Thank you for coming to my page. Please see below for relivant work experience and history.
Work Experience
Rapid Response Bio Clean - Director of Operations July 2016 - January 2021
Collaborate with executive-level management in the development of performance goals and long-term
operational
plans
Maximize efficiency and productivity through extensive process analysis and interdepartmental
collaboration.
Analyze current operational processes and performance, recommending solutions for improvement when
necessary.
Develop, implement, and monitor day-to-day operational systems and processes that provide visibility
into
goals, progress, and obstacles for our key initiatives.
Build and maintain relationships with all department heads, external partners, and vendors to make
decisions
regarding operational activity and strategic goals.
Plan, monitor, and analyze key metrics for the day-to-day performance of the operations to ensure
efficient
and timely completion of tasks.
Devise strategies to ensure growth of programs enterprise-wide, identifying and implementing process
improvements that will maximize output and minimize costs.
Uphold organization policies and standards, ensuring legislative regulations are followed.
Work closely with human resources to lead team with integrity and establish and maintain a trusting,
inclusive, and productive environment.
Execute and ensure that the core values and vision of the business remain intact within all aspects of
the
organization.
Pacific Patrol Services - Campsite Cleanup Team Leader/Sergeant September 2015 -
July
2016
Acting primary coordinator for the City of Portland and the abatement of illegal camping.
Duties included: coordination with local government agencies and advocacy groups that include Portland
Police
Neighborhood Response Team, Multnomah County Sheriff’s Office, JOIN Office of Homeless Services, and
Portland
Bureau of Office of Management and Finance.
Pacific Patrol Services - Security Officer February 2015 - September 2015
Provide a safe environment for students and staff members at the Pacific Northwest College of Art.
Duties included: acting as the roving security officer for the college ensuring no unwanted persons
entered
the building, communicating with local law enforcement agencies on property exclusions and emergency
situations,
maintaining the presence of security and working directly with the lead security officer on site.
Tualatin Hills Parks and Recreation - Building Monitor December 2014 - February
2015
Provided safe environment for children to learn and play basketball during the winter season.
Duties included: tracking all persons entering/exiting the facilities, providing communication between
coaches
and THPRD, providing basketball during THPRD’s winter season, and ensuring building was empty and secure
at
close of day.
Lifeworks Restaurant Group- Utilities and Food Preparer November 2013 - November
2014
Worked as catalyst to ensure the kitchen ran smoothly and properly on a day-to-day basis.
Duties included: receiving shipments, organizing, and rotating products, restocking dishes, assisting
in
food
preparation, and closing all facilities at the end of the day.
AmeriCorps National Civilian Community Corps - Assistant Team Leader October 2012 -
July
2013
Worked in a team-based environment for ten months, traveling around the country and performing over
2,000
hours of national and community service. Four rounds of Service included:
5 weeks of disaster relief in New York City, aiding survivors of Superstorm Sandy.
11 weeks of camping in Agua Caliente inside the Anza Borrego desert, working to preserve six natural
hot
springs that serve as the water source for the desert town.
8 weeks of infrastructure improvements in Washington State at YMCA Camp Bishop and Huston Camp and
Conference Center.
6 weeks working as a camp counselor for adults and children with special needs and disabilities at
Easter
Seals Camp Harmon.
Volunteer Experience
Beaverton Police Department - Victim’s Advocate May 2015 - October 2018
Act as a support network for victims of traumatic crimes and assist them with steps that come
after.
Completed 50+ hours of Advocate training through the police department.
Education
Epicodus March 2021 - September 2021
Currently attending school for computer programing and web development.
Udemy Web Development - Certification January 2012 - March 2021
Obtained a 63-hour certification in web development.
Portland Community College July 2016 - December 2018
Transfer degree in Business and Management.
National Academy of Sports Medicine- Personal Training Certification July
2014
Studied movement science, human anatomy, varying training methods and nutrition science.
Acheivements and Skills
CPR/AED/First Aid Certified.
Proficient in the following computer programs: VS Code, GitHub, Microsoft Office Word,
Excel,
PowerPoint, and
AppSheet.
Thorough experience in team-based work environments and leadership training, solid
communication
skills,
ability to take criticisms and apply them appropriately and a drive to better oneself
and
others
through
positive experiences and support.


class: center, middle, inverse, title-slide # Week 6 - Further into Data Viz ### Alex Lishinski and Joshua Rosenberg ### February 25, 2021 --- # Welcome! Welcome to *week 6*! **Record the meeting** --- # Breakout rooms! Starting with whomever most wants to go first: **One question:** - What is a weird, unusual, or surprising situation that you encountered when using R in the last week? **One reflection/discussion:** - The [Greenhalgh et al. (2020)](http://www.researchmerge.com/uploads/7/5/3/5/75356681/chapter_5_research_methods_in_learning_design_and_technology_pre-print.pdf) chapter outlines six considerations related to conducting ethical research. Which of these six do you think is *important but insufficiently emphasized* in your area of research? --- ### Review of last week's class Why visualize data? One answer: "You should look at your data." ([Healy, 2018](https://socviz.co/)) *To elaborate on this*: - Visualizations allow to _understand the structure and nature of your data_, and to begin to understand what might relate to what else - Just like we want to be constantly looking at our data in its spreadsheet/table/data frame format (e.g., `str()`, `glimpse()`, and `View()`), visualizing our data can help us to make sure our data contains what we think it does-and it can alert us to when it does not --- ### Review of last week's class * Exploratory visualization and presentation visualization * Basics of using base R plotting functions as well as ggplot --- ### Review of last week's class ```r data %>% ggplot(aes(x = name, y = value)) + geom_boxplot() ``` ```r data %>% ggplot(aes(x = value, y = name, fill = name)) + geom_jitter(color = "gray") + geom_violin() + theme_minimal() + scale_fill_brewer("", type = "qual") + ylab(NULL) + xlab(NULL) + theme(text = element_text(size = 16, family = "Times"), legend.position = "none") + ggtitle("Distributions for the Five Questions") ``` --- ### Review of last week's class ![](presentations-week-6_files/figure-html/unnamed-chunk-5-1.png) --- ### Review of last week's class ![](presentations-week-6_files/figure-html/unnamed-chunk-6-1.png) --- ### Homework highlights What do you notice? What do you wonder about?  --- ### This week's topics **Overview** 1. Data viz ideas and details 3. Data viz and tidying operations --- ### 2 overarching goals of learning data viz in R * Conceptual framework of visualization + Grammar of graphics and different mappings of data onto visual elements * Details of implementation + How to build and refine plots layer by layer + Eventually: Interactive data viz with ggviz and shiny --- class: inverse, center, middle # Part 1/2: Data Viz Ideas --- ### 1. Data Viz Ideas **Outline** A. Review of the grammar of graphics B. Understanding visualizations by layers C. Understanding mapping of data to geoms --- ### 1A: Grammar of Graphics Another way to think about visualizing data is in terms of the elements that make up a plot. The *grammar of graphics* ([Wickham, 2010](https://www.tandfonline.com/doi/abs/10.1198/jcgs.2009.07098), [Wilkinson, 2012](https://link.springer.com/chapter/10.1007/978-3-642-21551-3_13)) has a particular answer to the question of what a plot includes: Why a grammar of graphics? - gain insight into complex figures - reveal deeper relationships between what may appear to be unrelated visualizations - more flexibly and creatively visualize data--including in ways that do not fit well into one type of plot - suggest what makes a good figure --- ### 1A: More Data Viz Ideas #### Some general principles for effective data viz _Keep it simple_  --- ### 1A: More Data Viz Ideas #### Some general principles for effective data viz _Keep it simple_  --- ### 1A: More Data Viz Ideas #### Some general principles for effective data viz _Use color to draw attention_  --- ### 1A: More Data Viz Ideas #### Some general principles for effective data viz _Use color to draw attention_  --- ### 1A: More Data Viz Ideas #### Some general principles for effective data viz _Tell a story_  --- ### 1B: Understanding visualizations by layer Layers: 1. Data 2. One or more geometric objects (shape, point, line, etc.) 3. A mapping between variables in the data and the geometric objects and their characteristics (including their size and color) 4. A theme --- ### 1B: Understanding visualizations by layer _Data_ ```r ggplot(mtcars) ```  --- ### 1B: Understanding visualizations by layer _One geom_ ```r ggplot(mtcars) + geom_point(aes(x = disp, y = mpg)) ```  --- ### 1B: Understanding visualizations by layer _Additional Geoms_ ```r ggplot(mtcars) + geom_point(aes(x = disp, y = mpg)) + geom_smooth(aes(x = disp, y = mpg), method = "loess") ```  --- ### 1B: Understanding visualizations by layer _Additional Aesthetic Parameters: Color_ ```r ggplot(mtcars) + geom_point(aes(x = disp, y = mpg, color = am), size = 6) ```  --- ### 1B: Understanding visualizations by layer _Additional Aesthetic Parameters: Shape_ ```r ggplot(mtcars) + geom_point(aes(x = disp, y = mpg, shape = am), size = 6) ```  --- ### 1B: Understanding visualizations by layer _Additional Aesthetic Parameters: Size_ ```r ggplot(mtcars) + geom_point(aes(x = disp, y = mpg, size = hp)) ```  --- ### 1B: Understanding visualizations by layer _Theme: labels_ .pull-left[ ```r # code chunk here ggplot(mtcars) + geom_point(aes(x = disp, y = mpg, color = am), size = 6) + geom_smooth(aes(x = disp, y = mpg), method = "loess") + xlab("Displacement") + ylab("Miles per Gallon") + scale_color_discrete(name = "Transmission", labels = c("manual", "auto")) ``` ] .pull-right[  ] --- ### 1B: Understanding visualizations by layer _Theme: overall_ .pull-left[ ```r # code chunk here ggplot(mtcars) + geom_point(aes(x = disp, y = mpg, color = am), size = 6) + geom_smooth(aes(x = disp, y = mpg), method = "loess") + xlab("Displacement") + ylab("Miles per Gallon") + scale_color_discrete(name = "Transmission", labels = c("manual", "auto")) + theme_bw() ``` ] .pull-right[  ] --- ### 1B: Understanding visualizations by layer _ggthemes package_ ```r library(ggthemes) base_plot <- ggplot(mtcars) + geom_point(aes(x = disp, y = mpg, color = am), size = 6) + geom_smooth(aes(x = disp, y = mpg), method = "loess") + xlab("Displacement") + ylab("Miles per Gallon") + scale_color_discrete(name = "Transmission", labels = c("manual", "auto")) ``` --- ### 1B: Understanding visualizations by layer _Fivethirtyeight style_ ```r base_plot + theme_fivethirtyeight() ```  --- ### 1C: Understanding mapping data to geoms You can create different plots by: - Changing the aesthetic _mapping_ between variables in the data and geometric objects - Changing the geometric objects --- ### 1C: Understanding mapping data to geoms _Changing the mapping_ .pull-left[ ```r # code chunk here ggplot(mtcars) + geom_point(aes(y = disp, x = mpg, color = am), size = 6) + geom_smooth(aes(y = disp, x = mpg), method = "loess") + xlab("Displacement") + ylab("Miles per Gallon") + scale_color_discrete(name = "Transmission", labels = c("manual", "auto")) + theme_bw() ``` ] .pull-right[  ] --- ### 1C: Understanding mapping data to geoms _Changing geoms_ .pull-left[ ```r # code chunk here ggplot(mtcars) + geom_rug(aes(x = disp, y = mpg, color = am), size = 6) + geom_smooth(aes(x = disp, y = mpg), method = "loess") + xlab("Displacement") + ylab("Miles per Gallon") + scale_color_discrete(name = "Transmission", labels = c("manual", "auto")) + theme_bw() ``` ] .pull-right[  ] --- class: inverse, center, middle # Part 2/2: Data Viz and Tidying --- ### 2: How does tidying data relate to data viz? Often, we have to make changes to our data frame in order to create the visualization we would like to create. **Making a new variable prior to plotting the data** _Other data tidying steps_ we might take prior to visualizing data: - **recoding** variables - **creating a factor** (so that we can order elements of a plot as we wish for them to be ordered) - **grouping** and **summarizing** to plot a summary statistic - realizing that your data processing and tidying was not quite sufficient, so **returning to those stages** before finalizing your visualization - **re-running our analysis** (`.Rmd` file) because we discovered an issue with our data --- ### 2: How does tidying data relate to data viz? Sometimes we need to recode a variable or add a new one ```r tidykids <- read_csv(here("data", "tidykids.csv")) ``` ``` ## ## ── Column specification ──────────────────────────────────────────────────────── ## cols( ## state = col_character(), ## variable = col_character(), ## year = col_double(), ## raw = col_double(), ## inf_adj = col_double(), ## inf_adj_perchild = col_double() ## ) ``` ```r state_region <- data.frame(state.name, state.region) tidykids_reg <- left_join(tidykids, state_region, by = c("state" = "state.name")) tidykids_reg$timeblock <- recode(tidykids_reg$year, `1997` = "1997-2001", `1998` = "1997-2001", `1999` = "1997-2001", `2000` = "1997-2001", `2001` = "1997-2001", `2002` = "2002-2006", `2003` = "2002-2006", `2004` = "2002-2006", `2005` = "2002-2006", `2006` = "2002-2006", `2007` = "2007-2011", `2008` = "2007-2011", `2009` = "2007-2011", `2010` = "2007-2011", `2011` = "2007-2011", `2012` = "2012-2016", `2013` = "2012-2016", `2014` = "2012-2016", `2015` = "2012-2016", `2016` = "2012-2016") ``` --- ### 2: How does tidying data relate to data viz? Sometimes we need to recode a variable for plotting ```r ggplot(na.omit(tidykids_reg)) + geom_jitter(aes(x = state.region, y = inf_adj_perchild)) + xlab("Region") + ylab("Adj per child") ```  --- ### 2: How does tidying data relate to data viz? Creating and reordering factors is often useful ```r tidykids_reg <- tidykids_reg %>% mutate(timeblock = factor(timeblock)) ggplot(tidykids_reg) + geom_jitter(aes(timeblock, inf_adj_perchild)) ```  --- ### 2: How does tidying data relate to data viz? Creating and reordering factors is often useful ```r tidykids_reg$timeblock <- fct_relevel(tidykids_reg$timeblock, c("2012-2016", "2007-2011", "2002-2006", "1997-2001")) ggplot(tidykids_reg) + geom_jitter(aes(timeblock, inf_adj_perchild)) ```  --- ### 2: How does tidying data relate to data viz? When we do `group_by()` and `summarize()` we can plot summary statistics ```r summ_df <- na.omit(tidykids_reg) %>% group_by(state.region) %>% summarize(mean_perchild = mean(inf_adj_perchild, na.rm = T)) summ_df ``` ``` ## # A tibble: 4 x 2 ## state.region mean_perchild ## * <fct> <dbl> ## 1 Northeast 1.06 ## 2 South 0.855 ## 3 North Central 0.834 ## 4 West 0.865 ``` --- ### 2: How does tidying data relate to data viz? When we do `group_by()` and `summarize()` we can plot summary statistics ```r summ_df %>% ggplot() + geom_col(aes(state.region, mean_perchild)) ```  --- ### 2: How does tidying data relate to data viz? When we do `group_by()` and `summarize()` we can plot summary statistics .pull-left[ ```r # code chunk here summ_df %>% ggplot() + geom_bar(aes(state.region, mean_perchild), stat = "identity", fill = 6) + geom_errorbar(aes(x = state.region, ymin=mean_perchild-(1.96*sd(mean_perchild)), ymax=mean_perchild+(1.96*sd(mean_perchild))), width=.2, position=position_dodge(.9)) ``` ] .pull-right[  ] --- # Course Logistics **This week** * Homework 6: Available tommorow by noon; Due by Thursday, 3/4 * Readings * 1: https://clauswilke.com/dataviz/histograms-density-plots.html * 2: https://clauswilke.com/dataviz/visualizing-proportions.html **Coming up** * [Data ethics](https://making-data-science-count.github.io/s21-intro-to-data-sci-methods-in-ed/assignment-data-ethics.html) * *Just begin* to think and to ask questions about what you may want to do for [a final project](https://making-data-science-count.github.io/s21-intro-to-data-sci-methods-in-ed/assignments.html); something that will advance your research and allow you to exhibit and extend what you do in class --- # Random - https://educationdata.urban.org/documentation/schools.html - https://leanpub.com/tidyverseskillsdatascience --- # Wrapping up In your base group's Slack channel: - What is one thing you learned today? - What is something you want to learn more about? - _Also_, in GIF form (type `/giphy` in Slack, and then a random term), summarize how you are feeling about R


