FIT9137 ASSIGNMENT 1 - Computer Architecture Weeks1-3 ular,the .Theformatofthestudentsubmissionwillbeareportansweringquestionson memorymanagementtechniquesandarecordedvideodemonstratingthe understandingofVonNeumannarchitectureusingMARIE 1 3 gilatedValue20ofyourtotalmarksfortheunitDue DateWeek-5:11:55 pm Friday, 4April 2025 (Melbourne local t ● n.AssessmentCriteriaAllocatedmarksormarksbreakdownpertaskisgivenintheinstructionbelow ● lmarks per ( ). , marks ● markofzeroandnoassessmentfeedbackwillbeprovided ● xceedstheyondNOTbe mar ● such submissions will re It is the student's responsibility that the submitted video file can be opened on a standard Windows computer (without requiring specialised software), and that the images and texts shown in the video are understandable/readable (in English). If the video file cannot be opened, you will receive zero marks. After making a submission ( finalising it), we recommend you to download your submitted file and check that it opens and runs properly. Once you finalise your submission, you will mance.st submission.
Analytics Challenge: Store24 Instructions Please complete the following problems and submit a file named challenge__store24A.R using the Store24A data (Store24A_data.xls) available on CourseSite. Remember: • Do not rename external data files or edit them in any way. In other words, don’t modify Store24A__data.xls. Your code won’t work properly on my version of that data set, if you do. • Do not use global paths in your script. The directory structure of your machine is not the same as the one on Gradescope’s virtual machines, so it will won’t look in the right place. Data will always be stored in the base directory unless otherwise noted, so I suggest setting up your code to run like that. One way to do it is to use Rprojects or Rmarkdown files. You could also use setwd() interactively in the console, but do not forget to remove or comment out this part of the code before you submit. • Do not destroy or overwrite any variables in your program. I check them only after I have run your entire program from start to finish. • Check to make sure you do not have any syntax errors. – Tip: before submitting, it might help to clear all the objects from your workspace, and then source your file before you submit it. This will often uncover bugs. Packages You have access to the following packages. Do not load any other packages or your code may not function. If feel you need another package, let me know why and I may add it. library (dplyr) library (tidyr) library (ggplot2) library (stringr) library (readxl) library (modelsummary) Question 1 • Run a regression with profit as the dependent variable and manager tenure as the only independent variable • I will check that there is an object called “lm_ManagerTenure” and that the fitted coefficient are correct Question 2 • Run a regression with profit as the dependent variable and crew tenure as the only independent variable • I will check that there is an object called “lm_CrewTenure” and that the fitted coefficient are correct Question 3 • Run a regression with profit as the dependent variable and both manager and crew tenure as the only two independent variables • I will check that there is an object called “lm_BothTenure” and that the fitted coefficient are correct Question 4 • Run a regression with profit as the dependent variable and all control variables in the Store24A data (MTenure, CTenure, Pop, Comp, Visibility, PedCount, Res, Hours24) • I will check that there is an object called “lm_AllControl” and that the fitted coefficient are correct Question 5 • Run a regression with profit as the dependent variable and, in addition to all control variables in the Store24A data (MTenure, CTenure, Pop, Comp, Visibility, PedCount, Res, Hours24), include a squared term for manager tenure. Do not create a new variable for MTenureˆ2. Instead, add ‘I(MTenureˆ2)’ to your formula. • I will check that there is an object called “lm_AllControl.M2” and that the fitted coefficient for ‘I(MTenureˆ2)’ is correct Question 6 • Add all of your regressions to the same model summary table. You can use the code below. Feel free to tweak the parameters to add or remove information. model_list
Electrical Energy Systems M - Technical Essay Students on M level courses are required to show a deeper understanding of course material than level 4 students, and to be able to put that material into an industrial context. In order to show this deeper level of understanding, 20% of the final mark for Electrical Energy Systems M will result from the writing of a Technical Essay on an Energy topic. Technical Essay Assignment Considering the Net-Zero context, explain and discuss the following topics on Smart Grid (choose any one topic) from a technical perspective: • Variety of generation options (distributed generation) - focusing on the renewable power generation itegration • Demand-side response (electric vehicles charging and V2G) - focusing on low- carbon demands • Power outages and quality issues (protection / power conditioning) - focusing on how net-zero is going to affect the power quality • Electricity market (electricity trading practices) - focusing on the changes of market to fit for net-zero requirements • Self-healing (microgrids) - focusing on the carbon emission aspect of microgrids • Accommodates all generation and storage options (energy storage management) - focusing on low-carbon energy storage technologies • Optimises assets and operates efficiently (ICT and smart meters) - focusing on optimsing carbon emission. In your response include references to general low-carbon and Smart Grid concept and the technical topic you’ve chosen. Your report can be a technical review of a problem, a solution, or a combination of the two. Use annotated sketches and diagrams to illustrate your response. Logistics You must upload an electronic copy of your written Essay and this will become your final version once the ‘submit’ button is pushed. Please use your student ID in the filename of your submission, along with a electronically signed Declaration of Originality.
P8483 Application of Epidemiologic Research Methods Homework 7 Due Monday March 31 at 11:59 pm to CourseWorks. No late assignments accepted. Any assignment uploaded past the due date will not be graded. NOTE: ONLY .SAS files will be accepted for upload. Instructions for all homework assignments. · Each student must submit their own SAS EDITOR FILE onto Courseworks by the deadline specified on the front page of the homework assignment. · Please title your SAS EDITOR FILE as follows: o Example: “MRL2013_HW7.sas” · At the top of your SAS EDITOR file please put your first name, last name, and uni in a /*comment block*/ · Points will be deducted if the SAS code handed in does not run without errors from beginning to end. Introduction PLEASE USE THE SAS DATASET “Homework_7.sas7bdat”, available in RAW DATA/Homework on SAS On Demand. For this assignment, you are interested in looking at the effect of sedentary behavior. on cognitive function in older adults using NHANES 2001-2002 data. You hypothesize that gender, education level, and body mass index are potential confounders of the sedentary behavior—cognitive function association. Two of the variable definitions (from NHANES) are below. Refer to the NHANES documentation for other definitions. CFDRIGHT Number correct (range 0 to 100) on a cognitive functioning score DMDEDUC (educational attainment) 1 = less than high school, 2 = high school diploma (including GED), 3 = more than high school, 7 = refused, 9 = don’t know, . = missing PAD480 Over the past 30 days, on a typical day how much time altogether did {you/SP} spend on a typical day sitting and watching TV or videos or using a computer outside of work? 0 = less than 1 hour 1 = 1 hour; 2 = 2 hours; 3 = 3 hours; 4 = 4 hours 5 = 5 hours or more 6 = none 77 = refused; 99 = don’t know; . = missing Assignment 1. Create a copy of the dataset “homework_7” excluding patients with “refused”, “don’t know”, or “missing” responses to the educational attainment measure (variable DMDEDUC). Store it in the WORK library · You should have 1,551 observations. Please let us know you confirmed this in the comments (and tell us how you confirmed this)). · Produce a histogram of your outcome variable (CFDRIGHT) and comment on whether you think it is approximately normally distributed. 2. You decide to use the variable PAD480, which is a self-reported measure of time spent in front of the television or computer, to measure a construct of “sedentary behavior.” Create a categorical variable named SEDB that meets the following criteria: SEDB = 0 if participant reports one hour or fewer of time spent on a typical day sitting and watching TV or videos or using a computer outside of work SEDB = 1 if participant more than one hour of time spent on a typical day sitting and watching TV or videos or using a computer outside of work Create and apply an appropriate format to this new variable. Check your work! Use PROC FREQ to confirm (with code!) that all PAD480 variable values were applied to the correct category of new variable SEDB, and no variable values were mistakenly missed. 3. Use PROC TTEST to report the mean difference (and APPROPRIATE 95% CI; check the “equality of variance” test to know which 95% CI to report) in cognitive functioning score (variable CFDRIGHT) between those with different levels of the binary variable SEDB you just created. Interpret the mean difference and 95% CI in a sentence. If you were using an arbitrary 2-sided alpha of 0.05 at a cutoff against which to declare “statistical significance,” what would you conclude? 4. Use PROC REG or PROC GLM to do the same thing. Interpret the mean difference and 95% CI in a sentence. If you were using an arbitrary 2-sided alpha of 0.05 at a cutoff against which to declare “statistical significance,” what would you conclude? 5. How much of the total variability in CFDRIGHT is explained by its linear relationship with binary variable sedentary behavior. (SEDB)? Report where you got this information from. 6. Compare your crude estimate from Question 4 with a “fully adjusted” measure of the association between SEDB and CFDRIGHT after adjusting for age (variable RIDAGEYR) and educational attainment (variable DMDEDUC, treated as an unordered categorical variable) as sources of potential confounding. In a short paragraph, compare the crude (and 95% CI) to the fully adjusted (and 95% CI), tell me whether age and educational attainment are confounding the association between SEDB and CFDRIGHT, and whether after adjustment for age and DMDEDUC is there still a relationship between SEDB and CFDRIGHT? Show your work and tell me where you got the answer. Be sure to report all relevant regression parameter estimates and 95% CIs around these parameter estimates. 7. How much of the total variability in CFDRIGHT is explained by its linear relationship with SEDB, ridageyr, and DMDEDUC? Report where you got this information from. 8. From a causal perspective, can you interpret the partial regression parameter estimate for variable “DMDEDUC” in the model you used in Question 6? If so, interpret it. If not, tell me why you can’t interpret it. 9. Repeat question 6 with a different operationalization for your sedentary behavior. variable. This time, create an ordinal categorical variable with the lowest order corresponding to a response of “none” to variable PAD480, then next-lowest corresponding to “less than one hour,” etc. all the way to the highest category of “5 hours or more.” Report and interpret the crude estimate and 95% CI, the adjusted estimate and 95% CI. What do you conclude about the association between this operationalization of sedentary behavior. and CFRRIGHT score after adjusting for age and education? Which model do you prefer (the adjusted Question 6 model or the adjusted Question 9 model)? Why?
