ENSC252, Fall 2024 Practice questions 1) Convert the following as mentioned in the question. a) (1128)10 = (??)2 b) (1A28E)h = (??)2 c) (6756)8 = (??)2 d) (1010001110) b = (??)10 e) (1011000110111) b = (??)h f) (1110010100) b = (??)8 g) (123.25) d= (??)b h) (1010.1101) b = (??)d 2) Find theminterms expansion off(P, Q, R) = PQ + QR’ + PR’ 3) Derive the simplest sum-of-products expression for the function F (A, B, C, D) = AC’D’ + BC’D +AB’C’ 4) Design the simplest circuit that has four inputs, A, B, C and D, which produces an output value of 1 whenever three or more of the input variables have the value 1; otherwise, the output has to be 0. 5) For the timing diagram in Figure, synthesize the function f(X1, X2, X3) in the simplest sum of-products form. 6) Write the truth table for each gate shown below. Examine the gates whose outputs are same. 7) Find the minimum cost SOP and POS forms for the given function using Boolean algebra and draw the circuit using NAND(for SOP) and NOR(for POS) gates. f(X1 , X2, X3) = ∑ m(1,2,3,5). 8) Construct the truth table for the given circuit and find the minimum cost SOP form. 9) Design the simplest product-of-sums circuit that implements the function. f(X1 , X2 , X3) = ΠM(0,1,5) 10) The Boolean expression (in Sum of Products form) for the logic circuit that will have a 1 output when X = 0, Y = 0, Z = 1 and X = 1, Y = 1, Z = 0, and a zero (0) output for all other input states. Design the Logic circuit
Assignment Remit Programme Title BSc Accounting and Finance Module Title Numeracy, Statistical Analysis & Financial Literacy B Module Code 08 32427 Assignment Title Assessed Statistics Summary Exercise Level Undergraduate Weighting 30% Hand Out Date 28/11/2024 Deadline Date & Time 05/12/2024 12pm Feedback Post Date 16th working day after the deadline date Assignment Format Report Assignment Length 750 words Submission Format Online Individual Module Learning Outcomes: This assignment is designed to assess the following module learning outcomes. Your submission will be marked using the Grading Criteria given in the section below. LO 1. Demonstrate knowledge of descriptive statistics LO 2. Demonstrate knowledge of hypothesis testing LO 3. Demonstrate knowledge and understanding of the measure of association between two random variables Assignment: This assessment constitutes 30% of the total evaluation for the module. To gauge your current skills, you are expected to complete the following tasks. An electronic copy of these exercises should be submitted as required using Word or any other appropriate software package (converting it into PDF format is recommended). The cover sheet should include the title "Statistics Summary Exercise 2023," along with your student ID number and degree programme. Please submit only one file. You will need the Excel worksheet titled "Stock Returns Data Sample 2023" to successfully complete this exercise. This can be found on Canvas. Prepare a report of no more than 750 words (tables are not included in the word count) that summarises the investigated data. Your work should be presented clearly and concisely, utilising as many mathematical techniques as possible that have been covered this term. Tasks 1. Estimate the descriptive statistics of the data for both companies and the index, and provide commentary on various statistics, including measures such as mean, standard deviation, skewness, and kurtosis. (10 marks) 2. Using a 5% significance level, test whether a significant difference exists in the mean returns of the two stocks, GE and AAPL. You need to show the details of how you conduct the hypothesis test. (10 marks) 3. Imagine you are an investment manager currently holding the index. Would these two stocks, GE and AAPL, be suitable as target stocks for diversifying your portfolio's risk? Why? (10 marks) Grading Criteria / Marking Rubric Your submission will be graded according to the following criteria: This is a quantitatively based assignment, and your ability to apply mathematical and statistical methods will be a key part of the assessment. The following criteria will be used to evaluate your work: 1. 10 marks for Task 1, 10 marks for Task 2, and 10 marks for Task 3 (Total 30 marks). Marks will be awarded based on how well you address the specific requirements of each task. 2. A good report will demonstrate your understanding of key concepts, theories, and knowledge covered during the module. Strong responses will accurately apply statistical principles and mathematical techniques relevant to the data. 3. Support your discussion with relevant data and statistical evidence from the dataset provided. Clear and correct use of quantitative data is essential, and interpretations should align with the statistical findings. 4. The report should be well-organised and clearly written, with logical flow between sections. Proper formatting of tables is expected, along with concise yet comprehensive explanations of your findings. 5. Demonstrate critical thinking in your quantitative analysis. A strong report will go beyond basic calculations, offering thoughtful insights and original commentary, particularly in Task 3, where you evaluate portfolio diversification. Ethical Use of Generative AI (GenAI) You are permitted to use GenAIto support your submission for this assessment. You may use it for the following activities: • Researching and refining your ideas • Information retrieval or background research • Drafting an outline to organise or summarise your thoughts • Refining research questions • Checking spelling and grammar Applying GenAI tools should be done with human oversight and control. You should carefully review and use the results carefully as AI can generate authoritative-sounding output that can be incorrect, incomplete, uncritical, or biased. You may not submit any work generated by an AI tool as your own. Where you include any material generated by an AI tool, it should be properly declared just like any other reference material. Alongside your assignment you should also provide a commentary in the Cover Sheet detailing how GenAI has been used to develop your final submission. If you have not used GenAI tools, you should clearly state so. Plagiarism, including that which results from using GenAI, is a form of academic misconduct that will be dealt with under the University’s Code of Practice on Academic Integrity. https://intranet.birmingham.ac.uk/as/registry/policy/conduct/plagiarism/index.aspx University guidance on ethical use of GenAI can be found here: https://intranet.birmingham.ac.uk/as/libraryservices/asc/student-guidance-gai.aspx
CMPINF 0401 -- Intermediate Programming Assignment 4 Topics: Interfaces and implementing new data types Online: Monday, November 11, 2024 Due: All source (.java) files plus a completed Assignment Information Sheet zipped into a single .zip file and submitted via Canvas by 11:59PM on Wednesday, December 4, 2024. Late Due Date: 11:59PM on Friday, December 6, 2024 Submission note: Your .zip file should contain only .java files and your Assignment Information Sheet. There should be no project files or project subdirectories in the .zip file. Also, your TA must be able to compile your program from the command line using the javac command. Test your files to make sure this will work (especially if you use an IDE such as NetBeans for development). If the TA cannot compile your program you will receive minimal credit! Introduction: This term in lecture we have looked at building new Java classes using composition. In particular, we have looked at simple array-based data structures (SimpleAList) and simple linked-based data structures (SimpleLList). Recently we have also looked at using interfaces to access objects that implement those interfaces (see Lectures 21 and 22 and Lab 10). Furthermore, earlier in the term we did a simple in-class exercise in which we implemented a simple decimal "counter" - representing the number of times the "increment()" method was called using an array of ints to represent the digits. For details on this exercise, see the following files: InClass4 .java- driver program to demonstrate class Counter.java- simple implementation of a counter (my solution - you may prefer yours) These files are both available on Canvas at Files/InClass/InClass4/. Before starting this exercise, read over both of these files carefully, noting all of the comments. Be sure you understand what the Counter class is doing and how it is implemented in the simple solution. Details: In this exercise you will implement 2 classes: ArrayCounter in file ArrayCounter.java LinkedCounter in file LinkedCounter.java Both of these classes will implement the interface CountInterface (in file CountInterface.java). Note the methods within CountInterface: public void increment() // Add 1 to the counter value, increasing the values of all affected digits. This method may cause the // number of digits to increase, if the new value cannot be represented with the previous digits. public void decrement() // Remove one from the counter value, decreasing the values of all affected digits. This method may // cause the number of digits to decrease, if the leftmost digit would change from 1 to 0 (since we will // not show leading 0s). If the value of the counter is 0 prior to this call, this method should output an // error and not change the counter value. public void reset() // Reset the counter back to its initial state (and value of 0). After this call the counter should have only // a single digit. public int digits() // Return the number of digits being represented by the CountInterface. This should NOT include any // leading 0s. public int radix() // Return the radix value for this CountInterface. For a given radix,r, the digits in the CountInterface will // have values from 0 to (r-1), inclusive. public String toString() // Return a String representation of the counter with the digits shown correctly. It should contain no // extra characters – only the digits in order. Important Note on Implementations: The functionality of the CountInterface can be implemented in many ways. For this assignment you are required to implement it in the two ways specified below. If you do not implement it precisely as specified, even if it gives the correct output, you will receive minimal credit. Class ArrayCounter: Class ArrayCounter must have the following header: public class ArrayCounter implements CountInterface It should implement the methods in CountInterface as specified, with the following requirements: 1) The underlying data for your ArrayCounter must bean array of int. You may not use any Java collection class for this data. 2) The increment() method should always work and the count should always increase. In order to allow this, you must resize the underlying array when necessary to twice its previous size and copy the data to the new array. Resizing is demonstrated in handout SimpleAList.java – your resizing will be similar to that. 3) The constructor for the class will allow the radix and initial array size to be specified: public ArrayCounter(int rad, int asize) The rad value represents the radix, or base value for your counter and should be
Assignment 1 - Linear Discriminant Analysis Question 1. Using the comma separated multivariate data in the file fld.txt: (a) determine the discriminant line found by Fishers Linear Discriminant. (b) Plot both the data and the discriminant line on a scatter plot (c) Using this line, determine the class of each of the data points in the training dataset, assuming that the threshold is 0 (i.e. positive values are in one class and negative values in the other). (d) Determine what percentage of data points are incorrectly classified. NOTE: The first 2 columns in fld.txt are data columns. The third column is the class to which each data point belongs. Solve this problem in python using the implementation that I gave you in class AND the scikit learn (sklearn) library routine called discriminant_analysis.LinearDiscriminantAnalyis. In your code, display the parameters for the resulting discriminant line (i.e. the slope and the intercept) for both my code implementation and the sklearn implementation. To do this you must reconcile the difference in the parameters of the discriminant line for my representation and the parameters of the discriminant line as determined by sklearn. They are NOT the same - however when interpreted correctly the mean exactly the same thing. The discriminant line position and the classification results for both implementations should be almost identical, minus small differences in precision. Question 2. Using the multivariate data in the file spam.xlsx, determine the discriminant line found by Fishers Linear Discriminant. Using this line, determine the class of each of the data points in the training dataset, assuming that the threshold is 0. NOTE: The first 57 columns in spam.xlsx are data columns. The 58th column is the class to which each data point belongs. Determine what percentage of the training data points are incorrectly classified by your classifier. You will notice that most of the data in the first class is classified correctly while the data in the second class is not. Therefore, it makes sense to adjust the threshold. Try the classification again a few times while adjusting the threshold so that it is a small negative number to see if you can improve the overall classification error rate (percentage of errors in BOTH classes). FYI, information about the dataset is given in the folder with the spam dataset. This is a real dataset, from the Machine Learning Repository at UCI (University of California Irvine). I simply made it a bit smaller by only using the first 500 data points from the first class and the first 500 data points from the second. The original database has over 4000 data points and it was awkward to work with that in Excel. I have not, however, reduced the number of attributes. For those that are interested in a real-life example, this is one. It is a dataset with attributes used to try to detect spam from email. Perform. the above analysis using only my implementation in Python and report the minimum error you could obtain by adjusting the threshold (you could use a loop to search for the optimal threshold value if you like but this doesn’t need to be the exact “ minimum” error for the purposes of this assignment). For that minimum error value of the threshold, report the confusion matrix when classifying the training data.