Visit Beijing Forbidden City
A corner of the Palace.
To see the Orienta Pearl Tower.
After dinner,visit the Olympic Park.
Photographers.
It's me.
Silent.
The Venetian Macao.
People who don't put sheep.
The world is so big that i want to have a look.


|
Order
|
Buyer | Payment | Amount |
|---|---|---|---|
|
2194
|
Carla Leach | paypal | $59.85 |
|
2442
|
Kameron Burks | credit card | $32.50 |
|
4640
|
Jake Reese | paypal | $105.60 |
|
9390
|
Bryce Sellers | cash | $12.25 |


--- layout: default title: Some bad jokes keywords: bad jokes description: Some bad jokes ---
I'm a very stationery person; my favorite way to travel is by mail.
An ornithologist was studying the life cycles of fleas that live in the feathers of crows. She did not have any fancy equipment, but since the birds were smart, she trained them to count the number of fleas on each other and tap out the result with their beaks in Morse code. A friend asked, "Wouldn't it be easier on the crows if there was some technology to help out with this?" Picking up one of the crows, the scientist replied, "Eh, I've got the next best thing: my crows cope."


Khameni blames fuel protests on 'Iran's enemies'
Iranian supreme leader blames 'sabotage' by 'Iran's enemies' after government doubles fuel prices, sparking protests.
by Gary WilligIranian Supreme Leader Ayatollah Ali Khamenei on Sunday supported recent increases in gas and fuel protests which have sparked protests across Iran.
“Some people are no doubt worried by this decision ... but sabotage and arson is done by hooligans not our people. The counter-revolution and Iran’s enemies have always supported sabotage and breaches of security and continue to do so,” Khameini said on Iranian state television.
“Unfortunately some problems were caused, a number of people lost their lives and some centers were destroyed,” he added.
Protests erupted Saturday after the Iranian government raised the cost of regular gasoline to 15,000 rials ($0.13) a liter from 10,000 rials and instituted fuel rations. Additional liters would cost 30,000 rials.
One death was confirmed in the southeastern city of Sirjan. Several other deaths were reported on social media.
The increase in fuel prices was done in response the economic crisis Iran faces as a result of the renewed sanctions imposed by the United States.


{% extends "global/Page.html" %} {% load static otree %} {% block title %} SLIDER TESTING {% endblock %} {% block content %}
There's a slider Simply pick something
At the end, a random round will be chosen for prizes
In this round, you are the {{ player.role }}.
{% formfield player.age label="Enter your age" %} {% formfield player.name %} {% next_button %} {% endblock %}


I started Muay Thai at 17, fought all over England, Ireland and Thailand. I Ran my own gym for a number of years producing a strong stable of professional fighters and a world champion. I loved being "Coach" more than i did actually fighting, probably because I was much better at it.I like to try and get the best out of people around me as i think it brings out the best in Myself. I also compete in BJJ and had a couple of MMA Fights,here is a selection of highs and lows from my fighting career


Web drive was the very helpful system for our life. However, There will be some bugs on those system. Sometime some hacker can hack to your web drive account via the bugs
Three points let you more saftly
Do not upload any secret document such as bank statment


