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[SOLVED] Assignment 1

Assignment 1 Due date: 23:59  2024-04-04 The purpose of this assignment is to gain good understanding of the ordering property of multicast primitives through implementing the total order multicast primitive. Many applications need to use multicast mechanisms that satisfy various ordering properties to send information. To simplify the task of application development, many systems provide multicast mechanisms using middleware or OS services. In this assignment, you are required to implement a middleware that ensures total order of multicast messages. Figure 1 Multicasting through Middleware Figure 1 shows how the middleware is used. .   Each application program has a corresponding middleware. An application and its corresponding middleware reside on the same machine. .   The middleware uses a protocol to guarantee the ordering property of the multicast messages. .   When an application needs to multicast a message to other applications, the application sends the message to its corresponding middleware. The middleware is responsible  for multicasting the message to the corresponding middleware of the other applications through the network. .   When a middleware receives a multicast message from the network, it determines whether the message can be delivered to its corresponding application according to the protocol for guaranteeing the message ordering. The middleware buffers a message until the message can be delivered to the application. In this assignment, you are provided with a program for simulating the network. The multicast messages sent by the middleware on behalf of the application programs MUST be sent to the network program first. Subsequently, the network program will multicast the message to all the middleware in the system. Except for modifying the values stored in the file 'delays.txt', you MUST not alter any other file in the 'network' folder. The marker will employ the same network program during the assessment of your assignment. However, the marker may utilize a 'delays.txt' file with a different set of values. An example, which includes the network program and some simple programs for testing the network program, can be downloaded from Canvas. The details of the network program are as below: .   The middleware communicates with the network program through TCP sockets. .   The network program receives multicast messages sent by the middleware and broadcasts each received message to ALL the middleware in the system.  The broadcast is executed via point-to-point communication using TCP sockets. .   The network program listens for incoming messages on port 8081. .   File  'delays.txt' comprises several lines, with each line containing several  values separated by ','. The number of values in each line is equivalent to the number of middleware in the system. These values  represent the amount  of  delays  that the network program uses when sending a multicast message to middleware. Each value in a line corresponds to a middleware. For example, if there are two middleware M1 and M2, then each line of ‘delays.txt’ consists of two values, i.e., d1 and d2, and they are the amount of delay for M1 and M2 respectively. .   To simulate network delay, while broadcasting a message, the network program waits for a specific duration before sending the message to the middleware. The waiting time for different middleware may vary and is determined by the values in the file 'delays.txt'. For instance, assuming there are two middleware, M1 and M2, and the contents of 'delays.txt' are as follows: 100,2000 1000,30 … When the network receives the first multicast message (msg1), it reads the two values in the first line of 'delays.txt'. Based on these values, the network program will wait for 100ms and 2000ms before sending msg1 to M1 and M2, respectively. Similarly, when the network receives the second multicast message (msg2), it reads the two values in the second line of 'delays.txt'. Accordingly, the network program will wait for 1000ms and 30ms before sending msg2 to M1 and M2, respectively. .   Port 8081 is utilized by the network program for receiving multicast messages from the middleware. In this assignment, you are required to implement programs corresponding to Middleware 1 to 5 in Figure 1. You do not need to provide any of the application programs shown in Figure 1. You must use C# to do this assignment. Programming (12 marks) In this part, you are required to implement the middleware that ensures the total order of the multicast messages. In order to test the middleware, your implementation must include a GUI interface for the middleware. The layout of the interface for each middleware should be similar to Figure 2. Figure 2  The GUI for a Middleware The middleware and its GUI should satisfy the requirements below: .   Each middleware should have its own GUI. .   A GUI should have a title indicating the ID of the middleware, e.g. “Middleware 1”. .   Each GUI should have a button “Send”. When the button is clicked, the middleware should send a multicast message to the network. The contents of the message should be generated automatically by your program.  Each multicast  message sent by a middleware must include information that uniquely identifies the message.  For example, “Msg #1 from Middleware  1 xxx” indicates that this is the first message sent by Middleware 1. A message must be less than 1024 bytes. .   Each GUI should have three lists for displaying messages. o The “Sent” list shows the multicast messages that have been sent by the middleware to the network. The messages in the list should appear in the order in which they were sent to the network. o The “Received” list contains the multicast messages that have been received by the middleware from the network. The received messages should be displayed in the order in which they are received by the middleware. You should also show the proposed timestamp assigned to each message. o The  “Ready”  list presents the multicast messages that are ready to be delivered to the application program. This means that the messages in the “Ready” list should be a subset of the messages in the “Received” list. The order in which the messages appear in the “Ready” list should conform to the total-order property of the multicast messages. You should also show the final timestamp assigned to each message. .   Your implementation should have five middleware  programs.  Each middleware program should be stored in its own folder. There is NO need to run your programs concurrently on different machines. You only need to test them on one machine. .   Each middleware program should create a TCP socket for receiving messages from the network program. The sockets used by the five middleware programs MUST use ports 8082, 8083, 8084, 8085, and 8086 respectively. .   The middleware programs might need to exchange some “control” information amongst them when determining the order in which the multicast messages should be delivered. The middleware programs must exchange the control information between them directly using TCP sockets. That is, the control messages should not be sent to the ‘Network’. o On lab machines, you can only use port 8080 to 8099. Please make sure that your implementations take this into account. Your implementations MUST be able to run on a lab machine. .   You must NOT use any absolute path in your code, e.g., ‘c:xyzA1’. Given that your directory structure may differ from the marker's, using absolute paths may cause your program to fail to execute correctly on the marker's machine. Failure  to  execute correctly will result in receiving a mark of 0. Report (3 marks) Write a report about your implementation. Your report should include the following. (a) Clearly indicate whether you have tested your programs on a lab machine. (b) Instructions on how to run your programs. (c) What information is used to uniquely identify a multicast message. (d) Describe an application scenario in which the total-order multicast is needed to ensure the consistency of the data in the system. (e) Use an example to show the difference between total order and causal order multicast. Save your report as a PDF file. Name it as Report.pdf. Submission .   You MUST thoroughly test your programs on a lab machine. Programs that cannot be compiled or run on a lab machine will get NO marks. .   You only need to submit the implementations of the middleware.   Your implementations should have the following directory hierarchy.  The  directory structure must be similar to the example program. .   You should only submit the source code of your programs. .   You should create a windows batch file for compiling the submitted programs and for executing the programs. Name the batch file as run.bat. The batch file should be placed under folder ‘A1’. .   Pack your implementations, the batch file and the report into file A1.7z. The directory hierarchy of your implementation should be preserved when you pack your files. .   Submit A1.7z and report.pdf to Canvas. . NO email submission will be accepted. Marking Schedule Programming 1.   The batch file for compiling and executing programs works correctly. [0.5 marks] 2.   There are five middleware programs and the programs are placed in the right folders. [0.5 marks] 3.   Each GUI shows the ID of the middleware. [0.5 marks] 4.   When the “Send” button is clicked, a multicast message is sent to the network program. [0.75 marks] 5.   Each multicast message contains the information that uniquely identifies the message. [0.75 marks] 6.   Multicast messages are added to the “Sent” list correctly. [2 marks] 7.   Multicast messages are added to the “Received” list correctly. [2 marks] 8.   Multicast messages are added to the “Ready” list correctly. [5 marks] Report 1.   Statement about testing on a lab machine. [0.1 mark] 2.   Clear instructions on running the programs. [0.5 marks] 3.   Information on how to uniquely identify a multicast message. [0. 5 marks] 4.   Clear description about an application scenario. [1 mark] 5.   Clear demonstration of the difference between total order and causal order.  [0.9 marks]

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[SOLVED] FIN 400 ALGORITHMIC TRADING WITH

UNDERGRADUATE COURSE SYLLABUS FIN 400 – ALGORITHMIC TRADING WITH PYTHON DATE TENTATIVE  SCHEDULE estmentsIntroduction to PortfolioOptimizationDeterminingtheInitialInvestmentJan23Introduction to Bol radingBotMar4Developing a CryptoT radingBotMar20Visualizing Stock MarketDataMar25Introduction to RelativeStrengthIndex(RSI)Mar27Optimizing Trading StrategieswithRSIApr1Optimizing Trading StrategieswithRSIApr3Optimizing Trading StrategieswithRSIApr7-10Review; Midterm # 2(Asynchronous onlinetest)Apr15Apr17Optimizing Trading StrategieswithRSIOptimizing Trading StrategieswithRSIApr22Apr24Application ofTrading BotsApplication ofTrading BotsMay1-6Final Exam(Asynchronous online test)

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[SOLVED] EDUC2108 Managing Classroom Behaviour Web

Assessment brief Module Code EDUC2108 Module title Managing Classroom Behaviour Assignment title Essay title: Managing Students’ Behaviour in Schools Assignment type and description  For this assignment, you are required to write an essay by considering the following question: How should teachers manage students’ behaviour in schools, and why? In addressing the above question, you are required to critically Analyse and evaluate the government guidance (see the reference below) for headteachers and school staff on managing students’ behaviour in schools: Department for Education (DFE). 2022. Behaviour in schools: advice for headteachers and school staff 2022. [Online]. [Accessed 11th September 2024]. Available from: Behaviour in schools - advice for headteachers and school staff (education.gov.uk) Anonymous/Named Anonymous Rationale This essay-based assignment is designed to allow students to critically write about Behaviour Guidance/policy/ issues and ideologies, using UK government’s behaviour  guidance document, engaging with relevant empirical academic literature and by using theories on behaviour covered in the module to deconstruct and illuminate the debate around Managing Students’ Behaviour in Schools. Word limit and guidance Essay (2000 words) Use of Generative Artificial Intelligence in this assignment AMBER: AI tools can be used in an assistive preparatory role only You are permitted to use AI tools for specific defined processes within the assessment. See below for further details. Weighting 100% Draft The deadline submitting your essay (2 pages) plan for feedback is Tuesday by 9:00 am, 15th April, 2025 Deadline or date of assessment Submit your final assignment through Turnitin on Minerva by noon on Thursday 15th May 2025. Submission method Via Minerva Feedback provision Feedback on draft Learning outcomes assessed On completion of the module students should: 1.  Understand how theories of learning can be used to analyse children’s behaviour and the strategies for managing behaviour 2. Know about current government and school policies and understand the rationale for them 3. Understand that children’s behaviour is a result of a complex range of influences 4.  Know and understand some strategies for managing children’s behaviour Assignment guidance Word count:  2000 words – Essay Essay (2000 words) You are expected to show that you have engaged with content that has been covered in module sessions and to do plenty of reading from academic literature using the extensive module reading list (and beyond if you wish). We would like you to show that you can develop an argument and use supporting evidence to good effect. In particular the candate should address the following rubric criteria Knowledge/understanding: make sure you have done extensive reading from academic sources and show that he/she has critically engaged with the concepts and theory we have covered in the module; · Argument: make sure that you have a clear line of argument and are using source materials effectively to support this; · Effective communication: You need to be writing in an academic style, using effective language.  The candidate needs to structure your assignment in a logical way; Meeting the requirements of the assignment: making sure that you keep the essay question in mind so that your answer is relevant, pertinent and interesting Use of Generative Artificial Intelligence (Gen AI) This assessment is categorised as ‘amber’ according to the University’s Generative AI guidance for taught students. This designation is in line with the ‘amber’ specification that ‘you are permitted to use AI tools for specific defined processes within the assessment … as specified by the tutor’ (Guidance version 1.1, 11/2023, p. 10).   The purposes for which you may use Gen AI in this assessment are to generate ideas and to stimulate your own thinking; as a support tutor, for example to generate feedback on your work; to translate parts of source texts (words, phrases or sentences); or to proof-read your assignment text.  If you do use Gen AI, you must state clearly how you have used it, specifying the following:  · name and version of the Gen AI system used e.g. ChatGPT-4.0. · publisher (company that made the AI system) e.g. OpenAI. · the URL of the AI system. You may not use Gen AI to produce sentences or longer stretches of your assignment text. As an example, you may use an app such as Grammarly to proof-read your assignment text, but you may not use such tools to write or re-write text.  If you are in any doubt about the use of Gen AI, please consult your module leader. Supplementary guidance Please refer to the assessment section on Minerva General guidance  Follow this link to the skills@library academic skills resources: Academic skills | Library | University of Leeds Assessment criteria and process Your assignment will be assessed against the four module learning outcomes: as well as the undergraduate assessment criteria (available in Annex 10.1 of the Code of Practice on Assessment). Please also familiarise yourself with the information on second marking, moderation, etc. available in the Code of Practice on Assessment.  Presentation and referencing For the essay, please cite sources in the text and write a reference list at the end. For references and citations It is extremely important to differentiate between your own words and those of others when writing an essay.Failure to do so can lead to accusations of plagiarism, which is considered a serious academic offence. If your work is found to be plagiarised, you may fail the module and, in some cases, fail the entire course without the option to resubmit. I mention this not to cause alarm, but to stress the importance of developing good referencing habits. Always give clear references to your sources of information. You must develop the habit of recording bibliographic information while you read. This practice will save you time and effort when you write, and it will ensure that you give proper credit to your sources. Referencing systems work in two parts: the brief references that you make within the text; and complete references to books and articles at the end of a piece of work. The list of references at the end is called References and should include full details of all the sources you have quoted or referred to within your essay. Citations in the text: Give the author's surname and publication date in brackets at an appropriate point in the sentence. Page numbers are helpful but not essential, except in the case of quotations, when they should always be given. Citations should be included as part of the relevant sentence, so a full stop would go after the brackets. This applies to books, chapters from edited books, and articles. The University Library website has several excellent online resources on referencing: https://library.leeds.ac.uk/skills-referencing. Harvard referencing: https://library.leeds.ac.uk/skills-referencing-harvard • The reference list is written in alphabetical order by surname. • Surnames to be written in lowercase, e.g. SMITH, R. will be Smith, R. • No inversion of initials for 2nd author, e.g. KANE, M. and W. TROCHIM will be Kane, M. and Trochim, W. • If there are more than two authors include “and” before the final authors • Brackets are not used in the reference list. • Commas to be included in the Harvard in-text citation, e.g. (Smith, 2008) (Smith, 2008, p.3). The University Library website has a list of examples showing how you can include different kinds of references in your text and you should make sure that you look at it: https://library.leeds.ac.uk/info/311/referencing/38/citing_quotations_harvard_style/1. For modules in the School of Education students should use the official University of Leeds version of the Harvard referencing style. (called leeds Harvard). Guidance on how to source citations within the text and how to reference different types of material is available on the referencing pages of the Library website. Marking of all submitted coursework will be informed by this guidance and will correspond to the style. outlined on the Library’s referencing website pages.

