Most Popular Coding Languages of 2016

Most Popular Coding Languages of 2016

Data on the "Most Popular Coding Languages" based on hundreds of thousands of data points collected by processing over 1,200,000+ challenge submissions in (now) 26 different programming languages. This gives us a pretty valuable insight on what the trends are in hiring demand amongst tech companies for the upcoming year. It's data we hope will be especially helpful for new computer science graduates or coders looking to stay ahead of the curve. (CodeEval is now being used as a classroom tool in a number of schools, from university programs to boot camps.)

 

Results

For the fifth year in a row, Python retains it's #1 dominance followed by Java, C++, and Javascript.
This year's most noticeable changes were a 27% increase in C# submissions, a 15% surge in Java, as well as a 21% increase in C submissions. While still reigning champ we saw a 14% drop in Python submissions as well as a 17% decline in Ruby usage.

Programming language ranking change by year.

We've seen a triple digit surge with R and Visual basic but they still only account for less than 1%. This year we added 5 new languages D, Fortran, Guile, OCaml and Scheme.

Programming language change percentage by year.

It's interesting to note the rise of Java after several years of steady decline. Could this be the year that Java overtakes Python? On the TIOBE index, another major index and a good indicator of market share, Java has surpassed both Python and Visual basic for the top spot. This may indicate a big popularity growth in the coming year. Note: Some of the newer languages we've added; D, Guile, Fortran, OCaml, and Scheme, may have suffered somewhat since they haven't had a full year inside the platform.  

Artificial Intelligence Simplifies the Search for Relevant Scientific Literature

We’ve all been there, spending hours on end searching through the Internet for science papers. It feels like many of the scholar search engines have a hit an accuracy of Yahoo! circa 1998.

Needless to say, engineers and researchers have wasted many hours playing with keywords to find a stack of papers. And once they have that stack, another few hours sifting through abstracts to see if the stack of papers is useful, let alone on-topic.

To improve the quality of research journal searches, the Allen Institute for Artificial Intelligence (AI2) has released its free Semantic Scholar service. Semantic Scholar can automatically search the Internet for millions of science papers published every year and categorize them into usable topics.

Similar to Google, the Semantic Scholar will crawl the Internet using data-mining techniques to find publically-available science papers. Using computer vision tools, Semantic Scholar is able to extract the text, diagrams and captions for indexing and contextual determination. Finally, the tool uses natural language processing to filter the papers, extract who cites which and determine the paper’s quality.

Currently, the service has shifted through three million computer science papers and will continue to add categories in the future.

"Semantic Scholar is a first step toward AI-based discovery engines that will be able to connect the dots between disparate studies to identify novel hypotheses and suggest experiments that would otherwise be missed," said Oren Etzioni, CEO at AI2. “Our goal is to enable researchers to find answers to some of science's thorniest problems."

The mobile-ready Semantic Scholar interface will have various functions typical of scientific journal search engines. For instance, users can filter results by author, publication, topic and date. This is a standard in scholar search engines.

However, users will also be able to see who has cited the papers. This useful tool is only seen in some more advanced science search engines. Additionally, Semantic Scholar has a rare science search engine ability to give users access to the figures and findings in the paper.

At the end of the day, though, what really sets this search engine apart is its data mining and artificial intelligence capabilities.

"No one can keep up with the explosive growth of scientific literature," said Etzioni. "Which papers are most relevant? Which are considered the highest quality? Is anyone else working on this specific or related problem? Now, researchers can begin to answer these questions in seconds, speeding research and solving big problems faster."

I just wonder where this tool was during my thesis literature review.

Will you be using Semantic Scholar? What is your favorite scientific journal search engine?

Decision Making problem by means of fuzzy logic in program language MATLAB

DECISION MAKING PROBLEMS IN MATLAB

The M-file BF.m provides the calculation. See prog.1.

clear all
B1v = readfis('B1.fis');
UdajB1 = input('Input values in the form [I3a; I3b; I3c]: ');
VyhB1 = evalfis(UdajB1, B1v);
B2v = readfis('B2.fis');
UdajB2 = input('Input values in the form [I4a; I4b]: ');
VyhB2 = evalfis(UdajB2, B2v);
BFv = readfis('BF.fis');
UdajBF=input('Input values in the form [I1;I2]: ');
UdajBF(3) = VyhB1;
UdajBF(4) = VyhB2;
VyhBF = evalfis(UdajBF, BFv);
if VyhBF<0.5 'Reject'
elseif VyhBF<0.8 'Monitor'
else 'Accept'
end
fuzzy(BFv)
mfedit(BFv)
ruleedit(BFv)
surfview(BFv)
ruleview(BFv)


The results of calculation are presented by inputs I1,I2, I3a, I3b, I3c, I4a, I4b with values 0, 1 and 0.5. The results are Reject, Accept and Monitor.

Input values in the form [I3a; I3b; I3c]: [0;0;0]
Input values in the form [I4a; I4b]: [0;0]
Input values in the form [I1;I2]: [0;0]
ans =Reject
Input values in the form [I3a; I3b; I3c]: [1;1;1]
Input values in the form [I4a; I4b]: [1;1]
Input values in the form [I1;I2]: [1;1]
ans =Accept
Input values in the form [I3a; I3b; I3c]: [0.5; 0.5; 0.5]
Input values in the form [I4a; I4b]: [0.5; 0.5]
Input values in the form [I1;I2]: [0.5; 0.5]
ans =Monitor