2:length(h) tmp1 = h(:,i-1); tmp2 = h(:,i); quiver(tmp1(1),tmp1(2),tmp2(1)-tmp1(1),tmp2(2)-tmp1(2), 0, 'r','LineWidth',2) end
alpha = 0.2000 alpha = 0.2000 alpha = 1 alpha = 1 alpha = 0.2000 alpha = 1 alpha = 0.2000 alpha = 1 alpha = 0.2000 alpha = 1 alpha = 0.2000 x = 0.7960 1.2038 v = 1.7974 h = Columns 1 through 7 -1.0000 -1.0271 -0.4515 0.6432 0.8185 0.7755 0.7859 -1.0000 0.4778 0.2130 1.0809 1.1279 1.1801 1.2059 Columns 8 through 12 0.7925 0.7963 0.7956 0.7959 0.7960 1.2007 1.2024 1.2033 1.2039 1.2038 Warning: Gradient must be provided for trust-region algorithm; using line-search algorithm instead. Local minimum found. Optimization completed because the size of the gradient is less than the default value of the function tolerance. x = 0.7961 1.2039 v = 1.7974