ACCT2343 - Accounting Data Analytics and Visualisation Individual Assessment 3 (Part A) Due Date: Monday 14 April 2025 5:00PM Overview This final assessment, Assessment 3 - Part A (30%), consists of two sections as outlined below. The assessment is designed to evaluate your analytical and storytelling skills, based on the marking criteria provided. A rubric is also available to guide your work. Section 1: You are required to independently source a large dataset for analysis using Tableau or Power BI. The dataset should contain minimum 3,000 rows and can be collected from any accessible and relevant source available online. Superstore dataset and other datasets used in our workshops (eg Slainte) cannot be used. There will be no step-by-step instructions for this analysis. You are expected to identify and create a narrative (story) based on your data in Tableau Story and determine the appropriate dashboard with four visualisations to support that narrative. The story points in Tableau Story and accompanying visualisations should be presented in a written Word document. Section 2: In this section, you will create a 5-minute video presentation of your data story, tailored to a specified audience. The video should not simply be a reading of your written narrative from Section 1. Instead, you are expected to present your story creatively and engagingly, using visual and verbal elements to communicate the insights from your data analysis in Tableau Story. The video should effectively translate your written story into a dynamic format, making appropriate use of visual aids, such as the data visualisations developed in Section 1. You may use any video recording software or platform. of your choice. Your talking head should be clearly visible in the entire 5-minute video presentation. Assessment Details & Requirements Section 1 (15 marks): You are responsible for independently sourcing a large dataset consisting of minimum 3,000 rows from online sources. No dataset will be provided. Superstore dataset and other datasets used in our workshops (eg Slainte) cannot be used. You must thoroughly describe your dataset by answering specific questions (see below for further instructions) and use your dataset to create at least four (4) visualisations for a dashboard and story using either Tableau or Power BI (your choice). Based on your analysis and the visualisations you have developed; you will write an insightful story with a clear link to an accounting in any business decision-making aspect. Accounting must feature in at least one visualisation and in the narrative itself. You can connect accounting (from any domains that we covered in this course such as managerial, financial, audit, or taxation). In addition, you can also connect accounting aspect with any other broader issues like ESG, SDGs, sustainability, climate change, environmental management, or other human rights related issues. The objective is to find an interesting and meaningful story that can be told through the data analysis and visualisations you have created. You must submit both the written story (including the four visualisations and story) and answers to the dataset questions in a Word document (1,000 words), as well as your dataset in Excel and Tableau Packaged Workbook (.twbx) or Power BI workbook. Please include the following sections in your written report (note: dashboard visualisations and reference list do not count toward word count). 1. Introduction and Background. Discuss, in brief, your motivation for this dataset. Please include any sources that inspired you picking your topic. Give context and relevance. What do you want to learn and accomplish? What questions do you want to answer? How will your project benefit others? 2. Datasets. Describe the dataset(s) that you are using (you need collect at least one dataset). For the dataset include: • Who collected the data? Who funded the project that the data came from? Other important information about the dataset? • Why was the data created and for what purpose? • What is the timeline or lineage of the data? • Define and describe the variables included in the dataset. • How large is the dataset (cases, how many and what are the variables)? • What locations are included in the dataset? • Describe how the datasets will help you achieve your goals/questions that posed in your introduction, including any limitations. 3. Data Story. The weekly exercises and examples shown in our workshops (Weeks 1-12) can help you craft your data story. • Create at least four (4) visualisations for a dashboard and story in Tableau or Power BI. Please include different types of visualisations (tree map, bar, line chart etc.) • For each visualisation: o Tell the reader which data set (if using more than one) each variable comes from, the units (or categories) of the variable, and any other additional information. Please explore and reference accompanying documentation! Are there any limitations in the data set? Are there any definitions to share? o As mentioned above, make sure that your variables are clearly defined. o Please paste these visualisations in your document at appropriate points in your story/narrative. o Demonstrate how the visualisations addresses your question/goal. • Compose a story/narrative that describes your visualisations and tells your audience a story that meets your goals stated in your Introduction section. • Please apply the concepts, principles, frameworks that we have covered in this course. 4. Summary and Conclusions. 5. References. Please use quality references for in-text citations and end-reference list in your report. 6. Supporting Resources. Please see some references provided below as supporting resources for your dataset collection. Section 2 (15 marks): While data stories provide meaning and insights from analytics and visualisations, video stories add depth and engagement to these insights. For this task, you are expected to create a 5-minute video presentation for a target audience of board members and senior executives of an imagined organisation, based on the dataset you collected in Section 1. The video should not simply repeat the written story from Section 1 but should transform. it into an engaging, dynamic format that clearly conveys the deeper meaning behind your data analysis and the insights derived. The goal is to make the data accessible and meaningful, highlighting the key actionable insights that support decision-making. After successfully submitting your data analytics and visualisations to your manager (Section 1), you now have approval to present your findings to the board of directors and senior executives for their consideration. While these board members and senior managers are highly knowledgeable in their business domains, they may not have the technical expertise to fully grasp the complexities of your dataset, analytics, and visualisations. What they care about is how your analysis can help them make informed decisions. Your task is to present the data and insights in a succinct, digestible, and engaging manner through a 5-minute video presentation. The video should demonstrate how your findings address the question or business problem you set in Section 1 and the actionable insights they can use. To be successful in this assessment, you are encouraged to use Tableau Story (for submission using Tableau) or presentation slides (for submission using Power BI) that incorporate visual aids, such as the visualisations you developed in Section 1, to support your narrative. Your presentation should be tailored to the appropriate audience and use storytelling techniques to create a cohesive and engaging narrative. This approach will ensure that the board of directors and senior executives can clearly understand the key insights and take appropriate actions based on your analysis. You may use any video recording software or platform of your choice to create this presentation. Some supporting resources are provided below for Section 2. You can do research to find more relevant and appropriate tips and tools. To help you prepare for your video presentation, the following guidance and suggestions outline an effective structure, tips for recording, and recommended tools. Your talking head should be clearly visible in the entire 5-minute video presentation. Suggested Structure of the Video: 1. Explain the question(s) or business problem(s): o Begin by introducing the key question(s) or business problem(s) that your analysis seeks to address. 2. Incorporate your visualisation(s) into a visual narrative: o Use the visualisations from Section 1 to guide your audience through your data story. Ensure the visualisations are clear and relevant to your narrative. 3. Provide recommendations with actionable insights: o Based on your analysis, offer concrete recommendations or solutions. Highlight actionable insights that the board of directors or senior executives can use to inform. decision-making. 4. Provide a conclusion: o Summarise your key findings and recommendations in a clear and concise manner. 5. Include references: o Where relevant, cite any sources or references used. You may include a slide at the end of your presentation for references or appendices. 