Nyzo version 528 (commit on GitHub) corrects an issue with the block-freezing process, and it also corrects a separate sentinel initialization issue.
This version affects the verifier, sentinel, and client.
Recent performance improvements in the balance-list manager caused two different initialization issues. One of these issues potentially affected all three run modes. The other issue only affected new sentinels or sentinels that had been offline for more than 4 cycles.
BalanceListManager.updateFrozenEdge() was modified to accept the frozen-edge list as an argument. This method is only called from two places in BlockManager. Previously, the frozen-edge balance list was registered just before this method was called. Then, this method would retrieve the frozen-edge list by getting the frozen-edge block and the calling balanceListForBlock(). In some cases, however, balanceListForBlock() would return null, even though a balance list for the appropriate height had just been registered.
The balanceListForBlock() method needs to be re-examined and further refined, as its failure was the root cause of this problem. However, any changes made to that method will have further-reaching effects in the code than this change and will need to be considered more carefully and tested more thoroughly. This change is simple: we know what balance list the updateFrozenEdge() method needs, and we know that it is unable to obtain that balance list in some instances. We are now passing the balance list directly into the method instead of using a registration/lookup process that is more complicated and known to sometimes fail.
In the two places where BalanceListManager.updateFrozenEdge() is called, both in BlockManager, the frozen-edge list is now provided as an argument. The call to BalanceListManager.registerBalanceList() has been eliminated in both cases, as the balance-list map is cleared and rebuilt in the updateFrozenEdge() method.
The other correction is in the Sentinel class. Balance lists near the retention edge can no longer be fetched from verifiers. The sentinel now starts working from the frozen edge if it needs a balance list.
The unavailability of balance lists near the retention edge results in a significantly longer time that a new sentinel must wait before it can protect its verifiers. Before, the sentinel had to fetch 4 cycles of blocks before it could protect verifiers. Now, it must wait for 4 cycles of blocks to be created. At the current cycle size of 1181, this takes just over 9 hours. In future versions, a method for fetching historical blockchain data will be added, and this 9-hour wait should be reduced to just a few minutes.


@(url: String, token: String, name: String, email: String, name_project: String, name_event: String, date_event: String)
Hello @name,
I would like to inform you that you planned to start writing '@name_event' of your project '@name_project' on @date_event.
If you would like to go and see your planning for this project, or maybe would like to adjust is, please head over to this link.
Have a nice day,
your Personal Assistant


Whether it's to race your friends, shoot zombies or enjoy some of the classics, we have everything you could need in our one-of-a-kind arcade. Our arcade is loaded with award-winning games and an amazing atmosphere. All of this is located in our state-of-the-art facility, so come on over and see for yourself just why everyone has been talking about FunWorks Warehouse.


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Click icon to view source code on GitHub.
❐ Click icon to go full screen & get link to individual file.
Tooltips provide file size.
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--- layout: page title: About date: 2016-05-28 19:46:59.000000000 +02:00 type: page published: true status: publish categories: [] tags: [] meta: _wp_page_template: default author: login: saselli email<EMAIL_ADDRESS>display_name: rantonels first_name: '' last_name: '' ---
I'm a string theorist with a special place in my heart for holography and AdS/CFT. I dump my thoughts here. There is no guarantee of any kind of schedule.


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I was born in Chyenne, Wyoming but I have lived in Japan for 4 years and Israel for 2 years.
I now current attend the University of Washington in Electrical Engineering.
Find out more information at my personal website here.


On 15 July 2017, 5 new articles were published in 5 journals.
Transparency to Curb Corruption? concepts, Measures and Empirical Merit
Recurrent Childhood Animal Cruelty and Its Link to Recurrent Adult Interpersonal Violence
Autonomy and Responsibility As A Dual Construct
Considering the Role of Food Insecurity in Low Self-Control and Early Delinquency


CONTACT US ! Address: 9870 St Vincent Place, Glasgow, DC 45 Fr 45. Phones: +1<PHONE_NUMBER> +1<PHONE_NUMBER> Email<EMAIL_ADDRESS>THANKS FOR CHOOSING US


lightGallery
A customizable, modular, responsive, lightbox gallery plugin for jQuery.
A customizable, modular, responsive, lightbox gallery plugin for jQuery.
A touchable jQuery lightbox.
Lightbox is small javascript library used to overlay images on top of the current page. It's a snap to setup and works on all modern browsers.
A customizable lightbox plugin for jQuery.
A simple jQuery image viewing plugin.


Preparation time
less than 30 mins
Cooking time
10 to 30 mins
Serves
Serves 4
Poutine is a Canadian dish of chips and creamy curd cheese covered in a meaty gravy. Here it's served alongside butter-fried steak for top-notch comfort food!
Preheat the oven to 160C/140C Fan/Gas 3
To make the oxtail gravy, heat the dripping in a large casserole dish over a medium heat. Add the ribs in batches and seal until brown all over. Add the onion, carrot, celery and garlic and cook for 5–6 minutes. Add the red wine and simmer until the volume has reduced by half. Add the stock, return the ribs to the pan, cover and place in the oven for an hour.
Meanwhile, roast the oxtail in a frying pan and add it to the casserole pan and continue to cook for a further 3 hours. Remove from the oven and carefully flake the meat using a fork and stir. It should be a little lumpy.
Rinse the cut potatoes in plenty of cold water to remove the startch and place in a saucepan of boiling salted water and cook for 7 minutes. Drain in a colander and allow to dry.
Put the beef dripping in a deep-fat fryer or a large, deep, heavy-bottomed saucepan. Heat to 140C. (CAUTION: Hot fat can be dangerous. Do not leave unattended.) Fry the chips for 4–5 minutes, ensuring they are submerged. Remove from the fryer and drain. Shake the chips to chuff the edges and allow to cool completely in the basket.
Meanwhile, season the steaks all over with salt and pepper. Heat the dripping in a large cast-iron frying pan over a high heat. Once it is foaming, add the steaks and cook for 5 minutes, or until crusty on the bottom. Turn the steaks and add half the butter, the thyme, rosemary and garlic. Cook over a high heat, basting the steaks with the foaming butter, garlic and herbs, for 5–7 minutes, or until medium-rare. Transfer the steaks to a plate and leave to rest for 10 minutes. Meanwhile, remove the herbs from the pan and keep frying the garlic until soft and golden, adding the remaining butter.
Reheat the deep-fat fryer or heavy-bottomed saucepan to 190C. (CAUTION: Hot fat can be dangerous. Do not leave unattended.) Fry the chips again for 4–5 minutes, or until golden.
Strain 2 ladles of oxtail gravy into a pan places over a medium heat and simmer until thickened and heated through.
Cut the steaks off the bone and slice the meat across the grain. Serve with the fried garlic and the resting meat juices poured over. Toss the cooked chips with the cheese curds and place in a serving dish, pour over the gravy and serve with the steaks.


This is Pat and his family. He resides here in Seattle with his wife and two kids. He started his business right in his kitchen bake in 2013. In 2015 he got a "GoFundMe" started after many years of postive feed back from selling his cookies at craft conventions and having a little stand at Pike Place Market every Sunday. He was a one man show with the help of his wife on occasion. His business now is growing trumendously! With the local support from businesses near. You can find his cookies at most local coffee shops here in Seattle and few gift shops in Pike Place Market.
BUY SOME MERCH!!
Tee: $20


Ephesians 5:14
Wherefore he saith, Awake thou that sleepest, and arise from the dead, and Christ shall give thee light.