CDS532 Programming for Data Science Case Study Assignment (Due: 11th December 2024, 23:59) Introduction Air pollution refers to the contamination of the atmosphere by the pollutants released into the air when industrial and commercial activities are conducted. These pollutants are harmful to human health and the environment. Three major sources of air pollutants in Hong Kong are motor vehicles, marine vessels, and power plants. In this case study, we would like to investigate the air pollution problem in Hong Kong. To achieve our objective, we will write a python program that can automatically fetch data from the Hong Kong government’s data portal and visualize the data into charts. Program Requirement Write a program that prompts the user to select which year’s air quality health index (AQHI) dataset should be used for conducting air pollution data visualization. The program will then fetch the dataset that correspond to the year selected by the user from the data resource in JSON format in the data portal named "Past record of Air Quality Health Index (English Version)". The API for fetching data is shown as follows: https://api.data.gov.hk/v2/aggregate/hk-epd-past-record-of-aqhi-en?q={"section":, "format":} This API takes a JSON string which contains the parameter values for configuring which data should be fetched. The definitions of the parameters are listed below: Parameter Description section This parameter defines which year of the AQHI data to be fetched with a section code. The mapping between the section code and the year is shown in the table below: Section Code Year 2 2014 3 2015 4 2016 5 2017 6 2018 Note: This API only provides complete annual AQHI data from 2014 to 2018. The table above has already covered all section codes that can be mapped to a complete annual AQHI dataset. format This parameter defines the format of the return data. There are three different options: CSV, json and xml. In this assignment, json should be used After the AQHI data has been fetched, the program reads the AQHI data in JSON format downloaded from the data portal and conducts data preprocessing. A preview of the annual AQHI data is shown below: The annual AQHI data is a table where each row corresponds to 16 different AQHI values measured in 16 air quality monitoring stations in a particular hour in a particular day. The first column is the date when the AQHI value is measured. The second column is the hour when the AQHI value is measured. If a row has a value "Daily Max" in the hour column, that row is recording the maximum AQHI values of every air quality monitoring station in a particular day. Each of the remaining 16 columns represents the AQHI data collected by one of the 16 air quality monitoring stations located in the following 16 locations: Central/Western, Eastern, Kwun Tong, Sham Shui Po, Kwai Chung, Tsuen Wan, Tseung KwanO, Yuen Long, Tuen Mun, Tung Chung, Tai Po, Sha Tin, Tap Mun, Causeway Bay, Central, Mong Kok. Some AQHI values in the annual AQHI data may contain two extra symbols such as "+" and "*". These two symbols provide additional contextual information. However, these symbols may interfere with our analysis and have to be removed. On the other hand, some AQHI values maybe missing (i.e. empty value). Missing values are handled by filling with zeros. After performing data preprocessing, the program then performs the necessary calculations to plot the following two kinds of charts to visualize air population in the monthly scale: The first kind of chart is a bar chart which plots the monthly average AQHI of16 air quality monitoring stations in Hong Kong. The second kind of chart is a line chart which plot the changing trend of the AQHI averaged across 16 air quality monitoring stations in Hong Kong. Note: To keep things simple, the maximum AQHI value X measured at monitoring station S in date D will be considered as the overall AQHI value of the monitoring station S at date D. Sample Input Terminal Year Data available: 2014: section code 2 2015: section code 3 2016: section code 4 2017: section code 5 2018: section code 6 Please enter the section code corresponding to the target year: 4 Sample output Assumptions You may assume that every input of the program is valid in format. Submission Students should submit their source code as (1) a single Jupiter Notebook file (i.e. .ipynb file) OR (2) a zip file that contains standalone Python script. files (i.e. .py files) for answering the programing questions to the submission box on the Moodleelearning platform. on or before 11th December 2024, 23:59. Students are expected to name their file submission in the name of _case_study.ipynb OR _case_study.zip and their source code should follow the following format:
Assessment Brief 2024/2025 Course Code MGT5063 Course Title People in Organisations Weighting 40% Question release date Wednesday 30 October 2024 Submission date: Wednesday 04 December 2024 Grades and Feedback to be released on: Tuesday 07 January 2025 Word limit NA Action to be taken if word limit is exceeded Please confirm 1. QUESTION/ DESCRIPTION OF ACTIVITY This assignment makes up 40% of the overall mark for the course. It is a group assessment with individual contributions. The assessment should include an input from each group member. Throughout the course of this module, you have learned about the different stakeholders impacted by organisations, and about the dynamics and concepts that shape the behaviour of individuals, groups and leaders within them. You have also learned about the Scottish Fair Work Framework, and this knowledge will inform. this assessment. You have each been placed in teams. You are to select a real-life organisation based in the home country of one of your team members, and give a 15-20 minute presentation to the organisation to persuade the board of directors to implement the fair work framework for staff. The principles of this framework are effective voice, opportunity, security, fulfilment and respect. Your presentation should include: ● A very brief overview of the organisation ● Identification of the key stakeholders for the organisation (primary and secondary) ● Clear, detailed and evidence-based arguments explaining how these principles will impact positively on core areas of HR, including at a minimum: - Recruitment and selection - Retention - Skills and staff development - Diversity and inclusion - Individual performance ● You should then build on these arguments to present a convincing case of how this can improve overall organisational performance to benefit all stakeholders Please keep in mind that most board of directors are likely to be hesitant about aspects of the fair work framework, because of cost implications or autocratic management policies, however it is your job to persuade them with the strength of your arguments. Please also make sure you are drawing from strong academic sources and citing them properly using the Harvard system. 2. ADDITIONAL INFORMATION FOR GROUP ASSIGNMENTS You have been placed in your group at random and students are not allowed in any circumstances to choose your own teammates. You will know who you are working with by the second tutorial. You are expected to provide a groupwork contract agreed upon by all members of the team by a date provided to you by the course coordinator. In addition to this, students will be expected to complete an evaluation of all group members’ participation (including their own) in this group assessment as follows: ● A group task involves several distinct stages. In this self-assessment you are asked to consider the contributions made by each of the group, including yourself to these different stages. Members of your group may have contributed more to some areas than others. ● Please use the grid below and the 4-point scoring system.
BMEN90035 Biosignal Processing Final Exam, Semester 2, 2024 Number of questions: 2 Total Marks: 120 (1 mark per minute of writing time) • Question 1: 50 marks • Question 2: 70 marks Time allowed: • 40 minutes reading time - you MAY begin writing during this time • 120 minutes writing time • 30 minutes upload time - you must NOT write during this time The exam is openbook. You may use code from Class Exercises and Workshops as templates to modify to complete this exam. You may also consult resources on the internet, excluding artificial intelligence tools, such a Chat GPT or Wolfram Alpha. Additionally, a table of Fourier transforms available for download from the LMS Assignments page for this exam may be useful. A copy of this exam is available in pdf format from the LMS Assignments page in case any equations are difficult to read in the .mlx file. You may NOT share code or answers with other students during the exam. Data provided The following data are provided to be used in answering ALL Questions: • e1, e2 - two Matlab variables corresponding to two electromyogram (EMG) envelope signals elnl and , respectively, in units of volts. The two EMG signals were recorded simultaneously from different electrodes, on channels 1 and 2 (respectively), on the forearm of a subject while typing. • f_s - a Matlab variable corresponding the sampling frequency f = 1000 Hz used for elnl and eal ml . • cmap - a Matlab variable corresponding to a colour map that is useful for Q1f, Q2c, Q2d and Q2g. It is a 4x3 matrix, where rows 1 to 4 correspond to the colours cyan, blue, green, and red, respectively. Please note that answers to some questions are used in subsequent questions as part of a multi-step signal processing pipeline. Consequently, the following supplementary variables are provided to allow you to complete these subsequent questions in case you are unable to calculate these variables in the original question. These variables are all stored in a Matlab structure soln. • soln.P1_volts and soln.P2_volts - corresponding to key-press segments p1, m[k] and p2, m[k] calculated in Q1b. These variables are stored in the 1000x80 matrices, pi , as described in Q1b. • soln.p1_bar_volts and soln.p2_bar_volts - corresponding to the mean key-press segments, and , calculated in Q1c. These variables are 1000x1 vectors, . • soln.Q1_volts and soln.Q2_volts - corresponding to mean-subtracted key-press segments: q1, m[k] and q2, m[k] , calculated in Q1c. These variables are stored in 1000x80 matrices, , in the same format to the matrices pi described in Q1b. • soln.h1 and soln.h2 - corresponding to first two principal components, and , calculated in Q1d. • soln.ym1_volts and soln.ym2_volts - corresponding the projections Y1,m and )2m calculated in Q1f. These variables are in 1x80 vectors giving the projections to the key-presses m=1,.,80 . • soln.y1_volts and soln.y2_volts - corresponding to filter outputs y[n] and calculated in Q2c. • soln.y1_3rd_volts and soln.y2_3rd_volts - corresponding to filter outputs and for the third key press of each finger, as calculated in Q2d. These variables are 1000x4 matrices, where each column corresponds to a key-press for fingers 1 to 4, respectively. • soln.r_volts - corresponding to the distance, r[n] , calculated in Q2e. These variables can be loaded into your workspace by running the following lines of code. clear all load ExamData2024.mat Overview Electromyograms (EMG) measure the electrical activity of muscles during their contraction using electrodes placed on the surface of the skin. Most useful information in the EMG appears in the envelope of the signal (this is a positive, relatively slow signal that modulates a faster, noisy carrier signal). In this exam you will analyse EMG envelope signals obtained from recordings made during typing, for potential application in control of a robotic hand for typing. These EMG envelopes, eu[n] and , were obtained from simultaneous recordings on channels 1 and 2 (respectively), using from electrodes at different locations on the forearm. During the recordings, the subject pressed keys on a keyboard with one finger at a time. There were 80 key presses in all, made at a rate of 1 press per second as follows: • key presses 1-20: finger 1 = index finger, • key presses 21-40: finger 2 = middle finger, • key presses 41-60: finger 3 = ring finger, • key presses 61-80: finger 4 = little finger. The goal of the application is to use the EMG envelopes, and , to determine when a key press has taken place and which finger (1 to 4) this corresponds to. This will be done in two stages: • Question 1: Performing an off-line analysis using principal components analysis of the EMG envelopes. This will identify a two-dimensional projection space in which it is possible to classify which finger was used to press the key for each segment of the EMG envelope. • Question 2: Designing and implementing an on-line processing algorithm to determine when a key-press has taken place and which finger (1 to 4) was used. This is based on the principal components analysis from Question 1. Question 1: Performing an off-line analysis using principal components analysis of the EMG envelopes. This will identify a two-dimensional projection space in which it is possible to classify which finger was used to press the key for each segment of the EMG envelope. Q1a. [4 marks] Figure 1: Plot the two EMG envelopes, elnl and e a[n] , over the duration of the recording. Plot elnl in subplot(2,1,1) and ea[n] in subplot(2,1,2). Label your axes showing time in seconds and signal amplitude in volts. Give