6. Use and number of slides: o There are no strict guidelines on the number of slides. As a general rule, aim for a 5-minute video presentation but use your discretion in balancing the visuals and content. Supporting Resources for Section 1 (Data Collection & Analysis, Visualisations, and Written Report with a Narrative Data Story) Try to locate potential sources of relatively large dataset (with at least 3,000 rows) that are suitable for either Tableau or Power BI. You are allowed to use more than one dataset to prepare your visualisations for your storytelling. Please remember to check the terms and conditions associated with each dataset to ensure that it can be used for your assignment purposes. Remember that before using any dataset, you should understand its limitations and restrictions and ensure that it is suitable and legal for your intended use. You should also preprocess and clean the data before inputting it into Tableau or Power BI, to ensure it's in the right format and doesn't contain any errors or inconsistencies. Here are a few sources where you can find large datasets containing accounting variables that you can download for free and use in Tableau and Power BI: Kaggle: Kaggle is a platform for predictive modelling and analytics competitions. They host datasets in various formats, including CSV and SQL datasets, which can be integrated with Tableau or Power BI. There are several accounting and financial datasets available on Kaggle, contributed by users and organisations around the world. data.gov.au: This is the central source of Australian open government data. You can search for various datasets including financial and accounting related ones. Data.gov: This is the home of the U.S. Government’s open data. You can find federal, state, and local data, tools, and resources to conduct research, build apps, design data visualisations, and more. You can filter the datasets by topic or category to find accounting and finance related datasets. https://discover.data.vic.gov.au/dataset/: Data.Vic is the place to discover and access Victorian government open data. Australian Bureau of Statistics (ABS): ABS is Australia's national statistical agency, providing trusted official statistics on a wide range of economic, social, population and environmental matters. CSIRO Data61: CSIRO’s Data61 is Australia’s data innovation network that transforms existing industries and creates new ones through the application of science and technology. They have a range of datasets available, some of which are related to finance and economics. UCI Machine Learning Repository: This repository maintains a collection of databases, domain theories, and data generators that are used by the machine learning community. Although not specifically focused on accounting, you may find some financial datasets here. Google Dataset Search: This is a search engine from Google that helps researchers locate online data that is freely available for use. You can search for "accounting datasets" or "financial datasets" to find data relevant to your needs. EU Open Data Portal: This portal provides access to open data published by EU institutions and bodies. You can find various types of datasets including those related to economics and finance. World Bank Open Data: This is a comprehensive resource for global development data. The World Bank's Open Data initiative provides access to financial, economic, and various other datasets. Quandl: Quandl offers a vast collection of data from hundreds of publishers. It provides financial, economic, and alternative data that can be used in various data analytics tools. Some data are free, but for some others, you need a subscription. Financial Modeling Prep API: This provides real-time and historical stock data, as well as other financial data. It’s free to use up to a certain limit. Supporting Resources for Section 2 (5-minute Video Preparation) Things to Avoid in the Video: • Presenting verbatim from your written report: Your video should distill the essential insights from Section 1, rather than reading the full report. • Overly technical explanations: Avoid diving into the technical details of your dataset, analytics, or visualisations. Tailor your presentation for the board members and executives, who are more interested in insights and implications than in the technical process. • Exceeding the time limit: Ensure your video adheres to the 5-minute time limit. Brevity and clarity are essential. Tips for Recording Your Video: • Record in a quiet space: Ensure that your recording is free from distractions or background noise. • Test audio and video quality: Do a test run to verify that both audio and video are clear. You may want to seek feedback from peers. • Readable visuals: Make sure that any visuals or text used in your presentation are large enough to be clearly visible. • Dress professionally: Remember that your audience is composed of board members and senior executives, so dress appropriately for the occasion. • Engage with your audience: Be aware of your gestures, eye contact, and facial expressions, as they enhance engagement and help convey your message.
CENG0005 - Physical Chemistry - Project 2025 Consider the following system of a reactions in series and parallel reactions: A series of kinetic experiments of the above reaction system were carried out in a Continuous Stirred Tank Reactor (CSTR). In all experiments the feed stream solution contained only component A at concentration 1.000 mol dm -3. At each experiment the space time (mean residence time) changed by using different feed flow rates. At the outlet it was not possible to measure the concentration of the intermediate B. The measured outlet concentrations ofthe other reaction components (Ci,out) are shown in the following table: Experimental Run Space time [min] CA,out (mol dm-3) CC,out (mol dm-3) CD,out (mol dm-3) 1 0.2 0.703 0.084 0.034 2 0.4 0.587 0.168 0.067 3 0.6 0.518 0.235 0.094 4 0.8 0.470 0.287 0.115 5 1.0 0.434 0.327 0.131 Table: Experimental Data a) For each experimental run, calculate the net formation rate (Ri) of each reaction component, A, B, C, D in mol dm-3 min-1. Also, for each experimental run calculate the reaction rate (rj) of each of the three reactions (1: A → B, 2: B → C, 3: B → D,). Derive equations for RA, RB, RC, RD and r1, r2, r3. [15] Present the calculation results in the form. of tables. [15] b) Assuming power law kinetics and performing linear regression, estimate the reaction orders and kinetic constants (reaction rates constants) of the three reactions. Explain the method of estimation, presenting the kinetic equations. [10] Tabulate your calculation results. [15] Present the regression plots. [15] c) Consider now the following reaction system, where reaction component C reacts to form D too: How could the reaction rate of each of the four reactions be estimated from measurements ofthe concentrations ofthe four reaction components, A, B, C and D? [10] d) For the reaction system in question c, whose kinetics for all reactions is unknown, formulate the mole balance equations for each reaction component in the CSTR. Treat the kinetics of the four reactions as unknown. Write the equations for the net formation rates (Ri) of each reaction component, A, B, C and D, as function of the rates ofthe four reactions (rj). Use either one of the above systems of equations in this question d to explain your answer in question c. [20]
Midterm 2 SP22 Econ 4130--Cooper Part 1—Multiple Choice: Choose the 1 best answer, 2 points each. 1. In Guns, Germs, and Steel, Episode 2, did Ataxalpa, the Incan leader, underestimate or overestimate the threat of the conquistadores? A. Underestimate B. Overestimate C. Both D. It was unclear 2. What advantages did horses provide the Conquistadors? A. Speed: could run down the Inca in the open B. Platform. for battle C. Intimidation as Inca had never seen such powerful beasts D. All of the above 3. Which of the following is NOT an example of a poor French domestic economic policy? A. The College of the Fishery B. Over-regulation C. Sales of offices D. All of the Above were poor economic policies of the French Crown. 4. Which of the following was NOT a way the Spanish repudiated their debt? A. Extended the term of bonds B. Suspended interest C. Wrote down principle D. All of the above were ways they repudiated the debt 5. Which of the following was NOT given as a reason for the Great Divergence? A. A series of calamities resulted in the loss of Chinese Iron making B. Chinese Mines were dry, which meant mining coal was avoided; meanwhile British mines were wet leading to the invention of the steam pump/engine. C. Chinese stoves were less efficient than those in Europe D. All of the Above were reasons for the Great Divergence 6. Why was the Price Revolution bad? A. Erosion of value of wealth holding B. Decline in purchasing power of wage earners C. High prices for food D. All of the Above 7. According to Nathan Nunn the Atlantic Slave trade had the following effects EXCEPT A. Increased cooperation of diverse groups B. Lower government development in the 19th century C. Lower per capita incomes in the 20th century D. All of the above were cited as effects by Nunn 8. In the North and Thomas' "Economic Theory of the Growth of the Western World", which of following is NOT an example of things that decreased transaction costs? A. Less piracy due to stronger navies. B. Trade Networks C. Lower populations due to the plague D. All of the Above resulted in decreased transaction costs 9. Which of the following was the least developed during the Renaissance? A. Germany B. Italy C. Russia D. Sweden 10. Which of the following was a reason why Renaissance Christianity fostered growth versus classic beliefs? A. Anthropocentrism B. God seen as rational C. Spirits not seen as enmeshed in nature D. All of the Above 11. Which innovation allowed for iron to be smelted with coal instead of charcoal? A. The Flying Shuttle B. The Mule C. Cort's Furnance D. Watt's Engine 12. Which of the following was NOT a result of the New Husbandry? A. Increased divorce rate B. Better fed livestock produced more manure C. Fallow was eliminated D. Soil renewed by nitrogen fixing crops 13 Which of the following was not a way in which Britain's geography favorable for industrialization? A. Accessible deposits of iron and coal B. Being an island isolated them from the land wars of the rest of Europe. C. Favorable conditions for growing cotton. D. Good access to water transportation. 14. Which of the following is a reason why double entry bookkeeping was important? A. Standard way of evaluating the financial position of a firm. B. Necessary for the development of credit markets. C. Allowed for absentee ownership and thus larger firms D. All of the Above 15. What do data on wages during the Industrial Revolution show? A. Wages grew throughout the First Industrial Revolution. B. Wages fell during the First Industrial Revolution. C. Wages did not rise until late in First Industrialization D. There is no data available. Part 2—Short answer: 5 points each. 1. In the dual sector model discussed in class what industries were in the traditional sector during the 1st IR? What industries were in the modern sector? If 90% was initially in the traditional sector and it grew at 2% per year, and 10% was in the modern, growing at 5% per year, what percentage would be in each sector after 15 years (table below)? What would be the growth rate of the entire economy in that 15th year? 2. Using (thoroughly labelled) graphs, show how the Consumer Revolution led to the Industrial Revolution and explain the role of the “Industrious Revolution”. Part 3—Critical Thinking: 1-10 point essay. Rubric on next page. Based on our study of the First Revolution, what 2 policies would you recommend to a country stuck in persistent poverty which has not yet industrialized? Defend your answer as to why these policies are important with details from the course.
