I want to make things
that make a difference.
About
Searching for the opportunity to bring 2+ years experience with programming, Software Development position with CTrends incorporating critical thinking, problem solving, and leadership.
I'm a software developer, I write software for living. I work at Ctrends Software & Service Ltd. as a Software Developer which is amazing.
Apart from that I do a lot of other things such as-
- blogging at DZone, problem solving in Hacker Rank
- Professional meetup: at JUGBD & meetup
I mostly write code in Java, but I know Python and Php. I write code in JavaScript professionally when I work in front-end development. I have a few side projects running which are some what still in incubation, so when I make some good progress, I will share them here, hopefully, but chances are less. I love to listen to music, watch movies and series, read books (I love history) and whenever I get some free time, I try to sleep. If you would like to get in touch with me, whether it be for tech support or to just to say hi, feel free to send me an email. My email address is<EMAIL_ADDRESS>or find me on LinkedIn
Current Focus: Accessibility // React // Design Systems
Experience
Working as team leader to build ‘Financial Module’ to budgetary and quality requirements. Also involved in requirements gathering, demonstrating software prototypes, technical sales presentations, customer training. I have completed ‘SOD Module’ personally.
Develop Product
- Take part in software and architectural development activities.
- Contributing in all phases of the development lifecycle
- Designing, implementing, and maintaining Java applications that are high-volume and low-latency, required for mission-critical systems.
- Writing well-designed, efficient, and testable code.
- Tools: Java, J2EE,Spring Framework, Hibernate, PostgreSQL, JS, jQuery // Sass // HTML, JSX, Haml
Accomplishments
- Dramatically increased speed of UI development using atomic, scalable CSS
- Built tool to convert HRIS data into user accounts to simplify onboarding
- Implemented inline @tagging feature to increase app share-ability
- Developed Android application and modify existing Android application code for better performance tuning.
- To create offer
- To write content in prism (cloud based application) and creating offer.
8-week course for students enrolled in the Fundamentals Classes Intro to Web Design
- Teaching the basics of HTML, CSS, and the principles of responsive design.
- Currently redesigning and building a new, improved Web Design curriculum to be implemented across all SI channels in 2018
Accomplishments
- Increased the average student retention rate from less than 40% to more than 95%
- Doubled the average amount of students that move from Fundamentals Classes to the Full-Time program
Projects
Skills
HTML
CSS
Javascript
Back-End
Build Tools
CMS
Other
Awards
Won Award as ‘Best Performer’, round-27, J2EE, IDB-BISEW : https://goo.gl/gdmKhk
Won Award as ‘First Winner’, National Mobile Application Awareness Development & Capacity Building Program : https://goo.gl/4qDnNT
Education
Oracle Certified Professional, Java SE 6 Programmer (OCJP) | Nov 2016
Verify Link: https://goo.gl/T7haJY
Enterprise System Analysis & Design-J2EE (ESAD-J2EE) | Duration of course: 1080 Hours
Asia Pacific Communication Ltd. | Keari Plaza(4th floor), Plot # 83, Road # 8/A(Old 15), Satmosjid Road, Dhanmondi, Dhaka-1209 | Under the Supervision of IDB-BISEW IT Scholarship Project.


Connectivity analysis is based on graph theory. A connected graph is an undirected graph that has a path between every pair of vertices[1]. For urban networks, connectivity is used to understand spatial conditions affecting pedestrian activities and behaviors in cities.
Space Syntax brings together a series of theories for the analysis of spatial configurations in urban systems [2][3]. The Space Syntax approach represents streets as nodes and intersections as edges. According to Space Syntax, the three main conceptions of space are: isovist, axial space and convex space, and the street networks analyses are: Integration, Choice and Depth Distance.
In Space Syntax connectivity is a local property: it tells you how many elements (e.g. convex spaces) are directly connected with one certain element. A local property can be experienced directly from a static location in space. Depth Distance is a global property: it tells you how "far away" an element is from all the other elements. A global property can only be experienced from moving through space.
For our MOOC exercise you can intuitively work with connectivity or you can deepen your knowledge on Space Syntax theory or graph theory and use computational tools such as depthmap or any GIS software.