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[SOLVED] 125732 Advanced Corporate Finance 2025 S1R

125732, Advanced Corporate Finance, 2025 S1 Case Study, 20% of assessment Due date (time): 14 April (23:59 pm) Total 7 questions The chosen NZ firm for the case study is Fletcher Building Limited. The company’s financial statements, historic (stock) prices and market index (NZ50) are in the same file (FBU Statements and others), which is available in the Stream. You can also find the required risk-free rate (we use secondary market government bond closing yields, 10 years, in Wholesale Interest Rates sheet) in the file as well. Your tasks: 1)   Work out the answers for the following questions in Excel work sheets. You are required to use the formula to do the calculation (so that I can keep track of your calculation steps). You may fail to get the marks if the required formula is not provided. (You should talk to me if you don’t know how to use Excel formula.) 2)   You can provide your answer in the same Excel file (such as in the Workings sheet). For an excellent result, you need to discuss the implied assumption(s) used in answering each question. 3)   Submit your Case Study via Stream Drop Box before due date. 7 Questions to be answered in your study: (Q1) Transform. the “official” two years’ (ending 2020 and 2021) Balance Sheet (or Financial Position in the worksheet of FBU, in the file: FBU Statements and others. The simple version (in Workings sheet) is provided to help you organizing the transforming process) into “finance version” which should include four parts: Net Operating Assets, Other investments, Debt and Equity (See lecture notes of M1-2 for the steps). (Q2) Provide the Statement of Cash Flows (in indirect version, as shown in the textbook) for the last two years (2020 and 2021).  The procedures are explained in the Week 2 workshop and later lecture notes. Notice the difference between the indirect form (used in US and discussed the textbook) and direct form (used in New Zealand). The case is of a NZ firm and you are required to produce the statement in indirect form. (Q3) Calculate the company’s FCFs for the last two years (2020 and 2021). (using the contents in US version of balance sheet (created in Q1) to do the calculation to avoid the possible error of inconsistency). (Q4) Estimate the beta value (with market model explained below) for the company using the information provided: ●    Using the market model to estimate the equity beta: Re = Alpha  + Beta * Rm + ε Using historic data to run the above regression: Re is for FBU historic share returns (end-of-month or last trading date in a month, monthly returns from 3/1/20 to 1/7/22) and Rm is the same period NZ50 index monthly returns (as market return rate). The market prices of the stock are provided in the same file for financial statements. You are required to calculate the returns with the formula for the company and market index:  Rt(Firm)=(Pt-Pt-1)/Pt-1,  Rt(Index)=(It-It-1)/It-1  (for the month of t) where Pt  is the Last price of the stock and It  is the Open value of the market index. (Q5) Estimate the WACC for the company: Estimate the expected debt return with the following steps: ●   Calculate the last two years (2020 and 2021) actual interest rate for the debt: Debt Rate 2021= interest expense 2021/(D(2020) + Debt(2021)) Note: We use the “Average” level of Debt in the above rate calculation. Use this average debt rate in the following WACC calculation for future years (after 2021). Estimate the expected equity return: ●    Using the CAPM method to calculate the expected equity return: Re = Rf + (Rm-Rf) *Beta Beta: obtained from the above Q4 regression result Rf: Use recent (June 2021) Secondary Market Government Bond 10-years yields (in the sheet of Wholesale Interest Rates) as a proxy for future risk-free rate in the case study. Rm: we find the average return of NZ50 index is close to zero, which is not the case for the next future years (Explain this). we use 4% as the market index return for all future years in this case study.) Calculate the WACC ●   Calculate the equity market value (Ve): Ve= number of shares * share price (1 July 2021) (The number of shares is calculated from the form.: Net earnings/Net earnings per share, both values are provided in the  “SUPPLEMENTARY DISCLOSURES: EARNINGS PER SHARE” in the sheet of Financial Statements) ●    Using the debt book value as the current debt value (Vd) and the above calculated equity market value (Ve) to calculate the weights: We=Ve/(Ve+Vd), and Wd=Vd/(Ve+Vd) ●   Calculate the WACC=We Re + Wd Rd (1-T), using T=28% for this case. (Q5 end) (Q6) Forecast future three years (2022 to 2024) annual cash flows for the New Zealand case company: (The following procedures will be discussed in the lectures) ●    Estimate the growth rate of sales (using the average annual sales growth in the past  years (of 2018-2021, data is available in the sheet of Financial Summary in this case). ●    Estimate the future costs and investments (make your assumption, if necessary, e.g. constant ratio of W.C./sales). You need to make some assumption on how the fixed assets might be changed in the forecasting period. ●   Calculate the next three years (2022-2024) FCFs. (Q6 end) (Q7) Calculate the Present Value (discounting the FCF by the WACC of the operating assets, which is calculated in the above question) of the Net Operating Assets (assuming 2% of growing in FCF after 2024 and then get the firm value (which is the sum of Net Operating Assets and other investments) and equity value. Do a simple risk analysis (write a paragraph about 200 words). You may use the information provided in the annual report. (List and describe two risk factors that, you think, are important and discuss their possible influence on the firm value. For example, if the sales growing rate is 1% less than the planned, what will happen to the firm value? To get more ideas of this, you may read the contents of the annual report provided in the Stream. (Q7 end)  (and end of the case study)

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[SOLVED] MIS 632 Data Management LAB

MIS 632 Data Management LAB Catalog Description This 1-credit lab course provides an experiential learning component for MIS 631 Data Management for which it is a co-requisite. MIS 632 provides hands-on experience in designing, implementing, and querying databases.  The relevant software is introduced using demonstrations, in-class exercises and homework exercises that are closely tied to and executed in synch with the conceptual and theoretical material covered in MIS 631. Specifically, students will gain hands-on experience in: (i) ERWIN - a widely used commercial tool for representing conceptual (e.g., E-R diagrams) and logical data models (e.g., relational DBMS), (ii) PostgreSQL (relational database software), (iii) SQL Structured Query Language) and (iv) a NoSQL document data store. Students in MIS 632 must also be enrolled in the associated 2-credit lecture course MIS 631 Managing Data course.  Course Objectives Through immersion in practical applications of the concepts, data models and theory in the associated co-requisite course MIS 631, students are prepared for a world in which data and evidence-based decision making are increasingly important.  The skills gained in this course are essential for employees in the information technology and analytics departments in all major industries. List of Course Outcomes Through their exposure to case studies, in-class demonstrations and exercises, assigned homeworks and a team project, students will be able to: 1. Understand the major phases of the data life-cycle (analysis of business needs, design, implementation and use) 2. Develop conceptual data models (E-R diagrams to design relational databases) 3. Implement a database in PostgreSQL 4. Build SQL queries 5. Work in a team to execute a database development project. Corequisites: MIS 631 Students in MIS 632 must also be enrolled in the associated 2-credit lecture course MIS 631.  Grading Percentages:  HW  40%     Class work  40%     Mid-term      Final      Projects    Other  20% Class Participation   Credits:  1 credit      Other For Graduate Credit toward Degree or Certificate   Yes  No  Not for Dept. Majors  Other Textbook(s) or References  · Software and tool documentation for ERwin and PostgreSQL Support for Team Project in MIS 631       Students will be assisted in their work on the MIS 631 class project in which teams of 3-4 students will develop a database in an application of their choice. There will be several deliverables including a proposal, progress update(s), final report and a final in-class presentation. Students use this project to develop skills required for the entire data life cycle. · Project proposal: define a business problem and survey potential techniques for the data used to support the associated business process. · Model development through the development of a conceptual database model and its realization in a relational (or other) database · Application deployment and management: develop plans for deployment of the database     Mode of Delivery Class Online Modules Other Program/Department Ownership: MIS When first offered:   Fall 2024 Department Point of Contact and Title: Joe Morabito Syllabus Note:  Each MIS 632 lecture period will consist of a 10-20 minutes overview of the assignments, which will be completed by teams of students in the remainder of the class session. Date MIS 632 Subject MIS 632 Assignment Jan 27 Installation of relevant software Install ERwin Install PostgreSQL Feb 3 Relational database theory (Normal Forms) Normalization exercises ERwin tutorial Feb 10 Relational database theory (c.t.d) with examples Design simple E-R models Feb 18 Basic SQL: syntax & grammar Construct SQL queries Feb 24 Advanced SQL: update/insert/delete/subqueries Construct complex SQL queries   Mar 3 Advanced SQL (c.t.d) SQL queries exercise Mar 10 Extended ER data modeling Extended ER with examples Mar 17 Mar 24 Spring Break – No Class Schedule Conflict – No Class   Mar 31 Extended ER data modeling (c.t.d) Construct a use case for various business scenarios Apr 7 Introduction to DDL and performance tuning Develop a database model in PostgreSQL using DDL Apr 14 Application of data modeling in business domain Business examples Apr 21 Database organizational structure: star schema Use ERwin to construct a simple star schema Apr 28 Develop cloud architecture Install a database in the cloud (AWS) May 5 Student projects presentation    

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[SOLVED] EMATM0067 Text Analytics Coursework Spring 2025

