Applied Econometrics (2024/2025 Semester 1) -- Assignment 1 Instructions: 1. This assignment paper has a total of 100 marks, and contributes 25% to the course’s overall assessment. 2. Write down your solution/answer to each question in the space provided in THIS assignment paper. 3. Necessary calculations and/or formulas MUST be included in your solutions/answers. 4. Key concepts/methods/formulas and t-table can be found from the textbook or lecture notes, or from the review document available from the course iSpace. 5. Round your calculation results to THREE (3) decimals to achieve higher accuracy, unless clearly unnecessary. Miss Ariel, a smart girl of the beautiful Ultimate Imagination College (UIC) which currently has 9,260 students, is attending an Artistic Emotion (AE) class. Because of her learning experience, she is interested in examining whether or not harder study will lead to better learning outcome and, if yes, by how much, which can be simplified to examine the relationship between GPA (y) and average daily study-hour (x). For that purpose, yesterday she went to UIC’s University Road, to do a survey. She randomly surveyed 10 UIC students with each student’s GPA (y) and average daily study-hour (x) recorded as follows, where, taking student #5 for example, x5 = 12 andy5 = 3.3 imply that she (or he) studied 12 hours everyday in average and her (or his) latest GPA was 3.3. #: i12345678910Average daily study-hour: x : y Five sample sums have been calculated from this sample data set as follows: Part A. Basic concepts (15 marks) Q01 (2 marks): What is the appropriate population for the survey (or the random sample)? Q02 (3 marks): How many different samples of 10 students could be drawn from the population indicated in Q01? Q03 (10 marks): For and just for this question (Q03) only, suppose that Mr. Simon also randomly surveyed 10 students yesterday from the same population indicated in Q01. Q03a (2 marks): Are Ariel’s and Simon’s samples the same (i.e., do they have the same 10 students?) Q03b (2 marks): Will the two samples have the same average GPA? Q03c (2 marks): Which of the two samples will have an average GPA closer to the population average? Q03d (2 marks): Will the two samples produce the same sample regression model to explain GPA (y) using average daily study-hour (x)? Q03e (2 marks): Which of the two samples will produce a sample regression model closer to the population regression model? Part B. Calculate the sample statistics (20 marks) Q04 (1 mark): Sample mean ofx. _____________________________________________________________ Q05 (1 mark): Sample mean of y. _____________________________________________________________ Q06 (2 marks): Sample variance of x. _____________________________________________________________ Q07 (2 marks): Sample variance of y. _____________________________________________________________ Q08 (1 mark): Sample standard deviation of x. ______________________________________________________ Q09 (1 mark): Sample standard deviation of y. ______________________________________________________ Q10 (2 marks): Sample covariance between x and y. __________________________________________________ Q11 (2 marks): Sample correlation coefficient between x and y. ________________________________________ Q12 (2 marks): Standard error of sample mean of x. __________________________________________________ Q13 (2 marks): Standard error of sample mean of y. __________________________________________________ Q14 (4 marks): Briefly explain why standard error of sample mean of y (in Q13) is much smaller than the standard deviation of y (in Q09). What is the major implication of this? Q14a (2 marks): Reasons. _____________________________________________________________________ Q14b (2 marks): Implication. _____________________________________________________________________ Part C. Inference for population mean (30 marks) Q15 (10 marks): Test the null hypothesis (H0) that the population’s average GPA (μy) is 3.0 against a two-sided alternative hypothesis (H1) at the 5% significance level. Q15a (2 marks): State the two hypotheses formally in symbols. Q15b (3 marks): Calculate the sample t-statistic. __________________________________________________ Q15c (2 marks): Find the (two-sided) critical value from the t-distribution table. __________________________ Q15d (3 marks): Draw conclusions. _____________________________________________________________ Q16 (10 marks): Test the null hypothesis (H0) that the population’s average GPA (μy) is 2.7 against a right-sided alternative hypothesis (H1) at the 5% significance level. Q16a (2 marks): State the testing problem formally in symbols. _______________________________________ Q16b (3 marks): Calculate the sample t-statistic. __________________________________________________ Q16c (2 marks): Find the (one-sided) critical value from the t-distribution table. __________________________ Q16d (3 marks): Draw conclusions. _____________________________________________________________________ Q17 (6+4 marks): First