We know thta fillig the tax form can be difficult. So why do you struggle with it? By watching our videos all your issues will be resolved, taxes fully done, and no more stress!
We all know that cleaning your house can be very difficult and challenging. These videos will help you learn the basics of cleaning your new home.
 These videos will help you prioritize the most important factors of adulthood. All your hard work can be done in the beginning and you can relax after all the work that you have put in!
Purchasing a new home can be challenging. These videos will teach you what to look for in buying a new house, as well as how to make sure you don't end up with a mortgage.
Sometimes you have to start life with a random person that you never met. These videos will help you understand what it truly means to have a rommate, and what rules shuld be established between both of you.
Eventually, if you chosose, you will become a parent. These videos provide tips on how to be a parent, as well as what it actually means for your future. We will teach you how to make your own family.
Join our ever-expanding community of content creators and viewers alike! Sign up to gain access to uploading videos, rating videos, and our premium services as well.


So what does this do? It formats scripts to make them more readable.
For example, it would turn this:
begin GiveBonus
if ( GlobalVar == 0 )
set GlobalVar to 1
if ( Player->GetLevel >= 15 )
Player->AddSpell "Bonus3"
elseif ( Player->GetLevel <= 5 )
Player->AddSpell "Bonus1"
else
Player->AddSpell "Bonus2"
endif
else
set GlobalVar to 2
endif
StopScript GiveBonus
end
Into this:
begin GiveBonus
if ( GlobalVar == 0 )
set GlobalVar to 1
if ( Player->GetLevel >= 15 )
Player->AddSpell "Bonus3"
elseif ( Player->GetLevel <= 5 )
Player->AddSpell "Bonus1"
else
Player->AddSpell "Bonus2"
endif
else
set GlobalVar to 2
endif
StopScript GiveBonus
end
Just paste your script into the box, adjust the options as needed click
Indent Now, and copy your script back out.
Do you see ;ERR in your script? That means you have more close block
commands than open block commands. For example too many endifs. Likewise if the last
line of your script is indented, you have more close blocks than open blocks, just the
opposite problem.
Tip: You can use CTRL+C and CTRL+V to copy and paste in the editor.
Want to use Morrowind Script Indenter offline? Just press CTRL+S to save this page
on your computer.
If you use Firefox, you might also want to check out Yacoby's adaptation of my
code here as a
Fixefox Extension. It lets to indent scripts in your forum posts directly
with a right-click context menu.
* Thanks to Dave Humphrey for tweaking the code to work in more browsers.
|


--- layout: post status: publish published: true title: Modest Sam 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: 700 wordpress_url: http://winterdom.com/2002/06/modestsam date: '2002-06-26 16:26:50 +0000' date_gmt: '2002-06-26 16:26:50 +0000' categories: - Blogging tags: [] comments: [] ---
Sam Ruby says: "His description of me however is, shall we say, a wee bit over the top. I consider myself more of a teambuilder / cheerleader."
I'd like to point out that such a role can be just as important, as well. In fact, in some cases, it is even more important.


Introduction
The complexity of tax preparation is a pain for everyone. Managing the corporate tax is far more challenging for a Tax Executive of a multinational company. My client of this project would like to design a Tax Executive Dashboard as part of a platform to bring transparency and efficiency of corporate tax reporting.
My Role
- Created whiteboard sketches to communicate design ideas with designers and manager
- Created wireframes, data visualization, hi-fi mock-ups, and prototypes and iterated on an agile fashion based on the feedback from stakeholders
- Synthesized previous research and analyzed existing systems; summarized and presented key features and user journey to stakeholders to get alignment and priorities
Tool
- Sketch
- Invision