EMATM0067 Text Analytics Coursework Spring 2025 Deadline: 13.00 on Monday 28th April Overview This coursework is worth 50% of the unit. It wiII take you through severaI text anaIytics tasks to give you experience with appIying and anaIysing the techniques taught during the Iabs and Iectures. The work wiII be assessed through both your code and your written report, in which you shouId aim to demonstrate your understanding of text anaIytics methods, evaIuate the methods criticaIIy and incorporate ideas from the Iectures. We recommend that you first get a basic impIementation for aII parts of the required assignment, then start writing your report with some resuIts for aII tasks. You can then graduaIIy improve your impIementation and resuIts. TotaI time required: 40 hours. Support The Iecturers and teaching assistants are avaiIabIe to provide cIarifications about what you are required to do for any part of the coursework. You can ask questions during our Iab sessions, post questions on MS Teams, or to the BIackboard discussion forum. If you don’t want to share your question with the cIass, pIease contact Edwin by emaiI ([email protected]). Part 1: Climate Sentiment – Jupyter Notebook (max 32%) Many companies are required to pubIish (corporate discIosures’  documents containing usefuI information about the business and its finances. The information in these discIosures is very usefuI for investors, reguIators and other stakehoIders. For exampIe, discIosures may present cIimate-reIated deveIopments as risks or opportunities for the business. In this part of the assignment, you wiII compare cIassifiers that cIassify the sentiment of a text as a cIimate risk, an opportunity, or neutraI. We wiII be working with the CIimateBERT dataset:Webersinke et aI., 2022. Part 1 contains tasks 1.1 (13 marks), 1.2 (8 marks), and 1.3 (11 marks). Please see the accompanying Jupyter notebook text_analytics_part1.ipynb for detaiIs, which contains a series of tasks for you to compIete. Your answers to tasks 1.1, 1.2 and 1.3 shouId be saved in the notebook   itseIf, which you wiII need to submit. Submission detaiIs are in the notebook. Task 2: Climate Sentiment – Report (max. 38%) 2.1. Present an evaIuation of the three methods impIemented in Part 1. Your evaIuation shouId be presented as the first page of your report. Your evaIuation shouId incIude the foIIowing points: •     ExpIain the modifications you made to the naïve Bayes cIassifier in task 1.1c: what did you change, how does it heIp the cIassifier, and was there anything you tried that didn’t work? (3%, max. 150 words) •     Present a comparison of results in a table or plot, along with your interpretation of how well each method worked. Your discussion should mention concepts from the lectures (e.g., transfer learning) and what could be improved in future work. To inform. this discussion, you may want to analyse some examples of misclassified texts. (10%). 2.2. Using the dataset, can you identify topics that are associated with climate-related risks or opportunities? •     Explain the method you use to identify themes or topics. Make sure to motivate why you chose this approach and discuss its limitations. •     It is important to test and compare different approaches to find out what works best for this dataset. Compare two variations of your method, e.g., by changing an important step or parameter. •     Show your results (e.g., by listing or visualising topics associated with risks or opportunities). • Interpret the results and summarise the limitations of your approach.    (25%) Suggested length of report for task 2: 2.5 – 3 pages. Task 2: Named Entity Recognition on Twitter (max. 30%) Social media contains a wealth of information about public opinion and events, but this is often contained in unstructured text data. Your task is to build a tool for named entity recognition from Twitter posts that can help extract information about particular people, organisations and locations. To train and test the NER tagger, we will use the Broad Twitter Corpus (BTC) dataset, published by Derczynski et al., 2016.You can also find useful information on theHuggingFace dataset page. 2.1. Design and run a sequence tagger for the BTC dataset. Refer to the labs, lecture materials and textbook to identify a suitable method. You may choose any sequence tagging method you think is suitable, and you may wish to experiment with some variations in the choice of features or model  architecture to help justify your design. In your report: • Briefly explain your chosen method and its main strengths and limitations. •     If your model uses its own tokenizer, explain how you align the tokens with tags (this step is only needed if you use a neural sequence tagger that requires a particular tokenizer). •     Show an example entity span from the dataset, that illustrates how entity spans are encoded as tags in this dataset. •     Detail the features you have chosen, why you chose them, and hypothesise how your choice will affect your results. • Higher marks are given for good, well-justified model design.    (17 marks) 2.2. Evaluate your method, then interpret and discuss your results. Include the following points: • Explain your choice of performance metrics and their limitations. • Describe the testing procedure (e.g., how you used each split of the dataset). •     Show your results using suitable plots and/or tables. •     Do your methods make any particular kinds of error? Show some examples of mislabelled sentences and suggest how the methods could be improved in future.   (13 marks) Suggested length of report for task 2: 2 pages. Implementation The lab notebooks provide useful example Python code, which you may reuse. You may libraries introduced in the labs, or others of your choice. For tasks 2 and 3, you may write your code in either Jupyter notebooks or standard Python files. Report Formatting • Absolute maximum 5 pages o   References do not count toward the page limit. o  Aim for quality rather than quantity: you will receive higher marks if you write concisely. •     To set the page layout, fonts, margins, etc., we recommend using the template from an academic conference, such as LREC-COLING 2024 if writing the report in Latex o  You can use this template directly to write in Latex or follow the formatting style. in Word, Libreoffice, etc. o   You don’t need to include an abstract or introduction or conclusion. o   Please number your answers to each task clearly so that we can find them. o   No less than 11pt font o  Single line spacing o  A4 page format • The text in your figures must be big enough to read without zooming in. Citations and References Make sure to cite a relevant source when you introduce a method or discuss results from previous work. You can use the citation style. given in the LREC-COLING 2024 style. guide above. The details of  the cited papers must be given at the end in the references section (no page limits on the references list). Please only include papers that you discuss in the main body of the report. Google Scholar and similar tools are useful for finding relevant papers. The‘cite’link provides bibtex code for use with latex and references that you can copy, but beware that this often contains errors. Submission •     Deadline for report + code: please see first page. •     On Blackboard under the“assessment, submission and feedback”link. Please upload the following three files: 1.   Your submission for task 1: please see the details in the Jupyter notebook. It should be submitted to the submission point“Text Analytics Part 1 Notebook”. 2.   Your report for tasks 2 and 3 as a PDF with filename .pdf, where “”is repIaced by your student number from eVision (starting with a (2’, not your username). o   UpIoad this to the submission point marked“Turnitin submission point - Text AnaIytics Coursework”. o Please don’t include your name in the report itself: to ensure fairness, we mark the reports anonymousIy. 3.   Your code for tasks 2 and 3 a single zip file with filename .zip. o   Inside the zip fiIe there shouId be a singIe foIder containing your code, with your student number as the foIder name. o   PIease remove datasets and other Iarge fiIes to minimise the upIoad size. o   UpIoad this fiIe to the submission point“Text AnaIytics Parts 2 & 3 Code”. o   For tasks 2 and 3, your marks wiII be based on the contents of your report, rather than for good code structure or styIe. Assessment Criteria To gain high marks, your report wiII need to demonstrate a thorough understanding of the tasks and the methods used, backed up by a cIear expIanation of your resuIts and anaIysis or errors. Marks wiII be awarded for appropriateIy incIuding concepts and techniques from the Iectures.

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[SOLVED] ACCT7106 Financial Statement Analysis Individual Project Semester 1 2025

