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COMP501 – COMPUTING TECHNOLOGY IN SOCIETY Assignment 1: ICT Fundamentals

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COMP501 – COMPUTING TECHNOLOGY IN SOCIETY

Semester 2, 2024

Assignment 1: ICT Fundamentals

Total Marks: 100 Contribution to the final mark: 40%

Due: 6:00 pm, Friday, 23^rd August 2024

Assignment Aim

The assignment has 4 parts:

1. It aims to give students an understanding of how computer systems represent real-life data such as positive numbers, negative numbers, floating point numbers, text, and at the lowest level seen by the programmer, namely binary numbers.

2. It prepares students with the ability to install multiple operating systems using VirtualBox (https://www.virtualbox.org/) and comparatively evaluate the Linux/UNIX Operating Systems (OS). This assignment also prepares students to understand the basic concepts covering Linux/UNIX file systems, commands, and working environments.

3. It helps students understand the basic idea of contemporary Machine Learning and Deep Learning using Google Colab (https://colab.research.google.com/).

4. It aims to have students demonstrate an awareness of enterprise information systems, their application in the business environment, and modelling techniques for systems requirements:

a. It prepares students with the ability to analyse business cases and document the purpose, objectives, data requirements, data flows, input documents and output documents of common business functions and processes expressed in a range of information systems.

Submission Instructions and Academic Integrity

Submission Instructions

1. The assignment must be submitted on CANVAS.

2. Submit a zip file containing all your assignment work:

a. A .docx or .pdf file of your assignment.

b. A folder containing your images.

Compress these to “LAST NAME_Student ID.zip” and submit the zip file only.

Miscellaneous requirements:

· The assignment will not be marked if:

o It contains any form of malware (e.g., computer virus)

o Not submitted in correct file format

· Keep a backup copy of your assignments to be:

o uploaded to “Turnitin” anti-plagiarism service – if requested.

o submitted as a hard copy – if requested.

Part One: Conversions between Number Systems (25 marks):

Note: Remember to show all your workings where possible

Please fill your ID number in the box here:

ID =

Consider A = the last 2 non-zero digits of your AUT ID number:

For example:

· If your ID is 123286 then A = 86.

· If your ID is 123206 then A = 26.

A =

Question 1: Converting Between Number Bases (4 marks)

Perform the following conversions between different number-based systems (3 marks each):

Assume the number A is a decimal number (base 10):

1. Convert A₁₀ (from Decimal) to Binary à call the answer A₂

2. Convert A₁₀ (from Decimal) to Hexadecimal à call the answer A₁₆

Question 2: Unsigned Arithmetic Operations (4 marks)

Carry out the operations, assume that the numbers are unsigned and unlimited bits to represent:

a) Base 2: A₂ + 10101001₂

b) Base 16: A₁₆ + A7₁₆

Question 3: 2’s Complement Conversion (5 marks)

Assume that numbers are represented as signed, 8-bit 2’s complement representation.

If A = 86 then B₁₀ = -86 (Replace with the last 2 non-zero digits of your ID) to get:

B₁₀ =

Work out the following question:

Convert B₁₀ to 8-bit 2’s complement Binary; give the answer in 8 bits binary number à call the answer C₂

(*) note that the number is negative, so the answer will start with ‘1’.

C₂=

Question 4: 2’s Complement Operations (8 marks)

Assume that you have the number C₂ found in Question 3, represented as signed, 8-bit 2’s complement representation.

Carry out the following operations with signed 8-bit 2-s complement binary number C₂ found in Question 3. Indicate whether or not overflow occurs.

a) A₂+ C₂

b) C₂ + 0100 1101

Question 5: ASCII Characters (4 marks)

The Appendix gives a table for 7-bit ASCII. Using this table, we can work out the hexadecimal value corresponding to the encoding of this ASCII string “ABBA” (assume each 7-bit code occupies the space of an 8-bit byte with the MSB=0) as:

ABBA = 41424241₁₆

Using the Ascii table, find the hexadecimal and Binary values corresponding to your full name (note that there are spaces in the string your full name). Answer the following:

a) Your full name:

Your full name in Hexadecimal (base 16):

(2 marks)

b) How many bits (not bytes) are used (do not count the end of string byte) (2 marks).

Part Two: Linux Operating System (25 marks):

Question 1: Setting up one Linux operating system (5 marks)

Take screenshots during installation and when completed. Include screenshots in your assignment.

Note to Apple Mac Users: You will have to perform this section in the lab.

1. Download and install Virtual box if it isn’t already installed on your machine.

A tutorial on VirtualBox can be found at:

https://www.youtube.com/watch?v=sB_5fqiysi4.

Remember to submit at least 2 screenshots of installing Virtual box.