Twenty-seven new confirmed cases were reported from 4 to 10 December in the ongoing Ebola virus disease (EVD) outbreak in North Kivu and Ituri provinces. The confirmed cases in this week were reported from seven health areas in four health zones: Mabalako (67%, n=18), Beni (22%, n=6), Mandima (7%, n=2), and Oicha (4%, n=1). This is a substantial increase in the number of reported cases, compared to the average of seven confirmed cases for the previous three weeks. All of these cases are linked to three transmission chains. Of these 27 new cases, 20 (74%) were registered contacts, including 10 (37%) who were being followed up regularly. One community death was reported in an individual whose body was intercepted by response personnel at a point of control as they were being transported from Lwemba Health Area; a safe and dignified burial was performed. All other cases were referred to an Ebola Treatment Centre.
The majority of these new cases are linked to a single chain of transmission, in which one individual was a potential source of infection for 17 people. This is the second documentation of EVD illness in this individual within a 6-month period. An investigation is ongoing to understand the circumstances around this case. Among the possibilities being investigated are reinfection and relapse. Reinfection would mean a person who has recovered from EVD is infected with EVD from another person; there has never been a documented case of this. Rare cases of relapse have been documented, in which a person who has recovered from EVD gets disease symptoms again.
In the past week, there were six new cases among health workers; five of which were traditional practitioners, bringing the total number of health workers infected in this outbreak to 169 (5% of all reported cases).
In Beni and Mabalako Health Zones, the overall average percentage of contacts under surveillance in the last seven days has returned to levels seen prior to the security events in past weeks. The volume of alerts and the proportion of alerts investigated within 24 hours has made similar improvements.
In the past 21 days (20 November to 10 December), 42 confirmed cases were reported from 15 of the 71 (21%) health areas within four neighbouring active health zones in North Kivu and Ituri provinces (Figure 2, Table 1): Mabalako (60%, n=25), Mandima (19%, n=8), Beni (17%, n=7), and Oicha (5%, n=2). The majority of the cases (95%, n=40) are linked to known chains of transmission. Mambasa Health Zone recently cleared 42 days without any new confirmed cases.
As of 10 December, a total of 3340 EVD cases were reported, including 3222 confirmed and 118 probable cases, of which 2210 cases died (overall case fatality ratio 66%) (Table 1). Of the total confirmed and probable cases, 56% (n=1881) were female, and 28% (n=941) were children aged less than 18 years.
*3340 confirmed and probable cases, reported as of 10 December 2019. Excludes n=173 cases for whom onset dates not reported. Data in recent weeks are subject to delays in case confirmation and reporting, as well as ongoing data cleaning. Other health zones include: Alimbongo, Ariwara, Biena, Bunia, Butembo, Goma, Kalunguta, Katwa, Kayna, Komanda, Kyondo, Lolwa, Lubero, Mambasa, Manguredjipa, Masereka, Musienene, Mutwanga, Mwenga, Nyankunde, Nyiragongo, Pinga, Rwampara, Tchomia, and Vuhovi.
**Total cases and areas affected during the last 21 days are based on the initial date of case alert and may differ from date of confirmation and daily reporting by the Ministry of Health.
For further information about public health response actions by the Ministry of Health, WHO, and partners, please refer to the latest situation reports published by the WHO Regional Office for Africa:
WHO continuously monitors changes to the epidemiological situation and context of the outbreak to ensure that support to the response is adapted to the evolving circumstances. The last assessment concluded that the national and regional risk levels remain very high, while global risk levels remain low.
WHO advises against any restriction of travel to, and trade with, the Democratic Republic of the Congo based on the currently available information. Any requirements for certificates of Ebola vaccination are not a reasonable basis for restricting movement across borders or the issuance of visas for travellers to/from the affected countries. WHO continues to closely monitor and, if necessary, verify travel and trade measures in relation to this event. Currently, no country has implemented travel measures that significantly interfere with international traffic to and from the Democratic Republic of the Congo. Travellers should seek medical advice before travel and should practise good hygiene. Further information is available in the WHO recommendations for international traffic related to the Ebola Virus Disease outbreak in the Democratic Republic of the Congo.
For more information, please see:


Tiptoe Tiles
Room Code:
WXYZ
Player 1:
24
Player 2:
20
Player 3:
18
Player 4:
28
Player 5:
30
Player 6:
12
It's Mitch's Turn
Your Tiles


The class xml_io contains a descriptor list that represents structured data that can be encoded to or decoded from XML strings. Member functions to decode XML into this list, encode XML from this list are available, as well as functions to build up the list, or traverse the list to read the decoded data.
Each element in the descriptor list has a unique numeric ID.
The ID XML_ROOT_TAG (0xffff) represents the root element. It can contain no or a single element. Typically the root contains an object that holds all the data.
The following functions put an element into the XML structure. Note: The elements cannot be added in any sequence. An element is considered closed, when to its parent another element was added. Then you cannot add further elements to this.
Example:
word root = xml.add_tag(XML_ROOT_TAG,"root");
word base = xml.add_tag(root, "tag1");
xml.add_attribute(base, "attribute1")
xml.add_tag(root, "tag2");
// this does not work, because tag base is already closed
// by adding tag2 to its parent
xml.add_attribute(base, "attribute2");
Note: There is a size limitation for encoding, decoding XML. A maximum number of 4096 elements is supported. In this respect as element a tag, an attribute, an attribute value and a content value counts as element. So an XML <tag attribute="attribute-value"/> consists of 3 elements.
Parameters:
The following functions can be used for encoding.
Functions for decoding:
Elements can be accessed by name using the following functions.
Parameters:
To navigate in the element tree, the following functions are available
For a single XML data structure there is the above limitation of the maximum number of elements. Larger XML can be read, if they consist on some level of a list of sub structures which are limited in size. Such a XML can be decoded block by block. Decoding can be started with the first block. It will end with an incomplete XML. All sub tags which are complete, can be read. Then there are functions to remove all the complete blocks from the data, freeing space in the buffer, which can be filled with the next block and decoding can continue. For this the following functions are available:
Example:
class xml_io xml(buffer); // the xml_io class used for decoding
...
byte error = xml.read(); // decode the data copied to buffer
// do handling of all complete elements
...
if (error == XML_ERROR_INCOMPLETE) {
char b[XML_CHUNK_SIZE];
class xml_io save(b); // temporary xml_io to copy the data
// save the incomplete data to temporary xml_io base is the tag containing the
// tags of the limited size
xml.save_incomplete(base,step,save);
// in the temporary structure, navigate to the tag containing the tags of
// limited size. In this example it is test/steps
base = save.get_tag(0xffff,"test");
base = save.get_tag(base,"steps");
// get the handle of the first (incomplete) tag
step = save.get_first(XML_TYPE_TAG,base);
// copy back the saved XML to the original xml_io
save.save_incomplete(base,step,xml);
// restore base to test/steps and step to the first incomplete tag, so
// handling of the data can continue here
base = xml.get_tag(0xffff,"test");
base = xml.get_tag(base,"steps");
step = xml.get_first(XML_TYPE_TAG,base);
// get end of data in the buffer, to identify were new data has to be copied
// to
offset = xml.ofs();
}
// copy the data into the buffer, starting at offset. Make shure it is 0 terminated.
// after that handling of the next block can start with xml.read() again


{% extends 'common/base.html' %} {% load static %} {% block content %}
Uploaded by:{{ uploader.user_id }}
Description: {{ game.description }}
{{ review.text }}


It is an easiest example. We create a component in procedure without using template or binding.
You can open up the console and set app.what to a different value to see what will happen.


--- layout: page title: Articles tags: [audio, acoustics, technology, media, music, embedded, blog] comments: false ---


Toggle Button Bar can have only one selected item, and it has first item selected by default if none selected. This control is derived from AtomListBox, but it applies its own styles.


{% extends "base.html" %} {% block title %}
Medieval catalogues > SECULAR INSTITUTIONS: Secular Colleges > London, St Peter-upon-Cornhill > Leland, autumn 1534 × spring 1535
{% endif %}4 identified entries found.
All data was derived from the List of Identifications by Professor Richard Sharpe.
{% if not printing %}
A key to codes used in the List is available (opens in new tab).{% endif %}