the figure a title of Fig. 1. (Note here and throughout the exam, when giving a title to a figure with subplots, you may apply title to just the first subplot.) %% Q1a code figure(1), clf subplot(2,1,1) plot(t,e1,'k') title('Fig.1)') subplot(2,1,2) plot(t,e2,'k') xlabel('time [s]') ylabel('amplitude [V]') Q1b. [6 marks] The first step in performing principal components analysis is to partition each of the envelopes elnl and e a[n] into 80 one-second segments, each corresponding to a key-press. Call these key-press segments p1, m[k] and p2, m[k] , respectively, form=1,.,80 key-presses, and k=1,…,1000 samples. Extract each segment pj. m[k] , for each electrode. For the first segment of each electrode use samples n= l to 1000 (corresponding to the 1st to 1000th samples of eu[n] and eznl ); for the second segment use samples n= 1001 up to 2000, and so on for the 80 key-presses. (Hint: consider using the Matlab command reshape to produce a 1000x80 matrix, pi , for each electrode with pi. m[k] , k=1,…,1000 , as the columns). In Figure 2 plot all 80 key-press segments overlaid, plotting those for the first electrode, p1, m[n] , in subplot(2,1,1) and those for the second electrode, p2, m[n] , in subplot(2,1,2). Label your axes showing time in seconds and signal amplitude in volts. Give the figure a title of Fig. 2. %% Q1b code figure(2), clf Q1c. [8 marks] The next step in performing principal components analysis is to calculate the mean key-press segments, and , over the 80 key-presses for electrodes 1 and 2, respectively. Then use these to calculate the mean-subtracted key-press segments: and , for each key-press m=1,.,80 . Calculate the mean-subtracted key-press segments: q1, m[k] and q2, m[k] , m=1,.,80 . Plot them overlaid in Figure 3, plotting those for the first electrode, q1, m[k] , in subplot(2,1,1) and those for the second electrode, q2, m[k] , in subplot(2,1,2). Label your axes showing time in seconds and signal amplitude in volts. Give the figure a title of Fig. 3. (Note: if you were not able to calculate the key-press segments p1, m[k] and p2, m[k] you may use a version stored in soln.P1_volts and soln.P2_volts to complete this question. These variables are the 1000x80 matrices, pi , described in Q1a.) %% Q1c code figure(3), clf Q1d. [10 marks] Perform. principal component analysis on the combined mean-subtracted key-press segments. To combine them, concatenate the segments together for channels 1 and 2 for each key-press, m. I.e. if 1,m and q2m are the column vectors corresponding to segments q1, m[k] and q2, m[k] , respectively, make a vector of twice their length by appending 92m under 1,m : qm=Iq1, m;q2, m] . Plot the first two principal components, hi and hc in Figure 4, using sample numbers for the timeaxis. Label your axes showing time in samples and component amplitude (unitless). Give the figure a legend and a title of Fig. 4. (Note: if you were not able to calculate the mean-subtracted key-press segments, q1, m[k] and q2, m[k] , you may use a version stored in soln.Q1_volts and soln.Q2_volts to complete this question. These variables are 1000x80 matrices, , in the same format to the matrices pi described in Q1b.) %% Q1d code figure(4), clf Q1e. [6 marks] Interpret the two principal components in terms of the relative amplitude and polarity of the signals on each of the two electrodes. (Note: if you were not able to calculate the first two principal components, and , you may use a version stored in soln.h1 and soln.h2 to complete this question.) Q1f. [8 marks] Project the segments qm onto the 2-dimensional projection space defined by the first two principal components and : i.e. onto points (ym,yz m)=(hf qm, h⃞qm) (Eq.1) Plot the results in Figure 5 as a scatterplot of 2m versus Y1,m . Colour code the points in the figure so that "finger 1" = cyan, "finger 2" = blue, "finger 3" = green and "finger 4" = red. (The colour map cmap is provided for you. It is a 4x3 matrix, where rows 1 to 4 correspond to the colours cyan, blue, green, and red, respectively.) Label your axes, including showing the projection amplitude scale in volts. Give the figure a title of Fig. 5. (Note: if you were not able to calculate the first two principal components, and , you may use a version stored in soln.h1 and soln.h2 to complete this question. Similarly, if you were not able to calculate the mean-subtracted key-press segments: q1, m[k] and q2, m[k] , you may use a version stored in soln.Q1_volts and soln.Q2_volts to complete this question. These variables are 1000x80 matrices, Q; , in the same format to the matrices pi described in Q1b.) %% Q1f code figure(5), clf Q1g. [8 marks] Propose a criteria based on the projections y1,m and Y2,m in Figure 5 to classify each key-press as corresponding to one of the fingers 1 to 4 (the criteria need not have perfect accuracy). Explain the merits and deficiencies of your criteria. (Note: If you were not able to calculate the projections Y1,m and Y2,m , you may use a version stored in soln.ym1_volts and soln.ym2_volts to complete this question. These variables are 1x80 vectors giving the projections to the key-presses m=1,.,80). Question 2. Designing and implementing an on-line processing algorithm to determine when a key-press has taken place and which finger (1 to 4) this corresponds to. This is based on principal components analysis from Question 1. Consider the decomposition of the first principal component, h1=[h11;h12] , where • hn is the subcomponent of hi corresponding to signals from electrode 1, and • is the subcomponent of corresponding to signals from electrode 2. Similarly for the second principal component, h2= [h21;h22] , where • is the subcomponent of corresponding to signals from electrode 1, and • h22 is the subcomponent of hc corresponding to signals from electrode 2. Q2a. [14 marks] [This question requires handwritten answers to be scanned and uploaded]. Considering, applying these subcomponents, hj , as templates for matched filters, sol kl (k=0,…,999), to their corresponding envelopes eynl so as to correctly transform. them into the principal component projection space (y[n], ya[n]) defined by Eq.1 in Q1f. That is, at each time step, n, xnl is calculated by applying the steps of mean subtraction and projection described in Q1b, Q1c and Q1f to the two vectors e,in l=le, in-999),e,in-998],…e, in ll', (j=1,2) that contain the last 1000 samples of einl . Show that (y[n], ya[n]) are given by y[n]=(g11xe)[n]+(g12xe)[n]-h fp1-hp2 (Eq.2) (Eq.3) where (j=1,2) are the column vectors corresponding to the mean key-press segments, . Q2b. [10 marks] [This question requires handwritten answers to be scanned and uploaded]. Consider the pair of signals: • dI[n]=hu[n] for n=0,…,999 , and d[n]=0 otherwise, on channel 1, • dz[n]=h2[n] for n=0,…,999 , and d[n]=0 otherwise, on channel 2. That is,dnl is the subcomponent of the first principal component corresponding to channel j, starting at time zero. 1. Prove that the value of delay, na , from the onset of the signal at n=0 that will lead to a maximal output y[n] for the matched-filter is n a=999 samples? 2. Theoretically, what will the value of be at this delay time, ? Explain your answer. Q2c. [8 marks] Apply the formulae in Eqs. 2 & 3 to calculate the filter outputs (y[n], ya[n]) . Plot the trajectories of versus in the principal component projection space, in Figure 6. Colour-code the trace so that those parts of the signal corresponding fingers 1, 2, 3 and 4 are plotted in cyan, blue, green and red, respectively. In doing so, account for the delay, na , due to the matched filter (defined in Eqs. 2 & 3) that would be expected to result in an optimal match to the key-press segments, p1, m[k] and p2, m[k] , defined in Q1b. (Note: The colour map cmap is provided for you. It is a 4x3 matrix, where rows 1 to 4 correspond to the colours cyan, blue, green, and red, respectively.) Label your axes showing the projection amplitude in volts. Give the figure a legend and a title of Fig. 6. (Note: if you were not able to calculate the envelopes elnl and eznl you may use a version stored in soln.e1_volts and soln.e2_volts to complete this question. Similarly, if you were not able to calculate the first two principal components, hi and hc , you may use a version stored in soln.h1 and soln.h2 to complete this question. Similarly, if you were not able to calculate the mean key-press segments, and , you may use a version stored in soln.p1_bar_volts and soln.p2_bar_volts to complete this question. These variables are 1000x1 vectors, .) %% Q2c code figure(6), clf Q2d. [8 marks] Using your calculation of the filter outputs (y ln], ya[n]) , plot the trajectories of versus for just the third key-press for each finger, 1 to 4. To do this use the segment of the signal (y[n], ya[n]) that: • corresponds to 500 ms before the third key-press was initiated, to 500 ms after the third key-press was initiated, and • accounts for the delay, na , due to the matched filter (defined in Eqs. 2 & 3) that would be expected to result in an optimal match to the key-press segments, p1, m[k] and p2, m[k] , defined in Q1b. Once again, colour-code the trace for each finger 1, 2, 3 and 4 so they are plotted with cyan, blue, green and red, respectively. (The colour map cmap is provided for you. It is a 4x3 matrix, where rows 1 to 4 correspond to the colours cyan, blue, green, and red, respectively.) Label your axes showing projection amplitude in volts. Give the figure a legend and a title of Fig. 7. (Note: if you were not able to calculate the envelopes and you may use a version stored in soln.e1_volts and soln.e2_volts to complete this question. Similarly, if you were not able to calculate the first two principal components, and , you may use a version stored in soln.h1 and soln.h2 to complete this question. Similarly, if you were not able to calculate the mean key-press segments, and , you may use a version stored in soln.p1_bar_volts and soln.p2_bar_volts to complete this question. These variables are 1000x1 vectors, .) %% Q2d code figure(7), clf Q2e. [8 marks] Identify by sight the approximate "return" position (yr1, yr,.2) , evident in Figure 7; i.e. the approximate point that the trajectory (y[n], ya[n]) returns to between each key-press. In Figure 8 plot the distance, r[n] , of (y[n], yz[n]) from the "return" position (yr,1,yr,2) , as a function of time. (In your answer, specify the "return" position by giving values (yr,1, yr:2) to a 2-dimensional Matlab variable y_r in your code). (Note: if you were not able to calculate the segments of and corresponding to the third key-press in Q2d, you may use a version stored in soln.y1_3rd_volts and soln.y2_3rd_volts to complete this question. These variables are 1000x4 matrices, where each column corresponds to a key-press for fingers 1 to 4, respectively. ) %% Q2e code figure(8), clf Q2f. [8 marks] Propose (in words) a criterion based on the distance, r[n] , for first identifying when a key-press occurred, regardless of which finger was used. Justify your answer using Figures 6, 7 and 8. (Note: if you were not able to calculate r[n] in Q2e, you may use a version stored in soln.r_volts) Answer: Q2g. [14 marks] Based on your answers to Q1gand Q2f devise a method to determine from the EMG envelope data when a key-press has taken place and which finger (1 to 4) this corresponds to. Apply your method to the data in envelopes and . Report the results by first plotting the two EMG envelopes, elnj and elnl , in black over the duration of the recording in Figure 9 in subplot(2,1,1) and subplot(2,1,2), respectively. Then plot on top an asterisk (*) along the timeaxis of both subplots at the estimated time of each key-press from Q2f. Colour code each asterisk with black, blue, green or red, corresponding fingers 1, 2, 3 and 4, respectively. Label your axes showing time in seconds and envelope amplitude in volts. Give the figure a title of Fig. 9. %% Q2g code figure(9), clf Total marks: 120