construct a 95% confidence interval for the population mean (μx) of average daily study- hours, and then test whether μx is equal to 10 against a two-sided alternative hypothesis at the 5% significance level based on this confidence interval. How about μx = 11? Q17a (6 marks): Confidence interval. Q17b (4 marks): Hypothesis testing. Part D. Simple linear regression: basic calculations and interpretations (35 marks) This Part relates to a simple linear regression model estimated using David’s sample data (together with the results in Part B) and the ordinary least squares (OLS) method: yi = β(ˆ)0 + β(ˆ)1 xi + ûi ŷi + ûi, where ŷi = β(ˆ)0 + β(ˆ)1 xi is the model-fitted or forecast yi corresponding to xi and ûi is the corresponding residual for student i (i = 1, 2, … , 10). Q18 (3+2 marks): Find β(ˆ)1 , and explain its meaning. Q19 (2+2 marks): Find β(ˆ)0 , and explain its meaning. Q20 (2+3 marks): Find R2, which is just the squared sample correlation coefficient between y and x for simple regression, and explain its meaning. Q21 (1 mark): Find the total sum of squares of x (SSTx). _______________________________________________ Q22 (1 mark): Find the total sum of squares of y (SST). _______________________________________________ Q23 (3 marks): Find the residual sum of squares (SSR). _______________________________________________ Q24 (2 marks): Find the standard error of regression ( ). _____________________________________________ Q25 (3 marks): Find the standard error of βˆ1. _______________________________________________________ Q26 (2 marks): Is the standard error of βˆ1 small or big? _______________________________________________ Q27 (3 marks): Find the standard error of βˆ0. ______________________________________________________ Q28 (3+3 marks): For student #5, find her model-fitted GPA (ŷ5) and comment on her actual study performance. ______________________________________________________
Project Part 2: Marriage Artifact HHS4U It’s nearing the end of the semester, which means it’s time for your final project! You are going to create a marriage artifact to share with your peers, that demonstrates a thorough understanding of the rituals, customs, expectations and challenges of your chosen culture or type of marriage. Reminder: you have complete freedom regarding the format that your artifact takes, however, it must be something creative, informative, and visually appealing. Some suggestions are: ● video ● presentation ● website ● art piece (drawing or painting with explanation) ● animation ● case study! ● anything else you can think of! (If you’re unsure, ask your teacher!) Here are some guidelines and questions to help you consider and create your artifact to ensure that it is informative enough and comprehensive. (This is not a checklist of things to answer or include) ● introduce your culture or type of marriage - explain some background information ● explain what pre-wedding rituals, customs, expectations & challenges exist for couples who want to be married following that culture/type of marriage ○ What are the courtship rituals of your chosen culture? ○ How is the family involved in the relationship prior to the wedding? ○ What engagement rituals are specific to your chosen culture? ○ Is the couple allowed to live together before marriage? ○ How does the couple decide they want to marry? (Engagement, meeting parents, asking permission, etc.) ○ Are there any special rituals that occur before the wedding ceremony? ● explain what wedding rituals, customs, expectations & challenges exist for couples who want to be married following that culture/type of marriage ○ What does a typical wedding day look like? Briefly walk us through the ceremony. ○ Is there anything special that the bride/groom must do? ○ What role do the families play in the wedding ceremony? ○ Are there any specific customs/norms for the guests? (What they can wear, do they bring gifts, do they participate in the ceremony, etc.). ● explain what post-wedding rituals, customs, expectations & challenges exist for couples who want to be married following that culture/type of marriage ○ What are the expectations of each spouse after marriage (gender roles)? Are there specific gender roles that are enforced by society? ○ Are there any cultural/familial expectations for the couple? (Do they need to try to get pregnant straight away, go on a honeymoon, move in with one set of parents, etc.). ○ How is labour divided in the household after marriage? ○ Are there any expectations placed on either spouse after one spouse passes away (ex. Remaining celibate, moving in with a family member, etc.)? ○ What happens if the marriage breaks down? ● Your explanations of your pre-wedding, wedding, and post-wedding rituals must include some connection to theories we have discussed in class. (Minimum 2) ● Presentation will be up to 12 minutes Please note: you are not expected to answer every single question listed above - these are guidelines to inform. you of what kind of information you should include in your final marriage artifact.