Career Summary and my thoughts
| Time Frame | Summary | Skills Obtained/ Design Patterns Used | Links |
|---|---|---|---|
| 2018-Present | Left Fundrecs. For first time I heard Orchestration in micro services. I know micro services and have successfully implemented that kind of architecture, but Orchestration and Coreography was Alien to me. Received Camunda training and learnt how to model business process and that business process and business logic are seperate aspects. Spent great time with Jan doing pair programming and Strictly TDD. First time I was working in Agile/Scrum enviroment, not totally convinced with Manager's scrum, and the developer's scrum in practice, where i am working, totally opposite to Agile Manifesto. Most probably the bowing down to Atlassian JIRA has limited many people's imagination and convinced them it is the only and right way. I thought out that software delivery has everything to do with right approach to solve problems and not by micro management as mentioned in this anti pattern Death by Planning Right now I am investigating Java Connector Architecture to refactor the code from Ear to Rar structure. | Orchestration,TDD,Pair Programming, Death By Planning(Anti pattern) | N/A |
| 2017- Second Half | Working on my own from India after marriage on OCR Engine. Banging my head with severe pain in trying to figure out how to build a solution to read images in the code. After long struggle learnt techniques in Computer vision to play with drawings and felt like i can buid paint in the code. While i havent build paint, but i certainly did learn how to play with images in a program to extract proper text and pass them to tesseract OCR parser. | Open CV, Teseract. | N/A |
| 2017- First half | Build the UI for Parser Engine. Lots of investigation on React,Angular and Backbone. Worked on initial UI prototyping of the pages and functionality. Used Bootstrap to give look and feel. Didn't liked it. Used Material-UI for look and feel. Set up Js project from Scratch with webpack, babeljs pipeline. Loved the transition effects and super sexy UI. Lots of disagreement with Jason :) Wanted to buy amazon Share for around 800 Dollars, didnt had the courage. | Using mediator pattern to delegate the request from react to Backend rest services. Using reactjs in simple form and embraced it. Skill to build a professional UI from scratch. Great confidence. | N/A | 2016-Second Half | Fundrecs architecture is evolving with addition of Apache Camel to poll files from SFTP. Extended the apache camel sftp to multi channel SFTP polling mechanism. To maintain growing infrastructure, worked on Amazon Cloudformation to automate the deployments. Worked on Mongodb installation in AWS by cloudformation. Loved AWS too much. Amazed by AWS addition of product lines. | Automation of Infrastructure on AWS. Customizing the instances according to needs for the deployments in Cloudformation. JMS integration of micro service with Activemq. | N/A |
| 2016-First Half | Maintenence of Parser engine. Started working on Recs Engine. Designed Algorithm capable of reconcilliatin of a million data set on either side in couple of seconds. I started to enjoy writing test and wrote the unit test for the algorithm with 100% coverage. Embraced writing the unit test and statred to play with TDD. | N/A | |
| April 2015 ~ Dec 2015 | Given print out of Pdf files to add into Parser Engine by Alan. Banging my head on desk(virtually) to design a solution to extract data from PDF files and tranform it into tables. Worked with Padraig to build deployment infrastructure on AWS. Worked out on white board the VPC, security groups, subnets for fundrecs deployment. Use Amazon Web interface to build the infrastructure and deploy services to EBS. | Loved Amazon Elastic offerings(Had a presentation of How Elastic Compute works in Masters) | N/A |
| Dec 2014-March 2015 | Des convinced me to join Fundrecs. Worked hard with Padraig and Alan on first release. Deep dive into Groovy and Grails Framework. Obsession with groovy and functional Programming. Always thinking to solve problem in Java 8 Streams and groovy Closures. Advocated the usage of microservices pattern for Fundrecs. Worked out the communication in Hybrid Architecture to keep Direct HTTP only and done a POC to prove that it can be done. Solved bug in Spring framework to support multi tenancy and kept the design as stateless for performance. First Hands on Experience in using AWS for deployments. Wrote a java codebase to create deployment infrastructure on AWS. Writing code in Java looks ugly now, Groovy is class. Applied Domain Driven Design(I didnt know what i did was DDD until a podcast) to model the UI and domain objects seperately. Wrote first Integration test of my life. | Command Design Pattern, Domain Driven Design, Functional Paradiagm, Of course Micro Services! | N/A | 2013-2014 | Finished Msc in Computing. Formally studied Design Patterns, Distributed Systems, Agile Software Development. First class Honours. | Understanding of common GOF patterns(Strategy, Command, Facade, Mediator). Seen list of Enterprise Patterns. Guest Lecture on micro Services. Unconvinced with Docker craze in TSSG. Skills and aim to find design patterns and talk in patterns in the future work. TDD | Design Pattern Course Agile Soft Dev Course |
| 2013- Jan-August | Started multi module maven project from Scratch for HereIam. Hands on experience on working with POM and configuring multiple modules combined in parent project. Started working as Lead developer for Ezswype mobile application. Created the project backend from Scratch using Spring/hibernate. Coordinated skype sessions to gather requirements and solve all the hurdles in dev. Spent half nights with Sid and Hament working on it. Integrated Magnetic card reader device on Android by using Apache cordova Hooks. Struggled with performance issues in Cordova. Moved on to work on hereiam back again, worked on using Aache Shiro and Lucene search to feature the search functionality. Hands on experience with Mondodb and Java driver for Mongodb. Left Aranoah. | Self starter on any Greenfield project from scratch. Great confidence in development. Ony dev experience, no real production experience. | Project |
| 2012- September- December | Started woring in Aranoah. First experience of creating Web Services using Spring framework, Hibernate Criteria Queries and integration of the Http POST services with Jquery based javascript application. | REST(not proper) Services, MVC, Debugging a server side code and following the code of execution during request processing. Thinking about transaction in request processing, rollback in case of exception. Spring @Autowired, @RequestBody, @ResponseBody @Controller,@Service,@Component. Worked on Paginated views to display list of products. | Project |
| 2012- August | Finished Bachelor of Technology in IT | Understanding of Operating systems, Computer Architecture, Programming in Java using Spring framework, Hibernate APIs (both Hibernate own and JPA) | N/A |
| 2011 -Summer | Training in Java EE- Spring,Hibernate and Struts Framework | Facade,Factory, MVC, Inversion of Control, Loose coupling concepts | N/A |
| 2010 -Summer | Training in Core Java | Java Fundamentals- Class, Objects, Data structures in Java - Map, Set, List, Reflection, Basic Threading Concepts, Observer pattern in Swing | N/A |


When It Takes a Miracle, We’re There.
Care without any delay


HOLY MAN (1998)
A film review by Steve Kong Edited by Cher Johnson Copyright 1998 Steve Kong
Much like the goods pushed on Holy Man's Good Buy Shopping Network, what you see is not what you get with Holy Man. On TV, Holy Man is sold as a hilarious film starring Eddie Murphy, but onscreen it is a romantic comedy with Murphy as one of the supporting players. If you can get over the fact that what you get is not what you were sold, Holy Man is quite an enjoyable film.
Ricky Hayman (Jeff Goldblum) is the program director at the low-rated Good Buy Shopping Network. The ratings and sales are so low that his boss, McBainbridge (Robert Loggia), gives him two weeks to increase the sales. If sales don't go up, then Hayman is out. The ultimatum is not the only thing that McBainbridge gives Hayman; he also gives Hayman a marketing manager Kate Newell (Kelly Preston).
Off the bat Hayman is in love with Kate. But, Kate finds nothing to like about Hayman. Hayman is a self-centered and self-serving person. Can you see what kind of person Hayman is going to be by the end of the film? On their way to a meeting, Hayman's car blows a tire and they are stuck. Here is where they meet G (Eddie Murphy). G is a man on a pilgrimage that takes any given moment to say something spiritual. G is a smiling loveable guy, and Kate picks up on this immediately. Hayman, though, is not as accepting.
In a freak accident, Hayman almost runs G over. Hayman, under pressure from Kate, takes G in as a houseguest until G's all healed up. Hayman finds a good salesperson in G and persuades G to sell for GBSN. G is a hit on GBSN and sales increase sharply. G's mix of spirituality and good humor bring viewers in, even though G isn't pushing the product. Can all be so good? Of course not.
Murphy is surprisingly sedate as G. This doesn't mean that he's not funny. Murphy steals every scene he's in. Take, for example, an early scene when G is walking down a highway and a group of teens throws a Slurpee at G. Instead of scolding the teens or getting angry, G thanks them for not throwing a canned soda. Only Murphy could pull off such a line and make it a laugh-out-loud moment. The character G has a lot of pontential that is squandered by writer Tom Schulman.
Goldblum is serviceable as Hayman. Playing against the type of character he played in Jurassic Park and Lost World, Goldblum was able to make me like his character even though he was a slime ball.
Preston plays the "good girl" this time around. In Jerry Maguire, which is the same type of story, she played the "bad girl." Preston injects much life into the flat and boring character of Kate. She brings freshness to the mediocre and cliched script. Preston and Murphy both hold the film up.
For a horrible performance, look for Robert Loggia. He plays his part as if he were a cigar-chewing sergeant straight out of an old World War II film.
The script by Tom Schulman is a skimpier version of Jerry Maguire. The story is about a guy who loses himself to the pursuit of money and struggles to find his soul again. Preston and Murphy lift the thin script above mediocrity. I liked Holy Man in many ways. The primary one is that it's a feel-good film, and I walked out of the theatre with a smile. There are small things about the film that just make you smile, such as anything that Murphy's G does.
I recommend Holy Man. Catch it in the theatres or on video. You won't be disappointed as long as you keep in mind that this is not an Eddie Murphy comedy, but a romantic comedy. Maybe the producers of Holy Man will do a sequel with just G, since he is the most likeable character in Holy Man.
"Your good is better and better is blessed." - G.
--- Steve Kong<EMAIL_ADDRESS>recipe for a hard boiled review: one egg, two cups water, a pot, a helluva attitude, and a guy who loves the cinema. visit the hard boiled movie page.
http://boiledmovies.sbay.com/
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