CSDS 345: Programming Language Concepts, Written Exercise 2 due Sunday, March 30, 2025 Problem 1: Consider the following Java code (assuming Java allows methods to be declared inside other methods: public class AClass { private static int a = 10; private static int b = 20; public static int bmethod(int y) { int b = 30; public static int dmethod(int z) { int a = z; return a + cmethod(z); } return a + b + dmethod(y); } public static int cmethod(int x) { int a = 40; if (x == 0) { return a + b; } else { return bmethod(x-1) + a + b; } } public static void main (String[] args) { int b = 2; System .out .println(cmethod(b) + a + b); } } What is the value printed by the System .out .println statement when the main method is run if: a) Java uses static scoping b) Java uses dynamic scoping Be sure to trace your reasoning and justify your answer! Problem 2: Does Java use strict name equivalence, loose name equivalence, or structural equivalence when determining if two primitive types are compatible and/or equivalent? What about for non-primitive types? Give examples to justify your answer. Problem 3: Consider the following program written in a C-like language. What is the contents of values if we use: a) call-by-value b) call-by-value-result c) call-by-reference d) call-by-name ? If there is more than one possible answer, list all possibilities. Be sure to trace your reasoning and justify your answer! type coord = struct {int x; int y; int z}; function rotate(coord[] a, coord b) { if (a[0] .x >= a[1] .x) b.y = a[0] .y; else b.y = a[1] .y; if (a[0] .y > a[1] .y) b.z = a[0] .z; else b.z = a[1] .z; if (a[0] .z
CSC2141 Project Part 3 Designing and Building Your Database Due date: Monday, April 7, 2025, 11:59PM Overview and Grading In the first part of the course assignment, you assembled a dataset comprising at least four tables that will be used for design and query purposes. In the second part you designed and created your database and provided a few basic database queries. • Part 3a is optional and only needs to be completed if you wish to replace your grades on Parts 1 and 2 of the assignment. If your score on Part 3a exceeds the aggregated score of Assignment Parts 1 and 2, the Part 3a score can replace this 15% of your final grade. It is scored out of 30. • Part 3b is mandatory, is scored out of 25 and worth 10% of your final grade. Please refer to the main assignment document for general rules and guidelines. The assignment components are as follows. Details are provided on the following pages. Read the rubric at the end! It outlines the expectations. 3a (optional): A single PDF file with the following information 1. Introduce and describe your dataset 2. Show the internal model of your database 3. Show a dependency diagram of each table in your database, explaining why each table is in 3NF 3b (mandatory): A .zip file or .tar.gz archive containing the following: 1. SQL file #1 : A single file that contains all your data definition statements (CREATE TABLE / VIEW, INSERT INTO / LOAD DATA statements) 2. SQL file #2: A single file that contains a stored procedure 3. SQL file #3: A single file that contains the SQL queries specified below. 4. Any source files you used in LOAD DATA statements 5. A documentation file in .PDF format Part 3a: Describing Your Database (optional) This part has three sections. It mirrors some of the tasks from Parts 1 and 2, but also has some important modifications. Section 1: Overview of Your Dataset (10 points) Provide a description of your dataset, about two pages in length. We will not be strict about the submission length, but half a page is definitely too short, and five pages is too long. - What is the dataset? Describe the data you have obtained (including the parts you may have generated), answering the following questions: - What is the source of the data? Provide a URL or other reference. Please also clearly state the license information. - What have the data been used for in the past? Has anyone else done anything useful or fun with the dataset? Provide citations / URLs where appropriate. - Provide complete details of how you generated any simulated part of your data, including scripts, ChatGPT prompts, etc. - What do you plan to do with the dataset? Explain the key questions you would like to ask using SQL operations on the dataset. Section 2: Description of Your Tables (10 points) For each of your tables, provide the following information: - The name of the table - The attributes it contains, including brief explanations of any attributes that are particularly important or non-obvious - Any primary keys / foreign keys, including brief explanations of how the foreign keys link the tables to each other - The dimensions (rows and columns) Section 3: Internal Schema and Normalization (10 points) Note that these figures must not be hand drawn; they must be generated using tools such as the MySQL reverse engineering tool, diagrams.net, PowerPoint, etc. - Internal schema, including all tables, attributes, datatypes, relationships including cardinality information - Dependency diagrams for each table, with primary keys identified, and arrows showing determinant / dependent relationships - Explanation of why these tables are in 3NF, or cannot be converted to 3NF. To submit: A single PDF file, text in a legible font (e.g., 12 point Times New Roman), with reasonable margins (e.g., Word or LaTeX default, or narrow) Part 3b: Describing Your Database (4 sections) At long last, it is time to submit the database and any supporting data (if necessary). We will attempt to build your database, run your supplied queries, and test your stored procedures. Our intention is to test these in MySQL; if for some reason you used a different DBMS or made other nonstandard alterations or have a large file as part of your dataset, please let us know before the deadline. Section 1: Everything needed to construct the database (10 points) The first part of your submission is an .sql file that contains everything needed to construct your database. This includes all the DDL statements (which will probably be CREATE TABLEs) as well as the DMLs that populate those tables with data using INSERT INTO and/or LOAD DATA statements. Requirements: - The SQL file must run from start to finish, so for example if we load it into MySQL Workbench and hit Ctrl-Shift-Enter, your entire database should be created. - We should also be able to successfully re-run the script, so it should contain the appropriate DROP TABLE IF EXISTS statements and anything else needed to “clean” the database before it is re-run. - Every statement (CREATE TABLE, etc.) must be commented. Section 2: Stored procedures (5 points) You must submit a second .sql file that contains two stored procedures and CALL() statements to invoke them. These must satisfy the following requirements: - They must contain at least two separate DML statements that update, insert, and / or delete data in a related way, for example adding a row to a table and updating every other row in the table, or deleting a row if it is no longer needed. - They should use IN, OUT, or INOUT parameters - They should have basic transaction control to ensure that the entire sequence of statements either completes successfully or is rolled back. - The file should also contain at least one CALL() statement for each of the stored procedures. Since the procedures need to have at least one parameter, you will need to declare / use variables in this scope as well. - Each stored procedure must be commented. Section 3: SQL Queries (5 points) The third .sql file must contain five queries. Query 1. SELECT from a single table with a WHERE clause, producing a derived attribute. Query 2. A NATURAL, INNER, or OUTER JOIN between two of your tables. Query 3. A query covering one or more tables that uses a GROUP BY statement on at least one of your variables. Query 4: A query that makes use of at least one subquery in the FROM clause. Query 5: A sequence of queries that: - Creates a VIEW from two or more tables, including derived attributes - Runs a SELECT query on the view - Modifies one of the underlying tables - Re-runs the SELECT query on the view, reflecting changes in the underlying tables and the derived attributes Section 4: Documentation (5 points) The final task of the assignment is to write documentation for your database. This should consist of the following: - A brief summary of the database: data source, license information, number of tables, number of attributes. - Document at least three business rules that are enforced by your database. Explain how these rules are expressed as table constraints. - Briefly explain the five queries you have submitted. What is their purpose? - Explain how to use the stored procedures. The reader should be able to understand enough to know what information needs to be provided in the CALL(), what the procedure does, and what (if any) information is returned by OUT or INOUT variables.