Assignment Information Module Name: Security and Compliance in the Cloud Module Code: 6035 CEM / 303CEM Assignment Title: Coursework Assignment Due: 05/12/24 Assignment Credit: 20 Word Count (or equivalent): 2500 Part 1 / 1500 Part 2 Assignment Type: Group and Individual coursework Percentage Grade (Applied Core Assessment). You will be provided with an overall grade between 0% and 100%. You have one opportunity to pass the assignment at or above 40%. Assignment Task Overview ViewPoint Hotel (not an existing business) is a hotel chain with branches in Cardiff, Birmingham and London. The hotel remains busy throughout the year due to their unique business model. The hotel chain wishes to build a cloud network that hosts its online website and services, and securely link their two private networks. The management hopes that this secured network will enable clients to book accommodation, make changes (including cancellation), and pay using various means (cash and cards). They also hope to have rewards for their loyal customers. The requirements for the system areas follows: This assignment requires you to configure, deploy and test a working virtual cloud-based system. The assignment consists of two different elements: 1. Part 1: An individual practical section that requires you to develop a network- based system. The weighting of this work is 50% of the overall module grade. You must submit a section of 2500 words with the evidence of the designing, configuring and testing the developed system. 2. Part 2: An individual reflective section of 1500 words that considers business continuity and legal issues in relation to the network system provided in the practical element of the assignment. The section worth 50% of the overall module grade. (You must pass both parts of this task, pass mark is 40%). Part 1 - Practical Element Brief 1. Design the topology/layout of the system to be developed considering all the servers needed for the support of the core services of the business. 2. Develop the network system configuring and deploying the servers properly, and apply testing and security analysis to the developed system Practical Element Information During the practical/lab sessions you will be required to complete a set of workshops that will help you to acquire the skills needed for the completion of the practical coursework (CW1). This practical coursework covers the learning outcomes LO1, LO3, LO4 and LO5. The technical skills required to pass this element is covered in those sessions. Your work will be marked using the Practical Element Grading Rubric. Details of the Practical Element Design (10%): a. Produce an architecture of the cloud network to be implemented. Discuss the network topology of each site, the connectivity between sites, explain why this architecture is proposed and what are the IP address ranges which are used for each site. b. Explain which servers have been considered and why. Explain how the proposed servers can support the business needs, and how the features of these servers are used to enhance the functioning and security of the system. c. Describe how the proposed design supports the network scalability if the company plans to have more sites in the future, and how testing the security features is considered. Development (30%): d. Demonstrate how the servers at different sites are configured and tested to ensure the required functionality, and how are the settings of the cloud network are implemented. e. Demonstrate the appropriate techniques to show the proper connectivity of the servers, how local nodes of the private networks have access to public networks, how public networks have access to the promoted services, and how the local private servers and data are protected from public access. f. Demonstrate a proper level of manual/automated configurations of server, manual/automated deployment of tools, and how the workload has been balanced between the servers. g. Demonstrate how the security testing is implemented and with which tools to investigate the key security, what are the proposed solutions that have implemented to encounter these security issues, and how different privileges and rights have been applied to the user accounts. Structure and Presentation (10%): h. The section should have a good structure, nice presentation style and coherence of its content that will help the organisation. Figures should be added to present the design, development and testing of the proposed system. i. The section should have a clear narrative linking propositions, evidence and judgments. Facts will be clearly differentiated from opinions, all sources used must be evidenced by reference to other works following the APA Reference Style. Part 2 - Individual Reflective Section Brief (Overall 50%) You are asked to write a section that will consider business continuity issues in relation to the cloud network provided in the practical element of the assignment. Specifically, in the section, you need to consider the following: Threat Analysis and Business Impact Analysis: Identify the potential threats that could emerge with respect to the considered system which could lead to disruptions of the network and discuss the respective operational and financial impacts. Business Continuity Plan and Disaster Recovery Plan: Design and develop a detailed outline of a BCP for the produced system that will indicate the process steps that need to be followed. Create a plan that will demonstrate the appropriate actions for the recovery of the business after certain disaster scenarios. Legislation and Regulations: Discuss the national and international current legislation that should be considered for the developed system. Individual Reflective Section Information The is an individual section based on the practical element that you have completed and covers the learning outcomes LO1 and LO2. Your section will be marked using the Reflective Section Grading Rubric. Details of the Individual Reflective Section Threat Analysis and Business Impact Analysis (10%): a. Apply the threat analysis to the cloud network considering several different case scenarios, which should identify threats that could lead to the network disruptions including what are the servers and connectivity threats. b. Apply the business impact analysis to the cloud network considering several different case scenarios, which should identify the respective operational and financial impacts of each case. Business Continuity Plan and Disaster Recovery Plan (25%): a. Develop a business continuity plan that considers the appropriate process stages required for the successful continuation of the operation of the cloud network and its critical services, provide a thorough description of the actions, resources and requirements identified in each of these stages. b. Develop a detailed disaster recovery plan that enables the application of recovery strategies. These strategies should target the successful recovery of the cloud network and its critical services investigating the required resources, operation and services. Legislation and Regulations (10%): a. Indicate the current the national and international legislation that should be considered for the cloud network. b. Provide thorough explanation of how each of these legislations satisfies the security requirements of the cloud network. Structure and Presentation (5%): a. The section should have a good structure and nice presentation style that will help the organisation and coherence of its content. b. The section should have a clear narrative linking propositions, evidence and judgments. Facts will be clearly differentiated from opinions, all sources used must be evidenced by reference to other works following the APA Reference Style. Submission Instructions You are required to submit one report that includes two sections in a PDF format via Aula (TurnItIn). The first is a practical section which has a word limit of 2500 words while the second is a reflective section which has a word limit of 1500 words. The deadline for this coursework is on (12/12/24) Marking and Feedback How will my assignment be marked? Your assignment will be marked by the module team How willI receive my grades and feedback? Provisional marks will be released once internally moderated Feedback will be provided by the module team alongside grades release [Course teams to add a statement detailing how students can access their feedback here]. Your provisional marks and feedback should be available within 2 weeks (10 working days) What willI be marked against? Details of the marking criteria for this task can be found at the bottom of this assignment brief. Assessed Module Learning Outcomes The Learning Outcomes for this module align to the marking criteriawhich can be found at the end of this brief. Ensure you understand the marking criteria to ensure successful achievement of the assessment task. The following module learning outcomes are assessed in this task: Module Learning Outcomes LO1 Demonstrate a sound understanding of techniques for business continuity such as Business Impact Analysis. LO2 Apply continuity planning and disaster management on both local and cloud-based ICT resources to an organisational scenario. LO3 Design, build and test a range of secure, virtual networks to solve defined business requirements. LO4 Use a range of tools and techniques to carryout a security audit and develop strategies to reduce risk. LO5 Interpret and apply relevant legal considerations when storing data in the cloud.
ENSC252, Fall 2024 Practice Questions #2 1. (10marks) Simplify the following functions, F, together with the don’t-care conditions d, and then express the simplified function in sum of minterms: a) F(A, B, C, D) = Σ (0, 6, 8, 13, 14) + d(2, 4, 10) b) F(A, B, C, D) = Σ (1,3,5,7,9,15) + d(4,6,12,13) 2. (10 marks) Simplify the following expressions using K map, and implement them with NAND gate circuits: a) AB ’ + ABD + ABD ’ + A’C’D ’ + A’BC ’ b) BD + BCD ’ + AB’C’D ’ 3. (10 marks) Implement half adder circuit using only NAND gates. 4. (15marks) Construct a half subtractor circuit which performs subtraction of the two inputs A and B and Outputs are B(borrow) and D(difference). 5. (20 marks) Using 8 bit 2’s complement numbers, perform. the following. a)104 +22 b) 125- 45 c) – 98 - 21 d) -65-47 6. (20) Using half subtractor, implement full subtractor circuit whose inputs are X, Y, bin and outputs are B(Borrow) and D(difference). 7. (15 marks) Find the minimum cost POS form. for the function f (A, B,C, D,E) = πM (1, 4, 6, 7, 9, 12,15, 17, 20,21,22, 23,28, 31)
Assignment Information Module Name: Management Information System Module Code: 6042CEM Assignment Title: Building a Dashboard using Power BI Assignment Due: 05/12/2024 Assignment Credit: 15 credits Word Count (or equivalent): 2500 words +/- 10% Assignment Type: Individual Report with dashboard Percentage Grade (Applied Core Assessment). You will be provided with an overall grade between 0% and 100%. You have one opportunity to pass the assignment at or above 40%. Assignment Task Building a Dashboard using Power BI You have been provided with three datasets on Aula. Choose one of these datasets and create a report that analyses how information systems support business strategy, business processes, and decision-making within an organization. Your report should include the following components: 1. Dashboard Creation and Data Visualization (30 Marks): o Use Power BI to create a dashboard, providing a background on data visualisation and its importance. o Utilise KPIs and other visual elements to extract insights from your chosen dataset that can aid management decision-making. o Explain why you selected this dataset. 2. Data Exploration and Model Building (30 Marks): o Explore the dataset in depth and develop a relationship model of your choice. o Create an interactive dashboard report that supports business decision-making. 3. Model Evaluation (15 Marks): o Evaluate the accuracy, usability, and suitability of your model for decision-making. o Discuss how the model can contribute to sustaining a competitive advantage. o Identify any issues with the datasets or the model itself. 4. Insights and Conclusions (15 Marks): o Draw conclusions and provide insights based on the overall analytical and data models you have developed. 5. Self-Reflection (10 Marks): o Reflect on how using Power BI enhanced your analysis and decision-making process. Submission Instructions: • The report should be submitted electronically via the Aula • The deadline for submission is 5th December 2024. • You should submit your work by creating a report explaining your dashboard and uploading your Power BI dashboard on Aula as a PowerBI.Pbix form. • Your submission should be in Microsoft Word, or PDF format. • Late submissions will be awarded 0 mark. If you have a genuine reason for needing to submit late, you can request an extension from the faculty registry. • The report should be professionally formatted with clear headings, subheadings, and page numbers. Use 1.5 line spacing, a standard font (e.g., Arial or Times New Roman), and a font size of 12. • All work must be original and properly referenced. Plagiarism will not be tolerated and will be subject to university disciplinary procedures. • You must acknowledge the use of any AI tools in completing this assessment. Please familiarize yourself the withStudent guidanceon how to use Artificial Intelligence in studying and assessment. Marking and Feedback How will my assignment be marked? Your report will be marked by the module team. How will I receive my grades and feedback? Provisional marks will be released at 19/12/2024. Feedback will be provided by the module team alongside grades release on each script, which was submitted to Aula. Your provisional marks and feedback should be available within [2 weeks (11 working days)]. What will I be marked against? Details of the marking criteria for this task can be found at the bottom of this Coursework brief. Assessed Module Learning Outcomes The Learning Outcomes for this module align to the marking criteria which can be found at the end of this brief. Ensure you understand the marking criteria to ensure successful achievement of the assessment task. The following module learning outcomes are assessed in this task: 1. Recognise how information systems support business strategy, business processes, and practical applications in an organisation. 2. Interrelate how various support systems can be used for business decisions and to sustain competitive advantage. 3. Assess how the Internet and World Wide Web can provide a global platform. for e-business, business mobility and communications, collaboration, and cloud computing in an organisation 4. Evaluate the value of, and the relationship between business data, data management, and business intelligence.