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|
UltralightweightCalculator
|
#include "Symbols.h"
Functions | |
| void | createExactName (std::string &exactName, const std::string &baseName, const unsigned char argCount) |
| SymbolTableElement * | getElementFromSymbolTable (const std::string &exactName) noexcept |
| void | placeBuiltInSymbol (const std::string &baseName, const boundFunction func, const char argCount, const unsigned char delayMask) |
| void | placeUserSymbol (const std::string &baseName, const ManyType &mt, const char argCount, const unsigned char delayMask) |
| bool | removeUserSymbol (const std::string &baseName, const char argCount) |
| void | removeUserSymbolList (const std::string &baseName, std::vector< char > &vec) |
| const SymbolTableElement & | readSymbol (const std::string &baseName, const char argCount) |
Variables | |
| std::unordered_map< std::string, std::vector< char > > | overloadsTable |
| std::unordered_map< std::string, SymbolTableElement > | symbolTable |
| void createExactName | ( | std::string & | exactName, |
| const std::string & | baseName, | ||
| const unsigned char | argCount | ||
| ) |
Creates a string of the form baseName?argCount.
| exactName | where the result will be placed |
| baseName | the basename of the symbol |
| argCount | the number of arguments in the desired overload. -1 for n-matched symbols. |
|
noexcept |
Looks up a symbol.
| exactName | the symbol name to look up, should be of the form baseName?argCount |
| void placeBuiltInSymbol | ( | const std::string & | baseName, |
| const boundFunction | func, | ||
| const char | argCount, | ||
| const unsigned char | delayMask | ||
| ) |
Loads a language built-in function into the symbol table.
| baseName | the name of the symbol to be created |
| func | a pointer to the implementation of the language built-in function |
| argCount | the number of arguments accepted by the built-in function |
| delayMask | the delayMask of the language built-in function |
| void placeUserSymbol | ( | const std::string & | baseName, |
| const ManyType & | mt, | ||
| const char | argCount, | ||
| const unsigned char | delayMask | ||
| ) |
Loads a user-defined symbol into the symbol table. Overwrites if the specific user-defined overload already exists.
| baseName | the name of the symbol to be created |
| mt | the definition of the symbol, must conform to symbolTable structure |
| argCount | the number of arguments accepted by the user-defined symbol |
| delayMask | the delayMask of the user-defined symbol |
| UserAlert | if the specific overload is a built-in symbol |
| const SymbolTableElement& readSymbol | ( | const std::string & | baseName, |
| const char | argCount | ||
| ) |
Returns a reference to the SymbolTableElement corresponding to the given baseName and argCount. May return n-match if an exact match is not present.
| baseName | the baseName to look up |
| argCount | the number of arguments of the desired function / variable |
| UserAlert | if there is no match |
| bool removeUserSymbol | ( | const std::string & | baseName, |
| const char | argCount | ||
| ) |
Removes a user-defined overload from the symbol table.
| baseName | the name of the symbol to be removed |
| argCount | the number of arguments accepted by the user-defined symbol |
| void removeUserSymbolList | ( | const std::string & | baseName, |
| std::vector< char > & | vec | ||
| ) |
Generates a list of overloads that can be removed for a given symbol name. If no user-defined overloads exist, then the list will be empty. Appends the list to vec.
| baseName | the baseName to look up |
| vec | a vector onto which the result list will be appended in-place the values will have the same form as in overloadsTable |
| std::unordered_map< std::string, std::vector<char> > overloadsTable |
A table to record all the overloads of all defined language symbols. The vector<char> represents a set of overloads of a given basename. Values >=0 are for user symbols, the value itself gives the number of arguments. -1 is for n-matched symbols. Values <=-2 are for built-in symbols, -(value+2) gives the number of arguments for a built-in symbol.
| std::unordered_map< std::string, SymbolTableElement > symbolTable |
Records the values of all symbols. Keys have the form basename?argCount. Values are either a DataExpression or are a StructureVector of the form [expression,varnames...]. The values can never be a StructureString.


--- layout: about title: About ---
This site is where I post some of my CTF writeups and some stuff that I love (not yet).


2 writes to ExecutionPolicy
System.Management.Automation (2)
engine\InitialSessionState.cs (1)
1649
ss.
ExecutionPolicy
= this.ExecutionPolicy;
engine\remoting\fanin\InitialSessionStateProvider.cs (1)
2375
iss.
ExecutionPolicy
= executionPolicy;
2 references to ExecutionPolicy
System.Management.Automation (2)
engine\InitialSessionState.cs (2)
1649
ss.ExecutionPolicy = this.
ExecutionPolicy
;
2219
SecuritySupport.SetExecutionPolicy(Microsoft.PowerShell.ExecutionPolicyScope.Process,
ExecutionPolicy
, shellId);


Hi my name is Bob and this is my portfolio.
I am a digital designer and part time developer. I studied at the Global School of Design in London and have traveled the world working at many interesting companies. My passion for design has allowed me to work on some pretty awesome projects. Please take a look at the projects below and feel free to email me at
<EMAIL_ADDRESS>I am currently available for hire.


We have partnered with Bnk to the Future to create a rare, but limited opportunity for both large and small investors to own shares of Kraken stock. On May 20th, Bnk to the Future will offer preferred shares of Kraken stock to registered users on their platform. Preferred shares of Kraken stock can be purchased until June 20th, with a minimum investment of only $1,000.
Interested clients should register an account (If you do not have one already) with Bnk to the Future at https://bnktothefuture.com. If you already have an account with Bnk to the Future, please visit the offering page at: https://bnktothefuture.com/pitches/kraken/landing. Once registered, the Bnk to the Future platform will guide you through the investment process, including steps for compliance verification and accreditation. Any questions on the registration process can be answered at https://bnktothefuture.com/support.
We will continue to look for ways to democratize access to our equity, allowing more of our valued clients to become valued investors.
Thank you for choosing Kraken, the most trusted and secure digital asset exchange.
The Kraken Team
Hmm
crumb20-May-19 09:54 PM
how much are they selling and at what expected mcap level? wonder if this is attractive or just skinning noobs
and by how much i mean what percentage of company stock
Bitcoin is wreking everyone no matter what it does
Markus20-May-19 10:40 PM
everyone?
BCH is on steroids non stop moon
SHossain20-May-19 10:58 PM
to whom it may concern: K64 is being notarized
3
Hollowman900020-May-19 11:10 PM
@SHossain
Big things shaping up for K64
Alecandro20-May-19 11:14 PM
Yeeeee
siu - Chainmakers20-May-19 11:14 PM
Yes, we submitted PR today, and network started notarizing a couple of hours later.
Notary Network has gotten a lot more efficient integrating chains and starting notarizations lately.
Hollowman900020-May-19 11:23 PM
@siu - Chainmakers submitted PR?
siu - Chainmakers20-May-19 11:25 PM
Pull Request to iguana network.

Hollowman900021-May-19 02:35 AM
The party ended last episode
This one was the writers saying “hey turn out the lights when you’re done” to the janitor
Wife wants to watch the last 50 shades movie then we can cancel hbo
dukeleto21-May-19 02:54 AM
i muted every variation of every word and hashtag about GoT on twitter and life is much better since then
Hollowman900021-May-19 02:56 AM
It’s over now
It died cold and alone
An old man, filled with regret, waiting to die alone
awful
meatspoon21-May-19 02:57 AM
Eliminating #MurderingMyAuntWhileFrenchKissingHer didn't negatively impact your twitter feed experience since then?
dan.21-May-19 02:57 AM
im in disbelief how bad got became
1
Hollowman900021-May-19 02:58 AM
The midget plays with furniture was the most memorable scene
1
Awful
dan.21-May-19 02:58 AM
lol
Hollowman900021-May-19 02:58 AM
Banned
GoT is banned now
Alexfrog21-May-19 02:59 AM
GoT season 8 was better than recent komodo performance
2
only the writing was bad though, everything else about the show was still top quality
couldve been a masterpiece if the writing stayed great
meatspoon21-May-19 03:00 AM
GoT definitely had more rebranding this season than KMD


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| {{watch['typeName']}} | sell buy | < ≤ ≥ > | {{watch['threshold'] | number:'1.0-2'}} |


Availability: ALL
The EA (Enter Alias) command creates an alias (synonym) for a list of one or more commands. The alias can then be used as a command name. EA (Enter Alias) is normally used to create a short abbreviation (one or more characters) for a longer command or sequence of commands that are frequently used.
Usage
EA ident cmd_list
Arguments
Description
When a MON command is being processed, the MON first checks to see if the command name matches an existing alias name, ignoring alphabetic case. If an alias match is found, the alias name is replaced by the text of cmd_list, and the command is re-scanned. If an alias match is not found, the MON checks for built-in commands. This means that aliases can be used to re-define existing built-in commands, and the alias replacement text can contain other alias names. Recursive alias references are not supported, however.
If cmd_list includes a “FR (File Read) C file” command, the command file will be read in Quiet mode to provide the illusion that the alias name is a built-in command.
Aliases can be displayed with the DA (Display Alias) command, and removed with the KA (Kill Alias) command.
Examples
EA DIA DW @pc l 10,i // Disassemble instructions from current pc
ea ms fr c my_script // Run the command file named my_script.maj
ea rc fr c // Read command file without echo. Note that since
// the command file name is not specified, it must
// be provided when the rc alias is subsequently
// used.


--- layout: page title: "About Me" description: "Hey, this is Maosen Li, who is with Cooperative Medianet Innovation Center (CMIC), Shanghai Jiao Tong University (SJTU), China." header-img: "img/post-bg-sjtu.jpg" ---
Maosen Li
Ph.D candidate at Cooperative Medianet Innovation Center, Shanghai Jiao Tong University
Advisors: Prof. Ya Zhang,
Dr. Siheng Chen
E-mail<EMAIL_ADDRESS>Address: 800 Dongchuan Rd. Minhang, Shanghai 200240, China
Education
Shanghai Jiao Tong University (2017-)
Ph.D candidate
Lab: Cooperative Medianet Innovation Center
School: School of Electronic Information and Electrical Engineering
Major: Information and Communication Engineering
University of Electronic Science and Technology of China (2013-2017)
Bachelor Degree
School: School of Optical Information
Major: Information Display and Optical Technology
Shandong Experimental High School (2010-2013)
High School
Ph.D Research Area
Graph Representation Learning; Visual Reasoning; Video Analysis; Image/Video Synthesis; Machine Learning; Computer Vision; Deep Learning.
Publications
Actional-Structural Graph Convolutional Networks for Skeleton-based Action Recognition (CVPR 2019)
Class-Agnostic Action-Skeleton Graph Neural Networks for 3D Skeleton-based Human Motion Prediction (In Submission)
Unsupervised Local Facial Attributes Transfer Using Dual Discriminative Adversarial Networks (ICME 2018)
Decoupled Variational Embedding for Signed Directed Networks (In Submission)
Honors and Awards
Outstanding Graduates of UESTC (2017)
Undergraduate National Scholarship (2016)
Excellence Scholarship (A) of Cooperative Medianet Innovation Center, SJTU (2018)
First Award of National Post-Graduate Mathematical Contest in Modeling (2018)
Honorable Mentioned of Mathematical Contest in Modeling (2015)
Work and Internship
T.A. of 'Introduction to Electrical Engineering C' (instructors: Prof. Ya Zhang & A.R. Dazhi He).
Intern of Shanghai Media Intelligence Inc.