2. Download and install a Linux Operating System.

Obtain a copy of a mandatory Linux distribution such as Puppy-5.0. Download Puppy from AUT here: https://cv.aut.ac.nz/files/comp501/lucid-puppy-5.00.iso

You can download any ISO or VDI files from the Internet at: http://linuxlookup.com/linux_iso or

http://virtualboxes.org/images or

Puppy Linux | VirtualBoxes - Free VirtualBox® Images).

Once you download a copy of the ISO file, you can install it on Virtual Box.

Remember to submit at least 2 screenshots of installing Linux.

Question 2: Manipulate directory structures in Linux (20 marks – 2 marks each)

Note to Apple Mac Users: You can use Terminal to complete this question.

You will use the CLI (command-line interface) and provide:

a. The text or screenshots of command(s) that you type to perform a task.

b. The text or screenshots of any console output from those commands (the output from a directory listing, for example).

c. Make sure that you include ALL the commands you use to do a task. This includes any commands you have to type to move to a specific directory. Make sure your pasted texts or screenshots are clear enough to show where you are or have moved to.

Use any OS that you set up in Question 1 (Puppy is recommended).

Replace XXX below with your first name. If your first name is John, XXX = John.

Assume you have started in your home directory.

1. Perform a command that displays the absolute path of your home directory (your current location). Create a new directory inside your home directory and name it XXX.

2. Now navigate to the XXX directory and create directory ASSIGNMENT and change your current working directory to ASSIGNMENT.

3. Create three new subdirectories called XXXDirA, XXXDirB, and XXXDirC in ASSIGNMENT directory.

4. Create a new file called “MyInfo.txt” using the touch command and insert three lines into the file (you may use echo command and >> command).

a. The first line should contain your name and ID number.

b. The second line should be the name of your favourite movie.

c. The third line should be the first sentence of your favourite song.

And display the contents of the file “MyInfo.txt” to the standard output screen (you may use cat command).

5. Display the number of words in the file “MyInfo.txt” (you may use wc command).

6. Copy the file “MyInfo.txt” to directory “XXXDirA” and rename it to “MyInfoCopy.txt”.

Make another copy of “MyInfoCopy.txt” and name it “MyInfoCopy2.txt” (also store in the same directory “XXXDirA”).

Then, display the contents of the directory “XXXDirA” using the long format.

7. Change the current working directory to ASSIGNMENT if you are not there already. Create 10 new files (in directory ASSIGNMENT) named as follows:

a. FICTION.bak

b. unix.dak

c. thistest.bak

d. Zumbology.bak

e. more.woot

f. doodah.fil

g. Thiscourse.dat

h. Test-1.txt

i. File-1.bat

j. Assignment1.file

And display a listing of all the files and directories in long format in the current working directory ASSIGNMENT.

8. Display a listing of all the files in the current working directory starting with ‘T’ and ending with ‘t’ using one command, e.g. Test-1.txt.

9. Move all files containing letter ‘t’ to the directory XXXDirC using one command.

10. Display a listing of the contents of the current directory ASSIGNMENT. All files with names that contain letter ‘t’ should now be gone.

Note: Ensure your screenshots are clear and neat; Also look through your work in this section and resize screenshots such that question and answer are on the same page.

Part Three: Machine Learning and Deep Learning (25 marks)

This part of the assignment will help you to understand the basic idea of Machine Learning and Deep Learning using Google Colab (https://colab.research.google.com/ ). You will experiment with running a YOLO Python file on Google Colab and evaluate the results of your experiment.

Here you will test a fast version of an available object detection system - YOLO (https://pjreddie.com/darknet/yolo/). You will submit photos (taken by yourself) to YOLO; YOLO will try to identify the objects in the photos and will tag the objects and produce accuracy percentages of the detection/prediction. You will analyse the results to conclude if the tool is good enough for your task.

In the following section, we provide an overview of the process, followed by the questions you need to work through.

An overview – Using YOLO with Google Colab

1) Download the attached file: Assignment_1.ipynb from the Assessment page.

2) Open Google Colab: https://colab.research.google.com/ and login (using a gmail account).

3) Click File >> Upload Note Book to upload the .ipynb file

4) You will see only two cells of code. Click Run cell (see figure below) to run each cell. You will need to run the first cell once, and the last cell many times to finish the assignment):

· 1^st cell: where you can get the Yolo repo, pre-trained weights and set up the python environment (you only need to run this cell once)

· 2^nd cell: where you upload the necessary photos/images (.jpg) from your local drive to achieve prediction with YOLO

5) When you run cell 2, the program waits for you to upload a file. Click on Choose a file and select a file from your dataset of photos.

6) YOLO will then try to identify the objects in the photo and will give you the prediction results and percentage of accuracy of the prediction.