DTS101TC Introduction to Neural Networks Coursework Due: Sunday Apr.6th, 2025 @ 24:00 Weight: 100% Overview This coursework is the sole assessment for DTS101TC and aims to evaluate your comprehension of the module. It consists of two parts: five individual assignments/questions and an image object detection project. Each assignment/question must be completed according to the instructions provided in the Python Jupyter Notebook, with all output cells saved alongside the code. A report for the image object detection project must be submitted with the code and data. AIGC tools are not allowed in this coursework. Learning Outcomes A. Develop an understanding of neural networks – their architectures, applications and limitations. B. Demonstrate the ability to implement neural networks with a programming language C. Demonstrate the ability to provide critical analysis on real-world problems and design suitable solutions based on neural networks. Policy Please submit your assignments (notebooks) via Gradescope. For the project, please prepare your report in PDF format and package your code and data as a ZIP file. If there are any errors in the program, include debugging information and show your analysis. Submit both the report and the ZIP code file via Learning Mall Core. Electronic submission is the only accepted method; hard copies will not be accepted. After submission, you must download your file and check that it is viewable. Documents may become corrupted during the uploading process (e.g. due to slow internet connections). Students are responsible for submitting functional and correct files for assessment.
STAT 675: Final project 2024 The final project is required to be done by a group of two people (or three people for one group). The final group presentations will be given on Mon, May 5 and Wed, May 7, 2025 during the lecture time. Note that the class time for these two days will be extended to 2 hours (10:45am-12:45pm), and because of the extension, we will not have a lecture on April 30. Instead, I will hold an office hour during our lecture time 11am-12:20pm on April 30 to answer your questions. Each group needs to prepare slides and to give about 20-25 minutes presentations plus 10 minutes Q&A. Each group is required to send the final slides to me via email by 10:00am of your presentation date for preparation. Please send me your group members by April 16. Each group only needs to send me one email with all members cc’ed. If you’d like me to choose a group member for you, please also send me an email by April 16. Each group is required to submit one final project proposal by Mon, April 28, 2025. Each group only needs to submit one proposal but every group member should be involved with the proposal writing. The proposal should contain information regarding the topic that you choose, specific data (e.g., data name, sample size, variables, and resources) that you plan to study, and objectives. The proposal can be short (one or two paragraphs) within one page limit. The proposal will not be graded but I will take a look. The final report is due by 11:59pm, Friday, May 16, 2025. Each group member needs to submit an individually and independently written report of maximum ten pages (12 font, single or 1.5 lines spacing), not including computing codes. Computing codes should be given in a supplementary file along with any other materials that you might want to cover. Note that although this is a group project, each group member must submit an independently written report. Group members are not allowed to copy from each other. If such an issue is identified, students might receive zero point on the report. Note that since this is a group project, group members should collaborate and work together to solve problems that you might encounter (through zoom or in person meetings). You might not ask me questions without a group discussion. When you ask me questions via email (after group discussions), please cc all group members in the email. Please choose one of the following topics for your final project. Topics Topic 1: Study topics on modeling categorical data. This topic contains Part 1 and Part 2 (you need to complete both parts): Part 1: Apply Logistic Regression to a relatively large-scale dataset (large sample size, e.g., n > 200 and/or large number of predictors, e.g, p > 10 or 20) with binary outcomes. Your analysis should be complete and should incorporate at least the following procedures: exploratory data analysis, model building, model selection, model diagnostics, model interpretation, and prediction. In addition, you are required to fit Logistic Regression with Lasso Penalty and Generalized Additive Models (GAM) for the same data. Discuss if it might/might not work well, and compare them with the standard Logistic Regression. Part 2: Apply Multinomial Logistic Regression or Ordinal Logistic Regression to a large-scale dataset (large sample size, e.g., n > 200 or large number of predictors, e.g, p > 10) with multi- categorial outcomes or ordinal outcomes (more than two levels). Your analysis should be complete and should incorporate at least the following procedures: exploratory data analysis, model building, model selection, model diagnostics, model interpretation, and prediction. You might also try Lasso and GAM if possible and compare the results (optional). For both Part 1 and Part 2, you can find data on your own (e.g., you are particularly interested in some data) or you might choose datasets from online resources such as: https://www.kaggle.com/ https://archive.ics.uci.edu/ml/datasets.html Before data analysis, each group might need to perform. data cleaning and data processing (e.g., deal with missing values, covert to desired data format for analysis). To encourage group collaboration and independent study, I will not provide help on this step. You might encounter highly unbalanced data, e.g., proportion of events or non-events is very small. In this case, you need to do some study on how to address highly unbalanced data before your analysis. Topic 2: Study topics on modeling count data. This topic contains Part 1 and Part 2 (you need to complete both parts): Part 1: Apply Poisson and Negative Binomial regressions to a dataset with count outcome variables (different from lecture and textbook examples). Your analysis should be complete and should incorporate at least the following procedures: exploratory data analysis, model building, model selection, model diagnostics, model interpretation, and prediction. Part 2: Study topics on Modeling count data with zero inflated models. Real-life count data are frequently characterized by overdispersion and excess zeros (Lambert 1992; Greene 1994). Zero-inflated count models provide a way of modeling the excess zeros in addition to allowing for overdispersion. Such models assume that the data are a mixture of two separate data generation processes: one generates only zeros, and the other is either a Poisson or a negative binomial data-generating process. The result of a Bernoulli trial is used to determine which of the two processes generates an observation. Apply both Zero-inflated Poisson and Zero-inflated Negative Binomial models to a dataset with count outcomes (different from lecture and textbook examples). Your analysis should be complete and should incorporate at least the following procedures: exploratory data analysis, model building, model selection, model diagnostics, model interpretation, and prediction. You might compared with regular Poisson or Negative Binomial models. For both Part 1 and Part 2, you might find data on your own (e.g., you are particularly interested in some data) or you might choose datasets from online resources such as listed in Topic 1. Before data analysis, each group might need to perform. data cleaning and data processing (e.g., deal with missing values, covert to desired data format for analysis). To encourage group collaboration and independent study, I will not provide help on this step. Topic 3: A topic (related to logistic regression, GLM, or categorical data analysis) chosen by your own and approved by the instructor (should be comprehensive and might include advanced topics). If your project involves topics that are not covered by STAT675, you are required to provide introduction on these topics so other students in the class can understand. Your analysis should be complete and should incorporate at least the following procedures: exploratory data analysis, model building, model selection, model diagnostics, model interpretation, and prediction. NOTE: The evaluation of the project will be based both on your presentation and your final writing report. Again, to encourage group collaboration and independent study, I will only provide suggestions/advice on potential questions that you might have. I cannot help to solve specific problems/questions such as data cleaning/processing, programming/coding issues, etc. Each group should start the final project as early as possible (I will not answer questions at the last minutes, e.g. the day before/on the presentation date). Don’t wait till the last moment. See the “Instructions and Grading Criteria” file for further requirements and information on the final project.
