June 2023 - Page 66 of 1402 - InstantHomeworks

HW

External Factor Evaluation (EFE)

In this opening paragraph, explain what the significance is regarding an External Factor Evaluation for an organization (see Table 7). This explanation must include the rankings 1 to 4 as well as the weighting 0.00 to 1.00. This paragraph must have 5 to 6 academic sentences with 2 to 3 current scholarly sources. In addition, explain the results of your EFE analysis regarding the total weighted score. Indicate whether the value you obtained was greater than the average (2.5), less than or equal to and what this means. You should include Table 7, which will include the matrix of the evaluation of the 20 external factors.

Network 2

Answer the question in the word file

SOURCE CODE ANALYSIS

Students are provided with a vulnerable C code. You will analyze the C code by using two static analysis tools, e.g., FlawFinder and cppcheck, understand their outputs, and correct the vulnerabilities accordingly.

Lab Goal

The goal of the following laboratory exercises is for you to get familiar with source code analysis tools, understand their outputs, and improve your code accordingly. Lab Environment A personal computer with one of the Linux Operating Systems installed (strongly recommend Ubuntu 20.04). You can create an Ubuntu VM within VirtualBox. What to Submit Students will submit a document with: 1) The screenshot or printout of execution results of step C.

2) Your analysis and comparison description of step D. 3) For step E, write the corrected program, and printouts/screenshots of the results of the corrected program running with FlawFinder and cppcheck.

Machine Learning pytorch

CMSC 510 HW2 –

The goal of the homework is to gain familiarity with PyTorch (to install it, see:
https://pytorch.org/get-started/locally/ ), a machine learning library for python that allows for
defining the machine learning model and performing gradient descent for it in an automated
way.

Complete 4 exercises described below, and submit via Canvas a zip file with four Jupyter
Notebook files, one per each exercise. Each notebook should contain the code, as well as short
reports on the results of experiments.

Exercise 1.

Train a linear classifier for the Iris dataset (a 3-class classification problem, file iris.csv in
Canvas), using Mean Squared Error as loss (see pytorch_linear_Iris_MSE.py file on Canvas).
Perform an analysis of the behavior of training risk and accuracy for different learning rates.

Detailed steps:
a) Use pandas to load the iris dataset. Create dummy variables for the classes

b) Define pytorch tensors for the dataset using:
torch.tensor

c) Define pytorch tensors (with gradient) for weights and biases (W & b). W should be
n_features x n_classes, b should be 1 x n_classes. Initialize b to zeros (torch.zeros), and W to
random values sampled from a normal distribution with null mean – try different values for the
standard deviation and observe changes in the training behavior.

d) Define pytorch optimizer over variables W & b
torch.optim.SGD or torch.optim.Adam

e) Create the main loop that goes over the dataset in multiple epochs. In each epoch
e1) clear gradients (using optimizer.zero_grad)
e2) calculate linear predictions: pred=X W + b using
torch.matmul
e3) pass the linear predictions through the unipolar sigmoid: sigmoid(pred)=1/(1+exp(-
pred)). Use these functions:
torch.log, torch.exp
e4) calculate the squared difference between the predictions (after sigmoid) and the
true classes, for all three output neurons. Use:
torch.pow
e5) calculate risk = average the squared difference over the training samples. Use:
torch.mean e6) calculate gradients of risk with respect to W & b (call risk.backwards)
e7) make optimizer step (using optimizer.step)
e8) calculate accuracy

Experiment with different learning rates for the two optimizers and report the behavior of the
training loss and accuracy.

Exercise 2.

Train a linear classifier for the Iris dataset, using CrossEntropy as loss. Perform an analysis of the
behavior of training risk and accuracy for different learning rates.

Detailed steps – follow Exercise 1, but replace MSE with CrossEntropy:

e3) pass the linear predictions through softmax (i.e., normalize the unipolar sigmoids for classes
i=1,…,3 to sum up to 1 for each sample)

e4) calculate the cross entropy after softmax (sum_{i=1}^3 y_i ln(softmax_i)).
torch.multiply, torch.log, torch.sum

e5) calculate risk = average the cross entropy over the training samples

Experiment and report results as in Exercise 1.

Exercise 3.

Starting from Exercise 2, add a split of the Iris dataset into a training set and a test set. Also, in
the training loop, go over small batches of samples (e.g. 20 samples) instead of always over the
whole training set. Experiment with batch size and learning rate.

