PRO100_Assessment 2_Brief. ASSESSMENT 2 BRIEF Subject Code and Title PRO100 Information Systems Project Management Assessment Consultative Report Individual/Group Individual/Group Length 2000/3000 words (+/- 10%) Learning Outcomes The Subject Learning Outcomes demonstrated by successful completion of the task below include: b) Differentiate between PM methodologies and discuss relevance for Information Systems (IS) c) Apply PM concepts to IS projects within various organisations Submission Due by 11:55pm AEST/AEDT end of Module 4.2 (Week 8) Weighting 40% Total Marks 100 marks Task Summary In Assessment 1, you developed a business case and project charter for a real-world scenario project. In this assessment, you will progress the real-world scenario project from Assessment 1 and develop a project schedule for that project. You are required to answer questions related to project schedule management, cost management, resource management, risk management, communication management and stakeholder management. Context: In response to the case study provided, write a consultative report focusing on the various processes in project knowledge areas, including the management of project schedule, cost, resources, risk, communication and stakeholders. This assessment requires you to apply various tools and techniques you have learned in managing project schedule, cost, resources, risk, communication and stakeholders to a real-life case scenario. Assuming the role of the project manager, you will progress the real-world scenario project from Assessment 1. You will be asked to go through a number of processes in the project knowledge areas and generate outputs including subsidiary plans of a project management plan for those processes. This assessment provides you with an opportunity to view the various processes in managing a project and develop an appreciation of schedule-cost-scope-risk trade-off and the importance of communication in managing stakeholders. Instructions: Please read the PRO100_Real-World-Scenario-Project. You are required to complete the following tasks for this assessment. Task 1 (15 marks) – Produce Project Schedule and Cost Management Plans • Refer PRO100_Real-World-Scenario-Project for detailed task instructions. Task 2 (10 marks) – Produce a Project Resource Management Plan • Refer PRO100_Real-World-Scenario-Project for detailed task instructions. Task 3 (20 marks) – Produce a Project Risk Management Plan • Refer PRO100_Real-World-Scenario-Project for detailed task instructions. Task 4 (15 marks) – Produce a Project Stakeholder Management Plan • Refer PRO100_Real-World-Scenario-Project for detailed task instructions. Task 5 (20 marks) – Produce a Project Communication Plan • Refer PRO100_Real-World-Scenario-Project for detailed task instructions. Referencing There are requirements for referencing the consultative report using APA referencing style. Please see more information on referencing here: http://library.laureate.net.au/research_skills/referencing a. It is expected that you reference any source used. b. You are strongly advised to read the rubric which is an evaluation guide with criteria for grading the assessment. This rubric will give you a clear picture of what a successful report look like. Assessment Criteria Your answers will be assessed against the following criteria: • A report that focuses on the various processes in the project knowledge areas and demonstrates application of various tools and techniques in the management of project schedule, cost, resources, risk, communication and stakeholders for the real-world scenario project provided. • Use of Academic Conventions in APA Referencing style. Submission Instructions Please submit ONE Microsoft Word document (.doc or .docx) for the Consultative Report and any project files as required in Tasks 1-5 via the Assessment 2 section found in the main navigation menu of the subject’s Blackboard site. The Learning Facilitator will provide feedback via the Grade Centre in the LMS portal. Feedback can be viewed in My Grades. Academic Integrity Declaration I/we declare that except where I/we have referenced, the work I/we are submitting for this assessment task is my/our own work. I/we have read and am/are aware of Torrens University Australia Academic Integrity Policy and Procedure viewable online at http://www.torrens.edu.au/policies-and-forms I/we am/are aware that I/we need to keep a copy of all submitted material and their drafts, and I/we will do so accordingly. Learning Rubric: Assessment Two Assessment Attributes Fail (Unacceptable) 0-49% Pass (Functional) 50-64% Credit (Proficient) 65-74% Distinction (Advanced) 75 -84% High Distinction (Exceptional) 85-100% Knowledge and understanding (Tasks 1–5) Consultative report focuses on various processes in project knowledge areas. Application of various tools and techniques in management of project schedule, cost, resources, risk, communication and stakeholders. Lack of understanding of required concepts and knowledge of various processes in project knowledge areas. Key components of the assignment are not addressed. Lack of application of tools and techniques in management of project schedule, cost, resources, risk, communication and stakeholders to the case scenario provided. Limited understanding of required concepts and knowledge of various processes in project knowledge areas. Key components of the assignment are partially addressed with limited application of tools and techniques in management of project schedule, cost, resources, risk, communication and stakeholders to the case scenario provided. An adequate understanding of the various processes in project knowledge areas. All key components of the assignment are addressed with an adequate level of application of the tools and techniques in management of project schedule, cost, resources, risk, communication and stakeholders to the case scenario provided. A thorough understanding of the various processes in project knowledge areas. All key components of the assignment are addressed. Well demonstrated capacity to explain and apply relevant tools and techniques in management of project schedule, cost, resources, risk, communication and stakeholders to the case scenario provided. An exceptional understanding of the various processes in project knowledge areas. All key components of the assignment are addressed. Demonstrates mastery of concepts and application of relevant tools and techniques in management of project schedule, cost, resources, risk, communication and stakeholders to the case scenario provided. 80% Use of academic and discipline conventions Spelling, grammar, sentence construction, appropriate use of credible resources, Correct citation of key resources using APA style. of referencing 20% Poorly written with errors in spelling and grammar. Demonstrates Inconsistent use of good quality, credible and relevant resources to support and develop ideas. There are mistakes in using the APA style. Written according to academic genre and has accurate spelling, grammar, sentence and paragraph construction. Demonstrates consistent use of credible and relevant research sources to support and develop ideas, but these are not always explicit or well developed. There are some mistakes in using APA style. Written according to academic genre. Demonstrates consistent use of credible and relevant research sources to support and develop ideas. There are no mistakes in using the APA style. Well-written and adheres to the academic genre. Consistently demonstrates expert use of good quality, credible and relevant research sources to support and develop appropriate arguments and statements. Shows evidence of reading beyond the key reading. There are no mistakes in using the APA style. Expertly written and adheres to the academic genre. Demonstrates expert use of high-quality credible and relevant research sources to support and develop arguments and position statements. Shows extensive evidence of reading beyond the key reading. There are no mistakes in using the APA style.
ELEC1601 Introduction to Computer Systems Outline: • Introduction to course • Course admin • Computers at a high‐level (course overview) • Lab intro • Encoding numbers Introduction to Computer Systems Why are you here? Why are you here? • You have no choice… • It is a core unit Why are you here? • You want to understand how computers work • Not how to use MS Word/send email… Why are you here? • You want to understand how computers work • Not how to use MS Word/send email… • What is inside a computer • How could you design your own computer • Make the best better • How does a computer ‘talk’ to other devices • Hardware vs software • How do you use a computer efficiently What is involved in understanding computers • You may already have your favourite programming language • You are talking to them • But circuit only understands 0’s and 1’s • We will learn how to bridge the gap Why do people like this course? Why do people like this course? • You want to understand how computers work Why do people like this course? • You want to understand how computers work • Fun • Practical Fun/Practical • Learn how to control a real‐chip…ish • We use an Arduino • You should build skills and methodologies to talk to any computer Fun/Practical (e.g. lab1) Let’s talk about computer systems What is a computer system • https://padlet.com/davidboland/7sgxpv8xz8onrkh4 What is a computer system • Something you communicate through keyboard/mouse/touchscreen • Supporter of apps • Something that does mass arithmetic instructions • Something that integrates inputs and computation • A collection of digital logic that can execute machine instructions What is a computer system • Something you communicate through keyboard/mouse/touchscreen • Supporter of apps • Something that does mass arithmetic instructions • Something that integrates inputs and computation • A collection of digital logic that can execute machine instructions • An electronic brain?? Typical structure of a computer system What is a program • Written in a programming language • Meaning defined by language Program to execution • Sequence of steps Assembly language • Human readable machine language • Map directly to binary digits Can you program in assembly language? • Yes… • You can write extremely efficient code • But…You need to understand the processor in detail • You may not be smarter than a compiler… Program execution • Yes… • You can write extremely efficient code • But…You need to understand the processor in detail • You may not be smarter than a compiler… Abstraction Are all computers the same? Course Details Course scenario • Lectures • Under the hood ‐ Understand circuit/architecture • Lab • Designing a system ‐ Program it bottom up Learning Objectives • Content should always be linked to these objectives • We are here to help you achieve Additional Learning Objectives • We want to help you to learn how to learn • What are your learning strategies? • How did you study for your HSC/VCE/IBAC/A‐levels • https://padlet.com/davidboland/ol0rwdpyzw53ewu0 Strategies for sophisticated learners • You are going to run a marathon Strategies for sophisticated learners • Example learning strategy • Lectures • Attend • Tutorials • Attend • Do exercises as instructed by tutor • Labs • Attend • Complete as much as possible during lab session • Write lab report afterwards • Exam • Study during STUVAC • Make sure you complete all past lecture quizzes • Extra last minute study before relevant exam • Go to any ‘exam consultation sessions’ organised by lecturer Strategies for sophisticated learners • We want you to experience new options that can potentially help you to learn better. Dual‐process theories of reasoning • Brain processes in two ways • System 1: • Intuitive/instantaneous • Walk down street • What is 2+2? • Effortless • System 2: • Invoked when system 1 fails to solve problem • Tries to create a rational answer • You are not learning when you are using system 1 A better learning strategy • Lectures • Prepare for lectures • Not at the last moment • Distributed practice better • Attend • Participate diligently/actively • Be willing to make mistakes • Tutorials • Prepare for lectures • Attend • Do exercises as instructed by tutor • Participate diligently/actively • Be willing to make mistakes • Be willing to get ahead/go beyond/ask tutor more questions A better learning strategy • Labs • Prepare for lab in advance • Attend • Complete as much as possible during lab session • Build your team • Use the demonstrators • Write lab report afterwards • SPEAK UP EARLY ABOUT ANY ISSUES A better learning strategy • Exam • Study throughout semester • Have a study plan for exam period starting during STUVAC • Make sure you complete all past lecture quizzes • DO NOT DEPEND ON THIS • Extra last minute study before relevant exam • Go to any ‘exam consultation sessions’ organised by lecturer • Only if they can help you • Use your friends • Use the discussion forum Learning strategies Learning strategies • We want you to prepare before a lecture. • Really? Why? Learning strategies • We want you to prepare before a lecture. • Really? Why? • So you can work in lecture • Active participation • Talk to partners. • Lectures only contain a subset of the information in Canvas. Learning strategies • Tutorials should follow lectures • You are introduced to ideas, but build on it in tutorials. • Harder questions • More help Learning strategies • Labs arguably separate • More practical • But it should all come together at the end Bureaucratic stuff Your life for this course • https://canvas.sydney.edu.au/ • (https://canvas.sydney.edu.au/courses/35882) How do I pass? https://canvas.sydney.edu.au/courses/25935/pages/course‐organisation?module_item_id=901923 How do I pass? • Lab Completion • Marked as a group. • We will test anyone at random. You have the responsibility to teach your peers • Lab reports (weeks 3‐6) • Submit before start of next lab • Each group member will take a turn How do I pass? • Midterm • Give you an idea how things work/how you are tracking • Project • Changing focus to quality • Exam • Hurdle task. Can’t ride on your peers How this course is structured/What you need to do each week • Know what week you are in • Do not get behind • Do not do the wrong tasks • https://canvas.sydney.edu.au/courses/25935/pages/week‐ 2?module_item_id=901930 Plagiarism • Taken very seriously at university Edstem • https://edstem.org/courses/4704/discussion/278920 • Ask questions • Answer other questions • Does not matter if your answers are incorrect • Discussion will improve them Labs • Groups of 5. CC/RE same • If you want to create a team in advance OK • When you join a lab, change your name to be group1_ • Simulation based • Zoom breakout rooms • Discord chat encouraged • Screen sharing encouraged • Remote control encouraged • Codeshare encouraged • Mini‐demo Encoding Information Why do computers need to encode information? Why do computers need to encode information? • Because they can only deal with 0’s and 1’s What do we mean by encoding information for computers? What do we mean by encoding information for computers? • Numbers • (binary) What do we mean by encoding information for computers? • Numbers • (binary) • Colours • Red/green/blue • Machine