The output could be something like this:

test.jpg: Predicted in 18.271086 seconds.

truck: 92%

truck: 75%

truck: 68%

car: 97%

car: 96%

car: 87%

car: 78%

car: 65%

car: 59%

car: 50%

Prediction is done in 25.13233256340027 seconds.

And here, there are 11 cars/trucks successfully detected in the image.

Question 1: Collect dataset / photos (10 marks)

· Take at least 10 photos that include animals, people or other objects. Use pictures that have a mix of objects. You may use your cell phone to take the pictures or find them on the internet.

· Save all the pictures to a folder called Images.

· You may wish to resize the images so that each is 500 KB or less in size.

Question 2: Test the prediction performance of YOLO object detection system using your images as the inputs (10 marks)

· Start by running the first cell to set up the environment (you only need to run it once).

· Run the second cell and click “Choose Files” to upload a jpg image.

· Next you will first see the weights being downloaded and then the object detection system will search for all objects in the image and return output with bounding boxes and annotations of detected objects as in the following image:

· You will need to report on three things for each image from your experimentation:

i. how many object types (cars/animals/people) you see on the original image (left), (ignore very tiny ones),

ii. how many object types (cars/animals/people) YOLO recognized in the image on the right,

iii. the accuracy in percentage between the two (e.g. 0% - if none of the animals/people/objects is identified, 50% - if there are 10 animals/people/objects, but only 5 animals/people/objects are identified in bounding boxes, 100% - if all are identified.

· Repeat running the second cell to upload all your photos one by one and save all the prediction results.

· Copy and paste the original photos (left column) and the predicted photos (right column) in Table 1 below. Replace the images below with your images. Also collect the processing time, and the accuracy, e.g. 2/2 = 100% for each photo. (1 mark each photo).

Table 1: Experiment Results

Original images (Replace with yours)	AI detected images (Replace with yours)

Processing Time:	Accuracy:
Your 2^nd original image here	Replace with your AI detected image here
Processing Time:	Accuracy:

Question 3: Evaluation (5 marks)

Analyse the prediction results and write your evaluation summary. Please summarise the prediction accuracies by completing Table 2 (10 marks). Feel free to edit the Object Types column and add the objects detected in your photos (e.g., you may have dogs in your photos).

Table 2: Evaluation

Object types	Total number appeared in all images	Total number accurately detected in all images	Average accuracy (%)
Cars	0	0	0
Persons	0	0	0
	0	0	0


	0	0	0
Total	0	0	0

Part Four: Analyse a Business Case and document using Modelling Techniques (25 marks)

This part of the assignment presents the case study for this assignment. Please read the Rodney Gas Billing System case study below carefully before attempting the Questions that follow. Students may ask for additional clarification of the case study or assignment on the discussion board on Canvas.

The Process Modelling Workbook (see footnote) will be used as a resource for this assignment.

Tools & Templates

For this assignment, here are some options for the creating the diagrams:

• You may type in your answers,

• You may also use a drawing program to draw the Data Flow Diagrams and paste them into the assignment; drawing programs such as (i) Visio or (ii) Visual-paradigm with the Gane-Sarson modelling templates are an option you could use.

Here is the link for Visual-paradigm that may be helpful:

Gane Sarson Diagram | Visual Paradigm Online (visual-paradigm.com)

• Neatly handwritten diagrams with legible text will also be acceptable.

NOTE: The symbols in Visio or the symbols in the online Visual-paradigm tool, which are less explicit about naming and duplicates than those used in ‘The Process Modelling Workbook’, are acceptable.

Case Study - Rodney Gas Billing System

Rodney has just begun to provide gas to a limited number of customers. It is expected that the number of customers will increase as further gas pipes are installed. Currently the billing is done manually, but it is planned to computerise the system in anticipation of increased volumes of users. You have been requested to investigate the system and prepare a system proposal.

This is what you have found out from your information gathering:

The meter reader visits the gas customers every six weeks in order to read their gas meter. When all readings have been logged for the period, the meter reader brings the log into head office where the accounts clerk copies the readings into a “Customer Gas Meter Readings” file which contains, for each customer, details of their previous readings.

To produce the bills for the customers, the accounts clerk subtracts the customer’s previous meter reading from their current reading, in order to find the total volume of gas used for the period. This is multiplied by the gas charge rate which the clerk finds in a “Gas Charge Rates” file. If this is the first bill for the customer, a charge for installation may be added, and sometimes there are maintenance charges. These extra charges are supplied by the Maintenance Department. The gas charge rates are set annually by management. All charge amounts are added to any previous amounts owed which are found for the customer in the “Outstanding Bills” file. The current bill is posted to the customer.

Customers post their payments to the Rodney offices. The payments are banked, and a “List of Deposits” is returned by the bank. This list is compared to the “Outstanding Bills” and paid bills are placed in the “Paid Invoices” file. Partial payments are recorded on the matching bill which remains in the “Outstanding Bills” file.