My first experience with speed was pretty shitty (no pun intended) and occurred near the end of my sophomore year of high school. One of my friends called me and said he had heard I was curious about trying it. I verified his own curiosities and invited him over for the night.
He arrived at my house at 8 pm while my mother was asleep and my sister was out of the house.  Then I was interrupted by a phone call from a girl who had a crush on me and was extremely intoxicated. Putting that aside, we smoked a bowl before my sister got home and I was disappointed when I did not feel anything (that's what I thought).
When my sister left again around 10 pm we smoked another bowl or two and I started to play guitar. Matt turned to be a very good singer and he started to sing along to whatever I was playing. I began to feel very creative and euphoric. The music felt so good and his voice was beautiful. We smoked and played for a few hours until I decided it was too loud.
I started getting phone calls from the drunk girl again around 11 pm. She didn't stop calling until about 1 am and every time I talked to her I felt very nervous and anxious to get off the phone. I told my friends I wasn't feeling anything and kept asking Matt to pack another bowl.
We smoked until 7 am in the morning. I was convinced the whole time that I was not feeling the effects. I kept asking for more and agreed to pay Matt $10 next time I saw him. Most of my time was spent lying on the bed trying to be quiet. I was shaking very badly and sweating a lot.  Looking back, I was completely paranoid the whole time and was alarmed by the slightest of noises…that I was probably imagining anyway.
I did not eat the next day and had a terrible comedown that left me feeling both drained and depressed.  Until someone brought it up while we were hanging out, drinking some rum about three months later. We both denounced the drug for about two minutes before we decided we felt like doing it again.
The next week I was at his house with some friends when he told me to go to the bathroom.  Snorting it burned very badly for about two minutes. I felt on top of the world that whole day and had a deep conversation with my mother the entire ride home. Everything made me very happy and the comedown was not that bad.
It's been almost three months since that day and I have not stopped. Speed is pretty fun and enhances (not fuels) my musical creativity. I stopped buying it two weeks ago but I keep getting it for free anyway so that's pretty cool. I can't stand to smoke the stuff. Smoking seems like an extremely addicting method and after a few tries I still find it repulsive.
I find it very pleasing to mix speed with alcohol. My first week with the drug I was waiting for our dealer to arrive at a friend's house. A guy there rolled a joint and shared it with me. We then took a few shots of Cabo Wabo and sat outside to have some cigarettes. The 'man' showed up and we did lines which perfected our moods. The high from the weed went away very quickly but left me in a relaxed state while feeling lit and euphoric at the same time.  When I get too drunk while I'm on it I get very loud and extremely active. Other than that I'm just fine and have wonderful conversations with my friends.
Obviously this is a serious drug and I do take it seriously. Pretty fun to use but I've seen at least four guys I know turn into nasty looking fellows. All four of those guys are smokers and one is LITERALLY a psycho, but that happened to him before he started tweaking.  Also, everyone should learn how to play a musical instrument! Thanks for reading. -Pac-Man
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


An R interface for the remote file hosting service 'Box' (<https://www.box.com/>). In addition to uploading and downloading files, this package includes functions which mirror base R operations for local files, (e.g. box_load(), box_save(), box_read(), box_setwd(), etc.), as well as 'git' style functions for entire directories (e.g. box_fetch(), box_push()).
| Version: | 0.3.4 |
| Imports: | httr (≥ 1.1.0), httpuv, assertthat, stringr, dplyr, digest, bit64, rio, mime, utils, stats |
| Suggests: | testthat, knitr, rmarkdown |
| Published: | 2017-01-12 |
| Author: | Brendan Rocks [aut, cre], Ian Lyttle [ctb] |
| Maintainer: | Brendan Rocks <foss at brendanrocks.com> |
| BugReports: | https://github.com/brendan-r/boxr/issues |
| License: | MIT + file LICENSE |
| URL: | https://github.com/brendan-r/boxr/ |
| NeedsCompilation: | no |
| Materials: | NEWS |
| In views: | WebTechnologies |
| CRAN checks: | boxr results |
| Reference manual: | boxr.pdf |
| Vignettes: |
boxr: A high-level R interface for the box.com API |
| Package source: | boxr_0.3.4.tar.gz |
| Windows binaries: | r-devel: boxr_0.3.4.zip, r-release: boxr_0.3.4.zip, r-oldrel: boxr_0.3.4.zip |
| OS X binaries: | r-release: boxr_0.3.4.tgz, r-oldrel: boxr_0.3.4.tgz |
| Old sources: | boxr archive |
Please use the canonical form https://CRAN.R-project.org/package=boxr to link to this page.


Home
Kansas :: Locate The Law
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Last month I mentioned, in passing, Christopher Smart's poem Jubilate Agno. I am happy to report that a reading of the complete work, all three hours of it, by Mr Key and the spoken word performer Germander Speedwell, has now been recorded and will be broadcast (and podcast) by Resonance104.4FM on Thursday 27th December at 12 noon GMT. Further details here and here.
UPDATE : The podcast is permanently available here. I humbly suggest that you download it and set aside three hours every day to listen to it in full.


{% extends 'base.html' %} {% load staticfiles %} {% load i18n %} {% block title %}{{ repo.name }}{% endblock %} {% block head %} {% endblock head %} {% block main %}
{{ repo.name }} has been successfully connected to this project. This window will automatically close in 5 seconds.
{% endblock main %}


Vehicle
DeLorean
Millennium Falcon
Carreta
Vehicle
DeLorean
Millennium Falcon
Millennium Falcon
init ()
focus ()
change ()


News
You may be a RPLS Today, but you'll be a Land Surveying Legend Tomorrow
find out more
Employment
Finding a Job in Land Surveying has Never Been so Social
play video
Surveying History
Land Surveying Community preserving Surveying History for Future Generations
find out more
You may be a RPLS Today, but you'll be a Land Surveying Legend Tomorrow
Finding a Job in Land Surveying has Never Been so Social
Land Surveying Community preserving Surveying History for Future Generations


--- layout: post status: publish published: true title: WSIL? author: display_name: Tomas Restrepo login: tomasr email<EMAIL_ADDRESS>url: http://winterdom.com/ author_login: tomasr author_email<EMAIL_ADDRESS>author_url: http://winterdom.com/ wordpress_id: 709 wordpress_url: http://winterdom.com/2002/06/wsil date: '2002-06-26 16:30:04 +0000' date_gmt: '2002-06-26 16:30:04 +0000' categories: - Web Services tags: [] comments: [] ---
Timothy Appnel on WSIL.
OK, so I must be missing something.... How's this different from the old DISCO again? Can anyone explain? Simon?


Pepys Platform types
Attack Craft
Commercial Aircraft
Fishing Vessel
High speed craft
Law enforcement
Medical
Merchant
Naval - aircraft carrier
Naval - amphib
Naval - auxiliary
Naval - cruiser
Naval - destroyer
Naval - ExCon
Naval - fixed wing
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Naval - minewarfare
Naval - miscellaneous
Naval - patrol
Naval - rotary wing
Naval - submarine
Naval - survey
Passenger vessel
Pleasure Craft
Research vessel
Sailing vessel
Search and Rescue
Offshore support
Civilian - uncrewed surface vehicle
Civilian - uncrewed underwater vehicle
Civilian - uncrewed air vehicle
Naval - uncrewed surface vehicle
Naval - uncrewed underwater vehicle
Naval - uncrewed air vehicle


--- title: Make Foolproof Pasta Carbonara Without Scrambling layout: post date: 2015-12-24 16:23:54 -0500 type: post parent_id: 0 image: assets/images/post-img/20151118-spaghetti-carbonara-vicky-wasik-25-768x576.jpg status: publish categories: - Pasta - Italian meta: _wpas_done_all: 1 _thumbnail_id: 299 _edit_last: 1 _posts_by_tag_page_fields: a:2:{s:12:"widget_title";s:0:"";s:11:"widget_tags";s:0:"";} _wp_old_slug: dinner-in-20-make-foolproof-pasta-carbonara-without-scrambling-eggs-serious-eats _wpghs_github_path: _posts/2015-12-24-pasta-carbonara.md _sha: e0052237377e48ec0032a5984fb4f2250be92f12 author: login: mkelley email<EMAIL_ADDRESS>display_name: Mike first_name: Mike last_name: Kelley remote_url: http://www.seriouseats.com/2015/12/how-to-make-the-best-carbonara-sauce-spaghetti-pasta.html category: - Italian - Pasta ---
Pasta with carbonara sauce is a fundamentally simple and easy dish, made by coating pasta in a rich, creamy sauce of eggs, cheese, pork, and black pepper. The challenge is in combining the right ingredients for a sauce with a perfectly silky texture, and not accidentally scrambling those eggs in the process. Here's how to do it.
Source: Dinner in 20: Make Foolproof Pasta Carbonara Without Scrambling (Eggs) | Serious Eats


Fernando Juan Martin Amaya Moneza
Systems and Informatics Engineer
Martin Amaya Monteza
Help Desk Analyst
I describe myself as a skilled person to handle new work environments, friendly and friendly, team-oriented and under pressure keeping in mind the proposed objectives.
Design and programming
HTML5
CSS3
Javascript
Python
C++
Skills
Adaptability
Ability to solve problems
Positive attitude
Confidence
Honesty
Hobbies
Listen to music (varied) Watch fiction movies Play guitarProject 1