DAN Department of Management and Organizational Studies Intermediate Accounting I – MOS 3360A – Fall 2024 Research Case Assignment Introduction This assignment will require you to research and analyze financial accounting issues using the CPA Canada Handbook. Specific Case Analysis Requirements to complete the assignment are outlined in Appendix A to this document. Please review these requirements carefully to ensure you have the best chance of success. Students may complete this assignment individually or with one other student who is taught by the same instructor. If you choose to work with another student, only one student needs to submit the report. Completing this assignment is a requirement to pass the course. In the case of no submission, students will not be permitted to pass the course. The analysis and thoughts in your report must be in your own words with proper referencing to the CPA Canada Handbook. The CPA Canada Handbook is your main reference source to complete this assignment. If you take a passage from the CPA Canada Handbook, you must use proper referencing (further details on this are provided in Appendix A). Deliverables • All submissions should be completed in Microsoft Word (other formats will not be accepted). • Your response must be in 12-point font,single spaced, and can be a maximum often (10) pages (including any appendices, but not including your title page). You are not required to use all ten pages. • Please include a title page with the name of the instructor, student name, student number, and section number for each student. • The response should be well written, complete, concise, and easy to understand. Complete sentences and proper grammar should be used. Bullet points (as appropriate) are acceptable as long they are complete and understandable. Please DO NOT use incomplete thoughts or submit rough work. Submissions Students are required to submit both a hardcopy of the assignment AND an electronic copy of their final report by the due date listed below. • Hard copy: The report should be printed and stapled in the top left corner - no binders, plastic covers, etc., and must be submitted to the drop box outside the MOS office on the 4th floor of the Social Science building. The drop box is located to the left of SSC room 4304. The report submitted will not be returned. • Electronic copy: • The electronic copy of the report should be submitted in Word format. PDF formats will not be accepted. • Save the Word document in the following format: MOS3360 - Assignment_Last name_First name (of the student submitting the file) (example: MOS3360 – Assignment_Smith_Jane) • Submit your completed assignment through the “Assignment” tab on the OWL course site. • E-mailed responses will NOT be accepted. Appendix A - Case Analysis Requirements 1. The purpose of this assignment is to present you with an opportunity to read the CPA Canada Handbook and learn how to apply the accounting guidance contained therein. The CPA Canada Handbook can be accessed through the Western Libraries’ website at:https://edu-knotia-ca.proxy1.lib.uwo.ca/. You will need to login using your UWO credentials. You can also do a search on the Western Libraries’ website for “CPA Canada standards” . You will betaken to the CPA Handbook collection. Select "view online", and then click on the link "CPA Canada standards & guidance collection (CPACHB)". 2. You must base your response solely on the guidance contained within the CPA Canada Handbook. Any reference to textbooks, websites, or any other source will not be evaluated, and no marks will be awarded to any content referenced from these sources. Students are cautioned to only use sources outside of the CPA Canada Handbook for educational purposes, and to ensure your response to this case references the Handbook and nothing else. Relevant CPA Canada Handbook section references have been provided in the case to assist you in narrowing your focus. 3. You are expected to fully respond to all the accounting issues you identify in the case to demonstrate your depth of knowledge. Appendix B outlines a suggested format of how to analyze accounting issues. 4. A sample accounting issue with a solution has been provided for your review in Appendix C. You should format your response following a similar process as demonstrated in the sample solution. The accounting issue illustrated in the sample solution is very simplistic and you should assume the accounting issues you will be responding to in the case will be much more complex and require greater analysis. The accounting issues you will be analyzing in the case are detailed in nature. You should not assume that the in-class coverage was fully comprehensive. You will find extensive and detailed guidance in the relevant Handbook section referred to in this case. You should expect to read the entirety of the relevant Handbook section when analyzing the accounting issues. Not all sentences/paragraphs within the relevant Handbook section will apply to anyone accounting issue. You are expected to read each section in its entirety and to determine which guidance is applicable and which is not. The grade you receive on this assignment will greatly depend on the thoroughness of your response. 5. You are not required to discuss any financial statement note disclosure for these accounting issues. 6. You should aim to fulfill the following objectives for each accounting issue analyzed: a. Thoroughness: you MUST aim to analyze all relevant Handbook guidance (in the relevant section) for each issue. If the relevant Handbook section you are reviewing has multiple criteria within it you should analyze EACH relevant criterion. Analyzing only “some” criteria will result in you only receiving “some” marks. For example, if you were analyzing revenue recognition, you would never analyze just one revenue recognition criteria – you would analyze all the revenue recognition criteria. b. Case Facts: you MUST use case facts to support EVERY part of your analysis. For example, if you are analyzing multiple criteria for a particular accounting issue, you should have at least one relevant (and usually distinct) case fact to support your analysis of each criteria. c. Explain Everything: when writing your response, you should pretend that the person marking it knows absolutely nothing about the case and very little about accounting. Your response should explain everything to them. Do not assume or imply anything in your answer. If you don’t explicitly state something in your response you will not receive credit for it. For example, don’tsay, “You should take an umbrella outside when it is raining” . You didn’t explain WHY. Instead, do say, “You should take an umbrella outside when it is raining so you can hold it over your head to protect yourself from the rain” . d. Be Concise: you have a limited number of pages to work with for this assignment. As such, make your response concise. Your analysis should be complete and thorough, but also concise. There are no extra marks awarded for unnecessary words or fancy writing styles. 7. Handbook Referencing: you must copy and paste relevant sentences/paragraphs directly from the CPA Canada Handbook and include them in your response as demonstrated in the sample response in Appendix C. When doing this, please remember to: a. State which section/paragraph you are including, and b. Italicize anything copied and pasted from the Handbook. This will allow the person marking your assignment to know what is copied from the Handbook vs. what is your response. You will likely find it effective to copy and paste a portion of the Handbook section that applies to the accounting issue you are writing about, and then immediately analyze the guidance using applicable case facts. In the CPA Canada Handbook, you will see some text that is italicized and some that is not. The italicized paragraphs are the “ principles” . The text included underneath is to help provide further guidance on how to interpret and apply what is listed in the principle. For example: MEASUREMENT OF INVENTORIES Cost .04 Property, plant and equipment shall be recorded at cost. .05 The cost of an item of property, plant and equipment includes the purchase price and other acquisition costs such as option costs when an option is exercised, brokers' commissions, installation costs including architectural, design and engineering fees, legal fees, survey costs, site preparation costs, freight charges, transportation insurance costs, duties, testing and preparation charges. It may be appropriate to group together individually insignificant items of property, plant and equipment. It is acceptable to include information from both areas in your response if it is relevant to your discussion. It is important not to dump lengthy paragraphs from the Handbook into your response without applying the guidance to case facts. If something from the Handbook is in your response but you haven’t interpreted it in your own words afterwards as to how it applies to your case, or applied case facts against it, consider if you actually need to have that guidance in your response. 8. Conclude: you should remember to definitively conclude or make a recommendation on each accounting issue. Your conclusion / recommendation should: a. Summarize why you are reaching that conclusion or providing that recommendation, b. Discuss the required impact to the financial statements, and c. Include journal entries if you believe that will help illustrate your conclusion. Before you begin, please re-read these instructions again. Then, read them again before finalizing your assignment. Comprehensive professional answers are expected that are fully explained and supported by case facts. Goodluck! Appendix B - How to Analyze an Accounting Issue using the CPA Way You should follow the following format for analyzing your accounting issues in accordance with “The CPA Way” . 1. Assess the situation – This should be a brief description of what you think the accounting issue is. The purpose of this section is to highlight to your reader the key concerns in the case and explain what you hope to accomplish through your analysis. This should be 1-2 sentences to explain how the item has been accounted for using case facts and what you think the issue is (or state that it hasn’t been accounted for yet). You may pose a question at the end to make it clear what you hope to accomplish through your analysis (for example, “Does this item meet the definition of an asset under the conceptual framework?”). 2. Analyze the issue – In order to properly analyze an issue, you need to have a combination of relevant technical guidance AND relevant case facts to achieve depth. Handbook guidance with no case facts is not enough, nor is listing case facts that aren’t linked to Handbook guidance – you need BOTH. Simply inserting guidance from the handbook into your response without applying case facts will result in very few (or no) marks being awarded. Here are some potential formats you can use: Issue with criteria Handbook section says that the following 3 criteria must be met in order to : • Criteria 1 – MET/NOT MET – followed by case fact to support. • Criteria 2 – MET/NOT MET – followed by case fact to support. • Criteria 3 – MET/NOT MET – followed by case fact to support. Issue with a specific technical rule Handbook section says that …. In this case, … … . (discussion of how the technical rule applies using case facts) 3. Conclude and Advise - You need to make a clear conclusion or recommendation based on your analysis. In addition, ensure you answer the question “so what” after you are done concluding. If you are given information that allows you to quantify the impact from the accounting issue, do so here. Does this require an adjustment in the financial statements? If so, provide the journal entry if possible. Even if you don’t have numbers to quantify, providing the journal entry helps to show you understand the related accounting. Also, consider if the accounting issue impacts a central theme in the case, such as compliance with a debt covenant, a bonus calculation, etc.
CMPT115 - Final Project In this project, you will create a Python Program that will play Conway’s Game of Life. Game of Life Descrip/on John Horton Conway, a Bri@sh mathema@cian, created the cellular automaton known as The Game of Life in 1970. It is the most popular illustra@on of a cell machine. The "game" is in fact a zero-player game, which means that its development is determined by its ini@al state and does not require input from humans. The Game of Life can be played by seNng up a star@ng configura@on and watching it change over @me. The Game of Life universe consists of an infinite orthogonal grid of square cells in two dimensions. The cells in Conway’s Game of Life can be either alive or dead. To represent whether a cell is alive or dead, you will use 0 for dead and 1 for alive. For more informa@on seehQps://en.wikipedia.org/wiki/Conway%27s_Game_of_Life In this project, you will create a 35 x 35 grid of cells by making a 2-dimen@onal Python list named cells. Each element of the list which represent one cell is a Python Turtle. The ini@al state for each cell must be either 0 or 1 (see Figure 1). The goal is to simulate the evolu@on of the system over @me by applying the rules of Conway’s Game of Life and to display the state of the game at each @me step. The Grid: wn = turtle.Screen() wn.setup(width = 35*20 + 100, height = 35*20 + 100) wn.tracer(0) Figure 1: Example of a universe >>> print(cells[0][0].state) 1 >>> print(cells[3][0].state) 0 >>> print(cells[9][1].state) 1 >>> Note. For each cell (Turtle), if the state of the cell is 0, we set its color to "gray90" for the color gray and if the state of the cell is 1, we set its color to "gray0" for the color black. def initializeTheCells(): for i in range(35): cells.append([]) for j in range(35): newCell = turtle.Turtle() newCell.penup() newCell.shape("square") newCell.shapesize(stretch_wid = 0.9, stretch_len = 0.9) cells[i].append(newCell) newCell.state = 0 newCell.color("gray90") #gray90 almost white def selectCells(x, y): if onClick: if x > -350 and x -350 and y >>print(type(cells[0][0])) cells[i - 1][j - 1] cells[i - 1][j] cells[i - 1][j + 1] cells[i][j - 1] cells[i][j] cells[i][j + 1] cells[i + 1][j - 1] cells[i + 1][j] cells[i + 1][j + 1] Figure 2: Eight neighboring cells to a cell at row i and column j. The following shiZs take place at each @me step (see Figure 3): • If a state of a cell is 1 and has fewer than two neighbors that have states 1, it changes to 0. • If a state of a cell is 1 and has either two or three neighbors that have states 1, it remains 1. • If a state of a cell is 1 and has more than three neighbors that have states 1, it changes to 0. • If a state of a cell is 0 and has exactly three neighbors that have states 1, it changes to 1. Time step 1 Time step 2 Time step 3 Figure 3: An example showing the cells change from one generaFon to the next. Boundary Condi/ons How to deal with cells at the edge of the grid? Which cells are their neighbors? The answers to this ques@on are what are known as Boundary CondiFons. In this project we are going to look at two of them: The Constant Boundary CondiFon and Periodic Boundary CondiFon. Constant Boundary CondiFon: We consider all the cells adjacent to those on the edge of the grid are considered dead (see Figure 4). You can assume there are imaginary cells surrounding the grid where all of them are 0. all dead all dead all dead all dead Figure 4. Periodic Boundary CondiFon: We are going to assume that the neighbors to a cell at the edge of the grid are those cells at the opposite edge of the grid (The cells on the leZ edge of the grid are considered to be adjacent to the cell on the right edge and the cells on the top edge of the grid are considered to be adjacent to the boQom edge of the grid. (see Figure 5)). Figure 5: Three examples showing the periodic boundary condiFon. The Python Program I have included a starter Python program for this project on Moodle. You must use the provided starter code to complete the project. Algorithm: 1. Ini@alize the cells in the grid to all dead. 2. Using mouse click(s) to change cells’ states (alive to dead or dead to alive) 3. Ask the user to choose a boundary condi@on (enter 1 for constant and 2 for periodic) 4. At each itera@on (@me step), the next genera@on of cells is calculated for each cell at row i and column j, in two passes: Pass 1: For each cell record its number of live neighbors by considering the boundary condi@on. Pass 2: Update each cell to alive/dead based on the rules. Bonus for Extra Credit You do not need to complete the bonus sec@on, however students who manage to successfully complete the bonus sec@on will receive extra credit. We can add more colors into the game by using the following color paleQe. Rules on how to use the color paleNe: 1. Only use the color "gray0" to indicate the cell is alive and all other shades of gray indicate a dead cell. 2. If a living cell dies, its color is set to "gray50" and as the game progresses the color gradually fades out to "gray90", provided that the cell remains dead. SubmiAng Your Project You will find a submission link on the course Moodle page under "Final Project". You are required to submit your project as one your_full_name.py file that contains the given starter program and all the required custom func@ons. Submission Due Date and Time The due date and @me to submit your project is Thursday December 5th, 2024, at 4:59 PM. You are required to submit your project through Moodle before submission @me expires. If you don't submit your project through Moodle on Fme, then it will be considered as if you didn't aNempt the final project in which case, you will get a leNer grade N for the course. No email submission will be accepted for any reason.
