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Display the given SSNs from a text file in ascending order (C++)

I have a homework assignment that im not really sure how to approach as of right now, I've done some research but. I have not found really anything that would pertain to what I'm doing. Any help would be appreciated <3 . So far this is what I have:

/******************************************************************************
1.WRITE A PROGRAM THAT TAKES AT LEAST SIX PERSONS FROM A FILE, AND LISTS THEM IN
ORDER: (i) SSN ASCENDING, AND (ii) BMI DECREASING 

2. INPUT AT LEAST SIX PERSONS FROM A FILE. Prompt the user to enter one SSN of 
the given six. For that SSN, find the lowest and highest RBMI among the other 
five. Report the person names and RBMI values. 

3. Among all the persons, find the two persons having the least RBMI. Report 
their names and the value of their RBMI. 
*******************************************************************************/

#include <iostream>
#include <math.h>
#include <fstream>

using namespace std;

struct person
{               //define a struct

  string pName;
  float BMI;            // 18.4 
  long SSN;         // 10 digit 

};


int main ()
{

  string fLine;
  int pcount = 0;

  ifstream fRap;
  fRap.open ("Rappers.txt");

  while (!fRap.eof ())
    {

      getline (fRap, fLine);
      //myFavMovieStar[pcount].pname = fLine;
      getline (fRap, fLine);
      //myFavMovieStar[pcount].pname = fLine;
      getline (fRap, fLine);
      //myFavMovieStar[pcount].pname = fLine;

      pcount++;
    }

  fRap.close ();

  cout << "pcount is: " << pcount << endl;

  person myFavRapper[pcount];

  fRap.open ("Rappers.txt");

  for (int i = 0; i < pcount; i++)
    {

      getline (fRap, fLine);
      myFavRapper[i].pName = fLine;
      getline (fRap, fLine);
      myFavRapper[i].SSN = stol (fLine);
      getline (fRap, fLine);
      myFavRapper[i].BMI = stof (fLine);

    }

  fRap.close ();

  cout << "Done reading this file." << endl;

  for (int i = 0; i < pcount; i++)
    {
      cout << myFavRapper[i].pName << " | " << myFavRapper[i].
    SSN << " | " << myFavRapper[i].BMI << endl;
    }

  cout << "" << endl;

  cout << "SSN's Ascending: " << endl;





  cout << "" << endl;

  cout << "BMI's Decreasing: " << endl;



}

Here is the contents of the text file:

Jahseh Onfroy
123456789
19.4
Aristos Petrou
345678912
25.1
Gustav Ahr
234567891
21.5
Scott Arceneaux
101110111
22.4
Jazz Butler
134243332
22.2
Symere Woods
678912345
25.7

Technical Debt: Interview With Adam Tornhill and Alex Omeyer

Last week we hosted a webinar where Alex Omeyer interviewed Adam Tornhill about technical debt: what is it, why it's important, and how to manage it effectively. For this article, we've chosen some of the most interesting questions we've got from the audience. If you're curious to learn more — check out the full version of the webinar.

Alex: I'm Alex, the Co‑founder, and CEO of Stepsize. I spend all of my time talking about technical debt with Engineering team members, and I'm genuinely pumped to have Adam, CTO and Founder of CodeScene, with me today.

The Scaled Agile Framework (SAFe): Everything You Need to Know

Built for enterprise development teams, SAFe was first introduced in the year 2011. Dean Leffingwell was the brain behind it. The framework revolves around agile methodology, lean methodology, and systems thinking.

The said practices were used to manage teams, portfolios, and programs until SAFe 3.0. With the arrival of SAFe 4.0, an additional level, i.e., value streams, is also added to the framework.

5 of the Weirdest and Hardest Programming Languages

Programming languages, although difficult, are often created with the intent of making it easy to program something that is useful. However, there are programming languages out there that have the sole intent of making your life harder, or potentially even miserable. Below are some of the best 'worst' and weirdest programming languages around. 

1. Piet

Piet is a programming language made using colors. That means you have to create a small bitmap image that is converted into code your computer can understand. The code works by judging the difference between colors, in order to determine the action to take. Below is an example in Piet of how to say 'Hello World.'

Warning: Cannot modify header information – headers already sent in C:\xamp

please help me
Warning: Cannot modify header information - headers already sent in C:\xampp\htdocs\gauto-preview\gauto-preview\login(1).php on line 19

<?php
ob_start();
session_start();
require_once('connexion.php');
error_reporting(E_ALL | E_WARNING | E_NOTICE);
ini_set('display_errors', TRUE);
if(isset($_POST['login'])){
$username=$_POST['username'];
$motpasse=$_POST['motpasse'];
if($username&&$motpasse){
$query="SELECT*from user where username='$username' AND motpasse='$motpasse'";
$query_run=mysqli_query($conn,$query);
$rep = mysqli_fetch_assoc($query_run);
if($rep!=''){
$_SESSION['id_user'] = $rep['id_user'];}
$row=mysqli_num_rows($query_run);
if($row==1){
 flush();  
 header('Location: index(1).php'); 
 exit();

 //echo"<script type='text/javascript'>location.href='index(1).php';</script>";

//die('should have redirected by now');
}else{
echo "username ou password incorrect";
}                                                                         
}else{
echo "veuillez saisir tous les champs";
}
} 
?>

11 Popular Cities APIs

According to the United Nations, 55% of the world's population currently live in cities, and that percentage is estimated to jump to 68% by 2050. So it's no wonder city officials are embracing digital transformation to make their urban areas smarter.