Data Science Boot Camp
1
Welcome
I Core theory
2
Principles of data science
2.1
What is data science?
2.2
What is the data life cycle?
2.3
What is a pipeline?
2.4
Data science ‘in the wild’
2.5
The reproducibility crisis
3
Visualizing data
3.1
Bad examples
3.2
Good exaples
3.3
Edward Tufte
3.4
Grammar of graphics
3.5
Design principles
3.6
Plots & power: the politics of plotting
4
Writing about data
5
Data ethics
6
Visualizing data
6.1
Bad examples
6.2
Good exaples
6.3
Edward Tufte
6.4
Grammar of graphics
6.5
Design principles
6.6
Plots & power: the politics of plotting
7
Writing about data
8
Data ethics
9
Getting started in R
9.1
Downloading R and RStudio
9.2
Running R code: basic math & operators
9.3
RStudio: Tour, workflow, & scripts
9.4
Objects: variables and vectors
9.5
Calling functions
9.6
Base plots
9.7
Packages
9.8
ggplot
10
Working with data in R
10.1
Project workflows
10.2
Importing data
10.2.1
Working directories
10.2.2
Reading in data
10.3
Dataframes: exploration & summarization
10.4
Data wrangling
10.4.1
Data transformation, filtering, & grouping
10.4.2
The tidyverse and tibbles
10.4.3
Transformation with dplyr
10.5
Exploratory Data Analysis
10.5.1
Exploring distributions
10.5.2
Variable types & statistics
10.5.3
Descriptive statistics
10.6
Significance statistics
10.6.1
Comparison tests
10.6.2
Correlation tests
10.7
Customizing plots
10.7.1
Base plots
10.7.2
ggplot
10.8
Managing project files
10.9
Formatting your own data
11
Your core R toolkit
11.1
Uniting and joining data
11.2
For loops
11.3
Writing functions
11.4
Working with text
11.5
Cleaning messy data
11.6
Working with factors
11.7
Matrices & lists
11.8
Pipes
11.9
Exporting data & plots
11.10
Web-based data
11.11
For loops
12
Interactive dashboards in R
12.1
Shiny dashboards
12.2
Data entry apps
13
Databases
13.1
Intro: What, why, when (and when not)
13.2
Platforms: PostgreSQL, mySQL, SQLite, etc.
13.3
Alternatives: NoSQL
13.4
Practical: spinning up a local DB
14
Reproducible research & documentation in R
14.1
Introduction to Rmarkdown
14.2
Standards for tables
14.3
Standards for figures
15
Version control and teamwork
15.1
What is version control?
15.2
Github
15.3
Standard git operations
15.4
Other git platforms
16
Scientific writing techniques
17
Advanced skills
17.1
Geographic computing & GIS
17.2
Mapping
17.3
Statistical modeling
17.4
Apply family
17.5
Iterative analyses
17.5.1
Randomization
17.5.2
Simulations
References
Databrew LLC
Data Science Boot Camp
Module 9
Getting started in R
9.1
Downloading R and RStudio
9.2
Running R code: basic math & operators
9.3
RStudio: Tour, workflow, & scripts
9.4
Objects: variables and vectors
9.5
Calling functions
9.6
Base plots
9.7
Packages
9.8
ggplot


We enhance content!
- evolve turn-key communities
- revolutionize sticky schemas
- monetize vertical paradigms
- envisioneer web-enabled ROI
- expedite leading-edge ROI
- engineer world-class architectures
- optimize dynamic niches
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- aggregate next-generation ROI
- harness viral deliverables