ACCT7106 Financial Statement Analysis – Individual Project Semester 1, 2025 PROJECT OVERVIEW AND ADMINISTRATION Objective The project provides students with an important opportunity to ‘learn by doing’. You will be asked to apply most of the analysis from the first five seminars to an Australian public company. Start working on the project early. Leaving it to the last minute will be stressful and may result in poor performance. Company You need to analyse the financial statements of Super Retail Group (ASX ticker: SUL). The financial statements and notes in Excel format and the annual reports for Super Retail Group for FY 21, FY 22, FY23, and FY24 will be shared with you on Learn.UQ. Deliverables The project consists of both short answer questions and Excel spreadsheet work. Please submit answers to short answer questions in one Word or PDF document (PDF document is recommended) and answers to spreadsheet questions in one Excel workbook, as indicated in the questions. Page Limits I have specified page limits for the questions that are to be answered in the Word document. These are maximum page limits. I will enforce maximum page limits by simply not marking any material that appears beyond the page limit for a particular question. You do not necessarily need to write as much as the page limit if you can provide a correct, but succinct, answer. Cover pages and references lists are not counted towards the page limit. Formatting Your written answers should be in A4 page size, in Times New Roman font, 12pt font size, at least single line-spacing, margins at least 2cm on all sides. Please clearly separate and label your written answers to each question. Your Excel file should be formatted neatly and following the conventions described in class. Marking Guide A marking guide is provided at the end of the document. This contains the number of marks assigned to each part of the project. Frequently Asked Questions (FAQ) I will issue, and may update, an FAQ addressing frequently asked questions about the project. Please ensure that you check the Assessment tab on Learn.UQ on a regular basis. Submission of the Project The project is to be completed individually. The due date for the project is 13:00 (Monday) April 7, 2025. Cover Page Please include a cover page at the beginning of your Word or PDF document with your name and student number. This is not strictly necessary, as Learn.UQ manages assignment submissions and matches them to  students, but it provides a useful backup for me to identify the  author of each submission. Please include your name and student number in the excel file as well in “Student Details” worksheet. Project Submissions Instructions For the project, please submit two files by the submission date/time: -     Your written answers must be submitted through the Turnitin link on Learn.UQ. Please use a format that Turnitin can accept, such as a Word document or PDF. -     Your Excel file must be submitted through the Blackboard submission link on Learn.UQ. Note that both your written report and Excel workings must be submitted to achieve any marks. This is because I need to see your workings for financial calculations. Retention of Submission Receipts Please keep all submission receipt numbers provided by submission systems such as Turnitin and Learn.UQ as proof of your submission. Email Backstop If you encounter any problems with the submission systems (Turnitin and Learn.UQ), please alert technical support and me of the problem. (You may email me a copy of your assignment if you have trouble submitting through the normal systems and if you are concerned about incurring a late penalty due to technical problems.1) Late Submission of Assessment Unless you have an approved extension, you will lose 10% of your assessed grade for the assignment for each 24-hour period (or part thereof) after the designated due date-time that your submission remains outstanding.2   Your submission time is based on the later of when your written component is submitted and when your Excel file is submitted, so please make sure to submit both on time. To be clear, you will  incur  a  late  submission  penalty  even  if  one  component  is  submitted  on  time  and  the  other component is submitted late. Please note that the 10% penalty rate for submission are set by UQ policy, not by me. Multiple Submission ‘Attempts, Some submission systems, e.g. Learn.UQ, allow students to submit multiple ‘attempts’ at an assessment before the due date. Only the most recently submitted attempt will be graded, unless otherwise agreed between the course coordinator and the student involved. 1   Note that work received by email will still be subject to plagiarism checking through Turnitin. 2   Times will be based on the submission time recorded by Learn.UQ, Turnitin, the UQ email system, or any other authorised submission system as the case may be. For the avoidance of doubt, 24-hour periods will be based on calendar days, i.e. inclusive of weekends and public holidays. UQ sometimes grants extensions under limited circumstances. These extensions specify a later due date. The due time of day will remain the same, e.g. if the assignment is due by 13:00 on the due date, then it is due by 13:00 on the extended due date. For the avoidance of doubt, in the event that a student submits the assignment on time and then submits a different version of their assignment late, I will mark the late submission applying the normal late penalty. Applying for an Extension I do not decide on extensions. These are decided by UQ professional staff. Please see the instructions in the course profile. Getting Started To help you, I will provide my spreadsheet for JB Hi-Fi, which you can adapt to the allocated company. DETAILED PROJECT INSTRUCTIONS AND MARKING GUIDE Complete the following tasks in your Word document or Excel workbook as indicated in the question. A. Reformulation and Accounting Analysis (Classes 2-4) You will be given an Excel file with the raw financial statements and notes for the company, which you should use as inputs in the reformulation. This information was obtained from the 2022, 2023 and 2024 annual reports of the company. These annual reports have been shared with you on Learn.UQ under Assessment tab. You should go through the annual reports carefully to better understand the company’s accounting. Answer the following in your Excel workbook in the indicated worksheet/tab: A1 Balance Sheet Reformulation (Excel worksheet: BS_R) •    Reformulate the Balance Sheet (including any relevant information in the notes), following the approach taught in class, for FY21, FY22, FY23 and FY24. •    For ambiguous accounts, state any assumptions you made with justifications in the Excel file next to the relevant account (in red text). You should consult the annual reports to help you to interpret the accounts. •    Ensure you provide workings for how you separated operating and financing cash. •    Ensure you utilise any relevant information from the notes to the financial statements. A2 Income Statement Reformulation (Excel worksheet: IS_R) •    Reformulate  the  Income   Statement/Statement  of  Comprehensive  Income   (including  any relevant information in the notes), following the approach taught in class, for FY22, FY23 and FY24. •    For ambiguous accounts, state assumptions you made with justifications in the Excel file next to the relevant account (in red text). You should consult the annual reports to help you to interpret the accounts. •    Ensure you utilise any relevant information from the notes to the financial statements. A3 Cash Flow Statement Reformulation (Excel worksheet: CF_R) •    Reformulate  the  Cash  Flow  Statement  (including  any  relevant  information  in  the  notes), following the approach taught in class, for FY22, FY23 and FY24. •    For ambiguous accounts, state assumptions you made with justifications in the Excel file next to the relevant account (in red text). You should consult the annual reports to help you to interpret the accounts. A4 Check Financial Statement Relations (Excel worksheet: Relations) •    Calculate FCF = OI (after tax) – ∆ NOA for all four years using inputs from your reformulated Income Statement and Balance Sheet. •    Calculate  FCF = NFE  (after tax) – ∆ NFO + d for all four years using inputs from your reformulated Income Statement, Balance Sheet and Equity Statement. •    Calculate ∆ CSE for all four years using data from your reformulated Balance Sheet. •    Calculate ∆ CSE = CI – d using data from your reformulated Income  Statement and Equity Statement. •    If your two FCF calculations produce different numbers, double-check that your reformulation is correct. If it is correct, explain why there is a difference between the two FCF numbers. o Hint: this may be due to dirty surplus gains/losses recognised directly in equity. •    If  your   two   ∆   CSE   calculations  produce  different  numbers,  double-check  that  your reformulation is correct. If it is correct, explain why there is a difference between the two FCF numbers. o Hint: this may be due to dirty surplus gains/losses recognised directly in equity. Tips: Follow the approach in Classes 2-4 and my JB Hi-Fi example spreadsheet. Use the Penman (2013) textbook as an additional source, but note that we are not doing everything described in the textbook. Please follow the Excel formatting convention described in class and in the document ‘ACCT7106 Excel Formatting Conventions’. There are marks allocated for correct Excel formatting. B. Ratio Analysis (Class 5) Answer the following in your Excel workbook. Please label the Excel worksheet as indicated in each part of the question: B1 Ratio calculations (Excel worksheet: Ratios) •    Calculate RNOA and ROCE for 2022, 2023 and 2024 following the approach taught in class. Use averages in the denominators, e.g. average NOA and average CSE. • First-level break-down: Calculate FLEV, NBC and SPREAD for 2022, 2023 and 2024. Show that: ROCE = RNOA + FLEV x SPREAD. • Second-level break-down: Calculate PM and ATO for 2022, 2023 and 2024. Show that: RNOA = PM x ATO. • Break-down PM into different components of OI (after tax): Calculate Core Sales PM as Core OI from Sales (after tax)/Sales. Also, calculate the other components of PM as Core Other OI (after tax)/Sales, Unusual OI (after tax)/Sales, and Operating OCI (after tax)/Sales. Show that: PM = Core Sales PM + Core Other OI (after-tax)/Sales + Unusual OI (after-tax)/Sales + Operating OCI (after-tax)/Sales • Break-down RNOA into different components of OI (after tax): Calculate Core RNOA from Sales as Core OI from Sales (after tax)/AvgNOA. Also, calculate the other components of RNOA as Core Other OI (after tax)/AvgNOA, Unusual OI (after tax)/AvgNOA, and Operating OCI (after tax)/AvgNOA. Show that: RNOA = Core RNOA from Sales + Core Other OI (after-tax)/AvgNOA + Unusual OI (after-tax)/AvgNOA + Operating OCI (after-tax)/AvgNOA • Third-level break-down: Break-down core sales PM and FLEV into their individual accounts. • Change in ROCE: Decompose ∆ ROCE using the following relation: ∆ ROCE = ∆ RNOA + ∆ SPREAD x FLEV (beginning) + ∆ FLEV x SPREAD (ending) Answer the following in your Word document. B2 PM interpretation •    Explain the reasons for changes in PM over the period FY22-FY24. You will need to research what happened to the company over this period to understand the major causes of changes in PM. Refer to your calculations in B1 [the breakdown of PM and third-level break-down of core sales PM] to support your answer, e.g. “gross margin up 27 basis points to 22.2%, driven by a combination  of  lower  discounting  and  continued  buying  improvements”  (2021  Earnings Conference Call Transcripts)”. •    Your explanations must be supported by evidence. Make sure to include in-text references or footnotes to your sources of information. Make sure to provide answer in your own words. B3 RNOA interpretation •    Explain the operational factors/reasons for changes in RNOA over the period FY22-FY23. You will need to research what happened to the company over this period to understand the major causes of changes in RNOA [e.g. “the company opened ten new stores, driving an increase in sales (2023 Annual Report)”]. Refer to your calculations in B1 to support your answer, e.g. the breakdown of RNOA & PM. •    Your explanations must be supported by evidence. Make sure to include in-text references or footnotes to your sources of information. Make sure to provide answer in your own words. B4 ROCE interpretation •    Consider the first-level break-down: ROCE = RNOA + FLEV x SPREAD. How does financial leverage affect the company’s ROCE over the period FY22-FY24? Has FLEV increased or decreased ROCE compared to RNOA? Why? If there have been significant changes in FLEV, discuss why they occurred. How much of the change in ROCE during FY22-FY24 is due to operative activities and how much is due to financing activities? Make sure to assess and discuss the effects that changes in accounting standards may have had. Refer to your calculations in B1 to support your answer, e.g. the breakdown ofFLEV and the decomposition of ∆ROCE. •    Your explanations must be supported by evidence. Make sure to include in-text references or footnotes to your sources of information. Make sure to provide answer in your own words. You may need to refer to the commentary at the beginning of the company’s annual report, company’s investor presentation, earnings conference call transcripts or newspaper articles to discover the cause of changes in the company’s performance and financial leverage. Total Page limit (For B2, B3 and B4 combined): 3 pages (A4; Font: Times New Roman 12pt) Tips: Consult Class 5 to help you. You do not have to do the third-level break-down of ATO as discussed in Class 5 and Penman (2013) Chapter 12. All Parts of the Assignment: •    Marks will be  awarded for clear and correct English expression. Please ensure your Word document is carefully proof-read. •    Marks will be awarded for ensuring your Excel file is correctly formatted and clearly presented. Please follow the Excel conventions described at the end of Class 2 and applied throughout the course. •    Marks  will  be  awarded  for  correct  and  complete  referencing  of  sources.  See  below  for referencing requirements. •    Note  that the use  of AI technologies to develop responses is  strictly prohibited and may constitute student misconduct under the Student Code of Conduct. Referencing and Research Requirements Some parts of this assignment require students to conduct relevant research about the company. For written answers, evidence of appropriate research and understanding of the company is included in the marks  allocated  to  the  question.  Appropriate  references  help  to  provide  evidence  that  you  have conducted research to support your answers. There are also a small number of marks allocated to proper referencing formats/conventions. Research I expect you to conduct research into the company and its operating environment. To be clear, I do not want you to reference academic research (e.g. journal articles), unless they are highly relevant to the company or the point you are making. This is a practical course, not an academic research course. The emphasis  of  your  research   should  be  on  practical  materials,   such  as,  annual  reports,  investor presentations, earnings conference call transcripts, company websites, industry reports, newspaper articles, analyst reports etc. Source of information should be credible. For example, materials produced by a company, by a major financial institution, by a financial data platform (e.g. Morningstar or CapitalIQ), or by a mainstream newspaper are credible sources of information. Examples of low credibility sources include (but are not limited to): obscure websites not associated with a credible source, and articles from obscure news organisations. To be clear, references to low credibility sources will not be taken as evidence of research and will generally result in low grades. In addition, much of the information available from low- credibility sources is also low-quality and so relying on this information will usually result in low grades. Referencing Proper referencing is crucial for two reasons: •     To comply with plagiarism rules. •     To provide evidence that you have conducted research in answering your questions. You must follow one of the following two approaches to referencing for the project: •     Brief in-text references AND a reference list. The in-text reference should be brief but clear, while the reference list should provide sufficient detail for me to locate the source information. Use one consolidated reference list for the whole project, rather than a reference list for each answer. •     Footnote references with  similar detail to a reference list. You may use  standard footnote referencing conventions like ‘ibid’ if you like. In-text references and footnotes should be included in every sentence where you make a claim that requires supporting evidence. Do not simply put one reference at the end ofan entire paragraph or page. Please include the page number or numbers when referencing a lengthy document, such as an annual report or investor presentation document. If you are referring to a note to the financial statements, you can also mention the note number instead of a page number. References are essential for direct quotes, for example: JB Hi-Fi describes its one of the competitive advantages as follows: “#1player in the Australian market with the opportunity for further consolidation” (2020 Annual Report, p. 19). References should also be used when you make a claim about a company that requires some evidence to support it, for example: Most of JB Hi-Fi’s revenue is generated in Australia (2020 Annual Report, p. 70). The following is an example of what to avoid: In the future, more and more people will choose to travel by air, or prefer aircraft as the main long-distance transportation. This is a claim without any evidence to support it. Who says that ‘more and more people will choose to travel by air’? A statement like this needs to be supported by a reference or by a surrounding sentence which provides evidence. Certain claims are so self-evident or obvious that they don’t need a supporting reference. However, note that these should generally form. only a small part of your answer. For example: Qantas faces virtually no threat of substitutes on most of its international flights, as Australia’s major population centres are geographically distance from most overseas destinations, so that travelling by other means (e.g. by ship) would take a prohibitively long time. It is obvious the Australia is geographically isolated from most overseas destinations, so no reference would be needed for this claim. But be careful with relying on a statement being obvious. It is generally better to provide a reference. There is also no need to put a reference if you are citing the class PowerPoint or the textbook, unless you are directly quoting. The following is a guide to how you might reference some typical sources: ence()Example Reference ListEntry (Detailed)Company annual repofor the company youare analysing(2020 Annual Report, p. X)JB Hi-Fi 2020 Annual Re for another company(2020 Harvey Norman AnnualReport, p. X)Harvey Norman 2020 A you areanalysing(FY20 Investor Present 020.A company announcement,other than the annual reportorinvestor presentation(ASX announcement, 4 August2020) ement,ryclosureofmetropolitan YoumayabbreviateAustralian toR  JuneJBHiFisetforbiggestprofitgrowthinyears JB Hi-Fi investor re Notes: •     For sources not listed in the table, use your common sense, but make sure you provide enough information for me to clearly identify the source. •     If you provide footnotes instead of in-text references + a reference list, then please provide as much detail as the ‘Example Reference List Entry’ column of the table.

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[SOLVED] MATHS 7107 Data Taming Assignment 2 Trimester 1 2025