The Top 5 Big Data Applications in the Healthcare Industry

In this modern era of leveraging technology, the enhancement of healthcare sectors is crucial especially during the pandemic of COVID-19. Technological advancements can either make or break the future of healthcare and can control the second wave of coronavirus. One method which can be acquired to make healthcare more efficient, accurate, and affordable is by utilizing big data. 

Big data has completely revolutionized the way data is analyzed, managed, and leveraged across numerous industries. Noticeable sectors where data analytics is making prominent changes in healthcare. It is estimated that the global big data in the healthcare market will tend to reach $34.27 billion by the year 2022 at a CAGR of 22.07%. Moreover, big data in the healthcare market is expected to bypass the figure of $68.03 billion by the year 2024. 

Explore Clouddocs, One of the Efficient Azure Documentation Generator Tool With Useful Features

Introduction

Cloud Documentation is a terminology that has become popular among various Cloud Platform users today. Irrespective of the Cloud Platform being used, users are looking for a mechanism to get a full picture of the Cloud environment.

Cloud Documentation helps the management persona completely understand the cost invested on resources and for the people in the Senior Technical Lead role to get a holistic picture of the technical components in the Cloud Subscription underuse.

No, Automation Won’t Kill Banks, but It Will Change the Financial Services Game: Part 1

We live in a world where FinTech automation is forcing traditional banks to move faster and deliver better customer experiences. This new world demands a completely different business model from traditional financial institutions.

“The first time that I encountered FinTech, says FinTech commentator and best-selling author Chris Skinner, “was over a decade ago at a meeting in London, during which someone had the idea of launching a business that they called ‘an eBay for money.’”

give us the answer and help us

help me asp.....

In this assignment you have to write functions (in python) that applies various preprocessing
techniques which are popular in Natural Language Processing (NLP). For this purpose, you
are supplied with a Text Dataset (corona_data\test_small.tsv) and you have to process
that dataset using the following techniques:

  1. Tokenization
  2. Text Lowercase
  3. Remove HTML tags
  4. Convert number words to numeric form.
  5. Remove numbers
  6. Remove punctuation
  7. Remove extra whitespaces
  8. Convert accented characters to ASCII characters
  9. Expand contractions
  10. Remove special characters
  11. Remove default stopwords
  12. Stemming
  13. Lemmatization
  14. Part of Speech (POS) Tagging
  15. Named Entity Recognition
    You will find the details as well as the codes to do all of the above preprocessing in the
    Important Links section under LAB 7 in the Google Classroom.
    Important:
    Please follow these rules while you do the preprocessing:
  16. Use separate functions (modular programming) while you do each of the above
    preprocessing.
  17. Only preprocess the Example field of the dataset. Do NOT process the Labels.Page 2
  18. For each of the preprocessing steps do the following two things
    a. Apply it independently on the dataset and write the output as a text file (name
    the text file as <name_of_the_preprocessing>_out.txt)
    E.g., tokenization_out.txt text_lowercase_out.txt
    b. Apply it sequentially from the output of previous preprocessing(s), and finally
    save the complete output (as preprocessed_out.txt) when you are done
    with subsequent preprocessings steps (1 to 11).
    c. For outputting the TEXT file - You should write your own function that can take
    a list of strings (data) and write them into a .txt file under a directory named
    output automatically.
  19. Write proper Comments inside the code. Failing to do so will reduce your grades. Also
    copying others comments/code might give you a negative mark.
  20. Make sure you produce the output text files inside a folder named output which
    should be inside the same folder of your code. Rename the project folder having
    your code(s) and output files as your StudentID_Section and make it a .zip before you
    upload it as the solution to Assignment in Google Classroom. In case you use the
    Google CoLab, your code should automatically create the output folder in the
    corresponding google drive following the aforementioned folder structure.

Removing Duplicates – Java

Hi Experts,

I have a question. I have an object and want to insert in a text file. Before inserting, I want to check if it is duplicate. If no duplicate is found, I will insert the object. If yes, will not insert an object? Do I need a collection for this? This is for Java. Thank you!

convert to java

def findMaximumPath(mat):

rows = cols = len(mat)

count_list = []

for i in range(rows):

summ = 0

mat_index = [rows-1, cols-1]

curr_index = [0, i]

summ = mat[curr_index[0]][curr_index[1]]

while curr_index[0] != rows-1 and curr_index[1] != cols-1:

if mat[curr_index[0]][curr_index[1]+1] > mat[curr_index[0]+1][curr_index[1]]:

curr_index[1] = curr_index[1] + 1

else:

curr_index[0] = curr_index[0] + 1

summ += mat[curr_index[0]][curr_index[1]]

#print(str(curr_index) + " Sum: " + str(summ))

if curr_index[0] != rows-1 and curr_index[1] == cols-1:

for i in range(curr_index[0]+1, rows):

summ += mat[i][cols-1]

#print(str(i) + " Sum1: " +str(summ))

if curr_index[0] == rows-1 and curr_index[1] != cols-1:

for i in range(curr_index[1]+1, cols):

summ += mat[rows-1][i]

#print(str(i) + " Sum2: " +str(summ))

count_list.append(summ)

max_sum = max(count_list)

count = 0

for element in count_list:

if(element == max_sum):

count+= 1

print(count_list)

print("Maximum Sum: " + str(max_sum))

print("Number of Occurrences: " + str(count) + "\n")

mat1 = ([[3, 1, -2, 1, 1],

[-6, -1, 4, -1, -4],

[1, 1, 1, 1, 1],

[2, 2, 2, 2, 2],

[1, 1, 1, 1, 1]])

mat2 = ([[1, 1, 1],

[2, 2, 2],

[3, 3, 3]])

findMaximumPath(mat1)

findMaximumPath(mat2)