Subject: BRE2031 – Environmental Science Tutorial IAQ and Ventilation 1. A room is cooled in summer by an air conditioning system that provides an air flow rate of 5m3/s to remove heat gains (sensible) of 50kW. Room air temperature is maintained at 23oC. Derive the formula for calculating the supply air temperature and find its value. (the heat capacity of air can be considered as constant: 1005J / (kg . K) ; air density: 1.2929kg / m3 at 273K). (reference answer: about 14.9oC) 2. A room has heat gain (sensible) of 10kW and a supply air temperature of 10oC. Find the supply air rate required to keep the room air temperature at 20oC. (reference answer: 0.798m3/s) 3. A four-story commercial building is to be mechanically ventilated. Air-handling plant is to be sited on the roof. Each floor has dimensions 20m×10m×3m and is to have 6 air changes per hour (ACH). Of the air supply, 10% is allowed to exfiltrate naturally and the remainder (90%) is extracted to roof level. The supply and extract air ducts run vertically within a concrete service shaft and the limiting air velocity is 10m/s. Estimate the dimensions required for the service shaft. Square ducts are to be used and there is to be at least 150mm between the duct and any other surface. (reference answer: about 1680mmx 930mm)
ACTU PS5821 Actuarial Methods - Autumn 2024 Assignment - 5 Assigned 10/4/24, Due 10/12/24 Problem 1. Calculate 3p60 base on the following table x ex 60 15.96 61 15.27 62 14.60 63 13.94 Problem 2. Mortality, with a select period of 5 years, follows (a) Calculate 7p[70] (b) Calculate 1|2q[70]+2 Problem 3. For a 2-year select and ultimate mortality model, q[x]+1 = 0.95qx+1. Given the following expected number of survivors l76 = 98153 and l77 = 96124, calculate l[75]+1 Problem 4. Mortality, with a select period of 2 years, follows (a) Calculate the probability that a life aged 75 who has just been selected will survive to age 85 (b) Calculate the probability that a life aged 76 who was selected one year ago will die between ages 85 and 87 (c) Calculate 4|2q[77]+1
M426 H28 1. Two steps of Euler h = 0.1. y′ = −2ty; y(0) = 2; 0 ≤ t ≤ 0.2. Given solution y = 2e −t2. Find one step error and the global error. Show that they are bounded by the theory. 2. Two steps of Euler h = 0.2. y′ = y + t; y(0) = 2; 0 ≤ t ≤ 0.4. Given the solution y = −1 − t + 3et. Find one step error and the global error. Show that they are bounded by the theory. 3. Find y(T) by the adaptive Euler method with h = 0.2 and T L = 0.1. y′ = ty; y(1) = −2; 1 ≤ t ≤ 1.2. Check the final error. 4. Find y(T) by the adaptive Euler method with ini-tial h0 = 0.2 and T L = 0.1. y′ = 1 + 2y; y(0) = 1; 0 ≤ t ≤ 0.2. Check the final error.