Financial Statistics ST326 Assessed Coursework Deadline: 12pm, December 13, 2024 1 Questions This project is on the analysis of a bundle of stocks that are constituents of S&P500. You have the freedom to choose 10 stocks from the top 100 constituents by weight. The weights change everyday, but as long as the 10 stocks you have chosen have been the top 100 constituents on a particular day and have been traded over the past 5 years, it is fine. 1. Download the daily closing prices of the 10 stocks and the S&P500 index price for the past 5 years. Do not include 2 or more stocks from the same company but only of different classes. Plot their log-prices on the same plot. You can deal with potential missing values using R codes similar to Chapter 3 of your lecture notes, or any other methods, but you need to justify them. If you are downloading data using the quantmod package, you may want to export the data to a text file first using for example library(quantmod) getSymbols(’F’) F = as.data. frame(F) F = cbind(as. numeric(as. Date(rownames(F))), F) write. table(F, "F. txt", row. names=FALSE) getSymbols(’^GSPC’) GSPC = as.data. frame(GSPC) GSPC = cbind(as. numeric(as. Date(rownames(GSPC))), GSPC) write. table(GSPC, "GSPC. txt", row. names=FALSE) Then the corresponding lines in read. bossa. data inside a for loop should be changed to filename
Assessment Brief PROG2004 Object Oriented Programming (Assessment 1) Title Assessment 1 Type Programming Deadline 4 December 11:59 AM Weighting 20% Academic Integrity Contract cheating and the use of GenAI, such as ChatGPT, in this assignment are strictly prohibited. Any breach may have severe consequences. Submission You will need to submit 2 Java projects as detailed. Unit Learning Outcomes This assessment task maps to the following ULOs: • ULO1: explain, compare, and apply advanced class design concepts • ULO2: apply object-oriented programming principles to solve intermediate problems • ULO3: distinguish between and use advanced collection classes 2 Assessment Brief Task 1: In this assignment, you will write the code that manages the product categories on any website, such as Alibaba (Use another website). To get started: • Create a new Java project called Project1 in IntelliJ. • In the src directory, create a new class called AssignmentOne. • In the AssignmentOne class, create the main method. 3 Assessment Brief Module 1 - Advanced class design The following part of the assessment covers the content in Module 1. Part 1 – Creating abstract classes and interfaces Go to the website you choose and explore all the different categories of products that they sell. While you are doing this, have a look at the different product categories as they go from more general to more specific. At the top level of the Inheritance hierarchy, you need a generic product. In your Java project: • Create a new interface called Product with at least two generic methods that are suitable for ALL product categories on the the website you choose. Alibaba sells physical and digital products. Therefore, in the inheritance hierarchy, under products, you would need to handle physical products and digital products. In your Java project: • Create an abstract class called PhysicalProduct that implements the interface called Product. • Create an abstract class called DigitalProduct that implements the interface called Product. The PhysicalProduct and DigitalProduct abstract classes must have the following at a minimum that are suitable for ALL physical OR digital product categories on the Alibaba website: • At least two instance variables • A default constructor • A second constructor that sets all the instance variables • At least one abstract method • At least one non-abstract method • Any other methods or variables that you feel are necessary for the class to be usable in the program Part 2 – Using abstract classes and interfaces On the Alibaba website, choose one physical product category and one digital product category. In your project: • Create one concrete class that extends the PhysicalProduct abstract class. • Create one concrete class that extends the DigitalProduct abstract class. The classes should match the physical product category and digital product category you chose on the Alibaba site and have the following at a minimum: 4 Assessment Brief • At least two instance variables (one of these variables must be a boolean as a boolean instance variable is required in a later section of the assignment) • A default constructor • A second constructor that initialises all the instance variables (including the instance variables in the abstract superclass) • A method that prints the Product details, e.g. "The product details are:" followed by all instance variables formatted for readability (including the instance variables in the abstract superclass) • Any other methods or variables needed so that your products match the physical product category and digital product category you chose on the Alibaba site. For each class, in the class comments, you MUST provide a link to the product category on Alibaba that you based your class on. In the main method: • Add the following comment - // Part 2 – Using abstract classes and interfaces • Create an object for the concrete class that extends the PhysicalProduct abstract class using the constructor that sets all instance variables. • Create an object for the concrete class that extends the DigitalProduct abstract class using the constructor that sets all instance variables. • Add the following code - System.out.println(" -------------------- "); Part 3 – Polymorphism In the AssignmentOne class, write a method called demonstratePolymorphism that takes one parameter. The parameter type can be either a: • Product • PhysicalProduct • DigitalProduct In the main method: • Add the following comment - // Part 3 – Polymorphism • Add a second comment that explains how you are going to use the method you just created to demonstrate your understanding of polymorphism • Write the code to create an object and use the method you just created to demonstrate your understanding of polymorphism • Add the following code - System.out.println(" -------------------- "); 5 Assessment Brief NOTE: Do not remove the code in the main method for Part 2 (or any other part of the assignment). You can comment it out when you are developing; however, when you submit your assignment, the main method must contain all the code required for all parts of the assignment, i.e., your marker should be able to run your main method, and all parts of your assignment should run in one go. Module 2 – Generics and lambdas The following part of the assessment covers the content in Module 2. In Part 2 of this assessment, you created a concrete class that extends the PhysicalProduct abstract class. For the remainder of this assessment, this class will be referred to as yourClass as, in theory, all students should have a different name for this class. Part 4 – Generic classes In this part of the assignment, you are going to write the code for an ArrayList that can store instances (objects) of yourClass. In the main method, write the code to: • Add the following comment - // Part 4 – Generic classes • Declare an ArrayList • Add at least 5 instances of yourClass to the ArrayList • Add the following code - System.out.println(" -------------------- "); Part 5 – Generic methods In this part of the assignment, you are going to sort the ArrayList that we created in part 4. In yourClass, implement the Comparable interface. When you implement the compareTo() method from the Comparable interface, you must use at least two instance variables in the comparison. Once you have implemented the Comparable interface in the main method: • Add the following comment - // Part 5 - Generic methods • Write the code to sort the ArrayList that you created in Part 4. • Add the following code - System.out.println(" -------------------- "); 6 Assessment Brief Part 6 – Wildcards In the AssignmentOne class, create a method called DemonstrateWildcards that takes an ArrayList generic parameter . In the method, call one of the methods from PhysicalProduct abstract class. In the main method: • Add the following comment - // Part 6 - Wildcards • Add a second comment that explains how you are going to use the method you just created to demonstrate your understanding of wildcards • Write the code to create an object and use the method you just created to demonstrate your understanding of wildcards • Add the following code - System.out.println(" -------------------- "); Part 7 – Simple lambda expressions In the AssignmentOne class, create a method called DemonstrateLambdas that takes an ArrayList of yourClass and a Predicate as a parameter. In the method, test the Boolean instance variable from yourClass and print a suitable message based on whether it is true or false. In the main method: • Add the following comment - // Part 7 - Simple lambda expressions • Write the code to pass the Arraylist that you created in Part 4 to the DemonstrateLambdas method • Add the following code - System.out.println(" -------------------- "); • Add a default constructor and a second constructor that sets the instance variables (Staff and Person) using parameters • Add getters and setters for all Staff instance variables • In the Member class: • Extend the Person class • Add at least 2 instance variables suitable for a School member • Add a default constructor and a second constructor that sets the instance variables (Member and Person) using parameters • Add getters and setters for all Member instance variables In the Classroom class: • Add at least 3 instance variables suitable for a Clasrooms. One of these instance variables must be of type Staff, i.e. used to track the trainer running the Classroom. • Add a default constructor and a second constructor that sets the instance variables using parameters. • Add getters and setters for all Classroom instance variables. In the AssessmentTwo class add the following code: public class AssessmentTwo { public static void main(String[] args) { } public void partOne(){ } public void partTwo(){ } public void partThree(){ } public void partFour(){ } public void partFive(){ } public void partSix(){ } } 9 Assessment Brief Module 3 - Advanced Collections The following part of the assessment covers the content in Module 3. Part 1 – Lists The Classroom class is missing the ability to store a collection of Members who have signed up for the Classroom. For this part of the assignment: • Using a LinkedList, update Classroom so that a Classroom object can store a collection of Members (i.e. datatype Member) who have signed up for the Classroom. In addition to adding a LinkedList, you need to add the following methods to Classroom that work with the LinkedList: • A method to add a Member to the Classroom. • A method to check if a Member is in the Classroom. • A method to remove a Member from the Classroom. • A method that returns the number of Members in the Classroom. • A method that prints the details of all Members signed up for the Classroom (you must use an Iterator or you will get no marks). Note: Make sure all the above methods print suitable success/failure messages. Demonstration In the partOne method in the AssessmentTwo class: • Create a new Classroom object. • Using the methods you created: o Add a minimum of 5 Members to the LinkedList. o Check if a Member is in the LinkedList. o Remove a Member from the LinkedList. o Print the number of Members in the LinkedList. o Print all Members in the LinkedList. 10 Assessment Brief Part 2 – Collection Class There is no way to sort the Members who have signed up for a Classroom. For this part of the assignment: • Create a class (you can choose the name) that implements the Comparator interface. When you implement the compare method from the Comparator interface, you must use a minimum of two of the instance variables in your comparison. • Create a method in the Classroom class that sorts the LinkedList using the sort(List list, Comparator c) method in the Collections class. Note: You MUST use the Comparator interface. You CAN NOT use the Comparable interface. Demonstration In the partTwo method in the AssessmentTwo class: • Create a new Classroom object. • Using the methods you created: o Add a minimum of 5 Members to the LinkedList. o Print all Members in the LinkedList. o Sort the LinkedList o Print all Members in the LinkedList again to show that the LinkedList has been sorted. Part 3 – Queue Interface As most School classes would have a maximum number of members that can sign up, the program needs the ability to keep track of Members who are waiting to join the School class and the order in which they joined the waiting list, i.e., first in first out. For this part of the assignment: • Using a Queue, update the Classroom class so that a Classroom can store Members (i.e., Member objects) who are waiting to join the Classroom. In addition to adding a Queue, you need to add the following methods to the Classroom class that work with the Queue: • A method to add a Member to the Queue. • A method to remove a Member from the Queue. • A method that prints all the details for all Members in the Queue in the order they were added. Note: Make sure all the above methods print suitable success/failure messages. 11Submission ZIP file via USB drive (Name+ Complete student number + Assignment 1) You are required to submit Three items for this assessment, including: • Project 1 • Project 2 • A 10 minutes video explain Project 1 and Project 2 Assessment Criteria • Java code compiles with Java 17 LTS. • Use of correct coding style, including the use of comments. • Accuracy of coding. • Use of suitable coding structures. • Correct submission and naming conventions of assessment items as required.