[§356D-70] Rights and powers; supplemental. The rights and powers conferred upon the authority by this subpart shall be in addition and supplemental to the rights and powers conferred upon the authority by any other law. [L 2006, c 180, pt of §2]


| 002:062 Khan | : |
Verily! Those who believe and those who are Jews and Christians, and Sabians, whoever believes in Allah and the Last Day and do righteous good deeds shall have their reward with their Lord, on them shall be no fear, nor shall they grieve. |
| 002:062 Maulana | : |
Surely those who believe, and those who are Jews, and the Christians, and the Sabians, whoever believes in Allah and the Last Day and does good, they have their reward with their Lord, and there is no fear for them, nor shall they grieve. |
| 002:062 Pickthal | : |
Lo! Those who believe (in that which is revealed unto thee, Muhammad), and those who are Jews, and Christians, and Sabaeans - whoever believeth in Allah and the Last Day and doeth right - surely their reward is with their Lord, and there shall no fear come upon them neither shall they grieve. |
| 002:062 Rashad | : |
Surely, those who believe, those who are Jewish, the Christians, and the converts; anyone who (1) believes in GOD, and (2) believes in the Last Day, and (3) leads a righteous life, will receive their recompense from their Lord. They have nothing to fear, nor will they grieve. |
| 002:062 Sarwar | : |
However, those who have become believers (the Muslims), and the Jews, the Christians and the Sabaeans who believe in God and the Day of Judgment and strive righteously will receive their reward from the Lord and will have nothing to fear nor will they be grieved. |
| 002:062 Shakir | : |
Surely those who believe, and those who are Jews, and the for Christians, and the Sabians, whoever believes in Allah and the Last day and does good, they shall have their reward from their Lord, and there is no fear for them, nor shall they grieve. |
| 002:062 Sherali | : |
Surely, those who believe and the Jews and the Christians and the Sabians - whichever party from among these truly believes in ALLAH and the Last Day and does good deeds, shall have their reward with their Lord, and no fear shall come upon then nor shall they grieve. |
| 002:062 Yusufali | : |
Those who believe (in the Qur'an), and those who follow the Jewish (scriptures), and the Christians and the Sabians,- any who believe in Allah and the Last Day, and work righteousness, shall have their reward with their Lord; on them shall be no fear, nor shall they grieve. |
Concepts/Themes Covered: الإيمان > الإيمان بالله > الإيمان والعمل Faith> Belief In Allah> The Faith and the Action الإيمان > المؤمنون > لا خوف عليهم Faith> The Believers> There is no Fear on them الدعوة إلى الله > حدودها > التساهل مع المسالمين The Call for Allah> Its Limits> Lenient with the Peace-Loving الديانات > أهل الكتاب: ( اليهود و النصارى) > وجوب التساهل معهم(مع غير المحاربين) Religions> The People of the Book (the Jews and the Christians)> The Necessity to be Easy with them (with the Non-Fighters) الديانات > بنو إسرائيل > حالاتهم Religions>Children of Israel>Their Status الديانات > الصابئون Religions>The Sobians الديانات > النصارى > أجر المؤمنين منهم Religions>The Christians>The Believers' Reward from them
This tool uses eight English parallel translations for the Holy Quran imported from Mr. Clay Smith. You can also visit Qurany Concepts for browsing the Quran themes or topics. If you want to search the Holy Quran for keywords, please visit Qurany Keywords.


Although the desired effect should be different than the one seen below (if you check it in firefox) it is correct according to the specification:
Specifies a percentage for determining the used value. The percentage is calculated with respect to the height of the generated box's containing block. If the height of the containing block is not specified explicitly (i.e., it depends on content height), and this element is not absolutely positioned, the percentage value is treated as '0' (for 'min-height') or 'none' (for 'max-height').
Chromium, howver, renders it differently which looks like a bug


Behavior Trees (BTs) got the robotics society attention not least thanks to their modularity and reusability. The subtrees of BTs could be treated as separate behaviors and therefore reused. We address the following research question: do we exploit the full power of BT on these properties? We suggest to generalise the idea of subtree reuse to “node templates” concept, which allows to represent an arbitrary nodes collection. In addition, previously hardcoded behaviors such as Node* and many Decorator nodes could be implemented in a memory-based BT by node templates.
Robotic Learning from Demonstration (LfD) allows anyone, not just experts, to program a robot for an arbitrary task. Many LfD methods focus on low level primitive actions such as manipulator trajectories. Complex multistep task with many primitive actions must be learned from demonstration if LfD is to encompass the full range of task a user may desire. Existing methods represent the high level task in various forms including, finite state machines, decision trees, formal logic, among others. Behavior trees are proposed as an alternative representation of high level task. Behavior trees are an execution model for the control of a robot designed for real time execution, modularity, and, consequently, transparency. Real time execution allows the robot to reactively perform the task. Modularity allows the reuse of learned primitive actions and high level task in new situations, speeding up the process of learning in new scenarios. Transparency allows users to understand and interactively modify the learned model. Behavior trees are used to represent high level tasks by building on the relationship it has with decision trees. We demonstrate a human teaching our Fetch robot a household cleaning task.
In this paper, we present a modular mission
management system for micro aerial vehicles (MAVs), with
a successful application in real environments with the actual
platform. We use the behaviour tree as the main underlying
structure to represent and further execute the complex mission
plan. The proposed system is highly modular, allowing the
user to specify different missions by simply grouping various
mission-independent sub-behaviours together. The paper also
describes a set of commonly used behaviours for MAVs. The
proposed system has been applied to the 2017 International
Micro Air Vehicle Competition (IMAV 2017) and has helped
the team win both indoor and outdoor championships.
Reconfigure collaborative robots on new tasks quickly and efficiently is today one of the great challenges for manufacturing industries. In this respect, behavior trees already proved to be a great tool to design complex coordination schemes with important required characteristics, such as high modularity, predictability and reactivity. Nevertheless, BTs are also affected by some limitations that are preventing their widespread adoption. For example, being a state-less logic in their classical form, BTs have more problems representing sequences w.r.t. other approaches such as e.g. FSMs. We identified this and other issues while applying BTs to industrial robots task design. In this presentation we will discuss our experience and look at the solutions implemented to make the classical BT a more complete task programming framework for industrial applications.


This pie chart shows the usage by customers identifying under each of the passholder subsections. The four subsections of interest are Walk-up, Flex Pass, Monthly Pass, and Staff Annual. The usage percentages are derived from aggregating the total entries under each subsection in the dataset. Since it is a direct count of usage in each category over the 266 day period, it more accurately represents which categories rely more on the system rather than which categories houses more people, since this does not factor in repeat customers, which can be hard to do for non-regulars (Walk-in) customers.
This pie chart, seemingly simple, gives a large insight into a lurking problem in the Bike Share industry. The over saturation of users that are in it for a One-Way trip creates an issue of redistribution of bikes. A bike that may start at the residential part of the city, may end up elsewhere. If enough bikes get redistributed, it may effect business negatively and deter users from future usages.
Since the percentage of Round-Trip users is so low, and it is more beneficial to the Bike Share program to have customers bring back bikes to starting locations, an incentive can implemented to persuade users with an inelastic demand for Round-Trips. However, this creates the problem of users holding on to the bike for a longer amount of time, which may be solved with more bikes being deployed, but this bring total cost to the business up. There are many trade-offs to be considered here, given more time, a more thorough model could be built analyzing this very interesting proposition.
This graph shows the total number of trips by month. The upper bound seems to be around 25,000 rides per month, while the lower bound bottoms out around 10,000 rides per month. The interesting aspect of this is that how well it is tracing the average temperature in Los Angeles by Month. Although only a correlation, it gives some insight on the activity levels in Los Angeles as the seasons progress. There is a clear dip in Bike Share usage in the months of December and January when the weather gets cold relative to the Summer and Fall months.
Furthermore, this correlation may be attributed to various other factors like winter break for students, or the holidays and generally lesser outdoor activities in the winter.
(data source: https://www.wunderground.com/history/monthly/us/ca/ los-angeles-downtown/KCQT)


# -*- coding: utf-8 -*-
# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Encode using wordpiece models.
Implements the segmentation algorithm described in the last paragraph of
p. 5150, in the following publication:
M. Schuster and K. Nakajima, "Japanese and Korean voice
search," 2012 IEEE International Conference on Acoustics,
Speech and Signal Processing, 2012
https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/37842.pdf
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import sys
import tensorflow as tf
# Must be a large ID.
NO_TOKEN = sys.maxint
NO_TOKEN_STRING = '<unk>'
SENTENCE_START_STRING = '<s>'
SENTENCE_END_STRING = '</s>'
[docs]class WpmEncoder(object):
def __init__(self, wpm_filepath):
# Load vocabulary file.
self._piece2id = {}
self._pieces = []
with tf.gfile.Open(wpm_filepath, 'r') as f:
pid = 0
for line in f.readlines():
line = line.decode('utf-8')
piece = line.strip().split('\t')[0]
tf.logging.vlog(6, 'voc: %s -> %d', piece, pid)
self._piece2id[piece] = pid
pid += 1
self._pieces += [piece]
assert self._StringToToken(NO_TOKEN_STRING) != NO_TOKEN
[docs] def _TokenToString(self, token, safe=False):
if token == NO_TOKEN:
if safe:
return NO_TOKEN_STRING
else:
assert token != NO_TOKEN
return self._pieces[token]
[docs] def _TokensToString(self, tokens, sep=' ', safe=False):
return sep.join([self._TokenToString(t, safe) for t in tokens])
[docs] def _StringToToken(self, tokstr):
if tokstr in self._piece2id:
return self._piece2id[tokstr]
return NO_TOKEN
[docs] def _MergeTokens(self, token1, token2):
return self._StringToToken(
self._TokenToString(token1) + self._TokenToString(token2))
[docs] def _IsAllNull(self, candidates):
return len(candidates) == candidates.count(NO_TOKEN)
[docs] def _EncodeToIds(self, word):
if not isinstance(word, unicode):
word = word.decode('utf-8')
# First, match entire words. That will work to shortcut exception words
# such as <S>, even if there is no path to merge hierarchically. To wit,
# in this case, either '<S' or 'S>' must be present.
if word in self._piece2id:
return [self._piece2id[word]]
# Henceforth,
# * a token is a wordpiece ID.
# * the tokens array will be merged in-place.
# * the candidates array is an array of size len(tokens) - 1.
# It contains the token for the merged wordpiece, if it exists,
# -1 otherwise. For instance, candidate[3] = id(token[3] + token[4]).
# First, split into basic UTF-8 characters (letters).
tokens = [self._StringToToken(letter) for letter in word]
for t in range(len(tokens)):
if tokens[t] == NO_TOKEN:
# Unseen character.
tokens[t] = self._StringToToken(NO_TOKEN_STRING)
# Create initial candidate list.
candidates = []
for i in range(len(tokens) - 1):
merged = self._MergeTokens(tokens[i], tokens[i + 1])
candidates += [merged]
# Merge in the reverse binary tree until no more merges are possible.
while not self._IsAllNull(candidates):
best_id = np.argmin(candidates)
# Perform the merge at position best_id.
tokens = tokens[:best_id] + [candidates[best_id]] + tokens[best_id + 2:]
# Recompute the merge candidates to the right:
if best_id < len(tokens) - 1:
candidates[best_id] = self._MergeTokens(tokens[best_id],
tokens[best_id + 1])
else:
candidates.pop()
# Recompute the merge candidates to the left:
if best_id > 0:
candidates[best_id - 1] = self._MergeTokens(tokens[best_id - 1],
tokens[best_id])
candidates = candidates[:best_id + 1] + candidates[best_id + 2:]
assert len(tokens) == len(candidates) + 1
return tokens
[docs] def EncodeWord(self, word):
return [self._TokenToString(t) for t in self._EncodeToIds(word)]
[docs] def EncodeToStringAndIds(self, word):
return [(self._TokenToString(t), t) for t in self._EncodeToIds(word)]
[docs] def Encode(self, text):
"""Assumes that the text is utf-8 decoded."""
words = text.split(' ')
encoded_words = []
for w in words:
encoded_words += self.EncodeWord(w)
return ' '.join(encoded_words)
@property
def sentence_start_id(self):
return self._piece2id[SENTENCE_START_STRING]
@property
def sentence_start_string(self):
return SENTENCE_START_STRING
@property
def sentence_end_id(self):
return self._piece2id[SENTENCE_END_STRING]
@property
def sentence_end_string(self):
return SENTENCE_END_STRING