Code What do we mean by encoding information for computers? • Anything that you possibly want to do with a computer What do we mean by encoding information for computers? • Literally everything… Start with numbers… • Why? • Computers very good at arithmetic • Computers do a lot of arithmetic Start with natural numbers • What are they? How do we count numbers in base 10? How do we count numbers in binary? • Same way, but max of 2 What do numbers in base 10 mean • Consider the number 1429 • How do we break it down? What does a binary number mean • Consider the number 111010 mean • How do we break it down? How do we convert numbers between bases • Convert 382710 to binary? Why Octal/Hexadecimal • How do you think this monitor is encoded? Why Octal/Hexadecimal • Make binary more human readable • Conversion (from binary) is easy First exercise from worksheet Useful properties of base 10 • Is 1345 a multiple of 10? Is it a multiple of 100? • Is 13450 a multiple of 10? Is it a multiple of 100? • Is 10400 a multiple of 10? Is it a multiple of 100? • Is this hard? Useful properties of base 10 • How do you multiply 1274 by 10? • How do you multiply 1830 by 100? • Is this hard? Useful properties of base 10 • How do you divide 10400 by 10? • How do you divide 10400 by 100? • Is this hard? Useful properties of base 10 • Is 1345 a multiple of 7? Is it a multiple of 77? • Is 13450 a multiple of 70? Is it a multiple of 777? • Is 10400 a multiple of 70? Is it a multiple of 777? • How do you multiply 1274 by 7? • How do you multiply 1830 by 77? • How do you divide 10400 by 77? • How do you divide 10400 by 777? • Is this hard? Useful properties of base 2 • Is 101100 a multiple of 2? Is it a multiple of 4? • How do you multiply 1010010 by 2? • How do you multiply 1011110 by 4? • How do you divide 10101010 by 2? • How do you divide 10111100 by 4? • Is this hard? Useful properties of base 8, base 16 • Worksheet exercise 2 Couple of bonus questions • What is 10016 ‐1 ? How many different numbers can you represent? • 10 digits? • 10 octal values? • 10 hex values? • 10 bits? Encoding integers • What is the difference between the set of integers and set of natural numbers? Encoding integers • What is the difference between the set of integers and set of natural numbers? • Sign‐magnitude: the easiest way to represent negative numbers • Add a sign bit Encoding integers • What is the difference between the set of integers and set of natural numbers? • Sign‐magnitude: the easiest way to represent negative numbers • Add a sign bit • Is this a good representation? How do we add sign‐magnitude numbers How do we add positive numbers • How do we add 1503+1729? How do we add positive binary numbers • How do we add 1001+1011? How do we add sign‐magnitude numbers • Suppose we have two binary numbers in sign‐magnitude representation: • A+B (A+ve, B+ve) • A+B (A+ve, B‐ve) • A+B (A‐ve, B‐ve) One’s complement • Invert all bits • Simpler addition Two’s complement • Easy to compute • Invert all bits and add 1 • Has a direct understanding • Simpler addition still
ECMT2160: Computational Assignment Due: November 3, 11:59am This assessment task requires you to use MATLAB to run some Monte Carlo simulations. You should prepare your submission as a MATLAB Live Script file (i.e., a .mlx file). Submit your answers through the Canvas course website. Your submission should include a mixture of written responses formatted as text, blocks of MATLAB code, and MATLAB output, including graphs. You should submit two versions of your answers: the original .mlx file, and a version ex- ported to .html. You may work on this assessment individually, or in pairs. If you work in pairs, it is important that you clearly indicate the student ID number of your partner in your submission. Your submission should not be identical to your partner’s submission. The assignment consists of two questions, each with multiple parts. Answer all parts of both questions. The assignment is worth a total of 25 points towards your final assessment. The first question is worth 15 points and the second ques- tion is worth 10 points. Points will be deducted for poor presentation, including: excessive typos, poor written expression, poor organization, etcetera. Question 1 Before attempting Question 1, you should work through Sections 3.8 and 3.9 in the file IntroProb.mlx. Begin your submitted solution to Question 1 by running the command rng(STUDENTID) in MATLAB, where STUDENTID is your 9-digit Student ID number. This fixes the sequence of random numbers to be generated in your simulation. Suppose we roll two fair six sided dice. Let1 denote the sum of the numbers rolled, and let 2 denote the maximum of the numbers rolled. (a) (i) Create an 11 × 6 matrix containing the values taken by the joint prob- ability mass function of 1 and 2. The entry in row , column of this matrix should contain the probability P(1 = , 2 = ). (ii) Create a three-dimensional bar graph displaying the joint probability mass function of 1 and 2. (b) (i) Create a 1× 6 vector containing the values taken by the marginal prob- ability mass function of 2. The entry in column of this vector should contain the probability P(2 = ). (ii) Create a two-dimensional bar graph displaying themarginal probability mass function of 2. (c) (i) Create an 11 × 6 matrix containing the values taken by the conditional probabilitymass function of1 given2. The entry in row , column of this matrix should contain the conditional probability P(1 = |2 = ). (ii) Create six two-dimensional bar graphs, each displaying the conditional probability mass function of 1 given 2 = , with taking the values 1 through 6 in your six graphs. (d) In each of 10,000 iterations of a “for loop”, do the following. (i) Generate a discrete random variable whose probability mass function is the marginal probability mass function of 2 calculated in part (b). Hint: theMATLAB commandrandi(6,2,1) returns a 2×1 random vector whose entries are independent random variables each equal to the numbers 1 through 6 with equal probabilities. (ii) Calculate the conditional expectation E(1 | 2 = ), where is the random number generated in part (i). Calculate the average of the conditional expectations computed over all 10,000 iterations of the “for loop”. (e) Discuss how your findings in part (d) relate to the Law of Iterated Expecta- tions. Question 2 Let be a random variable with the standard normal distribution, and let() be the probability density function of the standard normal distribution. Let (1, 2) be the function (1, 2) = { 4(12)−4(−31 + −32 − 1)−7/3 if 0 < 1 < 1 and 0 < 2 < 1 0 otherwise. Suppose that 1 and 2 are a pair of continuous random variables whose joint probability density function is given by (1, 2) = (P( ≤ 1), P( ≤ 2))(1)(2) for all real 1 and 2. (a) Create a graph of the joint probability density function of1 and2 for values of 1 and 2 between −3 and 3. (b) Create a graph of the marginal probability density function of 1 for values of 1 between −3 and 3. Graph it alongside the standard normal probability density function. (c) Repeat part (b) for 2 instead of 1. (d) Based on your answers above, do you think that the joint distribution of 1 and 2 is multivariate normal? Why or why not?
AME 547 HW #5 (Due Thursday November 4th, 2021) Consider the workspace shown in the following figure. Obstacles are shown in red color and the robot is shown in black color. Robot will be approximated by lines. Obstacles will be approximated by circles. Implement RRT algorithm based on Single Tree Search Described in Section 5.5.3 in Chapter 5 of LaValle’s book (http://planning.cs.uiuc.edu/ch5.pdf) to find a path from the initial pose to the goal pose without intersecting with the obstacles. Test data will be released on Wednesday, November 3rd by 6PM. This will include initial pose (values of 1 ; 2 ; 3 ), goal pose (values of 1 ; 2 ; 3 ), and size and location for each obstacle. The maximum number of obstacles will be four. Please submit a zip file that includes the following: 1. Graphical representation of the computed path 2. Code to compute the path
FMAT3888 Projects in Financial Mathematics Semester 2, 2021 Interdisciplinary Project: Portfolio Optimisation with Market Data Below we provide some questions for the interdisciplinary project. You may want to make some adjustment for some parts of the questions, for example, but not limited to, adjusting the numbers in red or consider more periods for the dynamic optimisation, in order to have more interesting results. In that case, please feel free to do so. Setup. In reality each asset class in the spreadsheet provided on canvas, e.g., Dev. Equities and Hedge Funds, may contain several different assets. For the simplicity of analysis and presentation, without loss of generality we assume each asset class behaves like a single asset and admits some price process. We will work on six asset classes, including Cash (Asset Class 1), Dev. Equities (DEQ, Asset Class 2), Australian Equities (AEQ, Asset Class 3), Emerging Market Equities (EMEQ, Asset Class 4) Australian Fixed Interest (AFI, Asset Class 5) and Dev. Gov. Bonds (DGB, Asset Class 6). Let Si = (Sit)t∈N be the price process for Asset Class i, i = 1, 2, 3, 4, 5, 6. Here time t is in months and thus Sit is the price of Asset Class i at the end of the t-th month. For i = 1, 2, 3, 4, 5, 6, assume the dynamics of the price of Asset Class i satisfies Sit+1 = S i t · eX i t , t = 0, 1, 2, . . . . (1) DenoteXt = (X 1 t , X 2 t , ..., X 6 t ). AssumeX0,X1,X2, . . . are i.i.d., and each admits multivariate normal distribution with mean a = (a1, a2, ..., a6) ∈ R6 and covariance matrix B = (bij)i,j=1,2,3,4,5,6. For i = 1, 2, 3, 4, 5, 6, denote αit the monthly return of Asset Class i in the t-th month. By (1), αit = Sit Sit−1 − 1 = eXit − 1 =⇒ Xit = ln(1 + αit). Note that the realised monthly returns αit for these asset classes since January 2001 to April 2021 are provided in the spreadsheet. 1 Parameter Estimation Q1. Estimate the parameters ai and bij for i, j = 1, 2, 3, 4, 5, 6 using market data for the two time intervals: (A) from 1/1/2007 to 31/12/2010, (B) from 1/1/2011 to 31/12/2014. Q2. For n = 1, 2, . . . , by (1) the return for Asset Class i from the beginning of the t-th month to the beginning of the (t+ n)-th month is given by αit,n = Sit+n−1 Sit−1 − 1 = exp ( t+n−1∑ k=t Xik ) − 1. Show that αit,n d = eY i − 1, i = 1, 2, 3, 4, 5, 6 (2) where (Y 1, Y 2, ..., Y 6) admits multivariate normal distribution with mean na and covariance matrix nB. Q3. Let the random vector R(1) := (R (1) 1 , R (1) 2 , ..., R (1) 6 ) (resp. R (2) := (R (2) 1 , R (2) 2 , ..., R (2) 6 )) model the joint annual (resp. two-year) returns for six Asset Classes. For k = 1, 2 denote µ (k) i := E [ R (k) i ] , c (k) ij := Cov ( R (k) i , R (k) j ) , ρ (k) ij := c (k) ij√ c (k) ii √ c (k) jj , i, j = 1, 2, 3, 4, 5, 6. Use the results in Q1 and Q2 to compute/estimate µ (k) i , c (k) ij , ρ (k) ij for i, j = 1, 2, 3, 4, 5, 6 and k = 1, 2 for the two time intervals (A) and (B) from Q1. Remark: Here in the above we use lognormal distribution (instead of normal distribution) to model the annual and two-year returns R(1) and R(2). See (2). The reason is that we would like the return rate to be above −1 (why?). For computational purpose, it might be easier to use normal distribution instead for R(1) and R(2) in Q4 and Q6 later (if you use decide to use exponential utility). (Please check if that is the case or not.) You may do so if that is the case. Approximating lognormal by normal: Recall that ex− 1 ≈ x when x ≈ 0. Hence for Y ∼ N(µ, σ2), if Y ≈ 0 with large probability, i.e., when µ, σ2 ≈ 0 (why?), then with large probability eY − 1 ≈ Y and thus eY − 1 would behaves like a normal random variable for the most of the time. In this case it is reasonable to approximate eY − 1 by a normal random variable. One naive approach for the approximation is to use Y . However, as E[eY − 1] ̸= E[Y ] and Var[eY − 1] ̸= Var[Y ], it may be better to do moment matching and to approximate using normal distribution with mean E[eY − 1] and variance Var[eY − 1]. The same applies to the multivariate case. 2 Static Portfolio Optimisation Q4. Consider an investor who statically invests all her wealth in these six asset classes for two years. Answer the following questions for both cases where the estimation is based on two sets of market data, i.e., for time intervals (A) and (B) from Q1. (a) Solve the utility maximisation problem: max E[U(R(2)w)] subject to w1 + w2 + w3 + w4 + w5 + w6 = 1, where w = (w1, w2, w3, w4, w5, w6) T is the vector of weights, and U(x) = −e−γx with γ = 1. (b) Comment on the differences of your results corresponding to the two data sets. (c) Compare your result from (a) (with data set (B)) with the realised return on her portfolio using the market data for the period from 1/1/2015 to 31/12/2016. Q5. Under the setup of Q4, answer the following questions for both cases where the estimation is based on the time intervals (A) and (B) from Q1. (a) Find the efficient frontier for the market (Cash, DEG, AEQ, EMEQ, AFI, DGB) in the plane (σ, µ) using the estimated parameters µi := µ (2) i , cij := c (2) ij , ρij := ρ (2) ij for i, j = 1, 2, 3, 4, 5, 6. (b) Find the portfolio with the minimum variance which yields at least 12% for the expected return. To this end, solve the optimisation problem: min wTCw subject to w1µ1 + w2µ2 + w3µ3 + w4µ4 + w5µ5 + w6µ6 ≥ 0.12, w1 + w2 + w3 + w4 + w5 + w6 = 1, where w = (w1, w2, w3, w4, w5, w6) T is the vector of weights and C = [cij ] is the covariance matrix for R (2). 2 (c) Comment on the differences of your results corresponding to the two data sets. (d) Compare your result from (b) (with data set (B)) with the realised return on her portfolio using the market data for the period from 1/1/2015 to 31/12/2016. (e) Comment on the differences/similarities of your results from Q4 and Q5. 3 Dynamic Portfolio Optimisation Q6. Consider an investor who invests all her wealth in these six asset classes for two years, during which she will adjust her portfolio weights at the beginning of the second year. For k = 1, 2, denote ξk := (ξk1 , ξ k 2 , ..., ξ k 6 ) the returns of the six asset classes for the k-th year. Note that ξ1 and ξ2 are i.i.d. copies of R(1). Let w = (w1, w2, ..., w6) T (resp u = (u1, u2, ..., u6) T ) be the portfolio weights at the beginning of the first year (resp. second year). Then the return of the profolio over the two-year investment period is given by G(w,u) = (1 + ξ1w)(1 + ξ2u)− 1. (why?) Suppose the investor believes that parameters estimated using the data set (B) are valid. Assume short selling is not allowed. Answer the following questions. (a) Solve the utility maximisation problem: max E[U(G(w,u))] subject to w1 + w2 + ...+ w6 = 1, u1 + u2 + ...+ u6 = 1 where U(x) = −e−γx with γ = 1. Note u = u(ξ1) may depend on the realisation of ξ1. (b) Compare your result with that for Q4(a). Q7. Under the setup of Q6, answer the following questions. (a) Solve the portfolio optimisation problem: min Var[G(w,u)] subject to E[G(w,u)] ≥ 0.12, w1 + w2 + ...+ w6 = 1, u1 + u2 + ...+ u6 = 1. Note here the control u = u(ξ1) may depend on the realisation of ξ1. (b) Compare your result with that for Q5(b) (b) Comment on the differences/similarities of your results from Q6 and Q7.