N1569 Workshop 4 Use the S&P 500 data in the Excel Workbook for Topic 4. 1. Calculate the 90-day historical volatility and the EWMA volatility with a smooth- ing constant of 0.95, and plot them both on the same graph. Comment on your results. 2. Calculate the α% h-day VaR for the S&P 500 for α% = 0.1%, 1%, 5% and 10% and for h = 1 and 10 days. In each case, compare and contrast the results you obtain using the normal, historical and Monte Carlo VaR models. 3. Calculate the rolling 1% 10-day historical VaR based on 50 returns and make a time series graph to compare this with the normal VaR based on the same returns 4. Find the normal, historical and Monte Carlo VaR based on S&P prices between 31 Dec 2022 and 31 Dec 2023. How do the results change with the holding period and significance level chosen?
CIS 5450 Homework 4: Machine Learning Due Date: November 15th at 10:00PM EST, 103 points total (= 85 autograded + 18 manually graded). Imports/Setup Run the following cells to set up the notebook. Before you begin: · Be sure to click "Copy to Drive" to make sure you're working on your own personal version of the homework · Check the pinned FAQ post on Ed for updates! TAs work really hard to keep it updated with everything you might need to know or anything we might have failed to specify. Writing these HWs and test cases gets tricky since students always end up implementing solutions that we did not anticipate and thus could not have prepared the grader correctly for. · WARNING: You MUST check that your notebook displays ALL visualizations on the Gradescope preview AND verify that the autograder finishes running and gives you your expected score (not a 0). (Ed #251 (https://edstem.org/us/courses/44790/discussion/3426442)). Penalty: -10: If we have to resubmit your notebook to Gradescope for you after the deadline. (e.g. not naming your files correctly, not submitting .py and . ipynb , etc.). Penalty: -5: If your notebook fails show up in the Gradescope preview of your .ipynb (e.g. Large File Hidden Error ). o If you experience this issue, please try to remove print outputs the non-plot images in the notebook. Note: We will be manually checking your implementations and code for certain problems. If you incorrectly implemented a procedure using Scikit-learn and/or MLlib (e.g. creating predictions on training dataset, incorrectly process training data prior to running certain machine learning models, hardcoding values, etc.), we will beenforcing a penalty system up to the maximum value of points allocated to the problem. (e.g. if your problem is worth 4 points, the maximum number of points that can be deducted is 4 points). Note: If your plot is not run or not present after we open your notebook, we will deduct the entire manually graded point value of the plot. (e.g. if your plot is worth 4 points, we will deduct 4 points). Note: If your .py file is hidden because it's too large, that's ok! We only care about your .ipynb file. Please make sure you enter your 8 digit Penn ID in the student ID field below. In [ ]: %%capture !pip install penngrader-client from penngrader.grader import * #PLEASE ENSURE YOUR PENN-ID IS ENTERED CORRECTLY. IF NOT, THE AUTOGRADER WON'T KNOW WHO #TO ASSIGN POINTS TO YOU IN OUR BACKEND STUDENT_ID = # YOUR PENN-ID GOES HERE AS AN INTEGER File "", line 7 STUDENT_ID = # YOUR PENN-ID GOES HERE AS AN INTEGER ^ SyntaxError: invalid syntax In [ ]: %%writefile notebook-config.yaml grader_api_url: 'https://23whrwph9h.execute-api.us-east-1.amazonaws.com/default/Grader23' grader_api_key: 'flfkE736fA6Z8GxMDJe2q8Kfk8UDqjsG3GVqOFOa' Writing notebook-config.yaml In [ ]: grader = PennGrader('notebook-config.yaml', 'cis5450_fall24_HW4', STUDENT_ID, STUDENT_ID) --------------------------------------------------------------------------- NameError Traceback (most recent call last) in () ----> 1 grader = PennGrader('notebook-config.yaml', 'cis5450_fall24_HW4', STUDENT_ID, STUDENT_ID) NameError: name 'PennGrader' is not defined Part 0: Set up GPU capabilities (1 point) The cell below configures a CUDA device for use with PyTorch, if available. Remember to enable the GPU in Colab: Go to Runtime -> Change runtime type -> GPU. Part I: Preprocessing and Modeling in scikit-learn (45 points) 1.1 Data Loading and Preprocessing [0 Points] 1.1.1 Read and Load Data We are using a CSV for this part, winequalityN.csv from a Kaggle dataset (https://www.kaggle.com/datasets/dataregress/rajyellow46/wine-quality) . The dataset contains 13 columns and over 6000 wine entries. To get the data in here: 1. Go to this Kaggle link (https://www.kaggle.com) and create a Kaggle account (unless you already have one) 2. Go to Account and click on "Create New API Token" to get the API key in the form of a json file kaggle.json 3. Upload the kaggle.json file to the default location in your Google Drive (Please DO NOT upload the json file into any specific folder as it will be difficult for us to debug issues if you deviate from these instructions!). This can be helpful for your project if you decide to use Kaggle for your final project or for future projects! 1.1.2 Understanding Data A good practice before approaching any data science problem, is to understand the data you will be working with. This can be through descriptive statistics, datatypes, or just a quick tabular visualization. We will be walking through such tasks through Pandas. Let's also verify if there are any null values in our dataset. We will remove all null rows in wine_quality_df 1.2 EDA [subtotal 14 points] Exploratory Data Analysis (EDA) is an approach to analyzing datasets to summarize their main characteristics, often with visual methods. A statistical model can be used or not, but primarily EDA is for seeing what the data can tell us beyond the formal modeling or hypothesis testing task. 1.2.1 Visualization [10 points] (a) Quality Distribution [4 Points] Task: Find the distribution of the quality of wine in our dataset. The range of values should be integers in the range of 3-9, so we're expecting to have one bar per quality. You are required to use the Seaborn library for this problem to create a countplot (https://seaborn.pydata.org/generated/seaborn.countplot.html) . Requirements: You should use wine_quality_df for this problem. Your plot must: · Be of size (8,6) and use palette = 'viridis' . (ignore the deprecated warning) · Have appropriate titles and labels. · Be clearly legible and should not have overlapping text or bars. (b) 3D Scatterplot [6 Points] Task: We want to examine the relationship between three variables: alcohol , pH , and density . We also want to examine quality as well. You are required to use the Matplotlib library for this problem to create a 3D Scatterplot (https://matplotlib.org/stable/gallery/mplot3d/scatter3d.html) . Requirements: You should use wine_quality_df for this problem. Your plot must: · Be of size (6,6). · Have each data point be colored accordingly by quality . The color mapping should be: 1-5 is red and 6-10 is green. · Have alcohol content in the x-axis, pH level in the y-axis, and density in the z-axis. · Have appropriate titles, axes labels, and a legend. · Be clearly legible and should not have overlapping text or bars. Very Helpful Resources: · 3D Scatter Plotting in Python using Matplotlib (https://www.geeksforgeeks.org/3d-scatter-plotting-in-python-using-matplotlib/) · List of named colors (https://matplotlib.org/stable/gallery/color/named_colors.html) Since the dataset is large, we'll first sample 200 rows (roughly 10%) from our wine_quality_df using the random seed 42 and save the sampled dataframe into sample_wine_quality_df . 1.2.2 Correlation of Feature Variables [4 Points] With multiple features, it can be somewhat exhausting to do bivariate analysis on every possible pair of features. While you certainly should, your first instinct should be to check for the correlation between features since certain models (e.g. Linear Regression) won't work well if we have strong multicollinearity. Before finding our correlation matrix, we should filter out categorical features. Although quality is technically a categorical feature, we'll keep that column for now (and encode it later down the line). Drop any other categorical features and save this new dataframe into num_df . Correlation Heatmap Task: Create a correlation matrix using num_df and call it corr_mat . Using the correlation matrix, generate a correlation heatmap for these numeric features. You are required to use Seaborn library to create this heatmap (https://seaborn.pydata.org/generated/seaborn.heatmap.html) . Make sure your correlation heatmap meets the following criteria: · Ensure that your heatmap is sized (8,8): all feature labels should be visible on both the $x$-axis and $y$-axis · Use the RdBu color map to ensure that negative correlations are red and positive correlations are blue · Standardize the color scale so that -1 takes the darkest red color, 0 is totally white, and +1 takes the darkest blue color (2 manually graded points) As an added exercise, based off of the correlation matrix above, write down what you believe to be the two most highly correlated pairs of features (by magnitude), and briefly explain the numerical intuition of what that correlation means. Note that you don't need any scientific explanation for why those variables are correlated. Pair #1: Pair #2: 1.3 Feature Encoding [subtotal 8 points] 1.3.1 Encoding Wine Type [4 Points] Encoding is a process by which categorical variables are converted into a form. that could be provided to ML algorithms to do a better job in prediction. Task: · You should use wine_quality_df for this problem. · Let's first determine the number of unique values for the type column and save that value in a new constant NUM_UNIQUE_TYPES Since there are two unique values for wine quality (red and white), let's write a helper function to convert a string wine quality to an integer. As an example, the function can convert a wine type, like "white" to 0 and "red" to 1. Now, let's make a copy of wine_quality_df into encoded_wine_quality_df and encode the type column to numerical values, and rename the type column to red_wine . 1.3.2 Encode Classes in 'Quality' Column [4 Points] Task: We will be predicting the quality for our classification problem. We first want to transform our target into numerical values. Map the classes in the quality column in the following way: . 0-5: 0 . 6-10: 1 This encoding represents low and high quality respectively. These will be the two classes we will try to predict using the other features about the wine. You should use encoded_wine_quality_df for this problem. Save your results in encoded_wine_quality_df . 1.4 Random Forest Classification (sklearn) [23 points] 1.4.1 Preprocessing: Create Features and Target and Split Data into Train and Test [4 Points] Now that we have explored and cleaned our dataset, let's prepare it for a machine learning task. In this homework, you will work with various models and attempt to predict the quality of the wine. The features will be all the variables in the dataset except quality , which will act as the label for our problem. First, store these two as features (pd.DataFrame) and target (pd.Series), respectively. Now, use Scikit-learn's train_test_split (https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html) function to split data for classification into training and testing sets. The split should be 80-20 meaning 80% for training and the rest for testing. _IMPORTANT_: Please set the seed variable to 42 and then set the parameter to random_state = seed and store the resulting splits as X_train, X_test, y_train, and y_test . If you want to understand the purpose of seed, please feel free read over this concise yet thorough explanation on StackOverflow (https://stackoverflow.com/questions/21494489/what-does-numpy-random-seed0-do). Let's also use a StandardScaler to standardize the set of X values. Make sure that there's no data leakage, in that the scaler should be trained ONLY on the training data. Name into X_train_scaled and X_test_scaled . 