{% set active_page = "Registration" %} {% set page_title = "Registration" %} {% extends "base.html" %} {% block tabs %}
{% endblock %} {% block content %}The 2020 ACM Conference on Health, Inference, and Learning will be held online. Registration will remain open until July 24, 2020.
{{ components.subsubsection("Pricing") }}| Registration Type | Amount (USD) |
|---|---|
| ACM Member | 25 |
| Non-ACM Member | 30 |
If you have previously registered, you do not need to register again. We will refund the difference between the prices using the original method of payment. If you are not able to attend online, email<EMAIL_ADDRESS>to request a refund. Please include your registration number in the subject line. Refunds will be issued starting June 22, 2020 and may take up to 2 weeks before they are reflected on your bank or credit card statements.
Please cancel any travel and accommodation arrangements if you have not already done so. If you booked accommodation at the Sheraton Hotel, we have cancelled the reservation on your behalf and no further action is required.
Those who lack the means to pay for registration or who cannot afford to travel to attend the conference may apply for financial support, which consists of (1) a registration fee waiver and/or (2) a travel grant of a maximum of $1000.
Travel grants will be extremely limited and only offered to those in the most financially precarious positions. Please only apply for a travel grant if there is no other way for you to attend the conference. Applicants must declare explicitly they will not be able to attend without financial support from some source external to their own institute. Please try to get funding from other sources whenever possible, and apply for the minimum amount that you would require to come to the conference (considering travel, hotel, and subsistence costs).
Travel grants are offered in two modalities, of which you can choose one (if you are unsure, we recommend modality A):
Applicants for financial support must justify their need in their application. Among applicants who justify their need for support, priority will be given to those actively contributing to the conference. Any attendee who needs a travel grant to attend the conference will be eligible. Required application materials:
Possible outcomes are:
To apply for financial support, please fill out this form. If you are enrolled in a graduate program, please send your letters of support from your advisor and other relevant material to<EMAIL_ADDRESS>{{ components.subsection("Information for grantees") }}After receiving a travel grant, grantees will have to provide proof by e-mail to<EMAIL_ADDRESS>that they will attend the conference. This can be a flight/hotel reservation, a visa appointment or a visa, or similar materials. Not providing this proof will void the grant.
The travel grant cannot be advanced but is instead paid as a reimbursement or a lump sum after the conference takes place, as per ACM rules. Forms, instructions, and a contact e-mail address at ACM will be provided to all grantees.


--- layout: post title: A NServiceBus Unit Of Work implementation for RavenDB date: 2011-11-22 13:47:58.000000000 +01:00 categories: - NServiceBus - RavenDB tags: [] status: publish type: post published: true meta: dsq_thread_id: '480258277' _edit_last: '1' author: login: andreas.ohlund email<EMAIL_ADDRESS>display_name: Andreas Öhlund first_name: Andreas last_name: "Öhlund" ---
In my last post I introduced the new UoW support in NServiceBus 3.0. In this post I'll show you how to use it to create a UoW implementation for RavenDB. Ayende has already blogged about one way to do it but now there is a better way.
Sharing the session
The first thing we need to take care of is sharing the session between our message handler(s) and the actual unit of work implementation. We could do this using thread static but that has some issues that I have mentioned before.
Instead we will use the new support for child containers in NServiceBus 3.0. This means that all dependencies configured as single call effectively becomes static within the context of one transport message and this is exactly what we need.
To resolve a Raven document session from the container we'll add the following configuration (I'm using StructureMap but any one of the other containers except Spring and Unity would work)
The above code tells the container to create a new IDocumentSession using the lambda specified and the fact that all message processing is done using a child container means that all message handlers processing the message will get the same session instance.
Implementing the Unit Of Work
In RavenDB you need to explicitly call IDocumentSession.SaveChanges() in order to persist your data to the database. So to avoid making this call in all our handlers we add a unit of work implementation that takes care of it. This saves typing and makes sure that our developers won't introduce bugs by forgetting to make the call.
Note that we're taking a dependency on the IDocumentSession and given that the UoW is resolved from the same child container as the handlers we'll get the same session instance. Raven doesn't need any special setup so the only thing we need do is call SaveChanges if End() is called and no exception occurred.
The final thing we have to do is make NServiceBus use our UoW. We do this by configuring it in the container so that NServiceBus will find it and use it:
What about disposing the session?'
 NServiceBus is disposing the child container when the processing of a transport message is complete and this has the effect that any object implementing IDisposable will be disposed. Luckily for us IDocumentSession does just this!
With this in place we can create very clean message handlers that interact with Raven:
Working code please...
A working sample can be found over at my github account.
Any feedback is much appreciated!


Classify
-
beginning: 原始思想；前兆；初级阶段 the first or early ideas, signs or stages of sth 开头；开端；开始部分 the time when sth starts; the first part of an event, a story, etc.
-
rudimentary: 基础的；基本的 dealing with only the most basic matters or ideas 未充分发展的；原始的 not highly or fully developed
-
intermediate: 中级的；中等的；适合中等程度者的 having more than a basic knowledge of sth but not yet advanced; suitable for sb who is at this level
-
advanced: 高级的；高等的 at a high or difficult level 先进的 having the most modern and recently developed ideas, methods, etc.
-
full-fledged: 羽毛丰满的；发育充分的；有充分资格的
a full-fledged bird has grown all its feathers and is able to fly;
completely developed
-
under-developed: 发育不全的；不发达的；显影不足的
-
developed: 发达的；高度发展的having many industries and a complicated economic system 先进的；成熟的 in an advanced state
-
elementary: 初级的；基础的in or connected with the first stages of a course of study
-
fundamental: 基础的；基本的 central; forming the necessary basis of sth。也可以表示“重要性”，参考fundamental
优势
-
vantage: 优势;优越的地位 a position that provides an advantage
词组


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We help you clarify your thoughts and walk you through the process of confidently communicating them.
We help you clarify your thoughts and walk you through the process of confidently communicating them.
We help you understand the mechanics of reading and writing.
We help you build your communicatioin skills.
We help you clarify your thoughts and walk you through the process of confidently communicating them.
We help you understand the mechanics of reading and writing.


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This is a demo for mock server,it's a part of devServer which supports HTTP/WebSocket API.
Try it out:
A frontend productivity tool for Visual Studio Code.
This is a demo for mock server,it's a part of devServer which supports HTTP/WebSocket API.
Try it out:


UFC: Conor McGregor’s manager reveals Cerrone talks ‘progressing’
by FSA EditorAudie Attar is optimistic he will be able to make an announcement over Conor McGregor’s comeback fight in the near future.
Conor McGregor’s manager hopes to confirm a deal for the Irishman to make his UFC comeback against Donald Cerrone “in the coming weeks”.
The former two-weight world champion last month stated that he will make his return to the octagon on January 18.
McGregor has not fought since he was beaten by Khabib Nurmagomedov in a lightweight title bout in October 2018.
Cerrone urged the 31-year-old to “sign the deal” in October and Audie Attar, McGregor’s manager, is optimistic an agreement can soon be reached.
He told the South China Morning Post: “I would say that talks have been progressing. We’ve met them [Cerrone’s team], we’re in close communication with them.
“I would hope that an announcement could happen in the coming weeks, but at the end of the day, it’s still a work in progress.”
Attar believes McGregor can be crowned UFC champion again.
“I don’t see him slowing down,” he added.
“I think that fighting once a year for the last couple of years, it’s almost like it’s grown his appetite to stay active.
“In any sport, the more reps you take, the better you get. He’s no stranger to becoming a world champion multiple times over. He knows what it takes.”


class TDD_Scheduler(IntraSliceScheduler):
This class implements TDD intra slice scheduling.
| Method | __init__ |
Undocumented |
| Instance Variable | symMax |
Undocumented |
| Instance Variable | queue |
TDD scheduler TB queue. |
| Method | resAlloc |
This method implements resource allocation between the different connected UEs in a TDD slice. |
| Method | queueUpdate |
This method fills scheduler TB queue at each TTI with TBs built with UE data/signalling bytes. |
| Instance Variable | ueLst |
Undocumented |
| Instance Variable | sm_lim |
Undocumented |
| Method | rrcUncstSigIn |
Undocumented |
| Method | retransmitTB |
Undocumented |
| Method | dataPtoTB |
This method takes UE data bytes, builds TB and puts them in the scheduler TB queue. |
| Method | setTBS |
Undocumented |
| Method | printResAlloc |
Undocumented |
Undocumented
TDD scheduler TB queue.
IntraSliceScheduler class attribute queue is overwriten here by a new type of queue which handles symbols. This queue will contain as much TB as a slot can contain. If resource allocation is made in terms of slots, it will contain 1 element, else, it will contain as much mini-slots as can be supported in 1 slot.
This method implements resource allocation between the different connected UEs in a TDD slice.
It overwrites the resAlloc method from IntraSliceScheduler class. In this Py5cheSim version TDD scheduler allocates all PRBs in the slice to a UE during 1 slot. Future Py5cheSim versions could support mini-slot allocation by changing the UE symbol allocation in this method. Note that in that case, althoug there is no need to update the queueUpdate method, TBS calculation must be adjusted to avoid losing capacity when trunking the Nre__ value.
This method fills scheduler TB queue at each TTI with TBs built with UE data/signalling bytes.
It overwrites queueUpdate method from IntraSliceScheduler class, making Resource allocation in terms of slot Symbols and insert generated TBs into Scheduler queue in a TTI. Althoug in this version Resource allocation is made by slot, it is prepared to support mini-slot resource allocation by handling a scheduler TB queue in terms of symbols.


a self-taught software engineer👨🏻💻 based in Tokyo🗼. Focused on delivering user-friendly softwares.
Currently lead projects at Rakuten Inc. Previously a developer of JXPress.