Learning-Based Performance Specialization of Configurable Systems
A large scale configurable system typically offers thousands of options or parameters to let the engineers customize it for specific needs. Among the resulting many billions possible configurations, relating option and parameter values to desired performance is then a daunting task relying on a deep know how of the internals of the configurable system. We propose a staged configuration process to narrow the space of possible configurations to a good approximation of those satisfying the wanted high level customer requirements. Based on an oracle (e.g. a runtime test) that tells us whether a given configuration meets the non-functional requirements (e.g. speed or memory footprint), we leverage machine learning to retrofit the acquired knowledge into a variability model of the system that can be used to automatically specialize the configurable system.
SPLC’16: In the repository https://github.com/learningconstraints/SPLC-16, you will find all the required artefacts to replicate the experiments included in our paper submitted to the 20th International Systems and Software Product Line Conference. The goal was mostly to avoid faulty configurations and correct a video generator developed in the industry. The oracle was simply a yes/no procedure.
In RR’17, we have considered performance qualities. The oracle is parametrized with an objective value that users of our specialization method can control. Hence our proposal can be seen as a generalization of the idea originally proposed in SPLC’16. Moreover our empirical results cover more systems and classification metrics while taking training sizes and objective values into account https://github.com/learningconstraints/ICSE-17.
Objective of this page: Our RR’17 results show that, for many different kinds of objectives and performance qualities, the approach has interesting accuracy, precision and recall after a learning stage based on a relative small number of random samples. This page describes how we gather and process input data, how we compute classification metrics. Furthermore, all plots, heatmaps, and figures can be visualized. We aim here to supplement our original material (it is impossible to include all relevant figures we have generated!) and also support an independent verification and replication of our study. We also aim to provide a practical reading grid of classification metrics for users of our method.
A research report is also freely available online https://hal.archives-ouvertes.fr/hal-01467299:
+Paul Temple, Mathieu Acher, Jean-Marc A Jézéquel, Léo A Noel-Baron, and José A Galindo. "Learning-Based Performance Specialization of Configurable Systems" (2017), Research report, University of Rennes 1 (IRISA/Inria).
## Empirical Study ## Empirical Study
Empirical Study
Subject systems
We performed our experiments on a publicly-available dataset used in previous works. The dataset includes sets of configurations of real-world configurable systems that have been executed and measured over different performance metrics (more details hereafter). Configurable systems come from various domains and are implemented in different languages (C, C++ and Java). The number of options varies from as little as ten, up to about sixty; options can take Boolean and numerical values. The two last columns show, first, the total number of different possible configurations existing for the system and, last, the number of configurations that have been generated and measured in previous works (“All” meaning all different configurations have been measured). It should be noted that most benchmarks measure a single performance value, except for x264 where benchmarks evaluate 7 performance metrics.
Experimental setup
For each subject system, we have randomly selected a certain number of configurations from the ones available in the public dataset: our ML method operates over a training sample to learn a separating function. All remaining configurations form the test sample, i.e., it is the whole configuration set minus the training set. They are used to verify whether the classification of our ML is correct. For instance, the public dataset contains 69,000 different configurations for x264. If we used 500 of them to learn constraints, then we evaluate the classification quality of ML on the remaining 68,500 configurations.
For each configurable system, we make vary the sample size one by one, from 1 to the total number of configurations. We make vary the objective value between the lower and upper bounds of the performance measured in each dataset. We notice that threshold values have direct influence on the percentage of acceptable configurations. For instance, for x264, an execution time (speed) threshold less than 100s leads to only 21.4% of acceptable configurations. We thus vary the objective value from 0% to 100% of acceptable configuration with steps of 5%.
Classification measurements
We consider that configurations are acceptable (resp. non-acceptable) w.r.t a performance objective.
Our learning-based technique can introduce errors in the following cases:
- a configuration is valid (and thus classified as acceptable) despite being non-acceptable â€“ the system is under-constrained and unsafe;
- a configuration is not valid (and thus classified as non-acceptable) despite being acceptable (the system is over-constrained and lacks flexibility).
We expect to observe such cases:
- a configuration is valid and correctly classified as acceptable;
- a configuration is non-valid and correctly classified as non-acceptable.
In our case, positive class (also called class-1) corresponds to non-acceptable configurations that we want to discard. The negative class (also called class-0) corresponds to acceptable configurations that we want to keep. There are four possible situations represented in a confusion matrix (see Figure):
- True Positives (TP) and True Negatives (TN) are on the main diagonal and represent the set of correctly classified configurations;
- False Positive (FP) and False Negatives (FN), on the contrary, represent classification errors
For obtaining TP, TN, FP, and FN, we confront the classifications made by ML in the training set with the oracle decisions available in the testing set. Based on a confusion matrix, we can compute several classification metrics.
We rely on standard metrics used for binary classification problems. We used 5 metrics since they give a different and complementary perspective on the “quality” of our learning-based method.
Classification metrics
- Accuracy measures the portion of configurations accurately classified as acceptable and non-acceptable. It gives a global classification “performance” of the learning phase.
However accuracy alone can have limited interest since it hinders important phenomena. In the example (see Figure), you can have a high accuracy despite the clear limits of the learning phase of identifying non-acceptable configurations (precision is 29% and recall is 40%). So why is there a high accuracy and a low precision and recall? Because the class of “acceptable configurations” is much more represented (800+100=900 out of 1000). It is too easy to have a high accuracy with basically no specialization.
On the one hand, accuracy gives a global classification measurement of our method. On the other hand, accuracy is unable to show the weaknesses of a naÃ¯ve classification strategy (e.g., “keeping everything as acceptable”)
Intuitively, in our specific problem, we also need to measure the precision and recall of our classification. It can give better insights on our ability to (1) discard non-acceptable configurations and (2) to keep acceptable configurations.
-
Precision: measure our ability to correctly classify non-acceptable configurations. In our example, precision is 40 / (100 + 40) = 40 / 140 = 29%. The learning wrongly classifies a large portion of non-acceptable configurations.
-
Recall (also called true positive rate): measure our ability to comprehensively identify non-acceptable configurations. In our example, recall is 40 / (40 + 60) = 40%. Here, the learning misses a large portion of non-acceptable configurations.
A low recall may be problematic, since numerous non-acceptable configurations have not been classified as such: Users may still choose unsafe configurations. A low precision may be problematic as well since numerous non-acceptable configurations are actually acceptable: Users may lose some flexibility.
Precision and recall measure the ability of our system to classify non-acceptable configurations. However it says nothing on how safe and flexible is our resulting specialized configurable system:
- How many valid configurations of our resulting specialized configurable system are truly acceptable? A low proportion and users will perform numerous configuration errors since the system is not enough safe.
- How many configurations have been unnecessarily removed whereas they should still be reachable? A low proportion and users will have little configuration choices at their disposal.
We thus need two additional metrics. Specificity (also called true negative rate) quantifies how flexible is the specialized system. In the example, (800 / 800 + 100) = 89% of configurations will be reachable in the new specialized system.
NPV (also called negative predictive value) quantifies how safe is the specialized system. In In the example, (800 / 800 + 60) = 93% of configurations will be safe in the new specialized system.
How does a learning-based method compete with a non-learning based method?
The example shows that, despite a low precision (29%) and recall (40%), the resulting specialized configurable system is quite safe (93%) and flexible (89%). Our learning phase misses some opportunities and introduces some errors, but the overall process may still be of interest since we have a safer system than originally and the flexibility is arguably reasonable.
Without a specialization, all configurations are classified as acceptable, and the metrics are as follows in our specific example:
- accuracy is 90%
- precision is ~0
- recall is 0
- specificity is always 100% (the system is highly flexible)
- NPV is 90% (the system is quite safe), but it is less than 93%
It shows an improvement of 3% for the safety of the system. Obviously, it is just an example. The potential of our technique mostly resides in cases the performance objective leads to numerous non-acceptable configurations: We can learn and identify them, leading to safer configurable systems.
In general, a non-learning based method has always a precision is equal to 0, recall is equal to 0, specificity is equal to 100%. Hence we cannot beat a non-learning in terms of flexibility, since it keeps everything as acceptable.
However the safety of the resulting system may be very low. It is equal to: NPV_nonlearning = TN / (TN + TP) = number of acceptable configurations / number of configurations in total. We can thus compute a new metric, called negNPV, that is equal to NPV - NPV_nonlearning. The higher negNPV, the safer is our specialized system comparatively to a non-specialized system. Our empirical results confirm that we are safer in the vast majority of cases, even for a small training set.
Similarly, we can compute negAccuracy (difference of accuracy between a non-specialized method and our learning-based method).
Reading grid of classification metrics
To sum up:
- there are three classification metrics that measure our ability to classify (accuracy, precision, recall).
- accuracy gives a global classification “performance” of the learning phase
- accuracy alone has limitations since it does not quantify the classification of non-acceptable configurations; precision and recall should be used in complement
- high accuracy does not imply high precision or high recall: it is a theoretical statement (see formulae) and empirical results confirm there is no correlation between accuracy, precision and recall
- NPV and specificity characterize how safe and flexible is the resulting specialized system (precision and recall cannot)
- a high (resp. low) specifity means a high (resp. low) flexibility of the resulting system
- a high (resp. low) NPV means a high (resp. low) safety of the resulting system
- a low precision or low recall does not imply a low NPV or specificity. For instance, the classification metric can be low and, yet, leads to a highly safe and flexible system
A “specializer” (i.e., user of our specialization method) can employ:
- precision and recall for measuring the quality of the classification and perhaps identifying some “missing” opportunities. Overall it can drive the learning strategy: for example, with more (non-acceptable) configurations in the training set, precision and recall can perhaps be improved.
- NPV and specificity for measuring the safety and flexibility of the resulting system. In practice, there is (sometimes) a trade-off to find between flexibility and safety, depending on the specificities of the configurable system and its usage. Let us take two examples. On the one hand, critical configurable systems have to be highly safe: Leaving a large portion of non-acceptable (and thus unsafe) configurations is not suited. On the other hand, there are configurable systems for which flexibility is the key since it attracts lots of customers. The extent to which safety and flexibility can be degraded is the decision of a “specializer”: NPV and specificity help in such a task.
An important point is that some performance objectives (w.r.t. execution time, energy consumption, etc.) can lead to a high (resp. low) percentage of non-acceptable configurations. It can complicate the learning phase and thus both influence accuracy, precision, and recall. The definition of performance objectives can also influence NPV and specificity. For example, it is hard to be completely safe when numerous non-acceptable configurations have to be removed. You can miss a small portion of them and, yet, it is a lot of unsafe configurations that remain within the specialized system. Another factor is the size of the training set. It may influence both accuracy, precision, recall, NPV, and specificity.
With our empirical results, we aim to further understand the effectiveness of our learning-based method w.r.t any performance objective and training set. For each configurable system, performance objective (defining the percentage of acceptable configurations in the population), and size of the training set, we have computed accuracy, precision, recall, NPV, specificity, negNPV and negAccuracy.
Reproducibility
For the sake of verifiability and reproducibility of the experiments we provide: * All heatmaps, plots, and figures. We can only present a few figures in a typical academic paper (because of the page number limit) and we aim to supplement it. * A git repository containing all data and the scripts used to: i) execute the procedures over different configurable systems; ii) generate all the different graphs, heatmaps, etc.
* Details on how we have collected and prepared data
Details of the content
The repository is available here: https://github.com/learningconstraints/ICSE-17
Heatmaps
We have generated numerous heatmaps for each configurable system.
X-axis: size of the training set Y-axis: percentage of acceptable configurations (as influenced by the objective treshold value) Classification metric: the darker, the higher is the classification metric (usually from 0 to 1) It includes accuracy, precision, recall, NPV, specificity, negNPV and negAccuracy. It is a total of 7 heatmaps per system, with variations of training sets and performance objectives.
The tables (CSV files) out of which we have produced heatmaps are also available.
Graphs/tables
In addition we have fixed the number of acceptable configurations (in the population and as defined by an objective value) to 20%, 50%, and 80% and we have ploted the evolution of classification metric (accuracy, precision, recall, etc.) with regards to the size of the training set.
- 50% means that there are as non-acceptable configurations as there are acceptable ones.
- 20% means that 80% of configurations are non-acceptable: It is a challenge to discard all of them.
- 80% means that only 20% are non-acceptable: It can seen as challenging to learn from few non-acceptable configurations
Such graphs help to visualize the effect of the size of the training set. The general trend is that the more configurations we have in the training set, more effective our technique is. https://github.com/learningconstraints/ICSE-17/tree/master/2.metric_view/results
Code
- Learning phase
- Computation of classification scores (see https://github.com/learningconstraints/ICSE-17/tree/master/2.metric_view and https://github.com/learningconstraints/ICSE-17/tree/master/3.synthetic_view)
- Depicting heatmaps or plots (https://github.com/learningconstraints/ICSE-17/tree/master/1.heatmaps)
Input data
Performance measurements of subject systems. It is available here: https://github.com/learningconstraints/ICSE-17/tree/master/datasets


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This is where I will be posting some of the games I worked on in college. I have worked with many different languages and engines to create games.
- Unity using jquery and C#
- Adobe Animate(formally known as Flash) using Actionscript
- C++
Here are the games!