BLGY2155 Faculty of Biological Sciences Guidance on Level 2 Online Time-limited Assessment (OTLA) Essays You are required to write a concise and focused essay that addresses the question you are set making use of relevant knowledge and understanding you have acquired from lecture material and wider reading. Our expectation is that you will write the essay having spent considerable time studying the module content, engaging in wider reading, taking and organising notes, prior to the release of the OTLA question (i.e. as part of your revision). It is unlikely that the quality of the essay you produce will reach our expectations, and receive a decent mark, if you have not studied and engaged in wider reading before the OTLA questions are released. Wider reading could include information from textbooks or reviews that was not directly covered in the lectures and/or relevant primary sources (e.g. research publications, policy documents etc.). For each essay question you attempt, you should follow the guidelines outlined below. • Your answer should be a concise piece of writing that integrates material from different sources (lectures and wider reading) to provide a coherent overview of the topic area. Your essay should not be a loose collection of observations or thoughts. • The essay should have an introduction that provides the wider context to the topic and which leads up to the focus of the essay question. An abstract is not required. The introduction might for example include a definition for the discipline area/topic being covered and establish the scope of the material that will be covered. • The main body of your answer should be illustrated with relevant examples. The best examples are likely to be seminal and/or current (e.g. key advances in the field, cited widely and/or published in major journals). You should consider using headings to emphasise the structure of your answer. • Your answer should end with a final concluding paragraph that summarises your major observations and conclusions. • Consider illustrating your answer with sketches, figures and tables if this is appropriate for addressing the question that has been asked. In Biological Science-related topics this is often the case, as data, evidence and ideas (e.g. models) are usually presented in a visual form. A good illustration can save a considerable number of words. Each illustration should have a title and a brief legend and be referred to in the main body and be embedded at appropriate points in the main body of your essay. • Figures and tables can be adapted from a source such as a textbook, review or research paper, but this should be acknowledged, e.g. Adapted from Smith and Jones (2019). • Given the time available to you, it is likely that figures will often need to be hand drawn. Please embed an electronic version of the diagram into your essay question (e.g. by taking a photograph using the camera on your mobile phone and inserting it at the appropriate point). A full page can be used, if required. It is important that figures are legible, but figures will be marked for their content and relevance to the question asked and not for how neat and polished they are. • You should cite key references in the text at appropriate points and provide a reference list at the end of the essay. The referencing style. should be Leeds Harvard. • Please make sure each essay is completed as a word-processed document using 1.5 line spacing, Arial font (11 pt minimum) and margins of 2 cm on each side. All pages should be numbered in the footer. Each essay should be no longer than 1,000 words. Note, this is the maximum word limit and shorter answers may still gain a high score so long as they meet the criteria (see below) associated with a strong Level 2 essay The standard penalties will apply for exceeding this limit (see below). • Each essay should start with a title page that includes the following information: module number and module title, student number, section of paper (where appropriate) and question number (where appropriate), and title of question. The title of the question should be repeated at the start of your answer. Students who have a purple electronic coversheet provided by the Student Support team in relation to their specific learning difficulty, should make this their first page and then follow the above instructions for the second page. • Subheadings and in-text citations are included in the word count, but not the title page, the essay question itself when used as an essay title, reference lists, figures, tables or legends. The word count (excluding reference lists, figures, tables or legends) should be included at the end of the document. • As a guide, we suggest including 3 to 5 references for an essay. However, do bear in mind that it is not only about the number of references you include but the relevance and appropriateness of the references you select as well (e.g. have you identified, seminal works, does your references indicate up to date reading of the topic area). Each essay will be marked using the Qualitative Criteria for Judgmental Assessment of Level 1 & 2 Online Time-limited Assessment (OTLA) Essays (Undergraduate). These criteria award marks under three categories, i) content, ii) interpretation and iii) presentation. Therefore, in reviewing your essay, you should be aware that excellent answers have the following qualities: 1. presents authoritative material relevant to the question from supplementary reading as well as lectures; 2. demonstrates clear understanding of the material presented, e.g. by explaining concepts at a level appropriate for the level of study; 3. shows analytic ability, e.g. by identifying competing arguments or positions and evaluating the strengths and weaknesses of information; 4. makes judgements about concepts or ideas, e.g. by identifying the most important finding, approach, or argument; 5. has a structure that provides a logical flow of information, includes sub-titles as appropriate and uses a consistent and accurate referencing style.