BA Creative Media Arts 4MEST017W Space, Place and Experience: Moving Image, Interactivity, and Sound ASSESSMENT 2: Written Essay (2000 words) Worth 30% of the marks for the module Deadlines and submission: (i) Essay plan (formative): Please decide upon your topic and begin to put together an essay plan (structure and ideas to include from your reading) bring to the essay tutorial session on Tuesday 8th April. Submit with final essay. (ii) Written essay (summative): Monday 12th May 1pm through Blackboard This assignment is designed to allow you to respond to key ideas and examples of the representation of media culture in the form. of an academic essay. It develops your abilities in choosing a subject; framing an argument; selecting appropriate sources; reflecting on creative practice, and written communication skills. As such, you will be required to makes specific reference to examples of media practice and related theories. This is an individual assignment only, and tests learning outcomes 2, 6, 7 and 8. The Written Essay will be assessed on the extent to which you have demonstrated: • Engagement with researching information, theories and ideas appropriate to a set theme • Communication of theoretical ideas and analyses • Critical analysis of the topic using appropriate theoretical methods • Clarity of communication in written form. and coherence of essay structure Research and Referencing Please select one of the questions or topics provided on the next page and write a 2000-word essay (+/- 10%), referenced with examples and illustrations. Although most of the questions are linked directly to one of the lectures and a specific paper, you are expected to incorporate research from other (academic) sources also, and these should be included in your essay and referenced. As well as sources that are included in the module handbook, you might look at the reference list at the end of each paper, or sources provided in relevant lectures, in this or other modules. Have a look at this page for guidance: https://www.westminster.ac.uk/current-students/studies/study-skills-and- training/research-skills You are expected to include reference to underlying theories, and the essay should be fully referenced throughout using the Harvard referencing system. This requires that you include properly formatted in-text referencing in paraphrased sections, as well as properly formatted quotes where appropriate. Your bibliography/reference list should also be formatted according to the Harvard system. Failure to do so will result in a loss of marks. To learn more about referencing, look at the resources (i) here:https://www.westminster.ac.uk/current-students/studies/study-skills-and- training/research-skills/referencing-your-work (ii) https://www.citethemrightonline.com/how-to-use-cite-them-right(you will need to log in via the institutional login) (iii) https://libguides.westminster.ac.uk/referencing Please ensure that you fully understand the regulations around referencing, plagiarism, commissioning of work and use of Gen AI. You can find them here: https://www.westminster.ac.uk/current-students/guides-and-policies/academic-matters/academic-misconduct/plagiarism Questions: 1) Reality, Hyperreality and the Virtualisation of Space Rushton argues that “one should not concentrate on cinema’s capacities for representing reality, one should begin from the position that films are, in one way or another, part of reality”. (2011: 43-44) Using specific examples, examine the view that films and/or media are “part of reality". Reference: Rushton, R. (2013) ‘Realism, Reality and Authenticity’, in The Reality of Film: Theories of Filmic Reality, Manchester: Manchester University Press, pp. 42-78. https://ebookcentral.proquest.com/lib/westminster/reader.action?docID=1069669&ppg=1 2) Post-Industrialisation, Late-Capitalism and Electronic Dance Music Christodoulou argues that under “late capitalist conditions, the digitalisation of cultural production has intensified the appropriation of nature through technology” (2023). Using examples from music and/or media, examine the view that our understanding of nature and the human body is increasingly simulated by technology. Reference: Christodoulou, C. (2023) ‘Liquid Funk: Acceleration, Late Capitalism and the Signification of Nature in Jungle Drum and Bass Music’, Journal of Global Pop Cultures, ‘The Natures of Pop’, Issue 2 (2023) https://www.journalofglobalpopcultures.com/issues/the-natures-of-pop/liquid-funk-acceleration-late-capitalism-and-the-signification-of-nature-in-jungle-drum-and-bass- music 3) Accelerationism, Big Tech and the Future According to Andy Beckett, accelerationism addresses the “reshaping of our minds and bodies by ever-faster music and films” (2017). With reference to specific media and/or music, consider the impact of accelerationism and accelerated culture on our everyday lives. Reference: Beckett, A. (2017) ‘Accelerationism: how a fringe philosophy predicted the future we live in’, The Guardian, Thursday 11 May 2017. https://www.theguardian.com/world/2017/may/11/accelerationism-how-a-fringe- philosophy-predicted-the-future-we-live-in 4) The Past Inside the Present: Hauntology For Mark Fisher, " What haunts the digital cul-de-sacs of the twenty-first century is not so much the past as all the lost futures that the twentieth century taught us to anticipate” (2012: 16). Using specific examples, discuss the presence of the past and/or the role of “lost futures” in contemporary media forms. Reference: Fisher, M. (2012) ‘What is Hauntology?’ Film Quarterly, Vol. 66, No. 1 (Fall 2012), pp. 16-24. https://www.jstor.org/stable/10.1525/fq.2012.66.1.16 5) Psychogeography and sensing Psychogeography can change both you and society. Discuss with reference to relevant theory and specific examples from the lecture and other sources if necessary (including your own practice where appropriate). (other references are from artists, provided in class) 6) Failure Failure is not the opposite of success, but the opposite of perfection. How can art embrace this failure? Discuss with reference to relevant theory and specific examples from the lecture and other sources if necessary (including your own practice where appropriate). Reference: Hegel, G.W.F., 2010. The science of logic. Cambridge university press. (other references are from artists, provided in class) 7) Expanded Cinema Expanded cinema challenges traditional cinema. Discuss with reference to relevant theory and specific examples from the lecture and other sources if necessary (including your own practice where appropriate). References: Bovier, F., Mey, A., 2015. Exhibiting the moving image. Les Presses du réel: Zurich. Campany, D. (ed.), 2007. The cinematic, Documents of contemporary art. Whitechapel, MIT Press. Cubitt, S. (ed.), 2013. Relive: media art histories, Leonardo book series. MIT Press, Cambridge, MA. Forde, K., 2005. What sound does a color make? Independent Curators International, New York. Jennings, G., 2015. Abstract video: the moving image in contemporary art. University of California press: California. Mondloch, K., 2010. Screens: viewing media installation art. University of Minnesota Press. Moss, C., 2019. Expanded internet art: twenty-first century artistic practice and the informational milieu. Bloomsbury, New York. Rees, A.L. (ed.), 2011. Expanded cinema: art, performance, film. Tate: London. 8) The Question After Technology “The essence of technology is nothing technological.” Discuss with reference to relevant theory and specific examples from the lecture and other sources if necessary (including your own practice where appropriate). Reference: Heidegger, M. (1977) The question concerning technology and other essays. New York: Harper Colophon. Also available here: https://www2.hawaii.edu/~freeman/courses/phil394/The%20Question%20Concerni ng%20Technology.pdf Please ensure that the Written Essay for this module is: • Submitted as single softcopy through the Blackboard Turnitin submission link in the assessment folder for 4MEST017W; • Typed; • 1.5 or double-spaced for body text; single-spaced and indented for longer quotations (c. >30 words); • Fully and correctly referenced, using the Harvard referencing system and including captions and copyright information for any figures or illustrations (if used) • Carefully checked before submission for errors of content and presentation, with visual examples as appropriate, and your essay must; • Identified by module code on EVERY page, either at the top or bottom. • Anonymised, i.e. please do not include your name anywhere on the essay as it is going to be marked anonymously as per assessment regulations The moderation process for the Written Essay involves an internal moderator looking at a sample of essays in terms of marks and feedback drawn from the entire range of marks.
Assessment Proforma 2024-25 Module Code: CMT304 Module Title: Programming Paradigms Assessment Title: Logic Programming Assessment Number: 1 of 4 for single portfolio coursework Assessment Weighting: 25% The Assessment Calendar can be found under `Assessment & Feedback' in the COMSC- ORG-SCHOOL organisation on Learning Central. This is the single point of truth for (a) the hand out date and time, (b) the hand in date and time, and (c) the feedback return date for all assessments. Learning Outcomes • Explain the conceptual foundations, evaluate and apply various programming paradigms, such as logic, functional, scripting, filter-based programming, pattern matching and quantum computing, to solve practical problems. • Discuss and contrast the issues, features, design and concepts of a range of program- ming paradigms and languages to be able to select a suitable programming paradigm to solve a problem. Submission Instructions The coversheet can be found under `Assessment & Feedback' in the COMSC-ORG-SCHOOL organisation on Learning Central. All submissions must be via Learning Central. Upload the following files in a single zip file, [student number] .zip: If you are unable to submit your work due to technical difficulties, please submit your work via e-mail to [email protected] and notify the module leader. Any code submitted will be run on a system equivalent to the Linux laboratory machines and must be submitted as stipulated in the instructions above. Any deviation from the submission instructions above (including the number and types of files submitted) may result in a mark of zero for the assessment or question part. All submissions will be compared to each other and checked against other work available on the Internet and elsewhere to identify cases of potential unfair practice. Staff reserve the right to invite students to a meeting to discuss coursework submissions. Assessment Description Consider the following situation: Patent requests are submitted to the patent office and are reviewed by members of the tech- nical board. To find a good match between patent requests and board members (referees), every member of the technical board declares their expertise for each submitted request that needs to be reviewed: EXPERT, KNOWLEDGEABLE, FAMILIAR or INEXPERT For example, declaring EXPERT for a given patent request, means “I am an expert on the topic of this request”. The goal is to write a program to automate this process. Using a list of bids, indicating the level of expertise for each patent request and board member, it should assign each submitted patent request to a specific number of n members of the technical board such that • the workloads of the technical board members are approximately equal, that is, do not differ by more than m; • no member of the technical board is required to review a submission that is placed in the INEXPERT category; • no member of the technical board is required to review more than k submissions from the FAMILIAR category; • the total number of cases when a submission is assigned to a member who placed it in the EXPERT category is as large as possible. The parameters n, m and k are arguments set when calling the program. Task 1: Write a logic program in ASP which finds all solutions to the problem, given as input n, m and k and a list of bids. Task 1: Write an ASP program (problem encoding.lp) that solves the problem for any instance. Your program will receive as input a set bid/3 of triples mem, req, exp, such that the member mem has declared to have expertise exp for request req. (The parameters n, m and k are set when calling the program). The output of your program is a set assign/2 of pairs mem and req such that the member mem has been assigned to review request req. Make sure you document your code so it is clear how it should be used and what the approach to solving the problem is. Document your code so the following is clear. 1. How it should be used. 2. What the approach to solving the problem is. In particular, you need to explain what each rule achieves and how the rule achieves it. Include your name and student id in the comments. Task 2: Write a short report on logic programming related to the problem: 1. Provide, in up to 300 words, an analysis of the design and functioning of your program in terms of the Guess-and-Test modeling methodology. The word limits are an upper limit, not a target length. Text longer than the word limit will be ignored.