COMP5328 - Advanced Machine Learning Assignment 2 Due: 11 November 2021, 23:59PM This assignment is to be completed in groups of 2 to 3 students. It is worth 25% of your total mark. Introduction The objective of this assignment is to build a transition matrix estimator and two classification algorithms that are robust to label noise. Three input datasets are given. For each dataset, the training and validation data contains class-conditional random label noise, whereas the test data is clean. You need to build at least two different classifiers trained and validated on the noisy data, which can have a good classification accuracy on the clean test data. You are required to compare the robustness of the two algorithms to label noise. For the first two datasets, the transition matrices are provided. You can directly use the given transition matrices for designing classifiers that are robust to label noise. For the last dataset, the transition matrix is not provided. You are required to build a transition matrix estimator to estimate the transition matrix. Then, employ your estimated transition matrix for classification. Your estimated tran- sition matrix must be included in your final report. Note that to validate the effectiveness of your transition matrix estimator, you could use your estimator on the first two datasets and compare your estimation to the given transition matri- ces. The code contained in tutorial 9 could be a good starting point. Data prepossessing is allowed, but please remember to clarify and justify it in the report carefully. 1 A Guide to Using the Datasets Three image datasets with .npz format are provided. You can download them via canvas. 1.1 Attributes Contained in a Dataset The following code is used to load a dataset and check the shape of its attributes. import numpy as np # Remember to r ep l a c e the $FILE PATH datase t = np . load ($FILE PATH) Xtr va l = datase t [ ’ Xtr ’ ] S t r v a l = datase t [ ’ Str ’ ] Xts = datase t [ ’ Xts ’ ] Yts = datase t [ ’ Yts ’ ] print ( Xtr va l . shape ) print ( S t r v a l . shape ) print ( Xts . shape ) print ( Yts . shape ) 1.1.1 Training and validation data The variable Xtr val contains the features of the training and validation data. The shape is (n, image shape) where n represents the total number of the in- stances. The variable Str val contains the noisy labels of the n instances. The shape is (n, ). For all datasets, the class set of the noisy labels is {0, 1, 2}. Note that do not use all the n examples to train your models. You are re- quired to independently and randomly sample 80% of the n examples to train a model and use the rest 20% examples to validate the model. 1.1.2 Test data The variable Xts contains features of the test data. The shape is (m, image shape), where m represents the total number of the test instances. The variable Yts contains the clean labels of the m instances. The class set of the clean labels is also {0, 1, 2}. 1.2 Dateset Description 1.2.1 FashionMINIST0.3.npz Number of the training and validation examples n = 18000. Number of the test examples m = 3000. The shape of each example image shape = (28× 28). The transition matrix T = 0.7 0.3 00 0.7 0.3 0.3 0 0.7 . 1.2.2 FashionMINIST0.6.npz Number of the training and validation examples n = 18000. Number of the test examples m = 3000. The shape of each example image shape = (28× 28). The transition matrix T = 0.4 0.3 0.30.3 0.4 0.3 0.3 0.3 0.4 . 1.2.3 CIFAR.npz Number of the training and validation examples n = 15000. Number of the test examples m = 3000. The shape of each example image shape = (32× 32× 3). The transition matrix T is unknown. 2 Performance Evaluation The performance of each classifier will be evaluated with the top-1 accuracy metric, that is, top-1 accuracy = number of correctly classified examples total number of test examples ∗ 100%. To have a rigorous performance evaluation, you need to train each classifier at least 10 times with the different training and validation sets gener- ated by random sampling. Then report both the mean and the standard derivation of the test accuracy. 3 Tasks You need to implement at least two label noise robustness classifiers with at least one not taught in this course and test their performance on the three datasets. You need to implement an estimator to estimate the transition matrix. The code must be written in Python 3. You are allowed to use external libraries for optimization and linear algebraic calculation. If you have any ambiguity about whether you can use a particular library or a function, please post your question on canvas or Ed. 3.1 Image Classification with Known Flip Rates For the first two datasets, the transition matrices are provided. You can directly use the given transition matrices for designing classifiers that are robust to label noise. As mentioned in the section 2, for each classifier, you should report the mean and the standard derivation of the test accuracy. 3.2 Image Classification with Unknown Flip Rates For the last dataset, Since the transition matrix is not provided, you need to imple- ment an estimator to estimate the transition matrix. Then use the estimated transition matrix to build a noise robust classifier. Note that you can use the provided transition matrices of the first two datasets to validate the effectiveness of your transition matrix estimator. You need to include your estimated transition matrix in the final report. You also need to report the mean and the standard derivation of the test accuracy for each of your designed noise robustness classi- fiers. Both estimation accuracy of the transition matrix and the test accuracy on the last dataset contribute to the final mark. 3.3 Report The report should be organized similar to research papers, and should contain the following sections: • In abstract, you should briefly introduce the topic of this assignment, your methods, and describe the organization of your report. • In introduction, you should first introduce the problem of learning with label noise, and then its significance and applications. You should give an overview of the methods you want to use. • In related work, you are expected to review the main idea of related label noise methods (including their advantages and disadvantages). • In methods, you should describe the details of your classification models, including the formulation of the cost functions, the theoretical foundations or views (if any) of the cost functions, and the optimization methods. You should describe the details of the transition matrix estimation methods, the- oretical foundations (if any), and optimization algorithms. • In experiments, you should introduce your experimental setup (e.g., datasets, algorithms, evaluation metric, etc.). Then, you should show the experimen- tal results, compare, and analyze your results. If possible, give your personal reflection or thoughts on these results. • In conclusion, you should summarize your methods, results, and your in- sights for future work. • In references, you should list all references cited in your report and format- ted all references in a consistent way. • In appendix, you should provide instructions on how to run your code. The layout of the report: • Font: Times New Roman; Title: font size 14; Body: font size 12 • Length: Ideally 10 to 15 pages - maximum 20 pages Note: Submissions must be typeset in LaTex using the provided template. 4 Submissions Detailed instructions are as follows: 1. The submission contains two parts: report and source code. (a) report (a pdf file): the report should include each member’s details (student id and name). (b) code (a compressed folder) i. algorithm (a sub-folder): your code could be multiple files. ii. data (an empty sub-folder): although two datasets should be inside the data folder, please do not include them in the zip file. We will copy those datasets to the data folder when we test the code. 2. The report (file type: pdf) and the codes (file type: zip) must be named as student ID numbers of all group members separated by underscores. For example, “xxxxxxxx xxxxxxxx xxxxxxxx .pdf”. 3. Only one student needs to submit your report (file type: pdf) to Assignment 1 (report) and upload your codes (file type: zip) to Assignment 1 (codes). 4. Your submission should include the report and the code. A plagiarism checker will be used. 5. You need to clearly provide instructions on how to run your code in the appendix of the report. 6. Indicate the contribution of each group member. 7. A penalty of minus 5% marks per each day after due (email late submissions to TA and confirm late submission dates with TA). The maximum delay is 10 days, after that assignments will not be accepted. 8. Remember, the submission deadline is 11 November 2021, 23:59PM. 5 Marking scheme Category Criterion Marks Comments Report [80] Abstract [3] •problem, methods, and organization Introduction [6] •the problem you intend to solve •the importance of the problem Previous work [8] •previous relevant methods used in literature •their advantages and disadvantages Label noise methods with known flip rates [23] •pre-processing (if any) •label noise methods’ formulation •cross-validation method for model selection or avoiding overfitting (if any) •experiments •discussions Noise rate estimation method [12] •noise rate estimation method’s formulation •experiments •discussions Label noise methods with unknown flip rates [10] •pre-processing (if any) •label noise methods’ formulation (if different from above) •cross-validation method for model selection or avoiding overfitting (if any) •experiments •discussions Conclusions and future work [3] •meaningful conclusions based on the results •meaningful future work suggested Presentation [8] •academic style, grammatical sentences, no spelling mistakes •good structure and layout, consistent format- ting •appropriate citation and referencing •use graphs and tables to summarize data Other [7] •at the discretion of the assessor: illustrate outstanding comprehensive theoretical analy- sis, demonstrate the insightful and compre- hensive assessment of the significance of their results, provide descriptions and explanations that have depth but clarity, and are concisely worded Code [20] •reasonable code running time •well organized, commented and documented Note: Marks for each category is indicated in square brackets. The minimum mark for the assignment will be 0 (zero).
CSCI544: Homework Assignment №4 Due on Nov 09, 2021 (before class) Introduction This assignment gives you hands-on experience on building deep learning models on named entity recognition (NER). We will use the CoNLL-2003 corpus to build a neural network for NER. The same as HW3, in the folder named data, there are three files: train, dev and test. In the files of train and dev, we provide you with the sentences with human-annotated NER tags. In the file of test, we provide only the raw sentences. The data format is that, each line contains three items separated by a white space symbol. The first item is the index of the word in the sentence. The second item is the word type and the third item is the corresponding NER tag. There will be a blank line at the end of one sentence. We also provide you with a file named glove.6B.100d.gz, which is the GloVe word embeddings [1]. We also provide the official evaluation script. conll03eval to evaluate the results of the model. To use the script, you need to install perl and prepare your prediction file in the following format: idx word gold pred (1) where there is a white space between two columns. gold is the gold-standard NER tag and pred is the model-predicted tag. Then execute the command line: perl conll03eval < {predicted file} where {predicted file} is the prediction file in the prepared format. Task 1: Simple Bidirectional LSTM model (40 points) The first task is to build a simple bidirectional LSTM model (see slides page 43 in lecture 12 for the network architecture) for NER. Task. Implementing the bidirectional LSTM network with PyTorch. The architecture of the network is: Embedding→ BLSTM→ Linear→ ELU→ classifier The hyper-parameters of the network are listed in the following table: embedding dim 100 number of LSTM layers 1 LSTM hidden dim 256 LSTM Dropout 0.33 Linear output dim 128 Train this simple BLSTMmodel with the training data on NER with SGD as the optimizer. Please tune other parameters that are not specified in the above table, such as batch size, learning rate and learning rate scheduling. What are the precision, recall and F1 score on the dev data? (hint: the reasonable F1 score on dev is 77%. Task 2: Using GloVe word embeddings (60 points) The second task is to use the GloVe word embeddings to improve the BLSTM in Task 1. The way we use the GloVe word embeddings is straight forward: we initialize the embeddings in our neural network with the corresponding vectors in GloVe. Note that GloVe is case-insensitive, but our NER model should be case-sensitive because capitalization is an important information for NER. You are asked to find a way to deal with this conflict. What are the precision, recall and F1 score on the dev data? (hint: the reasonable F1 score on dev is 88%. Bonus: LSTM-CNN model (10 points) The bonus task is to equip the BLSTM model in Task 2 with a CNN module to capture character-level information (see slides page 45 in lecture 12 for the network architecture). The character embedding dimension is set to 30. You need to tune other hyper-parameters of CNN module, such as the number of CNN layers, the kernel size and output dimension of each CNN layer. What are the precision, recall and F1 score on the dev data? Predicting the NER tags of the sentences in the test data and output the predictions in a file named pred, in the same format of training data. (hint: the bonus points are assigned based on the ranking of your model F1 score on the test data). Submission Please follow the instructions and submit a zipped folder containing: 1. A model file named blstm1.pt for the trained model in Task 1. 2. A model file named blstm2.pt for the trained model in Task 2. 3. Predictions of both dev and test data from Task 1 and Task 2. Name the file with dev1.out, dev2.out, test1.out and test2.out, respectively. All these files should be in the same format of training data. 4. You also need to submit your python code and a README file to describe how to run your code to produce your prediction files. In the README file, you need to provide the command line to produce the prediction files. (We will execute your cmd to reproduce your reported results on dev). 5. A PDF file which contains answers to the questions in the assignment along with a clear description about your solution, including all the hyper-parameters used in network architecture and model training. References [1] Jeffrey Pennington, Richard Socher, and Christopher D Manning. Glove: Global vectors for word representation. In Proceedings of the 2014 con- ference on empirical methods in natural language processing (EMNLP), pages 1532–1543, 2014.