MATHS 7107 Data Taming Assignment 2 Trimester 1, 2025 1 Background The Australian magpie was named after the European magpie, which has a similar appearance, but is actually quite distantly related.  It is possible that the bird on the South Australian coat-of-arms is a magpie, although it could also be a magpie lark, which looks remarkably similar to an Australian magpie. When the coat-of-arms was designed, the bird was called a “piping shrike” which is not a scientific designation, and the word “piping” could be used to describe the call of either bird.  So it is not clear if the designer meant the magpie or the magpie lark. The Australian magpie is found all over the country, and in some small populations in New Zealand and New Guinea.  It is only found in the southern hemisphere.  While they are all the same species, there is considerable variation in the appearance of birds from around Australia.  In particular, the magpies from southern Australia tend to have more white plumage on their back than the magpies from northern Australia. Magpies are extremely intelligent birds and have adapted to live amongst humans, often in clans of up to 20–30 birds.  Many magpies are found in cities and towns, with some birds even developing relationships with humans. They are excellent problem solvers and can readily learn new things from other magpies, other non-magpie birds and from humans.  They have even been known to figure out how to remove tracking devices placed on them by scientists.  Another well known aspect of the magpies is their distinctive call, which is very complex and musical. These attributes led to magpies being rated Australia’s favourite bird in the inaugural Australian Bird of the Year survey, and ranking highly every year since. But an important attribute of Australian magpies is that they are quite territorial,  and can be extremely aggressive, particularly when the magpie’s clan has young chicks.  It is very common for male magpies to swoop down at humans and either threaten them by flying very close, or even striking the humans with their beaks.  They are one of the few bird species known to have killed humans: three people have been confirmed to have died from magpie attacks. Australian news reports regularly feature warnings about the dangers of magpies . There are many suspected risk factors that can increase the chance of being swooped, for example it seems that young children and old adults at more risk than the rest of the population.  The most common trigger seems to be riding a bicycle (although your lecturer has strong evidence that they hate unicyclists even more than cyclists), and there are some techniques that cyclists use to deter the birds from swooping. The South Australian Tourism Council is hoping to encourage more visitors to the south-east of the state, which is typically much more densely forested than the north and west of the state.  However, their research has indicated that potential overseas tourists are concerned about dangerous Australian animals, which is keeping tourists away. This isn’t helped, since one of the types of magpie subspecies in south east SA is called the tyrannica.  To combat this, the Tourism Council has a new advertising slogan: “Visit south-east SA.  At least the magpies are pretty safe!” which will be part of their new advertising campaign.   This campaign will run for two years.   As part of this campaign, they are planning to sponsor a group of 500 tourists to live in a small rural camp called Safety Hill, which is set in a heavily forested area of south-east SA for the duration of the campaign.  The land area of the camp is only 40,000 m2 , but it contains approximately 1,200 trees. To support their new advertising campaign, they would like you to do some data analysis.  They have provided four sets of data from different regions in Australia and they would like to know how likely it is the sponsored tourists would be swooped while living in Safety Hill.  The data sets are labelled suburbs 0 .csv, ..., suburbs 3 .csv. You’ll need to provide intervals around your estimates, and the Tourism Council wants the intervals at the 93% level.   They would also like your opinion on whether the Tourist Council would be better with the alternative slogan: “Visit south-east SA.  At least we  don’t have cassowaries!” Conveniently for you, they have just started using R  and R  Markdown,  so they want your report as a PDF generated using R  Markdown.  The Council’s CIO studied Data Taming last trimester, so she wants you to only use commands from the course, so that she can easily see what analysis you’ve done.  In your  R  Markdown  code chunks: make sure that you do not set echo  =  FALSE so that she can see what R  code you used to generate your output. But of course, she doesn’t want to see irrelevant warnings or messages. But remember that your report is for the Director of the Tourism Council, who is not really a technical person, and who certainly doesn’t know R. So make sure you include descriptions allowing the average person to understand what you are doing and what the output means. 1.1 Number of digits When writing your own text, or USING the output from R: •  For integer results, report the whole integer. •  For non-integers with absolute value > 1: use 2 decimal places •  For non-integers with absolute value < 1: use 3 significant figures. For example: ◦  135.5681 ≈ 135.57 ◦  −0.0004586 ≈ −0.000459 Exceptions: • If you’re just PRINTING the output from R, then just keep the output as it is. - But if you have R  do the rounding for you then you need to conform to these two conventions listed above. • If your data has fewer digits of precision than specified above (eg.  because of the way it was stored in the original data, or because of the way it was calculated) then only report that level of precision. •  In some cases, the question may specify a different level of precision — in which case, do what the question says. 2 The data The Tourist Council has four datasets labelled suburbs 0 .csv, suburbs 1 .csv, suburbs 2 .csv and suburbs 3 .csv, with data from different areas of Australia, collected at the end of the year 2029.  Each dataset contains 4 columns: • REGION: The name of the region where the data was collected. • POST  CODE: The postcode of the region.  These identifiers are managed by Australia Post.  While the initial development of the postcode system tried to assign the numbers in a sequential pattern, based on geographical location, this has been abandoned.   So  there  is  no  necessary relationship between two regions and their postcodes. • STATE: The Australian state or territory in which the region is located.  They are denoted by: SA, VIC, WA, NSW, NT, QLD, ACT, TAS. • ELV: The average elevation of the region (measured in metres). • POP: The number of people living in the region. • AREA: The surface area of the region (measured in square kilometres km2 ). • LAT: The latitude of the region’s centre. • LONG: The longitude of the region’s centre. • SWOOPS: The number of swoops recorded in the region for all years 2025–2029. • TREES: The number of trees in the region. Each dataset has data on 720 regions. There is likely to be some errors in the data, so make sure you clean it before you do any analysis. 3 Data cleaning IMPORTANT! Make sure you only remove data that you must remove.  Do not just delete data because it is inconvenient.  You must have specific instructions from the client, or it must be an impossible value, before you remove any data from your analysis. Even then, you need to describe why it was removed. Only perform cleaning operations if you know there is a problem.  (Performing unnecessary operations on data is a good way to accidentally introduce errors.) So make sure you have clearly identified the unclean piece of data before you clean it, and explain it in your report. Instructions: •  There may be some duplicated rows, in which case remove the row higher in the list (ie. the one with smaller row number). •  Some test data may have been left in. Remove it. • If there are any values that are impossible then remove the entire row. •  There may be some other typos, so fix them if possible.  If they’re not possible to fix, then delete the entire row. 4 Your job Note Make sure you write text to explain what you are doing at each point and why you are doing it. You need to justify all the things you do or claim.  Also describe the results.  This report is for the Director, so aim your explanations at the average person and avoid jargon wherever possible. 1.  Load the correct dataset directly as a tibble (don’t load it as a general dataset and then convert it, as that can introduce errors). Output the first 10 lines of the dataset and the dimensions of the data set. 2. We want to clean up our data, but first we’ll put in an extra column of row numbers, so we can track some changes we’ve made to the data. •  Add a column at the far left of the dataset called RN that contains the row numbers. Output the first 10 rows of the dataset. 3.  Using dot points, identify what types of variables we now have in our data set, i.e., “Quantitative Discrete”, “Quantitative Continuous”, “Categorical Nominal”, “Categorical Ordinal”.  (Don’t just describe what data type they are in the tibble — you need to think about the type of variable in the context of the meaning of the data.) Make sure you provide some justification for your choice of variable types. •  Don’t just provide vague statements, but be very concrete about describing this particular set of data. 4.  Now clean the data.  Make sure you justify every step of cleaning that you do.  Then display the first 10 rows of the dataset, and the dimensions of the dataset. Note If you discover any problems with the data in the following questions then you should come back and redo this question before you submit. Your data should be clean and shiny from this point. 5.  Now it’s time to tame our data. •  Make your data set correspond to the Tame Data conventions on page 3 of Module 2.  You’ll need to use your answers to Q3. •  Also make sure the R  data types in your tibble match the variable types that you identified in Q3. • (Reminder: Your data should already be clean by this point.   You may want to  check here if there  is any more cleaning required.  If so, go  back to  Q4 and try again.) Output the first 10 rows, and the dimensions, of your clean, tidy and tame data set. 6.  Making sure you set the seed correctly choose a random sample of 500 regions from the dataset, and order them by the row numbers that we introduced in Q2. Then output the first 10 lines of the dataset and the dimensions of the data set. Note Use this random subset from Q6 for the remainder of the assignment. 7.   (a)  Now let’s get on with some analysis. Add two new columns to the data set: • spt: the number of swoops per one thousand people in the region. • tph:  the number of trees per hectare in each region.   (Note  that  you  may  want to peform an intermediate calculation here, to get to the final answer.) and remove the columns containing the post codes and the number of trees.  Output the first  10 rows, and the dimensions, of the data set. (b)  Describe what type of variable these two new columns represent (“Quantitative Discrete”, “Quantitative Continuous”, “Categorical Nominal”, “Categorical Ordinal”). Are the data types correct in the tibble? (Explain your answer.) If they are not correct, make sure you change them. 8.  Report the following statistics: (a)  The region with the highest number of swoops per 1000 people. (b)  The region with the lowest number of swoops per 1000 people. (c)  The state with the highest number of swoops per 1000 people, averaged over all regions in the state. (d)  The state with the lowest number of swoops per 1000 people, averaged over all regions in the state. (Make sure you write some text for your answer — don’t just present some code output. 9.  Generate side-by-side boxplots for each state, with the number of swoops per 1000 people on the y-axis. 10.   (a)  Now make a scatterplot to see if tph is related to spt.  Put the independent/explanatory variable on the horizontal axis, and explain why this is the explanatory variable.  Include a straight line of best fit on your plot. (b)  Does it look like there is a linear relationship between the two variables?  Explain why/why not, and also give some reason (based on the context of the data) for why the data might be in this shape. 11. We would like to fit a linear model of tph against spt for this data.   But we will first apply a Box-Cox transformation. (a)  Use the Box-Cox algorithm described on page 7 of Module 5 to obtain an estimate of λ .  (Extend the range of the search for -4 ≤ λ ≤ 4, in steps of 0.075.)  What is the estimated λ? (b)  Apply the transformation to create a new column called spt bc on the right of your dataset. Output the first 10 rows, and the dimensions, of the data set. 12.  Produce the following plots: (a)  a scatterplot of the Box-Cox transformed data (with a line of best fit), (b)  a histogram of the Box-Cox transformed data, and the corresponding skewness, (c)  a histogram of the non-Box-Cox transformed data, and the corresponding skewness. Write 2–3 sentences about this output and how it compares to the untransformed data. 13. We will now try to fit a linear model to spt bc using tph as the predictor. (a) Write down the general equation for the true linear model when fit to the entire population. Make sure you define all of the notation you introduce. (Hint:  this  equation  should include the  error terms, and contain the true parameters.) (b)  Now write down the equation for the line of best fit for a sample of the population, with all the estimated parameters. Make sure you use the correct notation and define it. 14.  Build a linear model in R, and use the model summary to find estimates for the model parameters.  Use these estimates to rewrite your equation from Q13 giving the line of best fit for your model.  Also write down the estimated distribution for the errors. 15.  Before we use our model for anything, we need to check if it satisfies the 4 assumptions for a linear model (as described on pages 12–15 of Module 6).  So now check if our model satisfies these assumptions.  Importantly, the client needs to understand the implications, so as part of your answer: •  Describe in your own words what each assumption means in terms of the specific linear model you have fitted. • If you refer to any graphs make sure you describe (in plain language) what is on the horizontal and vertical axes of those graphs.  Your description should be specific to the data used in this assignment, not generic statements. •  Give an explanation of why each assumption is, or is not, satisfied. •  Make sure you identify at least one possible problem with the Independence assumption. (Note that we are going to use a linear model regardless  of any problems that you find in the  assumptions, but it is always good to highlight any shortcomings of the model so the  client knows about them.) 16.   (a)  Use your model to find the mean number of swoops (per thousand people) over five years in a region with the mean number of trees per hectare. You also need the correct interval. (b)  Then use your model to predict the actual number of swoops that will happen to the tourists at Safety Hill during the campaign.  Again, you also need to provide the correct interval.  Pay attention to the units. (Hint:  you will need to transform your predictions and intervals back into the scale of the original variables.) 17.  There is some suggestion that black-backed magpies are more aggressive swoopers than white-backed magpies. Make a scatterplot to check if your data confirms or contradicts this claim.  Explain how you plot shows this. 18. Write a paragraph or two describing what you have found,  and how that is likely to affect the Tourist Council’s campaign. You can also discuss any observations or conjectures that you have.  (Everybody’s data will be different so there is no right or wrong answer here, as long as you justify your claim with reasonable arguments.) As part of your answer, give a concrete recommendation for whether the Tourist Council should instead adopt the alternative slogan. That’s enough for this report.  We might investigate this data further in Assignment 3.  (Then we can charge the client more money for another deliverable!) 5 Submission You must submit your assignment via MyUni.  Do not email it to the teaching staff.  Detailed instructions are on the assignment submission page in MyUni.  Make sure that all your output is relevant to the questions being asked. 6 Deliverable Specifications (DS) Before you submit your assignment, make sure you have met all the criteria in the Deliverable Specifications (DS). The client will not be happy if you do not deliver your results in the format that they’ve asked for.

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[SOLVED] CHME0034 HDS Applied Computational Genomics - coursework 2025

CHME0034: HDS Applied Computational Genomics - coursework 2025 Instructions As a Computational Genetics researcher, you have been asked to analyse a new case control dataset to characterise the common genetic determinants of schizophrenia. You will need to run basic quality control of the data and then run a genome wide association study (GWAS), accounting for covariates, and finally examine and discuss your findings using relevant online tools and resources. In your report, you should provide a commentary explaining why you have undertaken each analytical step. You should also present your results in detail with the aid of tables and figures where appropriate, in the format of a formal scientific report. The report should be between 2000-2500 words and must include code snippits for the key commands you used. Data All schizophrenia cases and the control sample have been recruited in the UK. In addition  to  raw  non-quality  controlled  genetics  data  in  plink  format  you  have  a covariate phenotype file available with participants’ ages at recruitment. The data and a few helper scripts for this assignment can be found on Moodle along with this description. Make sure to copy over the files to aristotle (aristotle.rc.ucl.ac.uk) before you start working. Quality control and data description (40 marks) Give a short introduction to the analysis. Describe details of the sample you have received and perform. basic quality control of the sample as described in our lectures. Make sure you report the result obtained at each step and discuss and describe the rationale behind it. Genome wide association study (30 marks) Perform. a genome-wide association study of schizophrenia. Visualise the results using Manhattan and QQ-plots. Repeat the analysis with adjustment for covariates. Report the findings of the analyses and provide a discussion of any differences observed. Interpretation and discussion of findings (30 marks) Use  the  genome-wide  association  study   (GWAS)  summary  statistics  you   have generated to identify candidate variants and/or genes by applying at least two online tools introduced during the course. Justify your choice of these tools and provide a step-by-step outline of how you implemented them. Present an analysis of your findings and discuss their potential biological relevance to schizophrenia. Additionally, explain the challenges associated with identifying causal variants and genes from GWAS data, both in general and in relation to your specific findings.