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Hi there. We hoped you enjoyed the ride.
Comfort
1
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Chapter 4 Linear Regression
4.1 Simple Linear Regression
4.1.1 Example 1.
To investigate how the blood alcohol level rises as the beer consumption increase, we randomly selected 10 students and get the following data (Beers: in unit of cups; BAL: blood alcohol level):
Beers<-c(5,2,9,8,3,7,3,5,3,5)
BAL<-c(0.10,0.03,0.19,0.12,0.04,0.095,0.07,0.06,0.02,0.05)
plot(Beers, BAL) # Scatter plot for two variables (Independent variable vs. Dependent variable)
We can see the trend: as the beer amount increases, the BAL also increases. The trend looks like linear.
Assume that the true trend is a linear function, i.e., y = β1x + β0 (y=BAL, x=beer in cups), but unknown, how to use data to estimate the line (or equivalently, β1 and β0)?
The estimated line should best “explain” the data. A natural criteria for the line should be that its coefficients (β0 and β1) that minimize, the error
If a line satisfies the above criteria, we say that the line is the least square line.
The process of finding the least square line is called a linear regression.
In R, we use lm() to find the least square line.
regression <- lm(BAL~Beers) #lm( dependent variable~ independent varaible )
summary(regression)
##
## Call:
## lm(formula = BAL ~ Beers)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.0275 -0.0187 -0.0071 0.0194 0.0357
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.018500 0.019230 -0.962 0.364200
## Beers 0.019200 0.003511 5.469 0.000595 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02483 on 8 degrees of freedom
## Multiple R-squared: 0.789, Adjusted R-squared: 0.7626
## F-statistic: 29.91 on 1 and 8 DF, p-value: 0.0005953
We can also get confidence intervals for model parameters β0 and β1, using the confint() function and the lm() result. For example, let us find the 95% percent confidence interval for β0 and β1:
confint(regression)
## 2.5 % 97.5 %
## (Intercept) -0.06284414 0.02584414
## Beers 0.01110391 0.02729609
The column names are (1/2-95%/2) and (1/2+95%/2), where 95% is the confidence level. Notice that 97.5%-2.5%=95%. We can also predict the expected BAL for new Beers values, using the lm() result. For example, we can predict the expected BAL for Beers=4, Beers=6 and Beers=10:
predict(regression,newdata=data.frame(Beers=c(4,6,10))) #Name of the data frame column should match with the previous model
## 1 2 3
## 0.0583 0.0967 0.1735
4.2 Multiple Linear Regression
4.2.1 Example 1.
Consider the data set mtcars. It gives a comparison between different car models in terms of miles per gallon (mpg), cylinder displacement (disp, in unit of cubic inch), horse power (hp), weight of the car (wt , in unit of 1000 lbs) and some more parameters.
Suppose the true relation between mpg and disp, hp and wt is:
E mpg = β0 + β1 ⋅ disp + β2 ⋅ hp + β3 ⋅ wt how to predict mpg by disp, hp and wt?
This is similar to the previous BAL project. The only difference is that here we have 3 predictors (disp, hp and wt) and that we have only one predictor(Beer) in the previous BAL project.
– With a single predictor, the regression is called a simple linear regression; with more than one parameter the regression is called a multiple linear regression.
- To find the least square estimate, we can also use the lm() function.
data(mtcars) # Load dataset already validated by others in R.
mpg<-mtcars$mpg
disp<-mtcars$disp;
hp<-mtcars$hp
wt<-mtcars$wt
regressor.multiple <- lm(mpg~disp+hp+wt) # Add more varaibles with '+' sign
summary(regressor.multiple)
##
## Call:
## lm(formula = mpg ~ disp + hp + wt)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.891 -1.640 -0.172 1.061 5.861
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 37.105505 2.110815 17.579 < 2e-16 ***
## disp -0.000937 0.010350 -0.091 0.92851
## hp -0.031157 0.011436 -2.724 0.01097 *
## wt -3.800891 1.066191 -3.565 0.00133 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.639 on 28 degrees of freedom
## Multiple R-squared: 0.8268, Adjusted R-squared: 0.8083
## F-statistic: 44.57 on 3 and 28 DF, p-value: 8.65e-11
confint(regressor.multiple)
## 2.5 % 97.5 %
## (Intercept) 32.78169625 41.429314293
## disp -0.02213750 0.020263482
## hp -0.05458171 -0.007731388
## wt -5.98488310 -1.616898063
4.3 Linear models with categorical and binomial outputs
Let us consider the variable vs in the data set mtcars.
- It indicates whether a car has a V engine (vs=0) or a straight engine (vs=1).
- Now we wish to predict the engine type (vs) value using the cylinder displacement (disp) and the car weight (wt).
- Can we do this using lm()? In another word, can we perform a multiple regression?
It is not appropriate to use the lm(), because that we want to predict a binary outcome, not a continuous one. – We do not want our predictions to be a value other than 0 or 1. - In this scenario, we need to perform a logistic regression, which are commonly used to analyze binary outcomes.
In R, we use glm() with the argument family be “binomial”. – glm stands for “generalized linear model”.
vs<-mtcars$vs
glm(vs~disp+wt,family="binomial")
##
## Call: glm(formula = vs ~ disp + wt, family = "binomial")
##
## Coefficients:
## (Intercept) disp wt
## 1.60859 -0.03443 1.62635
##
## Degrees of Freedom: 31 Total (i.e. Null); 29 Residual
## Null Deviance: 43.86
## Residual Deviance: 21.4 AIC: 27.4
We specify the argument family as “binomial”, because the outcome is a binary outcome.
The model is a little different from that of linear regression.
Like in the linear regression, we can also use the glm() results to do many things, such as hypothesis testing, confidence interval and predictions.
To do the hypothesis testing and to compute confidence interval, we need to understand the model more. Therefore, in this class we only focus on prediction.
For example, we wish to predict the probability of a straight engine, for a car model with weight 2100 lbs and engine displacement of 180 cubic inches.
glm.result <- glm(vs~disp+wt,family="binomial")
predict(glm.result,data.frame(disp=180,wt=2.1), type="response")
## 1
## 0.2361081
The predicted probability is 0.2361081.
– Notice that we need to specify the type argument as “response”. What if we do not specify the type argument?
Video tutorial (simple linear regression):