Design an Analog Filter Introduction Filter circuits play an important role in many electronics designs. They are primarily used to pass desired signals while blocking unwanted signals. Filters can be divided into two main categories, analog and digital filters, and each category can be further divided into many sub-groups, such as passive filters, active filters, FIR filters or IIR filters. Each sub-group of filters has their own advantages and disadvantages. Learning the difference between each filter will allow a filter design engineer to choose the best type of filter for a given application. As for the specifications of a filter, bandwidth or 3dB cut-off frequency is obviously the most crucial one. In real-world situations, the boundaries of filters are less clearly defined, so that more detailed specifications are required apart from 3dB cut-off frequency, such as ripples in pass/stop bands, transition band/slope, etc. as shown in Figure 1 (a). For example, to implement a filter with very steep cut-off, filters of higher orders must be required, as shown in Figure 1 (b), and consequently the circuits become more complex. (a) (b) Figure 1. Detailed specifications for a low pass filter response Apart from filter specifications stated above, more factors are to be considered on the filter design, such as input/output impedance of the filter circuit and a load connected with it, as these affect the efficiency/performance of the filter. Furthermore, physical size, power consumption and economic cost are important to be taken into account when making a decision. In reality, filter design is a trade-off among these factors. Filters to be Designed In this project, we will focus on first-order analog filter design. You are required to design two types of first-order filters described below. Based on the requirements of the applications, you need to design the circuit topology, select components, conduct relevant calculations, simulate the filter performance in Multisim, and verify the performance for the application. Filter Type 1- Passive filter Passive filters are filters that consist of only passive components such as resistors, capacitors, and inductors (RLC) and attenuate the signal and have a gain of less than one (unity). As passive filters can handle hundreds of watts of input power, they are used in most home loudspeaker systems, such as in audio amplifiers and speaker systems to reduce any high frequency noise. For low frequency applications, RC filters are preferred since an inductor’s physical size can be very large. Here you are required to design a first-order RC passive filter for an audio application. Given that the audible range for humans is approximately 20 Hz to 20 kHz, the filter should be able to filter out noises above this frequency range. Also, in an audio application, the load of the filter normally has very low impedance, for example, an impedance of 8Ω for a speaker. Therefore, the values of the R and C for the filter are not only required to achieve a desired 3dB cut-off frequency but also to ensure it maximally match with the impedance of a load according to the principle of maximum power transfer. Filter Type 2 - Active Filter Active filters use transistor and op-amp as their basic components, along with resistors and capacitors, but not inductors. Due to the high input impedance and low output impedance of op- amps, active filters eliminate the loading effect at source and load. They can also provide a certain amplification for input signals, and therefore are widely employed in biomedical systems in which the weak bioelectrical signals typically, are in the range of 100 mV – 1 V while the frequencies are below 100 Hz. The design of very low-frequency filters (10 Hz) is not straightforward, many factors to be considered in practice, such as power consumption and low noise level, physical size, large time constant of RC, especially for integrated circuit implementation. Here you are required to design an active first-order RC filter with f3dB=10Hz and gain of 10 which is to be used in biomedical application. Software Platform Multisim is a platform. to be used as for other online labs. It can be accessed from Citrix Workspace in UniConnect Cloud or remotely access PCs in J03 Lab 440. Please check UniConnect Cloud for more details. Design Tasks For the two types of filters required above, you need to complete and include the tasks listed below but certainly not limited to them. 1. Draw the circuit diagrams for the two types of filters. Theoretically demonstrate the performance by deriving their transfer functions, identifying zeros/poles, as well as illustrating their bode plots. How do the poles/zeros affect the magnitude and phase response of filters? How to theoretically calculate the 3dB cut-off frequency? 2. To achieve a desired 3dB cut-off frequency required by the applications, there can be a few combinations for the product of R and C. To select a proper value of R and C for the specified application, you may need to consider the output/input impedance of the filters to ensure that the filters maximumly match the load/source, particularly for the RC passive filter. Demonstrate the calculations on the selection of R and C values for the filters. What are the calculated input and output impedance for the passive filter at the 3dB cut-off frequency? 3. List the model/ components selected for the filters in a Table with basic parameters. For example, for a capacitor, you need to provide more information than just capacitance, such as electrolytic or ceramic or tantalum (considering the physical size/accuracy/life span/cost), leaded type or surface mount technology (SMT) types as the parasitic effects are greater for the leaded types of capacitors with increasing of a signal frequency, as well as the voltage rating. For an op-amp, though there are a lot of specifications listed in the datasheet, such as large signal voltage gain, Gain Bandwidth Product, Slew Rate, input offset voltage, input resistance and common-mode rejection ratio (CMRR), what are the key specifications (i.e. illustrate 2 to 3) to be considered when used for a filter design? Why? You may do some research to find out the concepts mentioned in here or datasheet to help you to answer the questions. There are a number of options available in Multisim library for op-amp, for example a common-used op-amp uA741. 4. Use AC analysis in Multisim to simulate the filter frequency response, and identify 3dB cut-off frequency, voltage gain and phase at f3dB, transition period, ect. Are the results of simulation close to the theoretical calculations? What is the signal frequency range to make the output voltage having maximum magnitude and minimum phase shift (could be 180 if using inverting op-amp configuration)? How many dB can the filter attenuate for a frequency of 3 times of f3dB? 5. Set the input signal with a frequency from the range found in the AC analysis above (Step 4). Use Transient analysis in Multisim to simulate the output voltage of the filters. Plot the output and input of the filter in the same graph and work out the voltage gain and phase shift. Are they the same as what obtained from the AC analysis above? How about the Transient analysis for the filter when inputting a signal with a frequency of f3dB? 6. Impedance matching is important factor to be considered for filter design, particularly for a passive RC filter. To ensure it, you may simulate output and input impedance of the filters as function of the frequency. You can use AC analysis in Multisim by setting the output parameter as an impedance. What are the input and output impedance of the filters at 3dB cut-off frequency? How do the input and output impedance change in the frequency range of interest? With these simulations you may fine tune the values of R and C selected above to ensure the filter having the best possible input and output impedance for the specified application. 7. When a filter connected with a source and load, in order to work properly, the input impedance of the filter should be high per to the source impedance. While the output impedance of the filter should be low per to the load. To find out why, simulate the filter with different loads by using Parameter Sweep in Multisim. At what load can the filter deliver the maximum power to it? Comparing the simulation results for the two types of filters, can you identify the advantages and limits of them on this regard? 8. Unlike a passive filter not requiring a power source, an active filter needs DC power supplies to operate. In this step, you are to investigate how the parameters of the active filter impact on the input power consumption of the filter. Use Parameter Sweep in Multisim to simulate the total DC power consumed by the filter. You can choose different parameters as a variable to sweep, such as input signal amplitudes or loads. How is the power consumption of the filter affected by these factors? 9. In reality, the source is normally not a single frequency signal rather than having multiple frequencies (i.e. noises). Connect the filter with a source to mimic the scenarios and investigate the filter response. For example, set up a signal source as () = 1 sin(2) + 2 sin(2 ∗ 5), where f is the signal frequency and v1, v2 are the amplitude. The first item in the Equation is to mimic the signal while the second item is to mimic the noise which is 5th order harmonic. You can set up the frequency and amplitudes based on specified applications. For the filter with the signal source defined above, use Transient and Fourier analysis in Multisim to simulate the performance of the filter. Can the filters effectively attenuate 5th order harmonic? 10. Note this is an extra task and not compulsory. You would have a bonus mark but subjected to the full mark of this project. Single-pole arrangement of the first-order low pass RC filters gives a roll-off slope of -20dB/decade attenuation of frequencies above the cut-off point at ƒ- 3dB. However, sometimes this slope may not be sharp enough to remove an unwanted signal then two stages of filtering can be used. Cascade the two RC passive filters designed above into a two-stage filter. Simulate the bode plot in Multisim for this filter. Compare the frequency response with the first-order one (with the same RC values). Illustrate the commons and differences from their frequency response. Comments on the results. Assessment This project is to use Multisim as platform. to investigate the design and application of first-order RC filters. It takes 10% of UoS and is assessed by a group report (6 marks) and zoom demonstration (4 marks). The group report should include contributions from each group member, as well as the names, SID, lab session and group number. Each group only needs to submit one copy of the report to Canvas. Demonstration is no longer than 8 minutes for each group and the schedule will be posted on Canvas in week 12. The project will be starting in the week 10 and demonstration is scheduled in the week 13. Support and help will be available in the scheduled lab sessions via Zoom.
Faculty of Arts and Social Sciences School of Economics ECOS3002 Development Economics Mid-sem exam review Exam logistics and overview Exam logistics • The university exams office is responsible for administering the exam, through the special ‘In-semester Test for: ECOS3002’ Canvas site. • All official information on the exam and its administration, comes from them, and overrides anything I say in this video or elsewhere on the ECOS3002 Canvas site. • I will not be available on the day of the exam, the Ed site will be offline the day of the exam, and exams cannot be submitted by email. • Any exam-related issues will have to be dealt with through official University systems (e.g., Special Consideration), and approval of appeals is not guaranteed. • This video is providing an informal review of the logistics of the exam, and a review of some of the relevant content on the exam. • I highly recommend logging in to the exam site as soon as you have access, reading through everything, and making sure you have access to all the materials, resources, and software necessary for an online exam. Exam details Date of test: 11/10/2021 (Monday) Start: 14:00 AEST Duration: 1 hours and 30 minutes (90 minutes). This includes: • 10 minutes reading time, but you are free to start the test as soon as you are ready. • 30 minutes of upload time to allow you to upload your files as per your test instructions. Do NOT treat this as extra writing time. The upload time must be used solely to save and upload your files correctly as per the test instructions. Manage your time carefully. Check that you have saved and named your file correctly and uploaded the correct file. If your time runs out while you are uploading this is not considered a technical issue. • Materials required: (i) scientific calculator, and (ii) a sheet of blank paper with a writing instrument (pen or pencil), OR a digital drawing tool. • Your final exam submissions will be in the form. of a pdf (only). Analysis • The exam will not involve complex calculations or manipulations in Excel, however it will involve basic operations that you can implement on a scientific calculator. • You will also need to create a figure – you can do that using pen/pencil and paper, or a digital drawing tool. Either way you will need to upload your figure as a pdf. Exam format Question type Points Recommended time spent Question 1 Draw, calculate, interpret 15 15 minutes Question 2 Short answer: interpret a quasi-experiment 10 10 minutes Question 3 & 4 Short answer 5 each 5 minutes each Question 5 Short essay 15 15 minutes Academic honesty • It should go without saying that the exam is to be taken completely individually. Use of any method to communicate with classmates during the exam is forbidden. • Beyond that, it is an open book exam. The exam is designed so that you won’t get a huge benefit from searching online or in your textbook, so don’t get tempted to plan to just look things up for your exam responses. But you are certainly welcome to use either to look up concepts, definitions, etc. Mid-sem exam review Content overview Content of exam • Everything up to and including week 7 is fair game: lectures, tutorials, and textbook chapters. • In practice we the exam is most heavily focused through week 6, with light coverage of week 7 (enough to review the lecture video). Week Week Beginning Lecture Lecture Topic(s) / textbook chapter(s) 1 9 Aug Lecture 1 Chapter 1: What is development? Indicators and issuesChapter 4 (part 1): Impact evaluation 2 16 Aug Lecture 2 Chapter 4 (part 2): Impact evaluationChapter 3: History of thought in development economics 3 23 Aug Lecture 3 Chapter 5: Poverty and vulnerability analysisChapter 6: Inequality and inequity 4 30 Aug Lecture 4 Chapter 10: The economics of farm households 5 6 Sept Lecture 5 Chapter 18: Agriculture for development 6 13 Sept Lecture 6 Chapter 11: Population and development Chapter 12: Labour and migration • Chowdhury research vignette 7 20 Sept Lecture 7 Chapter 13: Financial services for the poor Chapter 1: What is development? Indicators and issues • The first question to answer about development is – what is it? How do we define it? How do we quantify it? • Our textbook posits 7 dimensions of development: 1. Income and income growth: totals like GDP, GNP, GNI, per capita, growth rate, PPP conversion. 2. Poverty and hunger: % below a poverty line (monetary), or a metric like calories. 3. Inequality and inequity: comparing top X% vs bottom Y%; inequity about opportunities. 4. Vulnerability: risk of poverty, vulnerability or susceptibility to adverse shocks (covariate and idiosyncratic risk), poverty traps? 5. Basic needs: human development: human capital (health, education), HDI, multidimensional poverty indices. 6. Environmental sustainability: intergenerational equity. 7. Quality of life: many broader theories, some outside economics. Within economics, Sen’s capabilities approach (focused on what you could do – freedom of choice), and Easterly’s 81 indicators of quality of life. Chapter 1: What is development? Indicators and issues • An approach to quantify well-being is through subjective measures, like “subjective well being” or happiness. • Provides a single-index measure, all encompassing, going beyond money. • But how well can we measure it? Easterlin paradox (1974), showing no correlation between income and happiness in OECD, seems to be overturned in developing countries (e.g., Deaton, 2008). • The dominant international development framework is the Sustainable Development Goals (declared in 2016 with targets for 2030) to replace the Millenium Development Goals (2000). Chapter 4: Introduction to impact evaluation and RCTs • An important trend in international development is to evaluate the effectiveness of international development programs and policies, using causal inference techniques. • The challenge for an impact evaluation researcher is that without a research design, data on programs and policies is almost always suffers from selection bias – because there are choices (on demand side or supply side of an intervention) about whether or not to take up an intervention, the take-up decision can be affected by hard-to-measure characteristics that also affect outcomes. • Then if outcomes differ between recipients and non-recipients of an intervention, was it because of those characteristics (which we can’t measure and control for), or the intervention? • Impact evaluation methods provide causal inference techniques to help us overcome selection bias. Chapter 4: Introduction to impact evaluation and RCTs • The randomized control trial (RCT) is considered the most rigorous or most scientific method to overcome selection bias. It is based on the clearest research design, with the weakest assumptions. • Because we explicitly randomize participants into treatment and control groups, we control the allocation of treatment, so treatment allocation shouldn’t be correlated with hard-to-measure characteristics. • Even here, whether “randomization worked” on unobservables is untestable, however we do balance checks on observables to verify. • With an up-front research design, RCTs lead to clear, simple analysis. Two common methods to estimate effects from RCTs are ITT (an average treatment effect) and ToT (a local average treatment effect). Chapter 4: Introduction to impact evaluation and RCTs • Because of randomization, RCTs are highly internally valid. But they may suffer from external validity issues, especially if we work with an opportunistic sample (e.g., a single NGO or company). This can also cause a pioneer effect. • Because RCTs are heavily controlled/planned, they can be subject to common experimental biases – e.g., Hawthorne effect (being studied changes behavior), John Henry effect (control group tries to catch up). • RCTs rely on the SUTVA assumption. Sometimes we may need to randomize a larger scale (e.g., village / neighborhood) to mitigate spillovers. • In some cases we can leverage “natural” randomization (e.g., that a government implemented). There we want to particularly check that randomization worked. Chapter 4: Introduction to impact evaluation and RCTs • There are other credible ways to do an impact evaluation. • In economics these are known as “quasi-experimental” methods because they try to imitate what a pure experiment does – separating treatment from the characteristics of the treated units. • Common methods in applied economics include: • Regression discontinuity design (RDD) • Differences-in-differences (DiD) • Instrumental variables (IV) • Propensity score matching (PSM) Chapter 4: Introduction to impact evaluation and RCTs • While we can learn a lot from these methods, they all suffer drawbacks relative to RCTs: ◦ RDD only estimates a local average treatment effect (LATE), though we model the treatment allocation process. ◦ DiD relies on assumptions about unobservable counterfactual trends. ◦ IV relies on an untestable assumption, the exclusion restriction, and again only gives us a LATE, typically for an undefined population. ◦ PSM relies on strong assumptions around how observables allow us to balance unobservables. • What are the threats to validity of these quasi-experimental designs, and how would you test for them? Chapter 3: History of thought in development economics (post-WWII) • 1950s-1960s: “glory years” of recovery, big push theories used to drive recovery in Europe. • 1970-1982: growth boom in 50s-60s didn’t lead to poverty reduction. Development agenda expanded beyond pure growth, to look at pro-poor growth and other dimensions of development. Lots of fiscal spending and debt accumulation. 