Exercise 4:

Linear classifier for MNIST Digits dataset. Explore the behavior of the code from Exercise 3 on a
larger, more complicated dataset and report the results.
The number of training samples is 50,000 – analyze training behavior if a random subset of 100,
500, 1000, 2000 samples is used instead. Also, experiment with the learning rate and the batch
size.

For loading the dataset, use: import torchvision.datasets as datasets
full_train_dataset = datasets.MNIST(root=’./data’, train=True, download=True, transform=None)
full_test_dataset = datasets.MNIST(root=’./data’, train=False, download=True, transform=None)
x_train = full_train_dataset.data.numpy().reshape(-1,n_features).astype(dtype=np.float)/255.0;
x_test = full_test_dataset.data.numpy().reshape(-1,n_features).astype(dtype=np.float)/255.0;
y_train_cat = full_train_dataset.targets.numpy()
y_test_cat = full_test_dataset.targets.numpy()
Note that the download of the dataset may take long time. As with Iris, convert categorical
variables for classes into dummy variables (there are 10 classes).

Cloud and Project Management

For Q1 and Q2 refer to the textbook: Cloud Computing by Dr. Kris Jamsa, 2nd ED

ISBN: 9781284248203

Publisher: Jones & Bartlett Learning

Publication Date: July 14, 2022

Q1. In paragraph form, respond to the following statements:

Many businesses were initially fearful to place files into cloud due to the perception that files in the cloud were less secure. Agree or disagree? Defend your answer.
To enhance security, most cloud-based file systems encrypt the underlying files. Agree or disagree? Defend your answer.
Because network communication is fast, cloud-based storage will always be faster than on-premise storage. Agree or disagree? Defend your answer.

The answer must be 300-350 words. A minimum of two scholarly journal articles are required. Please follow APA 7 Guidelines.

Q2. Write a research paper that contains the following:

Define and describe cloud-based collaboration.
Google Docs is a cloud-based tool used for document sharing.
Discuss pros and cons of using Google Docs for business-based documents.
Compare and contrast the use of Google Docs with Microsoft 365 Word Docs for business-based documents.

Paper should be approximately five pages in length, not including the title page and reference page. The body of the paper must include introduction and conclusion. A minimum of two scholarly journal articles are required. Please follow APA 7 Guidelines for in-text citations and references.

For Q3 refer to textbook: Systems Analysis and Design by Kenneth E. Kendall and Julie E Kendall, 10th ED

ISBN: 13: 978-0-13-478555-4

Q3. In a paragraph form, answer to the below topic:

“3-legged stool”: Name and describe how each leg of the 3-legged stool of project management affects the other legs.

The answer must be 200 words. Provide at least two APA references with in-text citations. The reference must include the URL/DOI of your resource.

response project

refer to the attached document.

Exp19_Excel_AppCapstone_CompAssessment_Manufacturing

You have recently become the CFO for Beta Manufacturing, a small cap company that produces auto parts. As you step into your new position, you have decided to compile a report that details all aspects of the business, including: employee tax withholding, facility management, sales data, and product inventory. To complete the task, you will duplicate existing formatting, utilize various conditional logic functions, complete an amortization table with financial functions, visualize data with PivotTables, and lastly import data from another source.

Start Excel. Download and open the file named Exp19_Excel_AppCapstone_ComprehensiveAssessment- Manufacturing.xlsx. Grader has automatically added your last name to the beginning of the filename.

Group all the worksheets in the workbook and fill the range A1:F1 from the Insurance worksheet across all worksheets including the formatting. Ungroup the worksheets after the fill is complete and ensure the Insurance worksheet is active.

Click cell I5, and enter a function that determines the number of full-time employees, (FT).

Enter a database function in cell I6 that determines the average salary of all full-time employees with at least one dependent. Format the results in Accounting Number Format.

Enter a lookup function in cell E5 that returns the tax deduction amount for the number of dependents listed in the cell C5. Use the table in range H13:I17 to complete the function. The maximum deduction is $500.00; therefore, employees with more than four dependents will receive no additional deductions.

Use Auto Fill to copy the function down, completing column E. Be sure to use the appropriate cell referencing. Format the data in column E with the Accounting Number Format.

Enter a logical function in cell F5 that calculates employee FICA withholding. If the employee is full-time and has at least one dependent, then he or she pays 7% of the annual salary minus any deductions. All other employees pay 5% of the annual salary minus any deductions. Copy the function down through column F. Format the data in column F with Accounting Number Format.