1.4.2 Random Forest Classification without Grid Search [4 points] Raw Random Forest Classifier Fit a Random Forest classifier on the X_train and y_train with the hyperparameters provided below. Calculate the accuracy of the model on the test set using the score method and store it in a variable named rf_acc . We're later going to use grid search to tune the hyperparameters, but for now, let's use the parameters below. Task: · Read the Scikit-learn documentation (https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html) for Random Forest Classifier. · For hyperparameters, set: class_weight = 'balanced' · Train the random forest classifier model and evaluate it using the score method. · Save your score in a variable rf_acc . · Use the scaled X data for all remaining sections 1.4.3 Random Forest Classification with Grid Search and Cross Validation [15 points] Now, we're interested in tuning the hyperparameters of the random forest model to see if we're able to achieve a higher testing score. We will be using sklearn's GridSearchCV utility to do this. After defining a set of parameters and respective values to check, grid search will check through all combinations of those parameters and test using a subset of the training data (cross validation). To learn more about GridSearchCV , we've attached the documentation here (https://scikit-learn.org/dev/modules/generated/sklearn.model_selection.GridSearchCV.html) Complete the following: 1. First, let's define the parameter grid. We're interested in tuning the following set of hyperparameters below with their corresponding ranges of values. Name the parameter grid param_grid . · class_weight : ['balanced'] · random_state : [42] · Second, let's instantiate our random forest model in the variable random_forest_model with default initialization. · Then, define the GridSearchCV object, using the estimator random_forest_model , the param grid defined above, a cv (cross validation) set to 5, scoring set to'accuracy', and verbose set to True · Then, fit the model and printout the best parameters / cross validation score. · Finally, we'll use our best model and evaluate it against our test data. Save the accuracy into test_accuracy 1.4.4 Random Forest Feature Importance [5 points] Now, let's find the relative feature importance for predicting the quality of wine. Use the best model from above, and create a Seaborn bar plot to display the feature importance. Save the feature importances into feature_importances . Specifications for plot: · The feature importances should be sorted in descending order · Use a Seaborn bar plot. If you're confused on the syntax, the documentation is here (https://seaborn.pydata.org/generated/seaborn.barplot.html) · Use a figure size of (10, 6) · Properly label the title and axes. 1.4.5 Random Forest Confusion Matrix [4 points] Finally, we will make use of a confusion matrix. It is used to consolidate the predictive performance of a model into a single table. In a binary classification scenario, it looks like this: Evaluate the performance using sklearn's confusion matrix like before, and we'll use a seaborn heatmap to display our results. Save the confusion matrix into conf_matrix . Use sklearn's confusion_matrix utility. Additionally, we will use the following set of parameters for our display: 1. set annot=True 2. fmt='d' 3. colormap set to 'Blues' 4. colorbar set to False 5. x and y tick labels set to True 6. Axes and title labels 1.4.6 Confusion Matrix Interpretation [Manually Graded 2 points] From the confusion matrix above, we see that the model is relatively balanced in its predictions, despite there being a class imbalance in our data. What technique did we use in previous steps helped address the class imbalance and why did it help?
COMP5565 Decentralized Apps Fundamentals and Development Group Project (25%) Form a group of five students. Nominate a group leader. Enroll in one of the groups in Blackboard by 1st November 2024. Background The project will focus on creating a dApp on either Ethereum, which would be public and permissionless, or Hyperledger Fabric, a private and permissioned blockchain, depending on the chosen privacy requirements. The dApp will enable a luxury jewelry maker to create digital certificates of authenticity for each item made of diamond they produce, which can be transferred to the buyer along with the physical product. Consumers will be able to verify the product’s authenticity by checking the certificate on the blockchain. What you have to do The main objective is to design and develop a user-friendly dApp that interacts with a blockchain to ensure the traceability and authenticity of luxury goods. In this project, choose ONE of the two blockchains (Ethereum OR Hyperledger Fabric). The dApp should: 1. Provide a transparent audit trail for the life cycle of each luxury item, including: a. mining the diamond (by mining company) b. cutting and polishing (by cutting company) c. controlling quality and laser engraving of unique ID (by grading lab) d. entering possession of the jewelry maker e. designing and inlaying the diamond into a jewelry f. customer purchasing the jewelry or transferring ownership. 2. Allow manufacturers to issue and transfer digital certificates tied to unique ID. 3. Enable consumers to verify the authenticity of their purchases easily via a front end. 4. Ensure the security, transparency, and privacy of the system. To this end: 1. Outline the architecture of the dApp, including smart contracts, user interfaces, and database design. Note that you will likely need to make compromise on the transparency vs. privacy criteria. 2. Implement your solution according to your architecture. Ensure smart contracts are secure and optimized for gas usage (if Ethereum is chosen). 3. Develop the frontend to interact with the blockchain, directly or via a centralized backend. 4. Deploy smart contracts on a local Ethereum blockchain (Hardhat) or set up a network (Hyperledger Fabric). Part 1: System Design (40 marks) Write a report to provide details of the design of your system. Elaborate clearly (1) the security, (2) the accessibility of information and (3) the efficiency of your implementation (Part 2). Justify critically your solution. Part 2: System Implementation (40 marks) Implement the system based on your design in Part 1. The system should use either blockchain frameworks, Ethereum and Hyperledger Fabric. For the front-end of your applications, it can be desktop software (GUI/Terminal-based), mobile app, website or a combination of them. In the same report in Part 1, include a detailed system installation guide. Part 3: Presentation (20 marks) You are going to give a 10-minute presentation on your project on 28 November 2024 (Thur) in the last lecture session. The presentation should include the design of your system. It can also include a brief (
Module Title: Coding for Medical Scientists Module Code: CSC2020 Module credits 15 % of Module Mark 60% 1. Required Task You will be provided with a data file containing electrophysiology experiments carried out as part of a neuroscience practical class. During this experiment, students evoked paired synaptic responses from hippocampal brain slices and then applied various cocktails of drugs to monitor how these responses were changed by the drugs. Your task is to write a Matlab function to analyse these data and write a short report on your findings. More details on the experiment carried out and what to analyse can be found on the module ELE page. Materials provided (on the ELE page): • One dataStruct.mat file, which is a struct with 10 fields. Each field contains a replicate of the experiment. • An Excel file called notebook.xlsx which contains information about when each drug cocktail was applied to the slice in each experiment. • A file containing important information about the experimental details which you will need to carryout your analysis and some additional information about how to process the data The report: The main body of the text should not exceed 2 page of A4. As guidance this is equivalent to about 1000 words. It should be formatted as follows: • Font: Calibri (body) 11pt • Line spacing: 1.15 • Margins: ‘Normal’ for a Word document: 2.54cm all sides (top, bottom, left and right) The page count does not include any figures or tables and associated legends. Nor does it include the code appendix (see below). Each figure and table, with associated legend, should be placed on a separate page at the end of the document. The text should be appropriately formatted to make it easily readable – i.e. break up text by including subheadings and avoiding long paragraphs. Your report should contain the following: • a very short introduction, setting out what is in the report. Probably no more than a paragraph or two. • a description of the data analysis methods and explanation of the code. • a results section containing: o A multi-panel figure showing details of a single example experiment. a. You should include example traces in each of the 4 conditions (i.e. control and the 3 drug conditions). b. A time course plot of a single experiment (e.g. a plot of time vs 1st fEPSP amplitude) c. As an example of the sort of thing required, see Fig 1A from Brown & Randall 2005 (link). o A figure summarising the average peak amplitude of the 1st response in the 4 conditions across all 10 of the experimental replicates o An appropriate statistical analysis to determine the effects of the various drug treatments on the peak amplitude of the 1st response. • a brief summary conclusion. NB for the purposes of this report, it is NOT important to know what the various drugs do ata pharmacological level – your conclusions should simply summarise the results. • an appendix containing the ‘published code’ (see attached file for details on how to do this). How to publish code. pdf The function : The function should: • have one input argument: the filename • have at least one output argument: a table array containing the results of the analyses of all the experiments. The table array should contain fields with the following information in them (at a minimum): o the filename of each recording (you can find this information in each field of dataStruct) o the average peak amplitude of the 1st fEPSP in each of the 3 drug conditions and in the baseline condition. The precise way in which this table is arranged is up to you but it should be sensibly organised with obvious but simple field names, so that it is clear what the output from the function is. • Be self-contained: i.e. if you write additional functions to make this run, they should be included as local subfunctions within the same m-file. • Be well-annotated with comments, including a brief ‘help’ description explaining how the function should be used (i.e. explaining what the inputs and outputs are). Use of Generative AI: This assessment falls under the category of AI-supported, which means that ethical and responsible use of Generative AI tools in the development of this assessment is supported. This may include using Generative AI tools to summarise literature, improve the structure of your work or quality of English language. It is not permitted to use Generative AI to help with writing any of your code or doing any of the data analysis (including generating figures, tables or other outputs). Please see the module specific Generative AI Statement template in the Assessment section (you can find it also at the end of this document) of the module ELE page for more details. Please keep a record of the tools, prompts and outputs used so you can produce these if necessary at aviva voce examination and demonstrate how you have built on this content to ensure your work is original. Submission For the coursework on this module you must submit both your report and the .m file containing your code. The report should be in either MS Word (.doc or .docx) or .pdf format. Please ensure that your name DOES NOT appear anywhere in the document, but that your personal ID number is included. The naming of your report file must follow the format below: ID number_ModuleCode_assessment name_AC year E.g. 91000000_CSC2020_report1_2022-23 The .m file and the report should be combined in a single .ZIP file and submitted to ELE by the stated deadline. For instructions on how to submit, please see the Medical Sciences Sharepoint (https://www.exeter.ac.uk/students/infopoints/yourinfopointservices/assessments/) If you submit your work late, but are within one hour of the deadline, a penalty of 5% will be deducted from your mark (0% if below the passmark). For any work submitted more than an hour late your mark will be capped at 40% (after 24 hours from the deadline a score of zero will be awarded). This is unless you have valid mitigation (please visit thePersonal & Pastoral support ELE pagefor more information.) In submitting this you are declaring the work is your own independent piece of work, not produced in collusion with a fellow student or plagiarized from a fellow student, or a web site, or a textbook, or any other information source. You must demonstrate good referencing practise and ensure you have sufficiently paraphrased all sources of information. Failure to do so may result in being referred to an academic misconduct panel which has the power to grant penalties based on specific criteria. For more details please revisit the Academic Honesty and Plagiarisminformation on ELE. 3.Acquired skills • Importing and analysing data in Matlab • Statistical analysis • Data interpretation • Data presentation • Report writing 4. Sources of support • Matlab help function, notes and reference book. • Assessment documentation • Computer lab practical classes • International students and those who have English as a second language can make use of theEnglish Language Skills Development programme. This programme provides face-to- face workshops and courses as well as one-to-one assignment writing tutorials and an extensive range of online resources via theirGuided Independent Learning site. Find out more by browsing the timetables: https://www.exeter.ac.uk/into/englishlanguage/howtoparticipate/timetablessupport/ or [email protected]