1970s were also a major inflationary period. • 1982-1997. Era starts with debt crises, as high inflation means high and unsustainable interest rates. To combat this, we get structural adjustment reforms under so-called Washington consensus, which was about opening up markets and reducing the role of the state in markets (deregulation, privatization, lowering of trade barriers, etc). Chapter 3: History of thought in development economics (post-WWII) • However Washington consensus was too abrupt a change, many countries couldn’t adapt. 1990s considered a “lost decade” for development, particularly in Africa. • 1997-2019. As a corrective, renewed role of the state in complementing the market, multidimensionality in development, more customized development policies. Emergence of MDG agenda (2000) with eye to 2015. • End of cold war (1989) brings greater interest in aid performance, and emergence of the impact evaluation revolution in development economics, in parallel to the credibility revolution in economics. Key leaders such as Abhijit Banerjee, Esther Duflo, Michael Kremer (Nobel Prize, 2019). Chapter 5: Poverty and vulnerability analysis • Poverty means not having a sufficient amount and/or quality of something. Measurement then involves defining that amount/quality, and then identifying which individuals/households don’t have a sufficient amount/quality. • Typically we want a monetary measure. While we might like to use income, in practice we typically use consumption (expenditure), adjusted for, e.g., CPI (inflation over time), PPP, access to public goods, converted to per capita level. • Set a poverty line – e.g., extreme poverty line (enough money for required daily caloric intake), normal poverty line (often 2x extreme poverty line), international poverty line (PPP$1.90 per day for extreme poverty, and PPP$3.10 per day for normal poverty). Chapter 5: Poverty and vulnerability analysis • A poverty profile graphs a ranking of households by expenditure (x-axis), then the level of expenditure on the y- axis. Poverty line is a horizontal line. Poverty gap is the gap between poverty line and y, for households below poverty line. • FGT developed a theory of poverty measures, build on Sen’s work. 1. Headcount ratio (proportion of poor in population): If = 0, 0 = / 2. Poverty gap index (average of poverty gap as a fraction of poverty line): if = 1, 1 = ∑=1 − 3. Severity of poverty index (average of square of poverty gap as a fraction of poverty line): If = 2, then 2 = 1� =1 − 2 Chapter 5: Poverty and vulnerability analysis • Other poverty measures consider multidimensionality within a period in time, or poverty over time (never poor, transitory poor, chronic poor, persistent poor). • Vulnerability summarizes poverty over time into the probability of being poor. Inverse of resilience. • Question about whether poverty traps exist. Usually build on a self-reinforcing dynamic whereby lacking enough of an asset (wealth, knowledge, health, psychological well-being, etc) makes it hard to climb out of the trap. Chapter 6: Inequality and inequity • Our tools for inequality analysis are built on the Lorenz curve, which plots cumulative % of population ranked by expenditure level (x-axis) against, the cumulative % of total expenditure (y-axis). • Poverty profile is like probability distribution function, Lorenz curve like cumulative distribution function. • 45-degree line shows perfect equality. • Gini coefficient: fraction of area between Lorenz curve and 45-degree line, compared to area under 45-degree line. • Runs between 0 (complete equality) to 1 (maximum inequality). • Income shares: income held by richest x% of population and poorest y% of population. Kuznets ratios then take ratios of these, removing units. Can have x=y, but don’t have to. Chapter 10: The economics of farm households • Farm households in developing countries: 25% of world population, 75% of world poverty. • First need to define a farm household – based on joint production, consumption and or reproduction. • The farm household model is one of the core models in development economics. • It captures labor allocation and leisure tradeoffs, alongside the role of land and capital • It provides a core model to analyze how market failures and frictions (in labor, goods, land, credit, etc), which impede the access of households to markets, can lead to behaviors and outcomes that might seem puzzling or irrational on first glance. Separability captures whether a household behaves as if its consumption and production decisions are independent, which is only possible if it is fully integrated in markets. If it is integrated, then it can set MC=MB on all margins. Chapter 10: The economics of farm households • Net buyer / net seller distinction, for interpreting welfare effects of prices. • A key question is whether family farms can compete – should we reinforce them (through policies), or encourage the movement out of agriculture? May really be about how fast. • Smallholder farmers are often highly exposed to uninsured risk. To deal with this, they can use: • Risk management: acting in advance to reduce probability and magnitude of risks. I.e., if expected impact is p*M, then try to reduce p and/or M. • Risk coping: dealing with risks after they happen. Chapter 10: The economics of farm households • The standard household model assumes unitary decision-making. • But in some contexts/decisions this may be too simplistic. Non-unitary decision-making models allow for multiple power brokers in the household. • Most common application husband and wife. • This provides another possibility for separability to be violated. In this case, the question is whether the power balance (e.g., between husband and wife) is affected by their production decisions or not. If it is, then separability may be violated (consumption and production choices intertwined).
Solved programming assignment available from Assignment Chef.
MTH3025: Financial Mathematics Project Project: Arbitrage Arbitrage is a key concept in financial mathematics. In this project, you are expected to consider some financial trading opportunities and identify, from the given data set, a mispricing that can lead in turn to an arbitrage opportunity. Then you will have to report a corresponding arbitrage strategy for returning a risk-free profit and estimate the magnitude for the expected profit, both in written and in oral form. 1 Report (20% of module mark) In the report, you will have to show the completion of three main tasks: Financial instruments. You will have to explain in your own words what is meant with the term arbitrage to someone with no prior knowledge of financial mathematics. Then, you are also expected to explain what is meant with the following types of financial instruments: – Foreign-exchange swap. – A rainbow option. – A lookback option with floating strike price. These financial instruments are not explained in the lectures. You are nevertheless expected to explain these instruments in your own words following independent con- sultation of external sources (to be duely acknowledged in your bibliography). Misprice identification. For all the four given trading opportunities, you will have to perform. the necessary calculations in order to identify a mispricing in one of them. Arbitrage strategy. You will have to describe the strategy that you can pursue to make a risk-free profit. You will have to explain why you have chosen a particular strategy, the asset(s) and derivative(s) to trade in, the investment that needs to be made at present time, and what will happen to the portfolios at the maturity date (final time). The report should be typed and checked for originality through Turnitin. The submitted file should be in PDF format. The expected length is no more than 5 pages (references excluded), using a reasonable font-size and reasonable line spacing (e.g., as the present document). Longer reports will be penalised. The marking scheme adopted is the following: Financial instruments: 30%. Misprice identification: 30%. Arbitrage strategy: 30%. quality of the report (grammar, layout, clarity of exposition): 10%. 2 Presentation (10% of module mark) You will also be expected to explain your trading strategy to the lecturer via a presentation that you have to upload on Canvas (via a separate upload with respect to the report, see corresponding Assignment). The presentation should highlight the arbitrage strategy that you would adopt to make a profit. It should explain what instruments you would prefer to trade in, and what profit you expect to make. You do not need to include trade opportunities that do not lead to a profit. The presentation should last between 5 and 8 minutes, and be aimed at a third-year student in mathematics with no knowledge of financial mathematics. The presentation will take the form. of a Spoken PowerPoint or a pre-recorded video. Your uploaded files should be readable with the tools available within the platform. Of- fice365, available via your QUB account, otherwise penalisations will be applied. When you upload your presentation please add a comment that indicate the total duration of your presentation. Specific instructions about the presentation can be found on Canvas (in due time). The marking scheme adopted is the following: Quality of the slides (fonts, formulas, tables,...): 30%. Quality of the presentation (timing, narrative, visual e↵ects, voice, ...): 30%. Quality of the technical explanations (assets to trade in, arbitrage strategy, ...): 40%. 3 Data A trader has access to the following four investment opportunities. One of them features a significant misprice that needs to be identified. Once identified, an arbitrage strategy can be devised to make a risk-free profit (see below for further guidance). The trader also has access to bonds at the UK market interest rate at 0.67%. Opportunity 1: Currency trading On a currency market, the following currency exchange rates are listed. GBP USD EUR CHF 1 GBP = 1.0000 1.2724 1.1491 1.3296 1 USD = 0.7859 1.0000 0.9031 1.0450 1 EUR = 0.8702 1.1072 1.0000 1.1638 1 CHF = 0.7521 0.9569 0.8592 1.0000 Opportunity 2: Futures for stocks with dividend Futures are similar to forward contracts. However, futures are traded on an exchange. As- sume that fair futures prices are equal to forward prices (see lecture notes Secs 4.8 and 4.9). The following financial data for the supermarket sector was available on 1 February 2021. The fair strike price for futures contracts is given for delivery of shares on 1 September 2021. All prices are given in GBX (pence). Share Value Dividend Payment date Futures Tesco 217.58 1.75 1 May 216.68 Sainsbury’s 280.75 0.75 1 March 281.10 Morrisons 245.76 1.25 1 June 245.47 Opportunity 3: Futures for commodities with storage costs The following financial data for trading in a variety of commodities was available on 1 January 2021. The fair strike price for the futures contracts is given for delivery of the commodity on 1 September 2021. All prices are given in GBP (pounds) per ounce. Storage costs are given per ounce for four months of storage and should be paid in advance. Spot price Storage cost per ounce Futures Gold 1093.22 8 1114.17 Iridium 1179.09 6 1196.41 Palladium 1032.11 5 1046.76 Opportunity 4: Option portfolio trading The following financial data for FTSE-traded companies was available on 1 February 2021. All prices are given in GBX (pence). The option price listed is for European options that can be exercised exclusively on 1 July 2021 for the stated strike price. You may assume that no dividend is payable between 1 February and 1 July 2021. Share Value Call option Put option Strike price AstraZeneca 5725 352.64 411.47 5800 Diageo 3025 197.87 264.22 3100 Unilever 4260 260.23 288.25 4300 4 Guidance about the report The report should contain the following elements: Your project number in the title. A short introduction. A first part (financial instruments) with an explanation of the term arbitrage and of the financial instruments listed on the first page. A second part (misprice identification) with an overview of the calculations carried out for all the four opportunities to identify the significant misprice in one of them (significant in this case means beyond rounding errors and leading to a risk-free profit via arbitrage above 2, see below). A third part (arbitrage strategy) in which you explain a possible arbitrage strategy, including an estimation of the expected risk-free profit. A short conclusion. A references section if deemed necessary. There is no need to include all data provided in this document in Sec. 3. The following points should be taken into account in your report: In opportunity 3, the trader can only buy or sell multiples of ounces. Regarding the first part (financial instruments) you should report the payo↵ (if rele- vant) of each financial instrument, the cost (if any) associated to the corresponding contract, and any additional information that you deem relevant and interesting to show your depth of understanding. Regarding the second part (misprice identification), you should present an overview of your calculations to prove that you have checked all the 4 opportunities, regardless where the arbitrage opportunity arises. For each opportunity, you do not need to repeat all the details of the calculations for all the cases; however, at least one representative case per opportunity should be analysed in full details. The arbitrage strategy needs to be self-financed, meaning that the trader does not need to invest their own money or assets. The trader can self-finance their arbitrage strategy constructing a portfolio by making as many financial transactions they desire at initial time. The transactions can consist of borrowing, buying, selling, exchanging and returning all the following: money (including di↵erent currencies), stocks, futures, options, bonds, or any instrument you think is relevant. However each initial transac- tion should not exceed the value of 10000 and only one transaction at most of each type can be made. For example, only 10000 can be borrowed from a bank and only once; or only stocks up to the value of 10000 can be borrowed and they can be bor- rowed only once. Initial transactions can be assumed to be made instantaneously and without fees. With the constraints above, the significant misprice that you have identified in the second part should allow you to devise an arbitrage strategy to make a profit greater than 2. You can conclude the third part (arbitrage strategy) with observations showing your critical assessment of the strategy that you laid out, for example describing the short- comings due to the assumptions made when compared to a real-world scenario. There is no need to write numerical codes to complete this project but you can certainly do that and include them in your report. However, if you wish to do so, please ensure that your codes are properly commented. The report should be uploaded via Canvas in pdf format, with automatically embedded Turnitin check for plagiarism. You do not need to worry as long as your Turnitin percentage is below 30%. Reports above this threshold will be considered on a case- by-case basis but, in general, in the past also reports above that threshold did not present genuine plagiarism issues. The lecturer will be happy to answer possible questions that you might have either via email or during the tutorials. However, notice that for what concerns the third part of the report (arbitrage strategy) no specific indication will be given. It is expected that the student should be able to devise an arbitrage strategy using the material available (notes, tutorials and homework problems, as well as additional resources). Due to the substantial nature of this assessment and the large number of students (approxi- mately 1 hour is needed to mark and give feedback to each student’s work, and approximately 80 studentes are enrolled), marks and feedback for the full project (thus including both re- port and presentation) is expected to be provided in 4 semester weeks from the deadline of the presentation — therefore by week 1 of the second semester.