---
layout: post
title: ''
date: '2008-10-16T00:06:00-07:00'
tags:
- concrete5,
- c5
- open source
- franz maruna
- kettle foods
- portland
tumblr_url: http://alexhwilliams.tumblr.com/post/54809091/why-do-agencies-suck-so-bad-at-making-web-sites
---
Why do agencies suck so bad at making web sites? That’s the question I posed to Franz Maruna. Franz is CEO at concrete5, which offers an open source content management system.
Franz should know why billboard makers make lousy web site creators. Until recently, Franz did a lot of work with agencies. He developed all sorts of web sites, including one for Kettle Foods that won his company some nice kudos back in the day.
Franz has pretty much stopped doing web site development work, trading it in for going open source with concrete5. You can get the concrete5 software for free.
concrete5 offers a hosting package. You can also get the c5 kit. It comes with the hipster c5 stickers and a set of screwdrivers! Awesome. The USB drive is made of sustainable hardwood.
Damn. Open source. Sustainable hardwood. These guys are Portland through and through. Just wait until you see the orange VW Franz speeds away in at the end of the video. Now, talk about a sweet ride.


To customize an icon button's color and properties, you can use the following mixins.
| Mixin | Description |
|---|---|
icon-button-size($width, $height, $padding) |
Sets the width, height, font-size and padding for the icon and ripple. $height is optional and
defaults to $width . $padding is optional and defaults to max($width, $height)/2 .
font-size is set to max($width, $height) . |
icon-button-ink-color($color) |
Sets the font color and the ripple color to the provided color value. |