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[SOLVED] INF1003H Assignment 2 Information Systems Evaluation Plan

INF1003H Assignment #2: Information Systems Evaluation Plan In this assignment, you are writing a pIan to evaIuate an information system of your choice, ideaIIy one where you have direct knowIedge or informaI reIationships with those who do, who can act as informants for you. The imaginary audience for your pIan couId be your supervisor at work, or a prospective cIient, perhaps  either way, you are proposing it in a way that you wouId be abIe to take responsibiIity for carrying it out. Your pIan shouId incIude the three sections beIow, and it shouId address aII questions beIow that are reIevant to your context, in no more than 2,000 words (excIuding any references or appendices). This evaIuation pIan needs to be based on the Iecture on evaIuation of information systems. If you incIude references, the format shouId be APA. Part 1: Context 1.  BriefIy describe the organization, and the information system to be studied. Be sure to incIude any paper- based parts of the system and system boundaries. What is the roIe of the system in the organization? 2.  Describe the technicaI environment of the system, if this information is avaiIabIe   what hardware and operating systems are invoIved. If it is“in the cIoud”, is there a specific browser that must be used? 3.  If this information is avaiIabIe: was the system purchased or deveIoped in-house? When? Is any information avaiIabIe on the deveIopment process and design decisions made? 4. Are there any external entities that the system interacts with? This information wiII heIp you define the boundaries of the system. 5.  Describe aII user groups for the system, and identify any major non-user stakehoIders, e.g., business owners. 6. What comparabIe or competitive information systems are avaiIabIe for comparison? This is important because you wiII need to identify a system against which yours can be measured. Part 2: Question for Research 1. What is the motivation, or reason for doing the evaIuation research? 2. State your research question(s) for this evaIuation (e.g.,“Is it meeting the organization’s needs?”) 3. What are the possibIe outcomes of this assessment? (e.g., no action, repIacement, modification …) 4. In what ways shouId the system owner be cautious about how much the assessment can achieve? That is, what are the Iimits to what you beIieve can be concIuded about the system? Part 3: Method 1. Your method wiII aImost certainIy be anaIyticaI rather than interpretive, but you shouId provide a brief rationaIe for this choice. 2. What is the evaIuator reIationship (independent vs participatory vs empowerment), and why? 3. What will you be measuring, exactly? How do you propose to do this measurement? (Remember you are not actually carrying out this measurement, you are simply planning it.) 4. What competitive/comparable information system identified in Part 1 will be your comparator? 5. Are you including consideration of cost effectiveness or ROI? If so, what costs need to be determined?

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[SOLVED] Assignment 2 Transaction Management Mechanisms in the Pager Layer

Assignment 2: Transaction Management Mechanisms in the Pager Layer Introduction In this assignment, you will explore the implementation of the Pager Layer, a critical component of the CapybaraDB database system. The Pager Layer is responsible for managing the paging of data in and out of storage, ensuring efficient memory usage, data integrity, and transaction handling.  The Pager Layer: ● Handles read and write transactions, ● Maintains an in-memory cache of database pages, and ● Enforces ACID properties to guarantee data consistency. Through this assignment, you will deepen your understanding of how CapybaraDB implements transaction management. You will also get hands-on experience with cache management and journaling mechanisms. The concepts of journaling and cache replacement strategies that you will implement are essential for any database system managing transactions and fault recovery. Task 1: Implement the Journaling Mechanism In Capybara DBMS, we execute each SQL statement in one transaction. There are two types of transactions: ● read transactions ● write transactions  In a database connection, we cannot have two concurrent write transactions, but it is possible to have one write transaction and multiple read transactions. Journaling and ACID Properties ACID is a crucial set of properties that guarantee reliable transaction processing. It's an acronym that stands for: ● Atomicity: This ensures that a transaction is treated as a single, indivisible unit of work. Either all operations within the transaction are completed successfully, or none of them are. If any part of the transaction fails, the entire transaction is rolled back, leaving the database in its original state. Think of it as an "all or nothing" principle.   ● Consistency: This property ensures that a transaction brings the database from one valid state to another. It maintains the integrity of the database by adhering to predefined rules and constraints. This prevents data corruption and ensures that the database remains in a correct state after each transaction. ● Isolation: This deals with concurrent transactions. It ensures that multiple transactions can occur simultaneously without interfering with each other. Each transaction is isolated from others as if it were the only transaction running. This prevents issues like "dirty reads" or "lost updates." ● Durability: This guarantees that once a transaction is committed, its changes are permanent and will survive even system failures, such as power outages or crashes. The changes are stored persistently, ensuring that data is not lost. CapybaraDB relies on multiple strategies, including Journaling to achieve ACID transactions . Journaling is a technique used to ensure data integrity, consistency, and recovery in the event of a failure. It involves recording changes to the database in a log (or journal) before they are committed to the actual database files. For example, ● Transaction Journaling to track changes for recovery. ● Checkpoint Journaling to manage long-running transactions and system recovery. Your primary task in this assignment is to implement the core logic of a journaling mechanism within a Transaction Manager. This involves completing the functionality of the Pager class, specifically the methods responsible for managing write transactions and ensuring data durability. TODOs The codebase contains several "TODO" comments to guide you. These markers indicate the locations where you need to insert the implementation logic. Pay close attention to these "TODOs" as they directly correspond to the following key functions: ● Pager::SqlitePagerBegin() ○ This method is responsible for initiating a write transaction. The "TODO" within this function will require you to implement the necessary steps to start the journaling process. This may include managing a journal file, and recording the initial state of the database before any changes are made. ● Pager::SqlitePagerWrite() ○ This method prepares a specific page for writing. The "TODO" within this function will require you to implement the logic that writes the page into the journal. This is critical for rollback functionality. ● Pager::SqlitePagerCommit() ○ This method commits the transaction changes to disk. The "TODO" within this function will require you to implement the logic that finalizes the transaction. ● Pager::SqlitePagerRollback() ○ This method reverts changes made during a transaction. The "TODO" within this function will require you to implement the logic that restores the database to its state before the transaction begins. This includes reading the original page data from the journal and writing it back to the database. By completing these "TODOs", you will effectively implement a journaling mechanism that provides ACID (Atomicity, Consistency, Isolation, Durability) properties for your Transaction Manager. Key Areas to Analyze 1. Transaction Lifecycle: ● Your implementation should correctly handle the write transaction lifecycle, including begin, write, commit, and rollback operations. ● Tests will validate: ○ Changes are committed to disk only after a successful commit. ○ Rollback operations restore the database to its pre-transaction state without persisting intermediate changes. 2. File Management: ● Journal files must be created, updated, and managed appropriately during a transaction's lifecycle. ● Tests will evaluate: ○ Proper creation and usage of journal files during write operations. ○ Reverting changes when a rollback is triggered, ensuring the journal restores the database correctly. ○ Cleanup or invalidation of journal files upon a successful commit to maintain consistency. 3. Concurrency Handling: ● Tests will simulate concurrent access to ensure proper handling of read and write transactions. ● Key checks include: ○ Write locks prevent multiple concurrent write transactions. ○ Read transactions can proceed concurrently without conflict. ○ Mechanisms effectively handle deadlocks, race conditions, and maintain consistency in multithreaded scenarios. 4. Resilience and Recovery: ● Your implementation will be tested for its ability to handle unexpected interruptions, such as crashes or power failures, that lead to corruption on the database. Task 2: Implement the LRU Cache Algorithm Once a page is loaded from the disk into the memory, CapybaraDB manages these pages in its cache. The purpose of caching is to keep as many pages in the memory as possible and to reduce the number of disk accesses since disk IO can be slow and is usually the bottleneck of performance. When the cache is full, CapybaraDB needs to decide which page to evict to make room for new pages. There are two eviction strategies and policies supported by CapybaraDB: FIRST_NON_DIRTY and  Least Recently Used (LRU). You will need to complete the logic related to the LRU policy. // define the eviction policy enum class EvictionPolicy {   FIRST_NON_DIRTY,   LRU }; Least Recently Used Policy (LRU) Under LRU policy, when the cache is full, CapybaraDB should evict the page that has been least recently accessed. A page access can be through a get, a lookup, or a write operation. To achieve the LRU behavior, CapybaraDB’s pager layer maintains a doubly-linked list (lru_list_) and a hashmap (lru_map_); they are members of the Pager class (pager.h). You need to use these two data structures to achieve LRU behavior. TODOs The majority of your implementation will be done in pager_cache.cc. The existing code in the Pager layer will give you a general understanding on caching mechanisms. The codebase contains several "TODO" comments to guide your implementation. These "TODOs" highlight the specific logic you must implement for effective page cache management: ● Pager::updateLRU() ○ This function is crucial for maintaining the LRU behavior. The "TODO" within this function will require you to implement the logic that updates a page's usage information whenever it's accessed. This typically involves moving the page "most recently used" to the front of a linked list or similar data structure. ● evictPage() ○ This function removes pages from the cache when it reaches its capacity. The "TODO" within this function will require you to implement the logic that identifies the least recently used page and removes it from the cache. ● SqlitePagerPrivateCacheLookup() ○ This is a private method that searches the page cache for a specific page. The "TODO" within this function will require you to implement the logic that checks if a page exists within the cache and returns it in case its found. By correctly implementing these "TODOs," you'll create a functional LRU page cache policy. Objectives and Requirements 1. Task 0: Understand the Pager Layer(existing code): ○ Grasp the functions and structure of the Pager Layer within CapybaraDB. ○ Review the provided code structure and focus on the interaction between Page Cache, Pager Operations, Transaction Manager, and Journal Manager. 2. Task 1: Implement the Journaling Mechanism: ○ Focus on implementing journaling functionality for transactions. This includes creating, writing to, and restoring from journal files to maintain ACID properties during database operations. 3. Task 2: Implement the LRU Cache Algorithm ○ Implement the LRU (Least Recently Used) algorithm to manage page evictions in memory.

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[SOLVED] Assembly Functions

Assembly Functions In this assignment, you have a given file (attached to the assignment: “main.c”). It contains different structs and some test cases that you need to verify. You need to write assembly code for the following functions. Each function in a separate file (sumOfPowers.s, compareAges.s, findPaymentsSum.s): -    int sumOfPowers(int n); -    This function takes one input (n) and it should find the following sum: 12 + 22  + 32  + 42  + ... + n2 -    int compareAges(CUSTOMER* a, CUSTOMER* b); -    This function should test if  a->age == b->age. it returns 1 if they are equal; 0 otherwise. -    int findPaymentsSum(CUSTOMER* c, int num_of_pamyments); -    This function should find the sum ofall payments made by a given customer. In “main.c”, you have given different test cases that can help you test your code. Be sure that you get the expected output when you run each test case as follows: TestCase -1 expected output:   TestCase -2 expected output:   TestCase -3 expected output:   Extra Credits (0.25 Point) Write assembly code for the following functions. Save its code in (findSalariesSum.s): -    int findSalariesSum(EMPLOYEE e[], int size); -    This function should find the sum of salaries of a given array of employees Extra Credit TestCase (TestCase -4) expected output:   Important Notes: -    It is so important to submit a working program (Non-working applications will not be considered). -    You must submit one zip file only (that includes all your code files).

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[SOLVED] STAT 302 Winter 2025 Final Project

STAT 302 Winter 2025 Final Project The assignment should be completed in an R Markdown document.  Submit the assignment electronically on Gradescope by March 21, at 11:59 PM as a PDF file. No late submission will accepted. Each answer has to be based on R code that shows how the result was obtained.  R code has to answer the question or solve the task. NOTE: Please keep the following guidelines in mind:: •  Choose the most efficient code whenever possible. •  Keep your code as short as possible. •  Use only base ‘R‘, unless the instructions explicitly request you to use a specific library. •  Use chunk options to suppress unnecessary output or messages in your code. •  Ensure your code fits within the page and does not extend beyond the margins in the PDF. •  Check the grading rubric for all the items which will be graded in this Assignment! The total number of points of this assignment is 60 points. Good luck! K means clustering In this project, you will implement an unsupervised learning technique called k-means clustering.  This method is used to partition a dataset into k distinct clusters based on the similarity of the data points.  The goal of k-means is to minimize the variance within each cluster, so that points within the same cluster are as similar as possible. While there is a built-in function in base R that can perform k-means clustering, you are not allowed to use it in this project. Instead, you will manually implement the algorithm from scratch. The k-means algorithm works as follows: •  Choose k initial centroids (typically randomly selected points from the dataset). •  Assign each data point to the closest centroid, forming KK clusters. •  Recompute the centroids of the clusters by calculating the mean of all points assigned to each cluster. •  Repeat the assignment and update steps until the centroids no longer change (or change very little), indicating convergence. Your task is to replicate this process and implement the algorithm manually using base R functions, without relying on any built-in R functions  (such as the knn() function) and without relying on any specialized clustering libraries. Consider one of the following datasets: This Taylor Swift dataset from a previous homework, urlRemote  

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[SOLVED] ECON10005 Quantitative Methods I 2025 Semester 1 Statistics