COMP9414 Artificial Intelligence Assignment 1 Project Goal The course projects in this class serve to explore the microarchitectural design space of uniprocessor design parameters. This project will help you to a) learn to read the provided framework code b) see how a simple cache simulation model can be developed c) once fully functional, explore the marginal benefits of different cache hierarchies to help develop the future project Instructions 1) Copy the tarball to a CSE machine and extract it into a local directory of your choosing. You should use CSE Linux Lab machines (i.e. e5-cse-135-01.cse.psu.edu through e5-cse-135-40.cse.psu.edu). To connect to these machines remotely, you should install a VPN on your computer and then use ssh to connect to one of the machines. For more details, please refer to the CSE Student Lab access information: https://www.eecs.psu.edu/cse-student-lab-access/index.aspx. This project simulates putting the memory references and values generated through naive ( O(N3 ) ) matrix multiplication of square matrices of size N through a parameterized cache hierarchy, that is, a specific cache will be generated respectively by the arguments in each test case in Makefile. The arguments represent as below: 1st, the size of the simulated matrix 2nd, the number of matrix multiplications to perform 3rd, the name of the cache level (for example, the second-level L2) 4th, the size of this level 5th, the associativity of this level 6th, the block size of this level 7th and the subsequent, the first-level L1 cache, and its arguments. If you implement it correctly, your logic would work for all these test cases. Most of the functionality for this program has already been provided. However, five key functions needed to properly perform. caching (setSizesOffsetsAndMaskFields, getindex, gettag, writeback, fill) are currently implemented as stub functions that either does nothing, causing the program to crash if they are relied upon. Your job will be to implement these missing functionalities within the functions defined in “YOURCODEHERE.c”, and descriptions of the functionality of each function are in YOURCODEHERE.h. You will need to read through the provided framework to figure out how to properly use the “performaccess function to set local contents based on another level’s data (fill) and to write data from the local contents into the next level of the memory hierarchy (writeback). You will need to familiarize yourself with the existing functions defined in csim.c, specifically “performaccess”, and the cache structure defined in csim.h, although you are not allowed to modify them. You are likewise not allowed to modify anything other than implementing the missing functionalities within the functions defined in “YOURCODEHERE.c”. You will invoke “performaccess” in your logistic, the input argument “size” could be fixed as 8. Your project, once complete, will be able to correctly execute all tests invoked by “make test” as well as other cache and matrix configurations not present in the test list. The test list was already included in the Makefile. Only cache hierarchies with monotonically nondecreasing block sizes (in integer multiples of 8 bytes) throughout the cache hierarchy will be tested. Similarly, only cache hierarchies with monotonically nondecreasing capacity from upper to lower caches will be tested. 2) Ensure that your environment is correctly configured (e.g. with default gcc, etc.) by running “make test”. You can verify the correct initial state of your environment/files by noting the following: a) the code should compile without any errors or warnings. b) the first test case (no cache instantiated) should run to completion and match the output in the included copy of the output from running “make test” on a completed version of the program c) the second test case should quickly generate a “Segmentation fault” due to the unimplemented stub functions 3) Modify YOURCODEHERE.c — this is the only file you will be modifying and turning in. Your project MUST compile without modification to the Makefile, or any other source files. Your code will be recompiled against the other files in their original state, on CSE servers. Any reliance on additional modifications will likely result in non-compiling status under test and a zero for the project. Please note that the CSE lab machines are 64-bits (represented as localVAbits variable), so 1 word = 8 bytes. You will reflect this in your implementation. Please ensure that any code you develop on a non-CSE platform. works on the CSE servers, as the code is NOT GENERALLY PORTABLE. 4) Continue to test your project. All tests in “make test” should run to completion (expected total run time 1-2 minutes, mostly in the last test). Statistics from your output file (NMM-csim.testout) for matrix sizes
CSC 110 Project 4 Programming Languages Concepts CMPSC 461 Project 2 Due August 5, 2019 at 11:59 pm Download Dr. Racket and use it to complete the following programming exercises. Rubrics: as shown on Canvas Write a function smooth that levels a nested list and returns only one simple list (not nested). A sample run is as follows: Implement a function to satisfy a predicate Write a function multiply that multiplies two lists. You need to test your function on various lists, where lists may be empty, simple or nested lists. (multiply ‘() ‘(4 5)) returns () iii.(multiply ‘(1 (10 2)) ‘(4 5)) returns Define a Scheme function called overlap that takes two sorted lists of numbers as arguments and that returns a list of the values in common between the two lists. For example: (1 2 2 3 7) 5- Implement list-tail and list-ref (list-tail ) (list-ref ) list-tail should return the sublist of obtained by omitting the first elements. For example: Implement assoc > (define e ‘((a 1) (b 2) (c 3))) (assoc ) Note that assoc is required to use equal? to compare with the items in . > (assoc ‘a e) (a 1) > (assoc ‘b e) (b 2) > (assoc ‘d e) 0 > (assoc (list ‘a) ‘(((a)) ((b)) ((c)))) ((a)) > (assoc 5 ‘((2 3) (5 7) (11 13))) (5 7)
A3 Repo for CS6310 Description In COMP1720/6720 your major project is an interactive p5 artwork for a new-media art installation. Here’s the scenario: gallery attendees are able to walk around and observe the various works (including yours) at their leisure. If they wish, they can pause at your sketch and interact with it, but they will receive no additional guidance/instruction on what to do. Your goal is to provide an engaging user experience of roughly three minutes, but the exact nature of that experience is up to you. It might be an interactive generative artwork, it might be an interactive movie/storytelling experience—you get to choose. Theme Each year, we choose a theme for the COMP1720/6720 major project. You shouldn’t feel limited by this—you have heaps of freedom to interpret and explore the theme however you like. Your artist statement (which must be more substantial this time than for your assignments) is your chance to explain how your interactive artwork relates to and explores the theme. This year’s theme is: You can interpret this theme however you wish, as long as you fulfill the requirements below. Getting started The process is exactly the same as for all the other assignments: fork & clone the major project repo make an interactive p5 artwork which explores the theme “new” as you go, commit and push your progress to the GitLab server As in the other assignments, the major project template repo sketch.js has some (minimal) starter code, and the template index.html file includes a “back to gallery” button (because your major project will be exhibited in a virtual “gallery exhibition”). As usual, there’s a submission checklist below to help you make sure you’ve completed everything you need to in your submission. Remember to take advantage of the Git help in the FAQ. Requirements Your major project is a p5 sketch which must: allow interaction using either the keyboard, mouse, microphone, camera, or some combination of those provide an engaging interactive user experience of roughly three minutes relate to the theme in a meaningful way be suitable for public presentation, viewing and interaction—it can’t be obscene! have well-organised source code, displaying the appropriate use of functions, arrays, objects, and the techniques discussed in code & design lectures include an artist-statement.md (max 1000 words) describing your artwork include an interaction-statement.md (max 500 words) which describes how a typical user will interact with your artwork include a statement-of-originality.yml describing any inspiration/code/assets you got from other places. It’s ok to use these external sources, but your major project must contain significant new work by you—you can’t just cobble together stuff from these other places (you’ll fail if you do) include a thumbnail.png image file with the resolution 1280×720 in the top-level folder of your submission repo to use in the “sketch selection” interface run smoothly in fullscreen at the test URL on any canvas size from 1920×1080 (in the CSIT labs) to 2560×1440 (in the PK iMac labs) make sure you test it out in the labs include a “back to gallery” button in the bottom right-hand corner (this is provided in the template—so as long as you don’t remove it then you’re fine) The artist statement Your submission must include a short (max. 1000 words) artist statement. Here are some questions to help you get started: how have you interpreted/explored your chosen theme? how have you structured the experience (e.g. beginning-middle-end, or something else)? what are you trying to make the viewer think? what are you trying to make the viewer feel? what do you hope the viewer tells their friends about your artwork after they leave the gallery? Your artist statement shouldn’t just be a list of “the first screen is this, the second screen is this…”; that’s what your interaction statement is for. Instead, the artist statement is your chance to explain the deeper story you’re trying to tell and the questions you’re trying to raise through your work. There’s no strict template for the artist statement—instead, you’ll be marked on how clearly you articulate your what you’ve tried to achieve artistically in your major project. The interaction statement Your submission must include a short interaction statement (max. 500 words) which describes how a typical user will interact with your artwork. This means a step-by-step discussion of your planned interaction experience from its beginning, to its middle, and its end. For each step in the interaction experience, you should describe what a user sees, what they should understand about the meaning of what they see, how they know what to do next, and what happens when they complete this step. When we assess your interaction statement, we will compare it with your sketch and decide whether or not it is realistic. Submission process Here’s the process (again, remember the Git help screencast videos) fork the major project template repository from the CECS GitLab server clone & work on your fork of the major project template repo, regularly committing & pushing your changes to the GitLab server at the submission deadline, the latest commit pushed to the GitLab server (not on your local machine!) will count as your submission
CS 2704 – Final Project INSTRUCTIONS For this assignment, you should carry out an evaluation of an existing app (mobile or desktop app, etc.). This should be a task-based app, i.e. in which a user needs to perform. one long task (up to 30 minutes) or a set of short tasks (that may take up to 30 minutes in total). Consider looking into open-source software alternatives (e.g. https://www.osalt.com/). To evaluate the app, you must use an expert evaluation technique (i.e. Nielsen’s heuristics). The evaluation will involve collecting data, drawing up suggestions for design improvements based on your findings, and submitting a report which summarises your research process (i.e. how you conducted the expert evaluation) and results (i.e. what problems you found and how you propose to fix them). You must conduct this evaluation with yourself and two further people (must be your classmates) acting as experts. The overall objective of the evaluation is to come up with suggestions on how to improve/enhance the usability or experience of the chosen app. You do not need to implement these improvements. You need to identify them in the expert evaluation and describe them clearly in your report. For each issue identified, you need to explain why it is a problem (i.e. justifying it based on the violation of Nielsen’s heuristics) and state what needs to be changed in the app to solve it. CHOICE OF APP You can choose any app that you like, with the following constraints: 1. The experts whom you invite to test the app of your choice should not be required to create an account with this app (and provide any personal information) just for the purpose of this evaluation (unless they want to do so) and they should not be required to pay anything, create a subscription, or provide any credit card/bank details. You can tackle this requirement by creating a dummy account for your invited experts. 2. The app should be single user (or at least have a single user mode). 3. It is possible to use the app intensively over a short period of time (e.g. 30 minutes), rather being used intermittently over a long period of time so that an evaluator can observe its use (i.e. don’t test an app that would require someone to use it daily for several days or weeks). 4. When completing the tasks, the app should not require personal information from the users (e.g. if you choose a journal app, you can ask users to navigate to various parts of the app, try different functionalities but do not ask them to, say, write a personal account of something that happened to them in the past). The app can be designed for any platform, including desktop, laptop, tablet or smartphone. However, in choosing an appropriate app, please also consider the following points: 1. General hardware/operating system constraints. Since you will be testing on your fellow students, try to choose an app which would be usable/testable by most people, rather than one which requires, say, a particular combination of model of smartphone and/or operating system. You can ignore this if an invited expert will be using your device. 2. If you will be communicating with the invited experts remotely, think about the practicalities of doing so (e.g. it would be quite straightforward to ask an expert to test a desktop/laptop-based app while using Zoom and sharing their screen. Other configurations may require a bit more thought). If you are in any doubt whether your chosen app is suitable, please discuss it with your seminar tutor. PROJECT TASKS AND METHOD Key project tasks: • Review three apps which are similar to the one you chose to evaluate, reflecting on their pros and cons compared to the app of your choice, and explain why you think your choice will reveal some usability or experience issues. • Explain how you used the expert evaluation technique in your project. • Carry out the evaluation and collect data. • Analyse the data collected and highlight the key implications for redesign (suggest improvements for the app, i.e. what would need to be changed in the future edition of this app so that it becomes easier to use and/or so that it delivers a better experience?). You should describe the improvement suggestions in the form. of text, but you may also find that adding figures to illustrate your proposed improvements makes them clearer to the reader. You do not need to test these improvement suggestions (i.e. you are not required to prototype and test them). • Write your report based on the above points complying with the word limit (see “General Information” above). PROJECT REPORT Reports must include: • Introduction & Background (~25% of the report): a mini app review, reviewing the minimum of three apps which are similar to the one you chose to evaluate. In this section, explain why you think the app of your choice will reveal some usability or experience issues. • Method (~15% of the report): procedure you have followed, what data was collected and how, how it was analysed. • Findings & Discussion (~50% of the report): presentation of the results, produce a set of detailed design improvement suggestions, including a discussion of how the results inform. a better design of the chosen app. • Conclusions & References (~10% of the report): summarise your evaluation, describe limitations of the current method, and present the future work directions. Also, please include a correctly formatted full list of references. You are welcome to use a referencing style. of your choice, but please make sure it remains consistent throughout the report. You should reference all the apps that you describe, as well as the guidance on Nielsen’s heuristics. MARKING CRITERIA 70-100% – Excellent report and evaluation. The explanation on why the chosen app may reveal some issues is excellent, very clear and backed up by the background research. The method is used effectively. Findings are presented clearly, and a detailed analysis has been carried out. Some highly relevant and appropriate implications for re-design are discussed, and limitations are discussed in a way which shows reflection. References are used effectively to back up the method used and background to project. 