Advanced Econometrics I (EMET 4314/8014) First Semester Final Examination– June, 2022 Beginning of Exam Questions 1. [1 mark total] Write the following statement by hand: I hereby declare ◦ to uphold the principles of academic integrity, as defined in the University Academic Misconduct Rules; ◦ that your work in the final exam in no part involves copying, cheating, collusion, fabrication, plagiarism or recycling. 2. [20 marks total] Consider the scalar model Yi = β0+ β 1Xi1+ ei where ei|Xi1 ~ N(0,1). You have available a random sample (Xi1, Yi), i = 1, . . . , N. Let β0and β 1be the OLS estimators obtained from a regression of Yi on a constant and Xi1 . (a) [2 marks] State β 1 in terms of sample moments ofthe data (that is, sample means, variances, and covariances). No derivation, just state the result. (b) [2 marks] Derive Var (β 1|Xi1). You observe an additional variable, Xi2 . Denote by 0 and 1 the OLS estimators from a regression of Xi1 on a constant and Xi2 . Define X-i1 := π-0+ π-1Xi2 . Let θ0and θ 1be the OLS estimators obtained from a regression of Yi on a constant and Xi1 . (c) [4 marks] Derive θ 1 in terms of sample moments of the data. (d) [2 marks] Derive Var (θ 1|Xi1, Xi2). (e) [5 marks] Prove or disprove: θ = β 1+ op (1). (f) [5 marks] Which estimator do you prefer: β 1 or θ 1? Why? 3. [20 marks total - 5 marks each] Are the following statements true or false? Provide a complete explanation. Use mathematical derivations where necessary. (Note: you will not receive any credit without providing a correct explanation.) (a) Let the discrete random variable have the following distribution: P(Y = 1) = π1, P(Y = 2) = π2, P(Y = 3) = π3, where π1 ∈ (0,1), π2 ∈ (0,1), π3 ∈ (0,1), and π1 + π2 + π3 = 1. In a random sample of size N you observe N1 realizations for which Y = 1, N2 realizations for which Y = 2, N3 realizations for which Y = 3, so that N1 + N2+ N3 = N. Then the maximum likelihood estimate of π1 is N1/N. (b) Let X be a Bernoulli random variable, that is, X = 1 with probability π and X = 0 with probability 1 — π where π ∈ (0,1). Let Y be another random variable (not Bernoulli distributed) and assume that Cov(X, Y) ≠ 0. Then Cov(X, XY) = E(Y) + (1 — π) · Cov(X, Y). (c) Let the random variable Z be such that E(Z) = 3 and E(Z2 ) = 13. Then a lower bound for P(—2 < Z < 8) is given by 21/25. (d) The Monte Carlo simulation of the simple schooling model from week 7, as summarized by the Julia code and corresponding output below, illustrates that the OLS estimator is a consistent estimator for the return to schooling. JULIA CODE 1 using Distributions , Random, Plots 2 3 function schooling__sample (b2 , n ; 4 p=13.2 , b1 =4.7 , b3=0, 5 su =0.175 , sa =7.2) 6 u = rand (Normal (0 , s q rt (su ) ) , n) 7 a = rand (Normal (0 , s q rt ( sa ) ) , n) 8 S = p .+ a 9 Y = b1 .+ b2∗S .+ b3∗a .+ u 10 return S , Y 11 end 12 13 rep = 100000 14 b2 = Array{ Floa t 6 4 } (undef , rep ) 15 for r in 1 : rep 16 n = 1000 17 x , y = schooling__sample (0 .075 , n) 18 b1_tmp , b2_tmp= [ones (n , 1) x ]y 19 b2 [ r ] = b2_tmp 20 end 21 histogram (b2 , normed = false ) OUTPUT 4. [20 marks total] Consider the model Yi = µ(Xi, θ) + ei, where ei|Xi ~ N(0, σ2e). The variables Yi and ei are scalars and dim(Xi) = K × 1 and dim(θ) = L × 1 where K ≠ L. The functional form. of µ is considered known but is left unspecified here. You have available a random sample (Xi, Yi), i = 1, . . . , N, to estimate the un- known parameters θ and the scalar σ2e. (a) [3 marks] Derive the conditional log likelihood function L(θ, σ2e). (b) [3 marks] Derive the score function. (c) [3 marks] Derive the expected value of the score conditional on Xi. (d) [2 marks] Determine the MLE of σ2e as a function of θ ML (the MLE of θ). (e) [3 marks] Derive the Hessian matrix as the derivative of the score. (f) [6 marks] Show that the information equality holds here.
CS5218: Assignment 3 Comparison of the CLANG and INFER Analyzers Deadline: 23:59 Sunday 6th April 2025 In this assignment, you will compare two well-established Static Analysis tools Clang and Inferon a set of programs with different sizes. Clang and Infer static analysers use flow and partial path-sensitive analyses. The set of benchmark programs contain defect types related to dynamic memory allocation, error handling, multi-threading, etc. You are to organize yourselves into three-person teams (with an exception of one size 4 team). Please indicate the team structure for this project in the document: Google Spreadsheet by 23:59 Monday 24th March 2025. We need this information by the given deadline as we are going to share different sets of programs to each team individually. 1 Academic Programs We consider two academic programs P1 and P2. The programs are written with a macro TEAM ID, whose value is to be instantiated by each team by its identification number, so each team will consider a slightly different version. We wish to determine if the error point (indicated) is reachable. The expression Ψ(x) represents some predicate on the variable x. This is the error condition. An analyzer reports an alarm if it cannot determine if the error point is unreachable; otherwise, it reports bug-free. #define TEAM ID . . . // ( #define N . . . // ( errorpoint } int error(int x) { Ψ(x) } #define TEAM ID . . . // ( errorpoint } int error(int x) { Ψ(x) } Program P1 has a straight line execution sequence. Thus whether an error is detected will depend on the analyzer to sufficient accurately track the value of x to the error point, and determine if error(x) is definitely false (and so no alarm is raised). Program P2, on the other hand, has many possible (in fact 2N ) execution paths can reach the error point. Thus to determine that there is no error, the analyzer needs to track up to 2N different values of x. Now consider the possible answers (alarm or bug-free) an analyzer can report, and what these answers can mean (true or false): FP: alarm but in fact the error point is unreachable. This is a false positive. (This occurrence is frequent in practice and is the biggest challenge to static analysis for bug finding). TP: alarm AND in fact the error point is reachable. This is a true positive. (This occurrence is probably due to “pure dumb luck”!) FN: bug-free but in fact the error point is reachable. This is a false negative. (This occurrence is probably due to the [criminal?] decision to reduce the frequency of false positives by remain silent.) TN: The analyzer reports bug-free AND in fact the error point is unreachable. This is a true negative. (Whether or not this is pure dumb luck is debatable, but this occurrence is the happiest outcome.) The four outcomes above can be summarized as a “confusion matrix” . The following are example conditions for Ψ(x) and what the results are for the analyzer Infer. • P1 x < TEAM ID - 99 returns BUG-FREE; result is a True Negative x != TEAM ID + N returns ALARM; False Positive x == TEAM ID + N + 1 returns ALARM; False Positive x == TEAM ID + N returns ALARM; True Positive • P2 x >= TEAM ID returns ALARM; result is a True Positive x == TEAM ID + 23 returns BUG-FREE; True Negative x == TEAM ID + 22 returns BUG-FREE; False Negative x
Assignment 1: Macroeconomics for Developing Countries Write a 2500 (+/- 10%) word essay on one of the following topics. The word limit doesn’t include references and footnotes. The essay is due on 28th April 2025. Topic 1: By referring to the relevant literature, critically evaluate the evidence on Solow’s theory of income convergence. In what way does the new growth theory proposed by Romer differ from Solow’s growth theory? Topic 2: Argue, using recent empirical literature, whether the experience of colonialism has long term effect on development. What are the possible channels of this long term effect? Topic 3: What does the literature in Development Economics say about the relationship between institutions and development? How do the different authors take into account the problem of endogeneity? Topic 4: Does the Human Development Index truly reflect the spirit of the “Capabilities Approach” put forward by Sen? What are the alternative indices that have been introduced in the literature? Instructions Your essay should be well structured (with an introduction, the main body and a conclusion). Make sure the work is well researched and not plagiarized. You are expected to conduct a thorough literature review as part of your assessment. The essential readings can be your starting points for your literature review, though you will be expected to go beyond these. The following outline plan is suggested: 1 Introduction: Comment on the topic, the treatment proposed and the limits of the study. Keep it concise and informative. You may explain the focus of your essay, define any key terms, etc. It is often useful to summarise briefly the overall theme or argument of the essay, listing the main points that you are going to make. Remember, this is the first thing that the reader sees. You have to convince them that your essay is interesting enough for them to continue. 2 Body: A comprehensive and coherent treatment of the topic as stated in the introduction. This will develop the main points/arguments and provide supporting evidence, in a logical order. It contains a critical evaluation and discussion of the material. 3 Conclusion: This should briefly summarise the argument or theme and indicate the conclusions drawn from it. You may add any final comments where relevant, e.g. main lessons, deductions, consequences. Conclusions are short, unambiguous and convincing. 4 Tables/Figures: In addition to the written section, you may provide supplementary figures, tables, etc. All exhibits provided should be referenced in the text, explained and add to your discussion. 5 Bibliography/References: All sources (e.g. books, articles, Internet sites,…) which you have used in the preparation of an essay must accurately be listed in a bibliography at the end. It is essential that references are given, indicated in the text by author's name and year of publication. I have no preference on the citation system, as long as you are consistent.
