Group Discussion Report Science Assignment Help

Background

• Both arrays have 10 integers
• Array
  B[] is placed right after array A[] in the data memory.
• Element A[0] is at memory
  address 0x0FED CBA0.
• Give the number of
  cold/compulsory cache misses, and the number of conflict misses at the end
  of 10 iterations of the code.
• Note that the cache is only
  for data values, i.e. you can ignore the impact of instruction fetching on
  this cache. (Focus on accesses caused by A[] and B[], ignore the first two
  lw outside of the loop)

MIPS Instructions

Attached as image.
Based on the instructions, please answer the following parts.

Questions

a. Provide number of
cold misses for direct mapped cache

b. Provide number of
conflict misses for direct mapped

c. Provide number of
cold misses for 2-way set associate cache

d. Provide number of
conflict misses for 2-way set associate cache

I have already
calculated the tag, offset, index for both A[] and B[].

So i want to check
my answer for the cold and conflict misses.

1. Find an article on the Internet outline a security breach or cyber attack. Provide a link to the article, what type of attack was used, why the attack was successful, and suggest a control that would mitigate against that attack. Clearly explain why that control would be an effective mitigation strategy (2-page minimum). A minimum of 4 sources is required.
2. APA must be observed when citing the source both in-text and in the reference section.
3. By submitting this paper, you agree: (1) that you are submitting your paper to be used and stored as part of the SafeAssign™ services in accordance with the Blackboard Privacy Policy; (2) that your institution may use your paper in accordance with your institution’s policies; and (3) that your use of SafeAssign will be without recourse against Blackboard Inc. and its affiliates

Option #2: Case: Generate Summary Statistics in Excel

Company summary

LendingClub is a peer-to-peer marketplace where borrowers and investors are matched together. The goal of LendingClub is to reduce the costs associated with these banking transactions and make borrowing less expensive and investment more engaging. LendingClub provides data on loans that have been approved and rejected since 2007, including the assigned interest rate and type of loan. This provides several opportunities for data analysis.

Calculate Summary Statistics in Excel

We use Excel for basic validation. Remember, there is a limitation on the number of records that Excel can handle, so this is best for smaller- to medium-sized files. Excel’s toolbar at the bottom of the window provides quick access to a summary of any selected values.

1. Open your web browser and go to: https://www.lendingclub.com/info/download-data.action (Links to an external site.).
2. In the Download Loan Data section, choose “2015”from the drop-down list, then click Download.
3. Locate your downloaded zip files on your computer, and extract the .csv files to a convenient location (e.g., desktop or Documents).
4. Open the LoanStats3c.csv file in Excel.
5. Select the [loan_amnt] column. At the bottom of the window, you will see the Average, Count, and Sumcalculations, shown in LAB Exhibit 2-4A (Links to an external site.). Compare those to the validation given by LendingClub:

• Funded loans: $3,503,840,175
• Number of approved loans: 235,629

Q1. Do your numbers match the numbers provided by LendingClub? What explains the discrepancy, if any?

1. Right-click on the summary toolbar and choose Numerical Count from the list. You should now see four values in the bar.

Q2. Does the Numerical Count provide a more useful/accurate value for validating your data? Why or why not do you think that is the case?

Q3. What other summary values might be useful for validating your data?

Required: Answer all of the questions from above and submit your Excel Data File.

Your well-written paper must be 3-4 pages, in addition to title and reference pages. The paper should be formatted according to the CSU-Global Guide to Writing and APA Requirements (Links to an external site.). Cite at least two peer-reviewed sources, in addition to the required reading for the module.

As a simplified example of character recognition, we will compare several supervised

learning classifiers with validation on a larger version of the MNIST digit recognition

dataset. In this assignment we will use a much larger dataset than that used for

assignment 1; this should represent a better distribution of the natural variability in hand

written 8s and 9s.

Download (from moodle), NumberRecognitionBigger.mat. Not the dataset includes data

samples for all handwritten digits 0 to 9, but we will be using only 8 and 9 for this

assignment. You can implement your assignment in either Matlab or python, with details

to follow:

Coding

Example Matlab and Python functions that can be relied upon are already outlined in

Assignment 1. Assignment 2 may also benefit from the following commands. You are

expected to read documentation on the commands available and try to get them

working, prior to asking for assistance. Please address questions to the course

Python

from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA

from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis as QDA

from sklearn.naive_bayes import GaussianNB as NB

Also strongly consider using:

from sklearn.model_selection import cross_validate

from sklearn.model_selection import StratifiedKFold, StratifiedShuffleSplit

and using the random_state argument for either StratifiedShuffleSplit or

StratifiedKFold.

Question 1: Implement K-Fold cross validation (K=5). Within the validation, you

will train and compare a Linear Discriminant Analysis Classifier, a Quadratic

Discriminant Analysis Classifier, a Bayesian Classifier (Naïve Bayes) and a K-NN

(K=1, K=5 and K=10) classifier. The validation loop will train these models for

predicting 8s and 9s. NOTE: for a fair comparison, K-Fold randomization should

only be performed once, with any selected samples for training applied to the

creation of all classifier types (LDA, QDA, Bayes, KNN) in an identical manner (i.e.

the exact same set of training data will be used to construct each model being

compared to ensure a fair comparison).

Provide a K Fold validated error rate for each of the classifiers. Provide a printout of your

code (Matlab or python). Answer the following questions:

a) Which classifier performs the best in this task?

b) Why do you think this classifier outperforms the others?

c) How does KNN compare to the results obtained in assignment 1? Why do you

observe this comparative pattern?

It was previously announced on multiple occasions that each student is required to

assemble their own dataset compatible with supervised learning based classification

(i.e. a collection of measurements across many samples/instances/subjects that include

a group of interest distinct from the rest of the samples). If you are happy with your

choice from assignment 1, then re-provide your answer to Assignment 1 Question 2

below. If you want to change your dataset for this assignment, for a future assignment or

for your graduate project, you are free to do so, but you have to update your answer to

Question 2 based on your new dataset choice.

Question 2: (Repeat) Describe the dataset you have collected: total number of

samples, total number of measurements, brief description of the measurements

included, nature of the group of interest and what differentiates it from the other

samples, sample counts for your group of interest and sample count for the group not of

interest. Write a program that analyzes each measurement/feature individually. For each

measurement, compute Cohen’s d statistic (the difference between the average value of

the group of interest and the average value of the group not of interest, divided by the

standard deviation of the joint distribution that includes both groups). Provide a printout

of the 10 leading measurements (d statistic furthest from zero), with their corresponding

d statistic, making it clear what those measurements represent in your dataset (these

are the measurements with the most obvious potential to inform prediction in any given

machine learning algorithm). Provide a printout of this code.

Question 3: Adapt your code from Question 1 to be applied to the dataset that you’ve

organized for yourself. Provide a printout of the error rates for the different classifiers

and your code. Answer the following question: is the best performing classifier from

Question 1 the same in Question 3? Elaborate on those similarities/differences – what

about your dataset may have contributed to the differences/similarities observed?

Deadline: October 24th, 2019.