arturmkrtchyan

public class AssemblyMain {
 
    public static void main(String[] args) {
        int number = 100000;
        System.out.println("Calculating SUM of the squares of 1 to " + number);
 
        long sum = sum(number);
 
        System.out.println("SUM: " + sum);
    }

arturmkrtchyan

public boolean isPrime(final int number) {  
    return number > 1 &&  
                IntStream.rangeClosed(2, (int) Math.sqrt(number))  
                        .noneMatch(index -> number % index == 0);
}

arturmkrtchyan

bash ./configure 
make all

arturmkrtchyan

public class BiasedLocking {

    private static final int LOOP_COUNT = 10000000; // 10 million

    public static void main(final String[] args) {
        incrementCounter();
    }

    public static void incrementCounter() {
        final long startTime = System.currentTimeMillis();

arturmkrtchyan

  private static void enumSwitch();
    Code:
       0: getstatic     #2                  // Field DayOfWeek.FRIDAY:LDayOfWeek;
       3: astore_0      
       4: iconst_0      
       5: istore_1      
       6: getstatic     #3                  // Field Switch$1.$SwitchMap$DayOfWeek:[I
       9: aload_0       
      10: invokevirtual #4                  // Method DayOfWeek.ordinal:()I
      13: iaload        

arturmkrtchyan

#include <pthread.h>
#include <stdlib.h>
#include <unistd.h>

void *thread_function(void *arg) {
  int i;
  for ( i=0; i<20; i++ ) {
    printf("Thread says hi!\n");
    sleep(1);
  }

arturmkrtchyan

10.64.200.48
10.64.200.49
10.64.200.50
10.64.200.51

arturmkrtchyan

Machine Learning Algorithm families

Supervised Learning (e.g. classification, anomaly detection, regression)

• Input data is called training data and has a known label or result such as spam/not-spam or a stock price at a time.
• A model is prepared through a training process where it is required to make predictions and is corrected when those predictions are wrong. The training process continues until the model achieves a desired level of accuracy on the training data.
• Example problems are classification and regression.
• Example algorithms include Logistic Regression and the Back Propagation Neural Network.

Unsupervised Learning (e.g. clustering and dimensionality reduction)

• Input data is not labelled and does not have a known result.

arturmkrtchyan

import jdk.incubator.http.*;
HttpClient client = HttpClient.newHttpClient();
HttpRequest request = HttpRequest.newBuilder(URI.create("http://www.google.com")).GET().build();
HttpResponse response = client.send(request, HttpResponse.BodyHandler.asString())
System.out.println(response.body())

arturmkrtchyan

<script>
    document.addEventListener("DOMContentLoaded", function () {

        const emailFieldName = 'Email';
        const jobIdFieldName = 'JobID';
        const formId = 'submitreferral';

        const userEmail = getUserEmail();
        const recordId = getUrlParam('recordId');