60-69% – Very good report and evaluation. The explanation on why the chosen app may reveal some issues is generally clear. The method is used appropriately, and there is some rigour and detail in the way it is used. Some relevant findings are presented, and an appropriate analysis has been carried out on the data collected. Some convincing re-design implications are drawn from the analysis, and there is a discussion of the limitations of the work carried out. Sources are correctly referenced. 50-59% – Fair report and evaluation. There is some attempt to explain why the chosen app may reveal some issues, but this may be unclear. The evaluation method may not have been used correctly or in sufficient depth. Some findings are presented, but these may lack detail. There is some effort to analyse the data collected, but there are problems with the way this is described. Re-design implications are discussed, but they may be too brief or may not match the findings and data presented. There is little reflection on the limitations of the project. There may be missing references. 40-49% – Poor report and evaluation. There is little discussion on why the chosen app may reveal some issues, or the discussion is very unclear. There are major problems with the use of the evaluation method. There are some findings presented, but these do not match the explanation of the method or are very brief. There may be no evidence of analysis, or a very poor analysis which is not well explained. Re-design implications are either missing or not backed up by any of the findings or analysis. There are major problems with the referencing. 30-39% – Very poor report and evaluation. Key elements of the project are missing or only covered very briefly. There is no convincing overview of what the evaluation set out to achieve, and the method used is not presented in any detail. There is little to no analysis, and findings are unconvincing, or not discussed. Re-design implications are not presented in any meaningful way. There are major problems with the referencing. Below 30% – Report has very little content of relevance.
COP2513 Spring 2020 Assignment 4 In this project, you will be developing a simple Java application (textprocessor) using an agile, test-driven process involving multiple deliverables. While you will receive one grade for the entire project, each deliverable must be completed by its own due date,and all deliverables will contribute to the overall project grade. textprocessor is a command-line utility written in Java with the following specification: textprocessor allows for simple text manipulation of the contents of a file. textprocessor [OPTIONS] FILE Program textprocessor performs basic text transformations on lines of text from an input FILE. Unless the -o option (see below) is specified, the program writes transformed text to stdout and errors/usage messages to stderr. The FILE parameter is required and must be the last parameter. OPTIONS may be zero or more of the following and may occur in any order: The program writes the output to output_file_name with transformed text instead of writing to stdout. If output_file_name already exists, the program shall result in an error. Used with the -r flag and -k flag ONLY; the search of -r or -k becomes case-insensitive. Keep only the lines containing substring. The search for substring is case-sensitive, unless option -i is set. This option must be mutually exclusive with -r below. Replaces the first instance of string old in each line with string new. The search for old is case-sensitive, unless option -i is set. This option must be mutually exclusive with -k above. Adds the string suffix at the end of each line.● -n This option must be mutually exclusive with -w below. Removes all whitespace from lines. For this assignment, whitespace will count as any spaces, ” “, or tabs, “t”, in the input file. It must be mutually exclusive with -n above. While the last command-line parameter provided is always treated as the filename,OPTIONS flags can be provided in any order and shall be applied as follows: ○ Options -k, -r, -n, -w, and -s, shall be processed in this order. That is: (1) if -k is present, then the file content is filtered based on the specified parameter, using a case insensitive search if -i is present; (2) if -r is present, then replacements are performed based on the option parameters, using a case insensitive search if -i is present; (3) if -n is present, then a line number is applied; (4) if -w is present, then whitespace from the line is removed; (5) if -s is present, then a suffix shall be applied. Specifying option -i without having specified option -r or -k shall result in an error. Specifying option -k with an empty string as the parameter should keep all input lines. Specifying options -r and -k simultaneously shall result in an error. Specifying option -n with a non-integer value or an integer out of range shall result in an error. test cases using these values as option parameters. IMPORTANT: You are expected to infer the expected program behavior. using the gradescope provided information alone. Submitting an implementation that prints out the instructor-provided test inputs is unfair to your classmates and will result in a significant grade penalty.EXAMPLES OF USAGE (In the following, “↵” represents a newline character.) textprocessor -o sample.txt FILE This is the first line of the input file.↵ This is the first line of the input file.↵ stderr: nothing sent to stderr textprocessor -r 02 two FILE Some words are: “one”, “02”, and “three”↵ stdout: stderr: nothing sent to stderr textprocessor -i -r the A FILE The file↵ output file: output file not created A file↵ stderr: nothing sent to stderr textprocessor -s er FILE This is cool↵ stdout: stderr: nothing sent to stderrExample 5: input FILE: Java is a programming language.↵ output file: output file not created Java is a programming language.↵ stderr: nothing sent to stderr textprocessor -r Question Exclamation -o text -s ! -w FILE This Sentence Ends In A Question Mark?↵ ThisSentenceEndsInAExclamationMark?!↵ stderr: nothing sent to stderr textprocessor -n 8 -n 2 -s ## –s ! FILE I wish this line had a line number..↵ output file: output file not created 01 I wish this line had a line number..!↵ stderr: nothing sent to stderr
FIN225 — Nordic Society, Politics, and Culture (Winter 2026) Department of Slavic Languages and Literatures Course Description: In the global imagination, Scandinavia has become synonymous with technological progress, modern design, gender equalitarianism, social welfare, solidarity, and wellness, or simply “happiness.” This utopianism associated with the Nordic region is often presented as model for other countries to follow, as testified by the Nordic lifestyle. exports, such as hygge, lagom, sauna, and sisu, gaining popularity in North America and beyond. In this class, we will examine how the core values and cultural features of the Nordic society emerged historically from the 18th century to the present and how they were shaped by and reflected in literature, drama, film, folklore and other kinds of cultural objects. In particular, we will examine the Nordic model/the welfare state, gender egalitarianism, environmental sustainability, and ideas about family and childhood, and situate these ideas within the broader historical, political, philosophical, and social contexts. Learning goals: After a semester of active participation, students will have… · Gained a basic overview of Nordic culture and history, including the key characteristics that the Nordic countries share and how they differ. · Rehearsed their interpretative skills in analyzing cultural objects against their historical, cultural, political, and aesthetic backgrounds. · Developed their ability to analyze key concepts, mindsets, and values in the Nordic region. Organization: · This class meets in person. · The course will have a flipped classroom and will consist of both short lectures and discussions. Students are asked to complete all the readings before the class for which they are due and come to class prepared to participate actively. · All works will be read in English translation, but if you have command of any of the Scandinavian languages, you are welcome to read some works in the original. · You will have to use Quercus to access assignments, readings and receive notifications, · Classes may not be recorded without the instructor’s permission. · My primary method of communicating with you is via Quercus messages, but if you prefer to communicate via email, let me know. · You are welcome to my drop-in office hours without prior communication. If you want to see me outside of those times, email me. Required readings: · Most readings will be made available on Quercus as PDFs or as electronic resources of the university library. A few items you will have to purchase on your own. · You are expected to make notes of the readings in preparation of class discussion. For this reason, please do not listen to the works as audiobooks or read them as Kindle editions even when available, but rather read as a paper copy (and less ideally from your screen as a markable PDF). Studies show that reading on paper versus on screen is more conducive to “deep reading.” · When buying paper copies, please use the editions listed above. We often discuss a specific passage, and it is important that we all read the same translation and can locate the same page/passage quickly. · All of the items you have to buy are available on Amazon and the University bookstore, but you can also check your local independent booksellers. · Some weeks are heavier than others regarding the works load, so plan ahead and start reading the novels ahead of schedule. Items students need to purchase for this class: · Nella Larsen, Quicksand. London: Penguin Classics, 2002 (1928). ISBN 9780141181271. · Tove Jansson, The Summer Book. Translated by Thomas Teal. New York: New York Revie of Books, 2008 (1972). ISBN 9781590172681. · Henrik Ibsen, Hedda Gabler and Other Plays. London: Penguin Classics, 2020. ISBN 978-0141194578. Course work and grading: NB: The course instructor reserves the right to make periodic adjustments to the schedule as a means of addressing the changing needs sometimes required to meet the course objectives. Attendance 10 % Every class Discussion participation 15 % Every class Short responses 15 % Every class Short quizzes (4) 30 % See class schedule Final Paper 30 % Due April 8, 23:59 Winter 2026 Topic Reading to be completed before class Jan 8Introduction & Lifestyle. Branding · Michael A. Livingston, “Utopia: The Ideal of Norden and its Overseas Marketing” · Charlotte Higgins, “The hygge Conspiracy”The Swedish Theory of Love: Individualism and Social Trust in Modern Sweden (2022), 3–49. Jan 22 Gender and Nation · Ibsen, Quiz 1Through a Glass Darkly (1961) Feb 5 The Nordic Family · Andersen, “The Little Match Girl” · Kaurismäki, Girl (1990) · Henrik Berggren and Lars Trädgårdh, “Pippi Longstocking: The Autonomous Child and the Moral Logic of the Swedish Welfare State” Feb 12 Who Belongs in the Welfare State? · , Let the right one in · Karlsson, “The Vampire and the Anxieties of a Globalizing Swedish Welfare State” Feb 26 Sexual Politics Quiz 2 · Moodysson, Mar 5 Human in Landscape · Tove Jansson, Summer Book (1972) Mar 12 Environmental Sustainability & Folk Environmentalism Icelandic Folktales and Legends, excerpts · John Lindow, “Introduction” to Mar 19 Nature, Nationalism, and the SámiLaplands resaGreetings from Lappland (1982) and Family day + Reading Week, Feb 16–20 (no classes)Quiz 4 · Larsen, The Bridge (Bron/Broen), episodes 1 & 2 · Gillis & Gudmundsdottir, “Introduction: Noir in the North” (2020) Final Paper due April 8, 23:59 Course Policies: Class Etiquette: Let us respect each other’s time by coming to class ready support one another’s learning. This includes: · Coming to class on time and being engaged. · Listening to other attentively and expressing our view with respect, even when amid intellectual disagreements. · Not using electronic devices for other purposes than accessing readings and other course content. Make sure your phone is on silent and stored away. · Please note that the lectures may not be recorded or photos may not be taken of the lecture slides. As your instructor, I am committed to… · helping you meet the goals you have set for yourself. · providing you written and oral feedback and being available for all questions and concerns. · recognizing that you have other classes and responsibilities beyond this class and respecting your need for a manageable workload. · returning assignments in a timely manner. · listening to student feedback and continuously reflecting on my teaching. · answering emails within 48 hours. Assignment submission: All discussion posts and the Final Paper will be submitted through Quercus. The short quizzes will be administered in class. Grading Criteria and Rubric: The University’s grading policies can be found here. Attendance (10 %) Regular attendance is a basic requirement of this class, as a lot of the learning happens in the class discussions. You will be awarded 2 points for each attended class (all or nothing). The exit tickets collected at the end of the class will be used as proof of attendance. All students can miss one class without an absence declaration, with no penalty (i.e. you will still receive 2 points for the class). You do not need to tell me the reason, although it is helpful for me to know ahead of time that you are not coming to class. After this one pass, you will either need to provide documentation of your absence, or you will lose your attendance score for the missed class. Students can submit an Absence Declaration form. directly available to them on ACORN, anytime they are absent from academic work. No additional information or documentation is required. Students will self-declare their medical exemption and will be responsible for contacting instructors to request the academic consideration they are seeking. When you miss a class, make sure to catch up on any missed readings and slides through Quercus, acquiring notes from your fellow students first and then following up with me in office hours. Discussion participation (15 %) Your participation grade is calculated for the whole course based on your overall engagement in class discussions (which includes making significant contributions to discussions by sharing one’s analysis and interpretation and listening and responding to others’ statements). Apart from in class-participation, there are also other ways of participating, such as coming