A filter that evaluates to true
iff both left
or right
evaluate to true
.
::DeveloperApi:: Represents a collection of tuples with a known schema.
::Experimental:: An interface for experimenting with a more direct connection to the query planner.
::DeveloperApi:: Data sources should implement this trait so that they can register an alias to their data source.
Performs equality comparison, similar to EqualTo.
A filter that evaluates to true
iff the attribute evaluates to a value
equal to value
.
A filter predicate for data sources.
A filter that evaluates to true
iff the attribute evaluates to a value
greater than value
.
A filter that evaluates to true
iff the attribute evaluates to a value
greater than or equal to value
.
::Experimental:: A BaseRelation that provides much of the common code required for relations that store their data to an HDFS compatible filesystem.
::Experimental:: Implemented by objects that produce relations for a specific kind of data source with a given schema and partitioned columns.
A filter that evaluates to true
iff the attribute evaluates to one of the values in the array.
::DeveloperApi:: A BaseRelation that can be used to insert data into it through the insert method.
A filter that evaluates to true
iff the attribute evaluates to a non-null value.
A filter that evaluates to true
iff the attribute evaluates to null.
A filter that evaluates to true
iff the attribute evaluates to a value
less than value
.
A filter that evaluates to true
iff the attribute evaluates to a value
less than or equal to value
.
A filter that evaluates to true
iff child
is evaluated to false
.
A filter that evaluates to true
iff at least one of left
or right
evaluates to true
.
::Experimental:: OutputWriter is used together with HadoopFsRelation for persisting rows to the underlying file system.
::Experimental:: A factory that produces OutputWriters.
::DeveloperApi:: A BaseRelation that can eliminate unneeded columns and filter using selected predicates before producing an RDD containing all matching tuples as Row objects.
::DeveloperApi:: A BaseRelation that can eliminate unneeded columns before producing an RDD containing all of its tuples as Row objects.
::DeveloperApi:: Implemented by objects that produce relations for a specific kind of data source.
::DeveloperApi:: Implemented by objects that produce relations for a specific kind of data source with a given schema.
A filter that evaluates to true
iff the attribute evaluates to
a string that contains the string value
.
A filter that evaluates to true
iff the attribute evaluates to
a string that starts with value
.
A filter that evaluates to true
iff the attribute evaluates to
a string that starts with value
.
::DeveloperApi:: A BaseRelation that can produce all of its tuples as an RDD of Row objects.
A set of APIs for adding data sources to Spark SQL.


2.

(The Daily News , 1985, August 8)


{% extends "human/base.html" %} {% block title %}{{ title }}{% endblock %} {% block content %}
In this module, we will display the miRNA:lncRNA interaction and related editings based on the miRNA expression. According to the analyses of miRNA expression profiles of 9566 small RNA samples from TCGA, we found that only 553 miRNAs (21% of miRBase annotated ) have a expression level >1 RPM (reads per million), the remaining 79% of miRNAs are at a very low or almost no expression level. Hence, we class all the miRNAs into four categories. Users can select interested miRNAs to obtain their target lncRNAs.


Consumer Watch: Faster deliveries in race for holiday shoppers
Brick and mortar stores battle with online outlets
ATLANTA - - There are 6 fewer shopping days between Thanksgiving and Christmas, and retailers are racing to win shoppers in less time.
ATLANTA - - There are 6 fewer shopping days between Thanksgiving and Christmas, and retailers are racing to win shoppers in less time.


This is a script designed to let Firefox’s “Email Link…” function work with web-based iCloud Mail.
To enable it, click the [Add Application] button in the top right.
You may view the source of this script, but per the LICENSE file in this repository, no privacy guarantees are offered.