ECON10005 – Quantitative Methods I Data Analysis Report 2025 Semester 1 1. Purpose This assessment task allows students to critically analyse real-world data and present their findings as a data analysis report. This task prepares students for the requirements of many business graduates employed within the public and private sectors. By completing this task, students are expected to develop their critical and creative thinking skills, presentation and written articulation of quantitative arguments, and collaborative skills. 2.   General Instructions to Students ●   Read all instructions in this document carefully. ●   The first Draft Task carries 3% of the total assessment for ECON10005. ● This is a compulsory group assignment: your tutor will put you into groups of four students from the same tutorial during week 3 tutorials. Please attend the tutorial you are registered for to facilitate the grouping. Individual assignments will not be accepted for this subject. ●   The first draft has a word limit of 700 words. o No penalty for submissions with a word count within 10% of the word limit. o Graphs, tables, equations and appendices do not count toward the word limit, provided they do not contain substantive explanation, analysis, or discussion. ●   All submissions are checked for plagiarism and originality. Students should be familiar with the university’s academic integrity policy, available here. o Explanation, analysis, or discussion generated by AI software (including ChatGPT) is not permitted in this assessment. More details are here. ●   Students should rely only on the material discussed in Weeks 1 and 2 lectures in   preparing this report. All statistical analyses should be conducted using Microsoft Excel. o Students are not required (nor would it be advantageous) to consult external material or provide a reference list. ●   Students may use the Ed Discussion Board to clarify questions about this assignment. o Students should NOT upload parts of their assignment (including calculations and written work) or discuss their exact approach to the assignment on the Ed Discussion Board. 3. Submission Guidelines ●   Assignment Submission o Submissions must be made using the link on Canvas before the deadline (4 p.m., Monday, 31 March). n Late submissions will receive no feedback and a mark of zero. n Students needing extensions for legitimate reasons must apply for an extension/special consideration. Information can be found in the subject guide. A faculty team oversees these applications. n Issues about formatting or incorrect/incomplete versions of submitted assignments are not considered valid grounds for an extension. o Student should submit two documents and a short survey for this assignment: n A PDF file with answers to the tasks described below (report). n An Excel file with the data and all the workings (descriptive statistics and graphs). Every graph and descriptive statistic created for the report should be included with clear labels in this Excel sheet. n A short survey assessing each member’s contributions. o Students should refer to the Subject Guide (available on Canvas) for the extension policy applicable to this assignment. ● Assignment Formatting o The report should include a cover page with your Student ID, tutorial time and location, and tutor’s name. o The report must be typed and compiled into a PDF before submission. n The  PDF  file  should  have  all  (and  only)  relevant  graphs  and  figures included. n In your writing, make sure to provide references to the tables and figures you use. Here is an example, “ … . as presented in Table 2, the mean of variable X (mean = xx) is higher than the mean ofY (mean = yy)” . n Handwritten assignments will not be marked, and any handwritten additions (including  graphs  or  equations) to  otherwise typed  assignments will be ignored. n Please make sure to label the graphs and tables correctly. o Excel file should be submitted without converting to PDF or any other format. 4. The Task You are a property market analyst working for a real estate investment firm that specialises in identifying trends in the Melbourne housing market. Your task is to analyse a random sample of 999 recently sold houses to uncover key insights about property values and the factors influencing them. The dataset includes the selling price of each property (in thousands of dollars), its distance from the CBD, the dwelling size, and the land size. Your analysis will help investors and homebuyers make informed decisions about Melbourne’s housing market. By exploring relationships between price, location, and property size, you will provide valuable insights into what drives property values and how different factors shape market trends. Here is a description of the data: Price:               Selling price (in thousands of dollars). Distance:         Distance from the CBD (in kilometres). Bld area:         Dwelling size (square metres). Landsize:        Land size (square metres). Large:              Whether the property is on a large lot (1 = Yes, 0 = No). CBD:               Whether the property is in the CBD area (1 = Yes, 0 = No). Here is the structure of the data analysis report: 1. Title: Give an interesting title to your report 2. Introduction- 100 words ●   Briefly introduce the purpose of your report. ●   Explain what the dataset is about and what insights you aim to uncover. 3. Data Exploration (Complete this section using Task 1 and Task 2 below)- 300 words ●   Provide a summary of key statistics for each variable. ●   Use graphs (such as histograms, box plots, etc.) to explore information in the data visually. 4. Analysis of house prices (Complete this section using Tasks 3 and 4 below)- 250 words ●   Investigate how different factors influence price. For example: o Does distance from the CBD affect property prices? o Do larger properties tend to be more expensive? o Do houses closer to the CBD tend to be smaller/larger than those farther away? 5. Summary and Conclusion- 50 words ●   Provide a brief conclusion summarising your key insights. ●   Suggest any further questions or areas for future investigation. Note: Each student should attempt to complete all four tasks below, discuss them among the group, and then write the report. Task 1: ●   Provide the summary statistics and at least one appropriate graphical method to describe the distribution of house prices. Compare and contrast these statistics. Ensure that the following information is provided. o A summary statistics table including the sample size, mean, median, standard deviation, range, interquartile range, minimum, maximum and 90th  percentile for the price. o A histogram and a box plot to visually describe the distribution of the price. o An  approximately  100-word  explanation  of  the  descriptive   statistics  and distribution of the price and any other interesting/noteworthy features. Task 2: ●   Provide appropriate  summary statistics and graphs to describe the other variables (distance, dwelling size, land size, whether the property is large or not and if the property is in the CBD area or not). Ensure that the following information is provided. o A summary statistics table including the sample size, mean, median, standard deviation, range, interquartile range, minimum, maximum and 90th  percentiles (note: calculating all these summary statistics may not be meaningful for the qualitative variables, choose the appropriate ways to summarise them). o A graph for each variable to present the distribution for the variables o An  approximately 200-word  explanation of the descriptive statistics and distribution of the variables and any other interesting/noteworthy features. Task 3: ●   Use descriptive statistics and graphical methods to present similarities/differences in the prices by the property’s distance from the CBD, dwelling size, land size, if the property is branded as large or not, and if the property is in the CBD area or not. o Use an appropriate graph and a measure of association to show the relationship between house price and the distance to CBD. o Use an appropriate graph and a measure of association to show the relationship between house price, land size, and dwelling size (note: you are creating two graphs here, one for price vs. land size and another for price vs. dwelling size). o Calculate two sets of descriptive statistics for house prices based on whether they are in the CBD area or not (column F in the Excel file). o Calculate two sets of descriptive statistics for house prices based on whether the house is deemed large (column E in the Excel file). o An  approximately  150-word  explanation  of the  relationship between house price and other variables you have looked at. Task 4 ●   An   analysis  of  the  relationship  between   the   distance  of  the  property   and  the land/dwelling size: o Using appropriate graphs, show the relationship between the distance of the property from the CBD and the land size and the dwelling size (note: you are creating two graphs here, one for distance vs land size and another for distance vs dwelling size). o An assessment of the  strength of the relationships, defended by appropriate descriptive statistics. o An  approximately  100-word  explanation  of  the  relationship  between  the distance of the property from CBD and its size using the statistics and the graphs you have above.

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[SOLVED] MIS631 A B Spring 2025 Data Management

MIS631 – A, B Spring 2025 Course Title: Data Management Program(s): BI&A, MSIS Description: This 2-credit course focuses on data and database management, with an emphasis on modeling and design, and their application to business decision making. The course provides a conceptual understanding of both organizational and technical issues associated with data. The central theme concerns data modeling and databases.  We examine organizational approaches to managing and integrating data. Among the topics included are normalization, entity-relationship modeling, relational database design, SQL, and data definition language (DDL). Discussed are specific applications such as strategic data management, master data management, and physical database design.  The course concludes with a brief overview of Decision Support Systems, data warehousing and business intelligence, NoSQL databases (e.g., MongoDB) and cloud computing. The course includes several case studies and modeling and design projects. Students in MIS 631 must also enroll in the associated 1-credit lab course MIS 632 Managing Data Lab. Course Objectives: 1. Understand the role of data in the competitiveness and strategy of organizations 2. Design relational databases in normalized form. 3. Build SQL queries 4. Develop and critique entity-relationship (ER) data models 5. Elucidate advanced data modeling issues e.g., temporal data modeling, meta-data, etc. 6. Identify and analyze data quality in a business context 7. Develop and evaluate strategic data plans; e.g., Master data management plan, enterprise data strategy & models 8. Understand the growing importance and issues associated with data warehousing, business intelligence and analytics, and big data. ASSIGNMENTS Individual & Team Assignments (10%) Review and comment on several assigned readings from the research and professional literature and modeling exercises. Developing a business case. SQL Certification (10%) Required SQL Module: https://www.sololearn.com/Course/SQL/ Information Modeling Exam (15%) Mid Term (15%) - Individual The mid-term paper is an individual assignment and is technology focused. References and all sources used (articles, texts, Web sites, etc.) must be provided in standard format. Team Database Project (40%) Students will develop a functional database that will be demonstrated at the conclusion of the course. The assignment includes business problem analysis, identification of database requirements, data modeling, creation of the database, and the development of business specifications for key processes. The final deliverable consists of a presentation and a set of PowerPoint slides. Participation (10%) Students will be assessed on their contributions to in-class discussions throughout the semester. Course Outcomes After taking this course, students are able to: · Understand basic deep modeling constructs · Creatively apply data modeling to solve real business problems · Assess model performance accurately to understand each model’s pros/cons · Explain models, interpret model outcomes, and understand potential bias · Execute SQL queries Prerequisites: Cross-listing: — show cross-listed course number(s) Grading Percentages:  HW 20%     Class work 10%     Mid-term 20% SQL Certificate 10%     Database Projects 40% Other (specify both percent and kind of work) Credits: 2 credits Other For Graduate Credit toward Degree or Certificate Yes No Not for Dept. Majors Other PREREQUISITES · Students must satisfy the requirement for enrollment in either the BI&A or MIS master’s degree programs. TEXTBOOK(S) OR REFERENCES Required Textbook: Database Concepts (9th Edition), by David Kroenke, David Auer, Scott L. Vandenberg, Robert C. Yoder. Pearson 2019. Required SQL Module: https://www.sololearn.com/Course/SQL/ Required Software: ERwin Department Point of Contact and Title: Joseph Morabito; [email protected] Syllabus: NOTE:  -à Indicates an MIS 632 lab exercise Session Subject(s) Assignment(s) Tuesday Jan 21 First Day of Spring 2025 Semester PART 1 - DATABASE FOUNDATIONS 1 Jan 27 Introduction to MIS631 Introduction to Data Management Functions Factors Affecting Data Management Functions Read Ch. 1 -à ERwin installation --> Install PostgreSQL 2 Feb 3 Tutorial on Database and Database Design Read Ch. 2 -à ERwin basics+ normalization 3 Feb 10 Organizations and Data Research paper presentations & discussion ) 7 Mar 10 Information Modeling – Advance Modeling (continued); Contracts and Formal Methods Discuss Key Semester Learnings -à SQL queries - advanced PART III - DATABASE MANAGEMENT Mar 16 – Mar 23 Spring Break 8 Mar 24 Data and Database Administration Functions Physical Database Design Strategic Data Modeling -à Extended ER data modeling è Major Project è Major Project (continued) 10 Apr 7 Data Warehouse, Business Intelligence, and Big Data Row & Column data storage 11 Apr 14 Cloud Computing and Data Architectures Review Final Project with Teams -à Install a database in the cloud (AWS) è Major Project (continued) 12 Apr 21 NoSQL: document, key-value, column-wide, graph Review Final Project with Teams è Major Project (continued) Sunday Apr 28 Final Team Projects must be submitted in Canvas May 5 13 May 12 -à Use Erwin for data design project: modeling and database design Last Day of Spring 2025 Classes: May 17 Final Exam Period: Sunday May 11 – Sunday May 18 14 May 19 15 Dec

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[SOLVED] ECE 132A Introduction to Communication Systems Homework 2 Winter 2025