{extend name="pc/base/base" /} {block name="title"}
| Driver Name | {$data.name} |
|---|---|
| Version | {$data.version} |
| Update At | {$data.create_time|strtotime|date="m/d/Y",###} |
| Support Product | {notempty name="data.products"} {volist name="data.products" id="vo"} {$vo.model} {/volist} {else /} If you lost the user guide and CD driver, you can turn to driver download online here. When you find no applicable model written in “Support Product”, please contact us via email to<EMAIL_ADDRESS>to get information, and we will tell you which one is suitable for your product. {/notempty} |
| Download Link | {notempty name="data.downloads"} {volist name="data.downloads" id="vo"} {$vo.requirement} {/volist} {/notempty} |
|
* Software update is usually for optimizing the product to support in special environments and applications. If your product works properly, it is not recommended to upgrade your product. * If you find the links are invalid or any other issues, please email to<EMAIL_ADDRESS>to make feedback or contact online customer service, thank you. |


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vue-vo·cab·u·lar·y
Vue components implementing a cohesive design system to unite the web-facing Creative Commons.
Apologies, but Vue Vocabulary cannot work without JavaScript enabled.
GitHub


| 547 | (A) | and fułł kenly thay come the castełłs to assayłłe |
| 549 | (C) | ¶ Kenely þe castelles . þey gan to a-sayle |
| 543 | (D) | kenely her castels they come to assaile |
| 535 | (E) | & kenely þe castelles . he caste hym to assayle . |
| (Ex) | ---- no text ---- | |
| 564 | (L) | kenely þe castels came to assayle |
| 519 | (P) | Kenely þe castels he come to assayle |
| 542 | (U) | kenely the castels / thei kome to assaile |
| (V) | ---- no text ---- |


|
CANopenNode
|
Stack configuration macros specify, which parts of the stack will be enabled. More...
Macros | |
| #define | CO_CONFIG_FLAG_CALLBACK_PRE 0x1000 |
| Enable custom callback after CAN receive. More... | |
| #define | CO_CONFIG_FLAG_TIMERNEXT 0x2000 |
| Enable calculation of timerNext_us variable. More... | |
| #define | CO_CONFIG_NMT (CO_CONFIG_FLAG_CALLBACK_PRE | CO_CONFIG_FLAG_TIMERNEXT | CO_CONFIG_NMT_CALLBACK_CHANGE | CO_CONFIG_NMT_MASTER0) |
| Configuration of NMT_Heartbeat object. More... | |
| #define | CO_CONFIG_SDO (CO_CONFIG_FLAG_CALLBACK_PRE | CO_CONFIG_FLAG_TIMERNEXT | CO_CONFIG_SDO_SEGMENTED | CO_CONFIG_SDO_BLOCK) |
| Configuration of SDO server object. More... | |
| #define | CO_CONFIG_SDO_BUFFER_SIZE 32 |
| Size of the internal data buffer for the SDO server. More... | |
| #define | CO_CONFIG_EM (CO_CONFIG_FLAG_CALLBACK_PRE | CO_CONFIG_FLAG_TIMERNEXT | CO_CONFIG_EM_CONSUMER) |
| Configuration of Emergency object. More... | |
| #define | CO_CONFIG_HB_CONS (CO_CONFIG_FLAG_CALLBACK_PRE | CO_CONFIG_FLAG_TIMERNEXT | CO_CONFIG_HB_CONS_CALLBACK_CHANGE | CO_CONFIG_HB_CONS_CALLBACK_MULTI | CO_CONFIG_HB_CONS_QUERY_FUNCT) |
| Configuration of Heartbeat Consumer. More... | |
| #define | CO_CONFIG_PDO (CO_CONFIG_FLAG_CALLBACK_PRE | CO_CONFIG_FLAG_TIMERNEXT | CO_CONFIG_PDO_SYNC_ENABLE | CO_CONFIG_RPDO_CALLS_EXTENSION | CO_CONFIG_TPDO_CALLS_EXTENSION) |
| Configuration of PDO. More... | |
| #define | CO_CONFIG_SYNC (CO_CONFIG_FLAG_CALLBACK_PRE | CO_CONFIG_FLAG_TIMERNEXT) |
| Configuration of SYNC. More... | |
| #define | CO_CONFIG_SDO_CLI (CO_CONFIG_FLAG_CALLBACK_PRE | CO_CONFIG_FLAG_TIMERNEXT | CO_CONFIG_SDO_CLI_SEGMENTED | CO_CONFIG_SDO_CLI_BLOCK | CO_CONFIG_SDO_CLI_LOCAL) |
| Configuration of SDO client object. More... | |
| #define | CO_CONFIG_SDO_CLI_BUFFER_SIZE 32 |
| Size of the internal data buffer for the SDO client. More... | |
| #define | CO_CONFIG_TIME (CO_CONFIG_FLAG_CALLBACK_PRE) |
| Configuration of TIME. More... | |
| #define | CO_CONFIG_LEDS (CO_CONFIG_FLAG_TIMERNEXT | CO_CONFIG_LEDS_ENABLE) |
| Configuration of LEDs object. More... | |
| #define | CO_CONFIG_LSS (CO_CONFIG_FLAG_CALLBACK_PRE | CO_CONFIG_LSS_SLAVE | CO_CONFIG_LSS_SLAVE_FASTSCAN_DIRECT_RESPOND | CO_CONFIG_LSS_MASTER ) |
| Configuration of LSS objects. More... | |
| #define | CO_CONFIG_GTW (CO_CONFIG_GTW_MULTI_NET | CO_CONFIG_GTW_ASCII | CO_CONFIG_GTW_ASCII_SDO | CO_CONFIG_GTW_ASCII_NMT | CO_CONFIG_GTW_ASCII_LSS | CO_CONFIG_GTW_ASCII_LOG | CO_CONFIG_GTW_ASCII_ERROR_DESC | CO_CONFIG_GTW_ASCII_PRINT_HELP | CO_CONFIG_GTW_ASCII_PRINT_LEDS) |
| Configuration of gateway object usage. More... | |
| #define | CO_CONFIG_GTW_BLOCK_DL_LOOP 1 |
| Number of loops of CO_SDOclientDownload() in case of block download. More... | |
| #define | CO_CONFIG_GTWA_COMM_BUF_SIZE 200 |
| Size of command buffer in ASCII gateway object. More... | |
| #define | CO_CONFIG_GTWA_LOG_BUF_SIZE 2000 |
| Size of message log buffer in ASCII gateway object. | |
Stack configuration macros specify, which parts of the stack will be enabled.
Default values for stack configuration macros are set in CO_driver.h file. They can be overridden by CO_driver_target.h file. If specified so, they can further be overridden by CO_driver_custom.h file.
Stack configuration macro is specified as bits, where each bit enables/disables some part of the configurable CANopenNode object. Flags are used for enabling or checking specific bit. Multiple flags can be ORed together.
Configuration macros may be in relation with CANopen configuration macros from CANopen.h file. Former enables/disables stack functionalities, latter enables/disables objects in object dictionary. Note that some objects in object dictionary may need some configuration macros to be enabled.
| #define CO_CONFIG_EM (CO_CONFIG_FLAG_CALLBACK_PRE | CO_CONFIG_FLAG_TIMERNEXT | CO_CONFIG_EM_CONSUMER) |
Configuration of Emergency object.
Possible flags, can be ORed:
| #define CO_CONFIG_FLAG_CALLBACK_PRE 0x1000 |
Enable custom callback after CAN receive.
Flag enables optional callback functions, which are part of some CANopenNode objects. Callbacks can optionally be registered by application, which configures threads in operating system. Callbacks are called after something has been preprocessed by higher priority thread and must be further processed by lower priority thread. For example when CAN message is received and preprocessed, callback should wake up mainline processing function. See also CO_process() function.
If callback functions are used, they must be initialized separately, after the object initialization.
This flag is common to multiple configuration macros.
| #define CO_CONFIG_FLAG_TIMERNEXT 0x2000 |
Enable calculation of timerNext_us variable.
Calculation of the timerNext_us variable is useful for smooth operation on operating system. See also CO_process() function.
This flag is common to multiple configuration macros.
| #define CO_CONFIG_GTW (CO_CONFIG_GTW_MULTI_NET | CO_CONFIG_GTW_ASCII | CO_CONFIG_GTW_ASCII_SDO | CO_CONFIG_GTW_ASCII_NMT | CO_CONFIG_GTW_ASCII_LSS | CO_CONFIG_GTW_ASCII_LOG | CO_CONFIG_GTW_ASCII_ERROR_DESC | CO_CONFIG_GTW_ASCII_PRINT_HELP | CO_CONFIG_GTW_ASCII_PRINT_LEDS) |
Configuration of gateway object usage.
Gateway object is covered by standard CiA 309 - CANopen access from other networks. It enables usage of the NMT master, SDO client and LSS master as a gateway device.
Possible flags, can be ORed:
| #define CO_CONFIG_GTW_BLOCK_DL_LOOP 1 |
Number of loops of CO_SDOclientDownload() in case of block download.
If SDO clint has block download in progress and OS has buffer for CAN tx messages, then CO_SDOclientDownload() functionion can be called multiple times within own loop (up to 127). This can speed-up SDO block transfer.
| #define CO_CONFIG_GTWA_COMM_BUF_SIZE 200 |
Size of command buffer in ASCII gateway object.
If large amount of data is transferred (block transfer), then this should be increased to 1000 or more. Buffer may be refilled between the block transfer.
| #define CO_CONFIG_HB_CONS (CO_CONFIG_FLAG_CALLBACK_PRE | CO_CONFIG_FLAG_TIMERNEXT | CO_CONFIG_HB_CONS_CALLBACK_CHANGE | CO_CONFIG_HB_CONS_CALLBACK_MULTI | CO_CONFIG_HB_CONS_QUERY_FUNCT) |
Configuration of Heartbeat Consumer.
Possible flags, can be ORed:
| #define CO_CONFIG_LEDS (CO_CONFIG_FLAG_TIMERNEXT | CO_CONFIG_LEDS_ENABLE) |
Configuration of LEDs object.
Possible flags, can be ORed:
| #define CO_CONFIG_LSS (CO_CONFIG_FLAG_CALLBACK_PRE | CO_CONFIG_LSS_SLAVE | CO_CONFIG_LSS_SLAVE_FASTSCAN_DIRECT_RESPOND | CO_CONFIG_LSS_MASTER ) |
Configuration of LSS objects.
Possible flags, can be ORed:
| #define CO_CONFIG_NMT (CO_CONFIG_FLAG_CALLBACK_PRE | CO_CONFIG_FLAG_TIMERNEXT | CO_CONFIG_NMT_CALLBACK_CHANGE | CO_CONFIG_NMT_MASTER0) |
Configuration of NMT_Heartbeat object.
Possible flags, can be ORed:
| #define CO_CONFIG_PDO (CO_CONFIG_FLAG_CALLBACK_PRE | CO_CONFIG_FLAG_TIMERNEXT | CO_CONFIG_PDO_SYNC_ENABLE | CO_CONFIG_RPDO_CALLS_EXTENSION | CO_CONFIG_TPDO_CALLS_EXTENSION) |
Configuration of PDO.
Possible flags, can be ORed:
| #define CO_CONFIG_SDO (CO_CONFIG_FLAG_CALLBACK_PRE | CO_CONFIG_FLAG_TIMERNEXT | CO_CONFIG_SDO_SEGMENTED | CO_CONFIG_SDO_BLOCK) |
Configuration of SDO server object.
Possible flags, can be ORed:
| #define CO_CONFIG_SDO_BUFFER_SIZE 32 |
Size of the internal data buffer for the SDO server.
Size must be at least equal to size of largest variable in Object dictionary. If data type is domain, data length is not limited to SDO buffer size. If block transfer is implemented, value should be set to 889.
Value can be in range from 7 to 889 bytes.
| #define CO_CONFIG_SDO_CLI (CO_CONFIG_FLAG_CALLBACK_PRE | CO_CONFIG_FLAG_TIMERNEXT | CO_CONFIG_SDO_CLI_SEGMENTED | CO_CONFIG_SDO_CLI_BLOCK | CO_CONFIG_SDO_CLI_LOCAL) |
Configuration of SDO client object.
Possible flags, can be ORed:
| #define CO_CONFIG_SDO_CLI_BUFFER_SIZE 32 |
Size of the internal data buffer for the SDO client.
Circular buffer is used for SDO communication. it can be read or written between successive SDO calls. So size of the buffer can be lower than size of the actual size of data transferred. If only segmented transfer is used, then buffer size can be as low as 7 bytes, if data are read/written each cycle. If block transfer is used, buffer size should be set to at least 889 bytes, so maximum blksize can be used. In that case data should be read/written proper time between cycles.
| #define CO_CONFIG_SYNC (CO_CONFIG_FLAG_CALLBACK_PRE | CO_CONFIG_FLAG_TIMERNEXT) |
Configuration of SYNC.
Possible flags, can be ORed:
| #define CO_CONFIG_TIME (CO_CONFIG_FLAG_CALLBACK_PRE) |
Configuration of TIME.
Possible flags, can be ORed:


R/summ_classmetric.R
summ_classmetric.Rd
Compute metric of the following one-dimensional binary classification setup:
any x
value not more than threshold
value is classified as "negative"; if
strictly greater - "positive". Classification metrics are computed based on
two pdqr-functions: f
, which represents the distribution of values which
should be classified as "negative" ("true negative"), and g
- the same
for "positive" ("true positive").
summ_classmetric(f, g, threshold, method = "F1")
summ_classmetric_df(f, g, threshold, method = "F1")
| f | A pdqr-function of any type and class. Represents distribution of "true negative" values. |
|---|---|
| g | A pdqr-function of any type and class. Represents distribution of "true positive" values. |
| threshold | A numeric vector of classification threshold(s). |
| method | Method of classification metric (might be a vector for
|
summ_classmetric()
returns a numeric vector, of the same length as
threshold
, representing classification metrics for different threshold
values.
summ_classmetric_df()
returns a data frame with rows corresponding to
threshold
values. First column is "threshold" (with threshold
values),
and all other represent classification metric for every input method (see
Examples).
Binary classification setup used here to compute metrics is a
simplified version of the most common one, when there is a finite set of
already classified objects. Usually, there are N
objects which are truly
"negative" and P
truly "positive" ones. Values N
and P
can vary, which
often results in class imbalance. However, in current setup both N
and
P
are equal to 1 (total probability of f
and g
).
In common setup, classification of all N + P
objects results into the
following values: "TP" (number of truly "positive" values classified as
"positive"), "TN" (number of negatives classified as "negative"), "FP"
(number of negatives falsely classified as "positive"), and "FN" (number of
positives falsely classified as "negative"). In current setup all those
values are equal to respective "rates" (because N
and P
are both equal to
1).
Both summ_classmetric()
and summ_classmetric_df()
allow aliases to some
classification metrics (for readability purposes).
Following classification metrics are available:
Simple metrics:
True positive rate, method
"TPR" (aliases: "TP", "sensitivity",
"recall"): proportion of actual positives correctly classified as such.
Computed as 1 - as_p(g)(threshold)
.
True negative rate, method
"TNR" (aliases: "TN", "specificity"):
proportion of actual negatives correctly classified as such. Computed as
as_p(f)(threshold)
.
False positive rate, method
"FPR" (aliases: "FP", "fall-out"):
proportion of actual negatives falsely classified as "positive". Computed
as 1 - as_p(f)(threshold)
.
False negative rate, method
"FNR" (aliases: "FN", "miss_rate"):
proportion of actual positives falsely classified as "negative". Computed
as as_p(g)(threshold)
.
Positive predictive value, method
"PPV" (alias: "precision"):
proportion of output positives that are actually "positive". Computed as
TP / (TP + FP)
.
Negative predictive value, method
"NPV": proportion of output
negatives that are actually "negative". Computed as TN / (TN + FN)
.
False discovery rate, method
"FDR": proportion of output positives
that are actually "negative". Computed as FP / (TP + FP)
.
False omission rate, method
"FOR": proportion of output negatives
that are actually "positive". Computed as FN / (TN + FN)
.
Positive likelihood, method
"LR+": measures how much the odds of
being "positive" increase when value is classified as "positive".
Computed as TPR / (1 - TNR)
.
Negative likelihood, method
"LR-": measures how much the odds of
being "positive" decrease when value is classified as "negative".
Computed as (1 - TPR) / TNR
.
Combined metrics (for all, except "error rate", bigger value represents better classification performance):
Accuracy, method
"Acc" (alias: "accuracy"): proportion of total
number of input values that were correctly classified. Computed as (TP + TN) / 2
(here 2 is used because of special classification setup,
TP + TN + FP + FN = 2
).
Error rate, method
"ER" (alias: "error_rate"): proportion of
total number of input values that were incorrectly classified. Computed
as (FP + FN) / 2
.
Geometric mean, method
"GM": geometric mean of TPR and TNR.
Computed as sqrt(TPR * TNR)
.
F1 score, method
"F1": harmonic mean of PPV and TPR. Computed as
2*TP / (2*TP + FP + FN)
.
Optimized precision, method
"OP": accuracy, penalized for
imbalanced class performance. Computed as Acc - abs(TPR - TNR) / (TPR + TNR)
.
Matthews correlation coefficient, method
"MCC" (alias: "corr"):
correlation between the observed and predicted classifications. Computed
as (TP*TN - FP*FN) / sqrt((TP+FP) * (TN+FN))
(here equalities TP+FN = 1
and TN+FP = 1
are used to simplify formula).
Youden’s index, method
"YI" (aliases: "youden", "informedness"):
evaluates the discriminative power of the classification setup. Computed
as TPR + TNR - 1
.
Markedness, method
"MK" (alias: "markedness"): evaluates the
predictive power of the classification setup. Computed as PPV + NPV - 1
.
Jaccard, method
"Jaccard": accuracy ignoring correct classification
of negatives. Computed as TP / (TP + FP + FN)
.
Diagnostic odds ratio, method
"DOR" (alias: "odds_ratio"): ratio
between positive and negative likelihoods. Computed as "LR+" / "LR-"
.
summ_separation for computing optimal separation threshold (which
is symmetrical with respect to f
and g
).
Other summary functions:
summ_center()
,
summ_distance()
,
summ_entropy()
,
summ_hdr()
,
summ_interval()
,
summ_moment()
,
summ_order()
,
summ_prob_true()
,
summ_pval()
,
summ_quantile()
,
summ_roc()
,
summ_separation()
,
summ_spread()
d_unif <- as_d(dunif)
d_norm <- as_d(dnorm)
t_vec <- c(0, 0.5, 0.75, 1.5)
summ_classmetric(d_unif, d_norm, threshold = t_vec, method = "F1")
#> [1] 0.4000000 0.3412008 0.3069521 0.1252455summ_classmetric(d_unif, d_norm, threshold = t_vec, method = "Acc")
#> [1] 0.2500000 0.4042686 0.4883134 0.5334032#> threshold F1 Acc
#> 1 0.00 0.4000000 0.2500000
#> 2 0.50 0.3412008 0.4042686
#> 3 0.75 0.3069521 0.4883134
#> 4 1.50 0.1252455 0.5334032
# Using method aliases
summ_classmetric_df(
d_unif, d_norm, threshold = t_vec, method = c("TPR", "sensitivity")
)
#> threshold TPR sensitivity
#> 1 0.00 0.50000000 0.50000000
#> 2 0.50 0.30853717 0.30853717
#> 3 0.75 0.22662682 0.22662682
#> 4 1.50 0.06680635 0.06680635


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Welcome to ConnQuality’s documentation!
Contents:
Module connquality
Connection quality measurement tools
Module connquality.graph
Graphing system
-
class connquality.graph.Graph(options)
-
run()
-
class connquality.graph.Reader
-
STATUSES = {'DEGRADED': 0.5, 'OK': 0, 'ERROR': 1}
-
read(filename, start=None, end=None, data_points=None)
-
connquality.graph.parse_options(args)
Parse commandline arguments into options for Graph
:param args:
:return:
-
connquality.graph.start_grapher()
Start the graphing application
Module connquality.monitor
Monitoring system
-
class connquality.monitor.Check(destination, logger=None)
Base class for all connection checks
-
check()
Run the connection check
| Returns: | Latency in seconds or None if failed |
-
parse_destination(destination)
Validate and parse destination address
-
class connquality.monitor.Monitor(options)
Manages connection monitoring and logging
-
STATUS_DEGRADED = 'DEGRADED'
-
STATUS_ERROR = 'ERROR'
-
STATUS_OK = 'OK'
-
run()
Run the monitor
-
class connquality.monitor.TCPCheck(destination, logger=None)
TCP/IP connection check
-
check()
-
parse_destination(destination)
-
connquality.monitor.get_clock()
Get a timestamp with high resolution for calculating elapsed time
| Returns: | A timestamp (not necessarily a unix timestamp) |
| Return type: | float |
-
connquality.monitor.parse_options(args)
Parse commandline arguments into options for Monitor
:param args:
:return:
-
connquality.monitor.start_monitor()
Start the monitoring application