Our founders planted a seed with the vision of a better future for members. Over the years, our reach and impact have grown, but a better future—together remains our focus. Today, we’re an influential and caring national organization.
The roots of the RTO/ERO go back to October
1966, when the Ontario Teachers’
Federation set up an interim committee of 10 superannuated teachers. Their
mandate – look into the possibility of forming an organization for all
superannuated
teachers and to seek improvements in their pensions and allowances.
In
December 1966, the interim STO/ERO committee presented a brief to Ontario
Minister of Education William Davis, requesting a recalculation of teacher
pensions. Two days later, Davis announced that minimum pensions would be raised
to $1,200.
In this original letter written by Art McAdam in Ontario, he asked Marion Tyrrell to join him at a meeting of pensioners representing the various Ontario teacher affiliates to formulate a proposal regarding the improvement of pensions.
The Teaching Profession Act was amended, and the Ontario Teachers’ Federation had the privilege of granting associate membership to superannuated teachers. This enabled those teachers to join OTF that way and, thereby, become members of STO/ERO.
The Superannuated Teachers of Ontario
(STO) formed an organization to safeguard and promote the
professional welfare of the 7,000 Superannuated Teachers of Ontario.
The
Superannuated
Teachers of Ontario/ Les enseignantes et enseignants retraités de l’Ontario
officially began on March 14-15, 1968, when its governing senate met for the
first time in the second-floor boardroom of the Ontario Public School Men
Teachers’ Federation in Toronto. The voting delegates were representatives of
the five affiliates of the Ontario Teachers’ Federation.
RTO/ERO’s first
newsletter was distributed in November 1968. It was a doubled-sided
sheet.
In December 1968, a committee was established to organize STO/ERO
into geographic areas across Ontario, based on the provincial county board
system. That resulted in 10 areas, which grew to 12 in 1970. In 1972, the
province was reorganized into 21 subdivisions, which would be known as
districts. The senate created the districts, which would each have at least
three executive members.
Between 1968 and 1969, STO/ERO met with
Minister of Education William Davis seven times to discuss increasing the teachers’
pensions.
In 1968 the first secretary-treasurer, Marion Tyrrell, was awarded $500 for past services and an honorarium of $150 a month. The title of executive director replaced that of secretary-treasurer in 1985.
In the early 1960s, the Teachers’ Superannuation Commission (TSC) introduced a health plan for the use of retired teachers. With the introduction of OHIP, Health Services began in 1969 with an agreement between STO/ERO, the Superannuation Commission and the Co-Operative Health Services with deductions made by the Commission. With deductions made by the commission, pensioners paid $2 per month. Those with a spouse paid $4 per month, and a family paid $6.50 per month.
A Travel Committee forms to act as an agent for the purchase of tours directed primarily at the market represented by STO teachers.
Associate Membership was introduced.
More than 12,000 persons (including dependents) received pensions from the Teachers’ Superannuation Commission. Of those, approximately 8,000 were members of STO/ERO. The 90 factor was introduced, enabling teachers to retire when their age plus years of credit equalled ninety.
Previously, Senators were appointed according to their former affiliate membership, with the introduction of Districts, Senators are elected or appointed by each District.
More than 12,000 persons (including dependents) received pensions from the Teachers’ Superannuation Commission. Of those, approximately 8,000 were members of STO/ERO. The 90 factor was introduced, enabling teachers to retire when their age plus years of credit equalled ninety.
Previously, Senators were appointed according to their former affiliate membership, with the introduction of Districts, Senators are elected or appointed by each District.
More than 12,000 persons (including dependents) received pensions from the Teachers’ Superannuation Commission. Of those, approximately 8,000 were members of STO/ERO. The 90 factor was introduced, enabling teachers to retire when their age plus years of credit equalled ninety.
More than 12,000 persons (including dependents) received pensions from the Teachers’ Superannuation Commission. Of those, approximately 8,000 were members of STO/ERO. The 90 factor was introduced, enabling teachers to retire when their age plus years of credit equalled ninety.
Previously, Senators were appointed according to their former affiliate membership, with the introduction of Districts, Senators are elected or appointed by each District.


<ywlib-0>
This is a second-header of ywlib-system.
Almost all contents defined in this header are constant-evaluable.
You can include this header unles including <ywlib>
if you want.
These headers are automatically included. If you want to include some of these headers and not the others, you can include them individually.
| Header | Description |
|---|---|
<ywlib-0/type.hpp> | is a second-header whose almost all contents are constant-evaluable. |