Joint mean and dispersion effects models fit the mean and dispersion parameters of a response variable by two separate linear models, the mean and dispersion submodels, simultaneously. It also allows the users to choose either the deviance or the Pearson residuals as the response variable of the dispersion submodel. Furthermore, the package provides the possibility to nest the submodels in one another, if one of the parameters has significant explanatory power on the other. Wu & Li (2016) <doi:10.1016/j.csda.2016.04.015>.
| Version: | 1.0 |
| Imports: | VGAM, statmod |
| Published: | 2018-04-27 |
| Author: | Ka Yui Karl Wu |
| Maintainer: | Ka Yui Karl Wu <kykarlwu at gmail.com> |
| License: | GPL-2 |
| NeedsCompilation: | no |
| CRAN checks: | jmdem results |
| Reference manual: | jmdem.pdf |
| Package source: | jmdem_1.0.tar.gz |
| Windows binaries: | r-devel: jmdem_1.0.zip, r-release: jmdem_1.0.zip, r-oldrel: jmdem_1.0.zip |
| OS X binaries: | r-release: jmdem_1.0.tgz, r-oldrel: jmdem_1.0.tgz |
Please use the canonical form https://CRAN.R-project.org/package=jmdem to link to this page.


Learn by doing.
Check out the projects I completed in my spare time. I enjoy learning about whatever gauges my interest at any particular time.
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| Apache Struts 2 Documentation > Home > Guides > Contributors Guide > Creating and Signing a Distribution > Creating and Signing a Struts 2.3.x Distribution > Building Struts 2 - Normal release |
| Before starting release process make sure that the Docs were exported to https://cwiki.apache.org/WW/! Check the Version Notes page! |
svn co https://svn.apache.org/repos/asf/struts/struts2/trunk STRUTS_#_#_#
Edit the top pom.xml file and replace 2.x the url below with major.minor.x part of version number that's going to be released (eg. version is 2.3.10 -> 2.3.x) and rename development folder to release.
<distributionManagement> <site> <id>apache-site</id> <url>scm:svn:https://svn.apache.org/repos/infra/websites/production/struts/content/development/2.x/</url> </site> </distributionManagement>
<distributionManagement> <site> <id>apache-site</id> <url>scm:svn:https://svn.apache.org/repos/infra/websites/production/struts/content/release/2.3.x/</url> </site> </distributionManagement>
commit the change
Edit src/site/resources/archetype-catalog.xml and change version of archetypes to current $VERSION, save and commit.
Tag the release by using the "release:prepare" goal of Maven:
mvn release:prepare -DautoVersionSubmodules=true -Dusername=yourSvnUsername -Dpassword=yourSvnPassword
For a dry run, add -DdryRun=true. If you do a dry run, use mvn release:clean to clean up after you have looked at the output.
When prompted for the SCM tag name, follow this pattern: STRUTS_2_3_[PATCH_VERSION]
| For some reason, when using svn client 1.5, the release plugin might fail to tag the release, if it fails, run: svn up -r head mvn release:prepare -Dresume |
[ERROR] Failed to execute goal org.apache.maven.plugins:maven-release-plugin:2.0-beta-7:prepare (default-cli) on project struts2-parent: Unable to tag SCM
[ERROR] Provider message:
[ERROR] The svn tag command failed.
[ERROR] Command output:
[ERROR] svn: Commit failed (details follow):
[ERROR] svn: No such revision 1223030
| If you get the error message above, try to re-run mvn release:prepare -Dusername=yourSvnUsername -Dpassword=yourSvnPassword -DautoVersionSubmodules=true command again, -Dresume flag is set to true by default and the plugin will resume the release process from where it failed before. |
This step will (more information):
mvn release:perform -Dusername=yourSvnUsername
This step will (more information):
After this step the artifacts will be hosted by Nexus
If you need to run perform again, (or in a different box), do:
svn co https://svn.apache.org/repos/asf/struts/struts2/tags/$VERSION cd $VERSION mvn javadoc:javadoc deploy --no-plugin-updates -DperformRelease=true -Papache-release
Next, log in to Nexus and close staging repository.
| Repository is identified by user name and public IP address, so if in meantime your IP changed, a new staging repository will be created so you must drop the old one (check the dates!) - if IP is the same, artifacts will be uploaded to the same repository as first attempt. |
To simplify testing, the assemblies have to be moved to the /www/people.apache.org/builds/struts/$VERSION dir.
After closing repository in Nexus, check if the release files are available from staging repository as bellow:
https://repository.apache.org/content/groups/staging/org/apache/struts/struts2-assembly/$VERSION/
In order to move the assemblies login to people.apache.org and execute the following code:
#!/bin/sh # create the destination directory mkdir $VERSION cd $VERSION # get the distro wget -erobots=off -nv -l 1 --accept=zip,md5,sha1,asc -r --no-check-certificate -nd -nH https://repository.apache.org/content/groups/staging/org/apache/struts/struts2-assembly/$VERSION # rename files for f in *2-assembly*.zip* do mv $f `echo $f | sed s/2-assembly//g` done # remove unneeded files for f in struts2-assembly-*.pom* do rm $f done
After that move the assemblies directory to the builds destination with
mv $VERSION /www/people.apache.org/builds/struts/
Send a short e-mail to<EMAIL_ADDRESS>informing about the new packages and to give people enough time to test the distribution (actual bits). Wait around a week before posting Vote. If no show-stoppers reported, start a vote thread for build quality designation.
Post a release/quality vote to the dev list (and only the dev list). The example mail is on Sample announcements page.
If the vote result is for an ASF release (i.e. not test build), update site, announce. If the vote result is for GA, push to central.
After the vote, if the distribution is being mirrored (there was a favourable release vote) copy the Sources and Binaries:
ssh people.apache.org cd /www/people.apache.org/builds/struts/$VERSION cp struts-$VERSION-src.* /www/www.apache.org/dist/struts/source cp struts-$VERSION-docs.* /www/www.apache.org/dist/struts/documentation cp struts-$VERSION-lib.* /www/www.apache.org/dist/struts/library cp struts-$VERSION-apps.* /www/www.apache.org/dist/struts/examples cp struts-$VERSION-all.* /www/www.apache.org/dist/struts/binaries
If a new DTD was defined, add it to https://svn.apache.org/repos/asf/struts/site/trunk/pom.xml, in get-dtds execution section.
| The default setup on people.apache.org will leave the files and directories only changeable by the user who creates them. The last two steps will allow future releases to go smoothly. |
Log in again to Nexus and release the repository, it will be automatically replicated across Maven Repositories
See Releasing a Maven-based project for further details.
Remove the old files from under /www/www.apache.org/dist/struts/ to synchronise only the latest version with peers. All the files from /www/www.apache.org/dist/ are always mirrored to http://archive.apache.org/dist/struts/. You can use the below command:
cd /www/www.apache.org/dist/struts/ find . -type f -name "struts-2.3.x*" -exec rm -f {} \;
where x is the previous version to remove (or one more previous to keep current and one version back).
Wait 24 hours before proceeding.
If the release will fix a - hopefully yet undisclosed - security issue, it's now time to update the Security Bulletins page and add a new announcement. For a template, just check former announcements
| The below is not true any more - please wait for update! |
ssh people.apache.org export VERSION=2.#.# unzip /www/people.apache.org/builds/struts/$VERSION/struts-$VERSION-docs.zip -d ~/docs #now the docs are under ~/docs/struts-$VERSION/docs mkdir -p /www/struts.apache.org/$VERSION mv ~/docs/struts-$VERSION/docs/* /www/struts.apache.org/$VERSION chmod -R g+w /www/struts.apache.org/$VERSION
Edit /www/struts.apache.org/$VERSION/archetype-catalog.xml and update the version of artifacts
svn co https://svn.apache.org/repos/asf/struts/site/ struts-site
mvn site:site
mvn site-deploy
It takes a few hours for the site changes to go live
Revert back the site target to 2.x and commit the change
Make public available all the pages that could leak confidential information about the vulnerability. Wait or manually export the space in Confluence. Build snapshot locally and copy assembly/target/assembly/out/struts2-#.#.#-SNAPSHOT-docs.zip to people.apache.org and update /www/struts.apache.org/2.x/docs
After updating the site, be sure that the permissions are set to group writeable ($ chmod -R g+w .)
We leave this as the last step, once the artifacts have had time to sync up on the mirrors. Target it to<EMAIL_ADDRESS>and<EMAIL_ADDRESS>samples are available at Sample announcements page