to office hours, forming student-lead study groups outside of the classroom, and emailing me about class contents and your learning. If you find speaking in class difficult, please come to see me in office hours so we can think of alternative ways. For further information of active participation in class, see the rubric on Quercus. Short responses (15 %) As part of preparation for each class, starting the second week of the class, you are expected to submit a short response to the readings on Quercus and to read the responses of the other class participants. The responses are due each Thursday at noon before the class. · Because the goal of short responses is to help us orientate towards class discussions, late submissions will not be accepted. · The responses should specifically address the weekly assigned readings and amount to about 150–200 words. I will drop three of your lowest scoring responses. · The responses should raise relevant issues and questions based on the weekly readings, expanding the discussions we have had in class. A good Quercus post might, for example, note recurring theme, pose a counterargument, or bring up a related, relevant topic or question. When discussing works of art such as literature and film, a good response will pay attention to the formal features and make at attempt to analyze in substantially. Good questions, comments, and responses will also draw links between topics and readings from different weeks of the course. Questions, comments, and responses will be evaluated based on their analysis, discussion, use of appropriate terminology, and the distinction between personal opinion and thoughtful criticism. · For more information, see the rubric on Quercus. In class quizzes (30 %) There will be four (4) short in-class quizzes, each 30 minutes long. They might include multiple choice questions or short open essay questions that ask you to explain a concept that came up in the readings, situate a passage, or synthesize class readings and discussions. Each quiz will cover all the readings, discussions, and lectures up to the day of the quiz. There will be no questions on minor details or trick questions, but you will need some details to understand the big picture. The quizzes are open notes quizzes, meaning that you can use all your notes that you have on paper. The use of any electronic devices is not permitted during the quizzes, so please put away your laptops, phones, and headphones. Any use of an electronic device during a quiz will result in 0%. In case of a missed quiz, each student has one chance to do a makeup quiz in office hours the following week, Mon & Wed 3–4pm. Final Paper (30 %) You will write one paper for this class. The required length is 7–8 pages. In the paper, you will respond to one of the several prompts handed out ahead of time. · The paper will be formatted with a standard font, 12 pt, and double-spaced and submitted via Quercus as PDFs or docx. · If citing or using secondary sources, use a standard manual for citations, such as the MLA or Chicago. · For further details on grading, see the rubric on Quercus. Late Assignments: · Late assignments will be subject to a 3% late penalty per day (including weekends), starting after the due date. · Only 48-hour extension will be granted unless there are extenuating circumstances and documentation is provided. · Day of extension requests will not be accepted. · Assignments will not be accepted 7 days after the due date. · If there are extenuating circumstances (illness, death in family) that prevent you from completing an assignment on-time you must email the instructor as soon as possible, preferably BEFORE the deadline and NO LATER than one week after the due date. In case of extenuating circumstances, the instructor and student will develop a plan for submitting assignments on time. · I will email students who are late on their assignments once, after which no follow-up reminders will be sent. · Students who have been absent from class for medical or other unavoidable reasons AND require an accommodation for missed or late term work must record their absence using the ROSI Absence Declaration. · The inability to upload your assignment to Quercus does not constitute a legitimate excuse for late assignments. In such a case, email the assignment directly to the instructor. Final Grade: Letter grades for the Midterm Exam, the Final Paper, and the entire course will be assigned as follows: 90–100 A+ 70–72 B- 57–59 D+ 85–89 A 67–69 C+ 53–56 D 80–84 A- 63–66 C 50–52 D- 77–79 B+ 60–62 C- 0–49 F 73–78 B Disputing a grade: If you wish to dispute an individual assessment mark, you should return a copy of your paper to me along with a detailed argument explaining why you think you deserved a higher grade attached to the paper. It will then be at the instructor’s discretion to decide whether to take the matter further. The final course grades are final, so if you are concerned about your performance, please make sure you come and talk to me in my office hours well before the end of the semester. Accessibility and Accommodations: Students with diverse learning styles and needs are welcome in this course. In particular, if you have a health consideration that may require accommodations, please contact the Accessibility Services Office as soon as possible. The Accessibility Services staff are available by appointment to assess specific needs, provide referrals and arrange appropriate accommodations. The sooner you let them and me know your needs, the quicker we can assist you in achieving your learning goals in this course. More info on registering with Accessibility Services: https://www.studentlife.utoronto.ca/as/new-registration
SUMMATIVE ASSESSMENT Module title: Behavioural Finance Module code: FINN3081 Assessment type: Assignment Word limit: 3000 words maximum Assignment Title Critically evaluate the behavioural explanations for major financial market anomalies, including excessive trading, bubbles, and herding, with reference to both empirical evidence and experimental techniques. In your answer: · Explain how cognitive biases, and psychological factors contribute to these anomalies. · Assess how limits to arbitrage interact with investor behaviour in these contexts. · Compare and critique at least two empirical or experimental papers, focusing on the robustness of their methodology and findings. · Apply behavioural concepts to a recent real-world case (for example meme stocks, crypto bubbles, or social media-driven sentiment), using evidence from professional data sources (for example Bloomberg, WRDS, Refinitiv, Orbis). Further guidance on how to conduct the relevant research and analysis will be discussed in lectures and workshops. Please also refer to the Assessment / Summative Assessment folder on the module’s Blackboard site for additional guidance. Learning outcomes This assessment addresses the following module learning outcomes: a. Demonstrate knowledge and understanding of questions in empirical finance linked to appropriate methodologies for their analysis. b. Explore issues in finance by using professional data sources. c. Provide students with the opportunity to develop the ability to critically evaluate academic literature relating to empirical and computational finance. Graduate attributes This assessment helps develop the following graduate attributes: Our graduates · are open minded, embrace diversity and listen to different viewpoints · are intellectually rigorous and courageous · are curious and creative · learn and grow from their experiences · are resourceful and could apply their knowledge and skills in a rapidly changing world Academic Integrity Please use the “Harvard” or the “APA” method of referencing. The Guide “Cite Them Right” provides detailed guidance on the relevant referencing conventions, both for in-text citations and the compilation of the list of references/bibliography. Citations must clearly show whether you use the authors’ actual words (direct quotes) or whether you summarise or paraphrase an argument they make (indirect quotes). When you read about an author’s arguments or findings, but you have not read the original source, you need to clearly identify this as a secondary quotation and you are not permitted to list the source you have read about (but not read yourself) in the bibliography. You must comply with the guidance on the use and referencing of generative AI explained in the Departmental Generative AI Policy here. As generative AI provides a different reply to the same prompt, students must provide an appendix of the prompts and the responses when generative AI is used. For each prompt or series of related prompts, students must establish a separate appendix. The AI response should be provided in form. of a transcript. of the text or screen shots of graphs or pictures. If you are conducting data analysis or empirical modelling, you must correctly identify the use of the statistical packages (such as Stata, Excel or MPlus) and databases (such as WRDS, Bloomberg) in the description of the analysis you conduct and the data you use. Students suspected of academic misconduct will be dealt with according to Business School and University guidelines. Marking guidelines Performance in the summative assessment for this module is judged against the following criteria: · Relevance to question(s) · Organisation, structure and presentation · Depth of understanding · Analysis and discussion · Use of sources and referencing · Overall conclusions Your assignment should be well organised and structured, using headings and sub-headings as appropriate to indicate topics discussed. You should carefully consider the differences in the quality and validity of different sources of information, including in the context of the use of generative AI. The assignment will be assessed based on the grade descriptors. DUBS Grade Descriptors for Undergraduate Programmes Class Mark (%) Descriptor First 86-100 Exemplary. Exceptional work showing insight into the topic; reflects a complete grasp of knowledge and understanding. Such work is only rarely encountered. 76-85 Outstanding. Comprehensive knowledge of the topic, showing depth of understanding with evidence of judgement in selection and critical analysis of relevant material. Logically structured and clearly written. 70-75 Excellent. Detailed knowledge of the topic, with evidence of judgement in selection and critical analysis of relevant material. Well written with good structure. Minor errors acceptable if compensated by excellence in other areas. Upper Second 65-69 Very Good. Displays good knowledge and thorough understanding of the topic with evidence of broader understanding informed by wider reading. Less critical grasp of the subject than evident in a First Class answer. 60-64 Good. Reasonably good knowledge and understanding, but little evidence of critical assessment or analysis. Coherent presentation but less well-structured than seen at higher grades. Lower Second 55-59 Adequate. Sound general knowledge of the subject as taught but lacks evidence of broader understanding. Presented in a satisfactory framework with relevance to the topic retained throughout. 50-54 Fair. Adequate, except that the work may be rather thin or unimaginative, missing some key points or lacking in clarity. Third 45-49 Weak. Exhibits defects such as: · factually correct, but at an elementary level · or a narrow selection of material with significant omissions · or significant errors of fact or understanding · or muddled; lacking cohesion and direction, or a misguided selection of material. 40-44 Poor. Typically includes several and sometimes significant defects and is thus barely acceptable. May include very short answers that nevertheless include key points. Fail 35-39 Very poor. A very thin piece of work containing evidence of only rudimentary knowledge of the topic. 30-34 Extremely poor. The work demonstrates little relevant knowledge and/or understanding of the subject. 20-29 Clear fail. Work that misses major elements of the knowledge base. Deserves recognition for making an effort to answer the question or address the essay title, but shows very little evidence of knowledge or understanding. 10-19 Serious fail. Significant inability to engage with the question or essay title. Marks are awarded within this range for overall presentation, the odd relevant word in context but negligible evidence of knowledge or understanding. 0-9 Outright fail. Work of very little or no value, or disqualified due to lateness, plagiarism or other disciplinary offences. Word limit Written assignments must not exceed the word count indicated above. The word count should: - Include all the text, including title, introduction, in-text citations, quotations, footnotes and any other item not specifically excluded below. - Exclude diagrams, tables (including tables/lists of contents and figures), equations, executive summary/abstract, acknowledgements, declaration, bibliography/list of references and appendices. However, it is not appropriate to use diagrams or tables merely as a way of circumventing the word limit. If a student uses a table or figure as a means of presenting his/her own words, then this is included in the word count. Students must report an accurate word count on the first page of their assignment. Examiners will stop reading once the word limit has been reached, and work beyond this point will not be assessed. Checks of word counts will be carried out on submitted work, including any assignments or dissertations/business projects that appear to be over-length. Checks may take place manually and/or with the aid of the word count provided via an electronic submission. Where a student has intentionally misrepresented their word count, the School may treat this as an offence under Section IV of the General Regulations of the University. Extreme cases may be viewed as dishonest practice under Section IV, 5 (a) - (x) of the General Regulations. Format Assignments should be typed, using 1.5 spacing and an easy-to-read 12-point font. Appendices In addition to appendices for the referencing of AI prompts, there might be other instances, where it may be appropriate to present material which does not properly belong in the main body of the assessment but which some students wish to provide for the sake of completeness in an appendix. Any such appendices will not have a role in the assessment – the examiners are under no obligation to read appendices, and they do not form. part of the word count. Please note that your assignment and any appendices must all form. part of the same electronic document, since only one file can be submitted using Blackboard. Submission instructions Your completed assignment must be uploaded to Blackboard no later than 12noon (UK time) on 16 January 2026. The submission title for this assessment should follow this format FINN1234_Z0****** (using the FINN-Code of your module and where Z0****** is your Anonymous Candidate Code). It is your responsibility to back up your work. You should back up your work on more than one device. A penalty will be applied for work uploaded after 12noon as detailed in the Late submission policy. You must leave sufficient time to fully complete the upload process before the deadline and check that you have received a receipt. At peak periods, it can take up to 30 minutes for a receipt to be generated. Feedback and marking Students will receive individual written feedback on the assignment and provisional marks by 13 February 2026 via the Blackboard submission site. Students’ summative marks are subject to rigorous quality assurance practices. This includes moderation of marking for each module, in which a second marker reviews a sample of assessments. The External Examiner for the programme then reviews a selection of assessments across all modules honours level. Finally, all marks are formally approved by the Board of Examiners. The marks remain provisional and potentially subject to change, until the exam board has confirmed them.