ECE 132A: Introduction to Communication Systems Homework #2 Winter 2025 Due: Monday, January 27, 2025 at 11:59 PM via Gradescope 1. (10 pts) Let z(t) be a complex-valued baseband signal with two-sided baseband bandwidth B, and let fc ≫ B. Show that the energy of is equal to that of z(t). 2. (10 pts) Let x(t) be any real-valued passband signal centered at fc, occupying a passband bandwidth B ≪ fc. Rigorously show that if then x(t) can be written as 3. (5 pts) Prove that the bandwidth of a signal output by an LTI system cannot be greater than that of its input signal. 4. (20 pts) Consider conventional AM transmission of the following message signal at a carrier frequency of 1 MHz. m(t) = cos(4000πt) − 3 · sin(6000πt) (a) Find and sketch/plot the spectrum of the transmit signal xAM(t), with appropriate labels on the horizontal and vertical axes. What is the passband bandwidth of xAM(t)? (b) What is the best-case transmit power efficiency, under the assumption an ideal envelope detector can be used to perfectly recover the message? (c) Suppose the received conventional AM signal (a time-varying voltage) is a perfect copy of the transmitted signal xAM(t) and is passed through an ideal full-wave rectifier followed by a simple RC circuit in an attempt to recover the message m(t). Using MATLAB or Python, plot the recovered message ˆm(t), i.e., the output voltage of the RC circuit, as a function of time t, comparing it against the original message m(t). What values of R and C yield satisfactory demodulation? Hint: For plotting, use a numerical approximation of the differential equation describing the output voltage at time t. 5. (20 pts) Suppose we construct the transmit signal where m1(t) and m2(t) are independent, real-valued baseband signals with zero mean, each of which occupies a two-sided baseband bandwidth much less than fc. This signal has a construction different from what we have talked about in class but resembles some sort of combination of two DSB signals. (a) What is x(f) in terms of m1(f) and m2(f)? (b) Is x(f) conjugate symmetric about f = 0? Is it locally conjugate symmetric about fc? (c) Mathematically show what happens if we apply a DSB demodulator to x(t). (d) Can we recover both m1(t) and m2(t) from x(t)? If so, how? Draw a block diagram of your technique and mathematically show that it works. (e) What takeaways might you draw from (d)? 6. (10 pts) Prove that the definition of the Hilbert transform. of a signal m(t) presented in class is equivalent to the convolution 7. (10 pts) Below, I have given you part of the spectra for a real-valued signal m(t). Fill in the rest, including all relevant labels on the horizontal/vertical axes, with ˘m(t) the Hilbert transform. of m(t). 8. (10 pts) Find the Hilbert transform. of m(t) = 20 · sinc(10t). 9. (25 pts) Good news! The US Federal Communications Commission (FCC) has just gifted me exclusive rights to the electromagnetic spectrum from 30 kHz to 80 kHz. After auctioning off licenses to this precious spectrum, I have granted four parties rights to their own exclusive channel within this 50 kHz band. Each of these four parties will operate their own radio station to broadcast music, news, talk shows, etc., but each station must abide by a few rules. • Each station is assigned to broadcast within a channel that is 8 kHz wide. • The carrier frequencies of neighboring channels will differ by 12 kHz. • Each station must broadcast conventional AM transmissions. All four of these stations are now on the air broadcasting, and I have directly sampled the signal received by an antenna tuned to this 50 kHz band. Your task is to engineer a software-based receiver in MATLAB/Python/etc. Create a script. to process the file radio.mat posted under Week 3 of the Modules tab on Bruin Learn. This file contains two fields: • y: samples of the received signal • fs: the rate at which the received signal was sampled (in units of samples per second). (a) Using the fast Fourier transform. (FFT), plot the magnitude of the (discrete) Fourier transform.|y(f)|2 of the received signal with the vertical axis in linear units and the horizontal axis in units of kHz. (b) Are you familiar with decibels (dB)? It is a common way engineers compress very large and very small numbers into a more manageable range, and it also makes calculating products and ratios much easier by transforming multiplication into addition via the logarithm. Feel free to read more about dB on your own; we may cover it more later in this course. Repeat (a) with the vertical axis in dB. This can be accomplished by simply plotting f vs. where [X]dB = 10 log10(X). (c) What carrier frequencies have each of the four channels been assigned? (d) Sketch a simple “band plan” of this spectrum, with frequency on the horizontal axis and labels showing where each of the four assigned channels, their bandwidth, and their carrier frequencies. (e) Draw a receiver block diagram (similar to those drawn in class) with an antenna, followed by the necessary components/processes to recover any one station’s broadcast message. (f) Process and demodulate the received signal in order to “tune in” to a particular channel. Following successful demodulation, listen to the message signal (e.g., using the soundsc(m,fs) function in MATLAB). What is being played on each of the four channels? (g) What might the reason be for a gap of 12 kHz between channels, considering the channels are only 8 kHz wide?

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[SOLVED] FIT5124 - Assessment 1

FIT5124 - Assessment 1 DEADLINE: 4 April 2025, 11.55PM (Melbourne time) TOTAL MARKS: 100 1 Overview The learning objective of this assessment is for you to gain experience in analysing and implementing zero- knowledge proofs. This is an individual assessment and you are not allowed to discuss any aspect of it with others (excluding teaching team members). Failing this requirement (e.g. helping other students, discussing solutions towards answering assessment questions in any platform) will result in penalties in accordance with the Uni- versity’s Academic Integrity guidelines: https://www.monash.edu/students/academic/policies/academic-integrity 2 Submission Policy You need to submit exactly two files on Moodle: (i) one written report answering the questions in Section 4, and (ii) one SageMath file answering the implementation questions in Section 5. Your report must be in PDF format and implementation code must have  .sage file extension. Name your files in the format: [Your Name]-[Student ID]-FIT5124-A1 (followed by file extension such as pdf, sage, etc). The report should include your name and student ID at the top of Page 1. Please do not spend time trying to come up with a ‘fancy’ cover page. The report should be prepared in 11pt Arial font. Important notes and penalties – It is the student’s responsibility that the submitted PDF file can be opened on a standard Windows computer  (without  requiring  specialised  software),  and that the images and texts included are clearly visible/understandable/readable (in English). If the PDF file cannot be opened, you will receive zero mark. Similarly, it is the student’s responsibility that the submitted SageMath implementation file can be run in SageMath 10.5. If the SageMath file cannot be run in SageMath 10.5, you will receive zero mark for the implementation question. After making a draft submission (before finalising it), we recommend you to download your uploaded assessment files and check that they open and run properly. Once you finalise your submission, you will not be able to revise it. – Note that draft files are NOT accepted and will not be marked. You must finalise your submission (with status shown as “submitted for grading”) for your assessment to be considered as valid. Otherwise, standard late submission penalty will apply. – Maximum number of pages allowed for the report is 3. Any content exceeding the 3-page limit will be disregarded and not marked. – Maximum number of characters allowed for the implementation code is 8,000. Any content exceeding the 8,000-character limit will be disregarded and not marked. – Late submissions incur a 5-point deduction per day. For example, if you submit 2 days and 1 hour late, that incurs 15-point deduction. Submissions more than 7 days late will receive a zero mark. – If you require extension or special consideration, refer to https://www.monash.edu/students/admin/ assessments/extensions-special-consideration. No teaching team member is allowed to give you extension or special consideration, so please do not reach out to a teaching team member about this. Follow the guidelines in the aforementioned link. – Zero tolerance on plagiarism and academic integrity violations: If you are found cheating, penalties will apply, e.g., a zero grade for the unit. The demonstration video is also used to detect/avoid plagiarism. Univer-sity policies can be found at https://www.monash.edu/students/academic/policies/academic-integrity. – All questions in the assessment are marked against technical correctness, clarity of explanations and quality of presentation. For example, if an answer is not presented well, you may not receive full marks even if the answer has the right technical ideas. 3 Scenario for the Assessment Suppose that we have a cyclic (multiplicative) group G = ⟨g〉of prime order n. We assume that the discrete logarithm problem in G is computationally hard (which implies that n is very large and particularly n > 2128). Let h be another random generator of G (i.e., the discrete logarithm relation between h and g is unknown). Compute the following value based on your Monash student ID id = Student_ID mod 1000. That means, id is the last three digits of your Monash student ID. The questions in this assessment will be based on the id value. IMPORTANT. You must use the correct id value to have your assessment considered for marking. Using an incorrect id value will make your assignment submission invalid and you will receive zero mark without further consideration. Let’s assume that we have a prover, Patrick, and a verifier, Victoria. Patrick wants to convince Victoria of the following “Patrick’s relation” R pat = { ((g, h, v); (m, s)) :    v = gm hs Λ m ∈ {0, id}  } . To prove the above relation, the interactive protocol between Patrick and Victoria works as follows. 0.  Patrick has ((g, h, v); (m, s)) and Victoria has (g, h, v) as protocol input. 1. Patrick: generate random f, r1 , r2  ∈R Zn 2. Patrick: compute a1 = gf hr1   and a2 = gf·m hr2 3. Patrick→ Victoria: send a1 , a2 4. Victoria: sample random c ∈R Zn 5. Victoria→ Patrick: send c 6. Patrick: compute z = f + cm, k1  = r1 + cs and k2  = r2 + s(id · c — z)     (all computed mod n) 7. Patrick→ Victoria: send z, k1 , k2 8. Victoria: Check all of the following: gz hk1   =? vca1 ;     and hk2   =?  vid·c—za2 . If all checks pass, then Victoria accepts the proof. Otherwise, she rejects the proof. (here, we assume that Victoria also checks a1 , a2  ∈? G; and z, k1 , k2  ∈? Zn, but don’t write them out explicitly) We call the above protocol “PV protocol”. 4 Zero-Knowledge Proof - Analysis [70 marks] 4.1 Protocol diagram [10 marks] Draw a diagram for the PV protocol in Section 3 clearly demonstrating the interaction between Patrick and Victoria. Your diagram must follow a similar structure as in the Schnorr’s ID protocol presented in the lecture notes. You can, for example, use any of the following methods for drawing: – hand drawn diagram (with its clearly visible screenshot attached to the assessment report), – PowerPoint, – cryptocode library in Latex (https://ctan.org/pkg/cryptocode?lang=en). You are welcome to use any other method. In the end, your diagram must be clearly visible without any confusing parts. The order of the operations must also be clearly understandable (as in the Schnorr’s ID protocol presented in the lecture notes). You may not get full marks if there is any unclarity. The only hand-written part allowed in the assessment is this question; for all other questions, no hand-written submission is accepted. The diagram must be added to the PDF report with label ‘Q1’. 4.2 Completeness analysis [10 marks] Prove the completeness of the PV protocol in Section 3. The proof must be added to the PDF report with label ‘Q2’. 4.3 Soundness analysis [25 marks] Prove the special soundness of the PV protocol in Section 3. The proof must be added to the PDF report with label ‘Q3’. 4.4 Zero-knowledge analysis [25 marks] Prove the honest-verifier zero-knowledge property of the PV protocol in Section 3. The proof must be added to the PDF report with label ‘Q4’. 5 Zero-Knowledge Proof - Implementation [30 marks] The implementation must be done in SageMath tool as used in the unit (https://www.sagemath.org/). If your submitted code does not run in SageMath 10.5, you will receive zero mark for the whole implementation part of the assessment. Your implementation must also properly and meaningfully build on the A1_code .sage file provided in the assessment Moodle page. Failure to meet this requirement will make your implementation invalid and you will receive zero mark for the whole implementation part of the assessment. 5.1    Interactive protocol implementation [22 marks] Implement the PV protocol in Section 3 in SageMath. The implementation must satisfy the following: 1. have the following functions: commitment, challenge, response, and verify, and 2. generate a protocol transcript using the above functions and run the verify algorithm to check and print the result of verification. 5.2    Conversion to non-interactive variant  [8 marks] Extend the implementation code above to support running the PV protocol in Section 3 as a non-interactive protocol via the Fiat-Shamir transformation. As instructed in the A1_code .sage file, your final implementation must use the variable MODE to switch between “Interactive” and “Non-interactive” modes of running the protocol. Submit your final implementation code as a  .sage file on Moodle.

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[SOLVED] Econ 413 Market Risk Forecasting and Control Coursework 1R

Market Risk Forecasting and Control (Econ 413) Coursework 1 Electronic submission due on Mar 28, 2025 by 12 noon Guidelines 1.  The first submitted file must be in pdf format including results and comments.  All results presented must be interpreted: only include the figures and tables you comment. 2.  The second submitted file must be a R code which replicates all results presented in the first file. 3. Word limits: no more than (a)  600 words in Part 1 (b)  850 words in Part 2 4.  Maximum filesize: 2MB. 5.  For support with submissions refer to John Sharples ([email protected]) Part 1 Download the Dow Jones Industrial Average in the sample that starts on your birthday in 2006 and ends on your birthday in 2022. (i) Visualise the data and comment on its features. (ii)  Consider a normal GARCH and use an appropriate method for choosing its specification. (iii)  Perform residual analysis on the chosen model. [100 marks] Part 2 Using the same data as in Part 1, perform the following analysis. 1. Use EWMA, MA, historical simulation and GARCH(1, 1) models to compute 5-percent daily value at risk forecasts for a testing window given by the last 10 years of data.  Explore different choices of estimation window. 2.  Perform backtesting analysis. 3.  Create an equally weighted portfolio of two assets of your choice from the US stock market in the same sample and repeat the above analysis. [100 marks]

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