Apply conditional formatting to the range C5:C34 that highlights any dependents that are greater than 3 with Light Red Fill and Dark Red Text.

Click cell H10, and enter an AVERAGEIFS function to determine the average salary of full-time employees with at least one dependent. Format the results in Accounting Number Format.

Use Advanced Filtering to restrict the data to only display full-time employees with at least one dependent. Place the results in cell A37. Use the criteria in the range H24:M25 to complete the function.

Ensure that the Facilities worksheet is active. Use Goal Seek to reduce the monthly payment in cell B6 to the optimal value of $6000. Complete this task by changing the Loan amount in cell E6.

Create the following three scenarios using Scenario Manager. The scenarios should change the cells B7, B8, and E6.

Good
  B7 = .0325
  B8 = 5
  E6 = 275000

Most   Likely
  B7 = .057
  B8 = 5
  E6 = 312227.32

Bad
  B7 = .0700
  B8 = 3
  E6 = 350000

Create a Scenario Summary Report based on the value in cell B6. Format the new report appropriately.

Ensure that the Facilities worksheet is active. Enter a reference to the beginning loan balance in cell B12 and enter a reference to the payment amount in cell C12.

Enter a function in cell D12, based on the payment and loan details, that calculates the amount of interest paid on the first payment. Be sure to use the appropriate absolute, relative, or mixed cell references.

Enter a function in cell E12, based on the payment and loan details, that calculates the amount of principal paid on the first payment. Be sure to use the appropriate absolute, relative, or mixed cell references.

Enter a formula in cell F12 to calculate the remaining balance after the current payment. The remaining balance is calculated by subtracting the principal payment from the balance in column B.

Enter a function in cell G12, based on the payment and loan details, that calculates the amount of cumulative interest paid on the first payment. Be sure to use the appropriate absolute, relative, or mixed cell references.

Enter a function in cell H12, based on the payment and loan details, that calculates the amount of cumulative principal paid on the first payment. Be sure to use the appropriate absolute, relative, or mixed cell references.

Enter a reference to the remaining balance of payment 1 in cell B13. Use the fill handle to copy the functions created in the prior steps down to complete the amortization table.

Ensure the Sales worksheet is active. Enter a function in cell B8 to create a custom transaction number. The transaction number should be comprised of the item number listed in cell C8 combined with the quantity in cell D8 and the first initial of the payment type in cell E8. Use Auto Fill to copy the function down, completing the data in column B.

Enter a nested function in cell G8 that displays the word Flag if the Payment Type is Credit and the Amount is greater than or equal to $4000. Otherwise, the function will display a blank cell. Use Auto Fill to copy the function down, completing the data in column G.

Create a data validation list in cell D5 that displays Quantity, Payment Type, and Amount (in that order).

Type the Trans# 30038C in cell B5, and select Quantity from the validation list in cell D5.

Enter a nested lookup function in cell F5 that evaluates the Trans # in cell B5 as well as the Category in cell D5, and returns the results based on the data in the range A8:F32.

Create a PivotTable based on the range A7:G32. Place the PivotTable in cell I17 on the current worksheet. Place Payment Type in the Rows box and Amount in the Values box. Format the Amount with Accounting Number Format.

Insert a PivotChart using the Pie chart type based on the data. Place the upper-left corner of the chart inside cell I22. Format the Legend of the chart to appear at the bottom of the chart area. Format the Data Labels to appear on the Outside end of the chart.

Insert a Slicer based on Date. Place the upper-left corner of the Slicer inside cell L8.

Use PowerQuery to connect to the Access database Exp19_Excel_AppCapstone_Comprehensive_Inventory.accdb. Load the Inventory table into a new worksheet named Inventory.

Note, Mac users, download and import the delimited Inventory.txt file into a new worksheet named Inventory starting in cell A1.

Create a footer with your name on the left, the sheet code in the center, and the file name on the right for each worksheet.

Save the file Exp19_Excel_AppCapstone_ComprehensiveAssessment- Manufacturing.xlsx. Exit Excel. Submit the file as directed.

Review a peer-reviewed article that addresses how demographic and psychographic attributes influence usability measurement.

Review a peer-reviewed article that addresses how demographic and psychographic attributes influence usability measurement. 4-5 paragraphs, APA citation required