ECOS3035: Economics of Political Institutions Homework II 1. Consider the two-period agency model of elections from class. There are three players: two politicians (an incumbent and a challenger), and a voter. A politician can be either of high quality H with probability p or of low quality L with probability 1−p, where p = 0.2. Quality is privately observed by the politician. A politician in office in period t, t ∈ {1, 2}, exerts effort et ∈ [0, 1] which she privately observes and which costs her . In each period t, a politician in office can deliver two outcomes: good or bad. The type H politician delivers the good outcome for sure. The type L politician, by contrast, delivers the good outcome in period t with probability et , and the bad outcome with the complementary probability. A politician gets a payoff of B ∈ (0, 1) for each period that she is in office, and otherwise she gets a payoff of 0. The voter gets a payoff of 1 for each period where the outcome is good, otherwise he gets a payoff of 0. The timing of the game is as follows. In period 1, the incumbent chooses e1. The outcome for period 1 is realized. The voter observes the outcome and chooses whether to reelect the incumbent or to replace the incumbent with the challenger. In period 2, the politician in office chooses e2. The outcome for period 2 is realized. The game ends. (a) What is the equilibrium level of effort for the high type politician and the low type politician in the second period? (b) Consider the stage of the first period where the voter observes the outcome (good or bad). For each of these outcomes compute the posterior probabilities that the voter assigns to the type of incumbent (H or L). How does this depend on the conjectured effort of the incumbent? Why? (c) Specify the voter’s optimal reelection strategy in period 1 following the realization of the outcome. (d) Compute the incumbent’s equilibrium level of effort in period 1 for each type. (e) Given this equilibrium level of effort in period 1, compute the probability of a good outcome at the start of the game. 2. Consider Case I in Banerjee, Hanna and Mullainathan (2012). Suppose H = h = yH > L = l = yL > 0, and NH < 1. Suppose there is no testing. (a) Find the efficient (social welfare maximizing) probabilities πH and πL for this case. (b) Consider the mechanism: Verify that the slot probabilities satisfy the slot constraint (total number of slots equals 1). Verify that pL satisfies the affordability constraint for L. Also find the maximum value of ϵ (call this ϵmax) that satisfies both the affordability and incentive constraints for type H. (All of these should convince you that the mechanism is feasible for the bureaucrat to use – it satisfies incentives, participation, the slot constraint, and the affordability constraint). (c) Suppose the government imposes a rule that the bureaucrat has to use the mechanism Find conditions under which there is no corruption – that is, the rule is never broken by the bureaucrat. (d) Suppose the government imposes a rule that the bureaucrat has to use the mechanism where ϵmax is the same as in the part above. Find conditions under which there is corruption – that is, the rule is broken if the bureaucrat has a very low cost of breaking the rule. Which type of agent does the bureaucrat get a bribe from? What is the value of the bribe? (e) Consider the same mechanism above but replace yL with y < yL. Would there be more corruption or less corruption by the bureaucrat – in terms of breaking the rule and the bribe charged? Be very brief.
Assessment Proforma 2024–25 Key Information Module Code CMT304 Module Title Programming Paradigms Assessment Title Quantum Computing Assessment Number Part 4 of the 4-part portfolio coursework Assessment Weighting 25% of the portfolio coursework Assessment Limits Hand-out: 6th of March 2025 Hand-in: 10th of April 2025, 9:30am Feedback expected by: 13th of May 2025 Limits are per task as set in the instructions The Assessment Calendar can be found under ‘Assessment & Feedback’ in the COMSC–ORG– SCHOOL organisation on Learning Central. This is the single point of truth for (a) the hand out date and time, (b) the hand in date and time, and (c) the feedback return date for all assessments. 1 Learning Outcomes The learning outcomes for this assessment are • Explain the conceptual foundations, evaluate and apply various programming paradigms, such as logic, functional, scripting, filter-based programming, pattern matching and quantum com-puting, to solve practical problems. • Discuss and contrast the issues, features, design and concepts of a range of programming paradigms and languages to be able to select a suitable programming paradigm to solve a problem. 2 Submission Instructions The coversheet can be found under ‘Assessment & Feedback’ in the COMSC–ORG–SCHOOL or- ganisation on Learning Central. All files should be submitted via Learning Central. The submission page can be found under ‘As- sessment & Feedback’ in the CMT304 module on Learning Central. Your submission should consist of these files: Description Type Name Coversheet Compulsory One PDF ( . pdf) file coversheet. pdf Task 1 Compulsory One PDF ( . pdf) file task1 . pdf If you are unable to submit your work due to technical difficulties, please submit your work via e- mail to [email protected] and notify the module leader (and ideally the setter, if different). Any code will be tested on a Linux system equivalent to COMSC’s Linux lab machines and must run there. 3 Assessment Description Consider the following quantum circuit: It consists of two CNOT gates in the middle of the circuit. The two-qubit input quantum register |x〉is an arbitrary quantum state and can be set by the user. The other two-qubit input quantum register |00〉is in the ground state and cannot be changed. The gate F is an unknown quantum operation (this means it is an arbitrary, but fixed gate on two qubits, but you do not know what it does). The gate F-1 computes the inverse operation of F. Task 1: 1. Analyse the operation of the circuit to determine what the values of the two two-qubit output quantum registers |A〉and |B〉are, depending on the properties of F and the user-selectable input |x〉. Clearly justify your answer. 2. Explain how you could, if possible, determine the operation of the gate F from this circuit (you can execute the circuit as many times as you wish). 3. Furthermore, discuss what this means for the difference between quantum computing and a classical computing paradigm of your choice (working with bits instead of qubits). Answers should be provided in a report of up to 500 words (formulae and code do not count towards this limit, but ensure you explain any formula and code included). The word limit is an upper limit, not a target length. Text longer than the word limit may be ignored. The circuit operation has not been identified correctly and the justification is not correct. There is no discussion of how to identify F and the related difference between classical and quantum computing. 4 Assessment Criteria Task 1 worth 25% of the coursework High Distinction 80% - 100% Distinction 70% - 79% The circuit operation has been correctly identified, depending on F and |x〉, and the justification is complete. The report shows a clear understanding of the quantum operations and the underlying theory, considering all cases involved in the full operation. The approach to identify F, where possible, is suitable, fully explained. It clearly considers the related differences between classical and quantum computing. Merit 60% - 69% The circuit operation has been correctly identified, depending on F and |x〉, and the justification is complete. The report shows a clear understanding of the quantum operations, considering all cases involved in the full operation. The approach to identify F, where possible, is suitable, and relates to the differnces between classical and quantum computing. Pass 50% - 59% The circuit operation has been correctly identified, depending on F and |x〉, and the justification is suitable, even if there are minor mistakes or incom- plete arguments. The report shows a clear understanding of the quantum operations, even if not all cases have been considered. The approach to try to identify F is suitable and well explained, but it focuses mainly on either the quantum or the classical computing context. Marginal Fail 40% - 49% The circuit operation has been correctly identified, with some mistakes, and the justification shows some understanding of the involved quantum oper- ations. The approach of how to identify F points in the right direction, but incompletely considers related classical as well as quantum computing con- cepts. Fail 0% - 39% There is a discussion of the circuit operation that shows some insights, but the operation is not correctly identified and the justification is incomplete. The approach of how to identify F shows some insights, but is not suitable and failed to consider the related differences between classical and quantum computing.