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Package org.bitcoinj.net
Interface StreamConnection
-
- All Known Implementing Classes:
Peer
,PeerSocketHandler
,ProtobufConnection
public interface StreamConnection
A generic handler which is used inNioServer
,NioClient
andBlockingClient
to handle incoming data streams. Used to be called StreamParser.
-
-
Method Summary
All Methods Instance Methods Abstract Methods Modifier and Type Method Description void
connectionClosed()
Called when the connection socket is closedvoid
connectionOpened()
Called when the connection socket is first openedint
getMaxMessageSize()
Returns the maximum message size of a message on the socket.int
receiveBytes(java.nio.ByteBuffer buff)
Called when new bytes are available from the remote end.void
setWriteTarget(MessageWriteTarget writeTarget)
Called when this connection is attached to an upstream write target (ie a low-level connection handler).
-
-
-
Method Detail
-
connectionClosed
void connectionClosed()
Called when the connection socket is closed
-
connectionOpened
void connectionOpened()
Called when the connection socket is first opened
-
receiveBytes
int receiveBytes(java.nio.ByteBuffer buff) throws java.lang.Exception
Called when new bytes are available from the remote end. This should only ever be called by the single writeTarget associated with any given StreamConnection, multiple callers will likely confuse implementations.
Implementers/callers must follow the following conventions exactly:- buff will start with its limit set to the position we can read to and its position set to the location we will start reading at (always 0)
- May read more than one message (recursively) if there are enough bytes available
- Uses some internal buffering to store message which are larger (incl their length prefix) than buff's capacity(), ie it is up to this method to ensure we don't run out of buffer space to decode the next message.
- buff will end with its limit the same as it was previously, and its position set to the position up to which bytes have been read (the same as its return value)
- buff must be at least the size of a Bitcoin header (incl magic bytes).
- Returns:
- The amount of bytes consumed which should not be provided again
- Throws:
java.lang.Exception
-
setWriteTarget
void setWriteTarget(MessageWriteTarget writeTarget)
Called when this connection is attached to an upstream write target (ie a low-level connection handler). This writeTarget should be stored and used to close the connection or write data to the socket.
-
getMaxMessageSize
int getMaxMessageSize()
Returns the maximum message size of a message on the socket. This is used in calculating size of buffers to allocate.
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In 2015 I completed my Computer Systems Technology diploma at the British Columbia Institute of Technology and specialized in Web and Mobile development. I plan to continue on to the degree program at BCIT in the Network Security and Administration Option
My main focus of my studies in the diploma program was backend development and the exploration of RESTful services and system management. This included development in PHP and ASP.NET where I developed WebApps and RESTful API's using a number of frameworks. I also explored front-end development with Android, iOS and some web development. I have become an avid fan of the Nodejs engine and Angularjs framework for its amazing power and intricacy it adds to a typical website and the development process.
Additionally in my studies, I have worked with Java, C, and C++. One of my first projects I created on my own was a Chess simulator that was developed in Java. This simple yet somewhat complicated project taught me alot about inheritance and polymorphism. The project was large enough also to be an example of the value of code re-use and to develop with code re-use in mind. With Java being the first language I was taught while attending BCIT, many of my coding 'best practices' are based on standards enforced by this language and its developers. During my second and third semester at BCIT I used C and C++ to make various consol utilities including a grocery store simulator in C++ and a number of file manipulating tools in C.
While at BCIT I was also accepted into Co-op for my program and had an 8 month internship at Cirius Secure Messaging as a Quality Assurance Analyst. Periodically though I had the opportunity to do some development on some internal tools. Along with learning C# from ASP.NET at BCIT, I also gained valuable experience and learned a large amount about C# while at Cirius.
I've always been a technology person. My family is very technical and so many of my external activites have always has some technology related twist. During high school I took guitar lessons which sparked my interest in sound production. I was involved in many concerts for my high school; manning the soundboard and organizing what was needed for a given concert. With my involvement, I slowly began building my own small studio where I was able to record an EP with my brother and experimented with works created with friends. Lately my sound and production interests have evolved into DJing where I have performed at weddings of family and friends.
During the summer I enjoy doing various outdoor activities. I am a 4 year volunteer at the Vancouver Folk Music festival which I got involved in through my cousins. I have recently become re-interested in backpack camping and last summer hiked out to the Joffre Upper Lake with my sister and friends where we stayed for the night.
During the winter you can find me on the mountains snowboarding or spending time with family. Christmas is an important time for our family and we host and attend many social events during this time of the year.


ProtographQL is a full-service protoyping tool for GraphQL servers, empowering developers to build, visualize, validate and test GraphQL schemas and queries without writing any code. You can import or manually create your relational database tables and easily generate a package to spin up a functional GraphQL Apollo Server.
Create tables manually or import from a PostgreSQL database, visualize your schema, and export an Apollo Server.
Validate and export query and response pairs from a custom GraphQL Endpoint for testing.
ProtoGraphQL is in beta. Please post any issues to our GitHub - we would love to hear from you!
Haris Hambasic
@hambasicharis1995Jessica Vaughan
@jessicavaughan820Michele Moody
@MilmoodyVance Wallace
@VancitoAlena Budzko
@AlenaBudzkoBryan Fong
@bryanfong-devGeoffery Lin
@geofflinJarred Jack Harewood
@jackhajbRondolfo Guzman
@Rondolfoguzman25


Bad Design Is Bad for Your Health: Why Data Visualization Details Matter
Presentation Abstract by Jody Butts, Sr. User Experience Designer Given on August 11, 2017 at the UX Australia Conference in Sydney
Full presentation audio and slides are available here: http://www.uxaustralia.com.au/conferences/uxaustralia-2017/presentation/bad-design-is-bad-for-your-health-why-data-visualization-details-matter/
The proper design of data visualization details is imperative for accurate and actionable data visualizations and dashboards. In the health care technology industry, even the smallest design detail on a graph of patient data can have a dramatic impact on patient safety and outcomes. For example, auto-adjusting y-axes in vitals graphs across patient charts in the EMR could cause change blindness which impacts the perception of patient data trends.
Patient Safety
As a user experience designer for Cerner, a health care IT company, patient safety is the foundation of my work. However, we see startling patient safety statistics around the world today. According to a Johns Hopkins study from 2016, medical error is the third leading cause of death in the United States, following heart disease and cancer. The World Health Organization (WHO) estimates that in developed countries across the globe, as many as one in ten patients are harmed while receiving hospital care.
Based on research the User Experience team at Cerner has done, there are several challenges the health care industry faces to ensure patient safety. From workflow and communication to external rules and regulation, there is a complex and interconnected web of factors.
While human error is unavoidable, there should be several defensive layers for every system. Despite the training, regulation, and process our health systems have, health care providers are still working in high-risk environments.
Understaffed health systems are flooded by people in need of care. On a bustling hospital floor with alarms going off at all hours, care team members running between patient rooms, and the need to use a computer to document patient data to meet government regulations, it is no wonder that medical error occurs.
Although we cannot ensure software will always work perfectly, we do have responsibility to ensure that the UI and UX layers of our systems are designed to protect our users and enable them to perform their tasks well.
User Experience at Cerner
I work for Cerner, a company at the intersection of health care and IT. Cerner technologies support the full continuum of care including clinical, financial, and patient engagement solutions. We have products licensed at more than 25,000 facilities across 35 countries.
As a part of Cerner’s UX team, I spend most of my time designing in the clinical space, working on projects for our main product: the electronic medical record (EMR), mainly used by doctors and nurses. Our technology is subject to a long list of legislation and regulations. Cerner has an entire team dedicated to reviewing standards for each country to ensure we are meeting all health IT requirements.
Our UX team also has training on how these standards impact our designs and research. Accessibility guidelines, patient privacy, and medication safety are just a few examples of regulation themes that guide our work. I focus on the rules that impact data visualizations and incorporate additional research to create safe and usable experiences.
The accurate graphical display of patient data has become my passion over the past two and a half years. Data visualization is a very powerful tool across industries, but I’m biased toward believing that its most amazing applications are in health care technology.
Data Visualization in Pediatric Health Care
For many years, the EMR was simply an input tool for charting patient data–most data review was solely displayed in rows and columns. Now we are creating meaningful displays of information for clinicians to support their decisions.
For example, the pediatric growth chart has been used by health care providers to track children’s growth from birth into adulthood for almost 40 years, starting on paper. The WHO and Centers for Disease Control and Prevention (CDC) have standards for healthy growth of height, weight, head circumference and more based on age, sex, and so on.
These standards are displayed as percentile lines that create a unique curve for each measure. When patient data is plotted, it becomes easy to identify if a child’s growth is progressing normally, or if there are abnormal patterns. For the first 20 years of an individual’s life, the growth chart is essential for tracking healthy growth progression. It is also an invaluable tool for communication between parents and clinicians.
As I have worked on a redesign of Cerner’s interactive growth chart tools, I have had the opportunity to observe clinicians using the growth chart in hospitals. In a Neonatal Intensive Care Unit (NICU) setting, or neonatal intensive care unit, physicians are making life-saving decisions for premature infants based on this information. For specialists, such as pediatric endocrinologists, the pattern of the trend line is often the first indication of diagnoses such as hypothyroidism. Can you imagine being able to recognize a disease by looking at a line graph?
Data Visualization Design Details
In a matter of clicks, we can take tables with thousands of numbers and transform them into graphics that provide immediate insights. From problem solving to storytelling, this information drives action across every industry.
There are countless examples of confusing and even misleading graphs–just try a Google search for “bad data visualizations,” and you’ll see what I mean. This assumed legitimacy is dangerous, especially when the design is sleek and appears trustworthy. The details that lead to accurate and impactful data visualizations fall into three categories: clarity, context, and creativity.
Clarity
Clarity is the foundation of data visualization design. We want to avoid all potential for confusion in order to empower our users to confidently make decisions. To achieve this, we must start with the basics. The title of a visualization should be succinct, yet provide the information needed to understand what data are being viewed and how they are being measured. If additional details are important to include, consider using supporting text below the title.
A dangerous mistake I regularly see while consulting on visualizations and dashboards is improper or missing labels. Do not make users guess what data points are being shown or how they are being measured. All axes in visualizations should be labeled. It is also important to include the unit of measure, which I like to include in parentheses next to the label.
While labels are important, I believe the most powerful element for clear data visualizations is proper scale. A simple mistake I often see is to start the y-axis of a bar graph at a value other than 0. Human eyes interpret the value of a bar to be equal to its length, so when the y-axis does not begin at 0, the values are not interpreted accurately. Similarly, the perception of data measured in percentages is also often skewed by scale. This sometimes occurs because a tool automatically adjusts the scale to fit to the data, but sometimes it is done to manipulate the data display. This example of an ad from Chevy illustrates the power scale has on perception (starting at 0:45).
Context
Once there is a truthful and clear foundation for data, the following techniques can bring users quicker insights and increase their confidence in data interpretation.
If there is a normal, expected, or ideal range of values for the data, adding a target range is a simple but powerful way to add context. A light shaded region behind the grid lines can allow users to easily identify high and low values. Similar to a target range, a goal line can be added to indicate a discrete value that the plotted data should be achieving. A goal line and target range can be used together as well.
It’s important to mark visualizations and dashboards with the date it was published. I also recommend including the date or date range of the data as well in order to maintain trust and relevancy of your visualization.
Annotations are another simple way to provide your user with context. On-canvas notes and labels should be used sparingly, but can help highlight insights. These can be placed in context of a specific data point, a date or timeframe, etc.
Creativity
Once we have created a clear visualization and provided our user with additional context, we are ready to polish our design. There is great potential for creativity in data visualization, but it can cause more harm than good if done poorly. Let’s look at visual encoding methods, colors, and other visual design details that can enhance your visualization or dashboard.
Qualitative and quantitative data is visually encoded, or translated, through a variety of graphical methods that make up each visualization type. Research has been done on these visual encoding methods, giving us insights for human graphical perception. Statistician William Cleveland and his colleague, Robert McGill, tested the visual encoding methods and ranked them by accuracy of perception. Their findings give us a scientific foundation for data visualization.
Position is the best encoding method for accuracy, followed by length, angle, area, volume, and finally, with color taking the last spot. This means that color is not reliable for communicating data information in a graph. This aligns to the WCAG 2.0 accessibility guidelines, which state that color cannot be used as the only visual means of conveying information, indicating an action, and so on. Second, bar, and line graphs are common for good reason–they are most effective for our brains to accurately understand. Ultimately, it’s always best to try a couple of options and compare their effectiveness.
Use color sparingly. Think of it as a tool for drawing attention to the most important insights. If everything has color, especially on a dashboard, nothing will stand out. Since approximately 10% of the population is color blind, we should avoid using red and green together on a visualization. Color Oracle is a great color blindness simulator that you can use to see how your colors appear for users with these vision deficiencies.
Ensure you maintain high contrast between colors by avoiding very light or very dark shades. The Delta-E calculation measures the distance between two colors. At Cerner, we ensure a difference of at least 3.5.
The best way to test the colors you are using is to print it out in grayscale. Make sure you can still match plotted data to its labels. Better yet, get additional eyes on it. This test ensures that color is not being utilized as the only way to communicate meaning.
Visualizations are the Key to Understanding Data
The world is overflowing with big, complex problems, but we also have big, complex data to solve them. This big, complex data exists as numbers that can go on forever. Data visualizations are the key for humans to understand it all. Data must be visualized clearly and effectively to enable professionals across industries to understand it, make decisions, and take action based on it.
We can revolutionize health care and other fields through the way we visualize data. The design details matter. Through clear, contextual, and creative data visualizations, we can drive action that will change the world and even save lives.


About Me
Student of Web Development
Hi, I'm Lane Garner. I am a musician and web develpment student currently attending the fullstack bootcamp at Austin Coding Academy. I am interested in learning about topics such as HTML, CSS, Javascript and eventually the back end as well.


This is where you can find the lyrics of all Lord Raghavendra Swami mantras or devotional hymns in English and Devanagari script. You will also find the meaning of Raghavendra Swami mantras and devotional hymns in English. Raghavendra Swami mantras are read to please Lord Raghavendra Swami.
| Raghavendra Swami - Anu Stotra |
| Raghavendra Swami - Guru Stotra |
| Raghavendra Swami - Ashtotara Shatanamavali |


Coronavirus disease (COVID-19) is an infectious disease caused by a newly discovered coronavirus. Most people infected with the COVID-19 virus will experience mild to moderate respiratory illness and recover without requiring special treatment. Older people, and those with underlying medical problems like cardiovascular disease, diabetes, chronic respiratory disease, and cancer are more likely to develop serious illness. The best way to prevent and slow down transmission is be well informed about the COVID-19 virus, the disease it causes and how it spreads. Protect yourself and others from infection by washing your hands or using an alcohol based rub frequently and not touching your face. The COVID-19 virus spreads primarily through droplets of saliva or discharge from the nose when an infected person coughs or sneezes, so it’s important that you also practice respiratory etiquette (for example, by coughing into a flexed elbow). At this time, there are no specific vaccines or treatments for COVID-19. However, there are many ongoing clinical trials evaluating potential treatments. WHO will continue to provide updated information as soon as clinical findings become available.
To prevent infection and to slow transmission of COVID-19, do the following: Wash your hands regularly with soap and water, or clean them with alcohol-based hand rub. Maintain at least 1 metre distance between you and people coughing or sneezing. Avoid touching your face.  Stay home if you feel unwell. Refrain from smoking and other activities that weaken the lungs. Practice physical distancing by avoiding unnecessary travel and staying away from large groups of people.
COVID-19 affects different people in different ways. Most infected people will develop mild to moderate illness and recover without hospitalization. Most common symptoms: fever. dry cough. tiredness. Less common symptoms: aches and pains. sore throat. diarrhoea. conjunctivitis. headache. loss of taste or smell. a rash on skin, or discolouration of fingers or toes. Serious symptoms: difficulty breathing or shortness of breath. chest pain or pressure. loss of speech or movement. Seek immediate medical attention if you have serious symptoms. Always call before visiting your doctor or health facility. People with mild symptoms who are otherwise healthy should manage their symptoms at home. On average it takes 5–6 days from when someone is infected with the virus for symptoms to show, however it can take up to 14 days.