Comprehension Questions (Include sketches and undertake any numerical calculations that assist in your discussion. All questions are worth 16 points). Attempt all questions. Question 1 Consider a reaction that takes place at 298 K in which two Cl atoms (radius 0.15 nm) approach each other with the possibility of forming a Cl2 molecule. Note a Cl-Cl bond has a strength of 6 x 10-19 J. Discuss the factors that affect the outcome of this interaction. Include the following in your discussion: (i) How the potential energy, kinetic energy, and total energy change along the trajectory as the atoms approach starting from a large separation to the point where they collide. (ii) Discuss the different possible outcomes of this collision process and the factors that determine these outcomes. Please be very specific. (iii) Is there an energy barrier to the formation of Cl2. If not, why? (iv) How it is possible to overcome a barrier to reaction (whether present in this case or not). Please discuss in terms of your response and any sketches used in (i) above. Question 2 Archaeological records clearly show the sudden emergence and then the dominance of hot-blooded mammals over cold-blooded reptiles. Discuss the implications of the switch from cold to hot blooded species from the standpoint of thermodynamics. How might this have provided an evolutionary advantage to mammals. Are there disadvantages? Are there any implications for extra-terrestrial life? Question 3 (i) What do you understand by the concept of state function in thermodynamics? Give examples of quantities that are state functions and contrast their properties with related thermodynamic quantities that are not state functions. (ii) Distinguish between reversible and non-reversible (irreversible) processes in thermodynamics, giving examples in each case. (iii) Consider the case of entropy S for which changes ΔS during a thermodynamic process can be expressed in two different ways (i) heat adsorbed, and (ii) changes in the number of available microstates. With the help of examples, discuss the applicability of the descriptions (i) and (ii) to both reversible and non- reversible thermodynamic processes. (iv) Can you think of an example of an irreversible process that can be modelled as a series of reversible processes. Explain why this might be helpful in describing thermodynamic processes. Question 4 Consider a liquid droplet in air that rests at the bottom of a cylinder that is enclosed by a frictionless piston. In stage 1, the piston containing the droplet is placed in a constant heat reservoir (temperature T1) and the size of the drop is observed to decrease while the piston moves upwards. In stage 2, the piston stops moving when it reaches a height H at which point a small quantity of the liquid droplet remains. In stage 3, the temperature of the reservoir is changed to T2 and the piston moves downwards so that its final height is ½H. Discuss these observations from a thermodynamic perspective. Identify the system and the surroundings. Indicate the sign of the changes (+/-) of relevant system quantities (q, w, U, H, G, S etc) during stages 1, 2 and 3. What can you say about the reservoir temperatures T1 and T2 - how they compare to each other and the temperature of the ambient in stage 1 prior to placing the cylinder in the heat reservoir? Question 5 (carefully read the entire question before you start) (i) Describe in detail how you would go about setting up experiments to determine the rate law for a reaction that involve two reactants, which can be any molecules of your choosing. (ii) Demonstrate your approach by providing a numerical example, showing concentrations and reaction rates as appropriate. (Yes, I want a table with real numbers and correct units!!) (iii) Determine the rate law and the order of the reaction. What is the rate constant? (please specify the units). (iv) Write down a reaction mechanism for your reaction that includes a rate determining step and the presence of a single reaction intermediate. Show that the mechanism you propose is consistent with the overall stoichiometry of the reaction in (i). (v) Show the mechanism you propose is consistent with the rate law you specified in (iii). What is the molecularity of the rate determining step. Express the observed rate constant in term of the rate constants associated with individual steps in your mechanism. (vi) What experiments and analyses would you perform. to determine the activation energy for this reaction. For your reaction explain how the measured activation energy depends on the different reaction steps in your mechanism.
MTH205 Introduction to Statistical Methods Tutorial 3 Based on Chapter 3 1. State the null and alternative hypotheses to be used in testing the following claims and determine generally where the critical region is located: (i) The mean snowfall at Lake George during the month of February is 21.8 cm. (ii) The mean monthly household income is no more than $8000/mth. (iii) The average rib-eye steak at the Longhorn Steak house weighs at least 340 g. . 2. In a research paper, it is claimed that mice with an average life span of 32 months will live to be about 40 months old when 40% of the calories in their diet are replaced by vitamins and protein. Is there any reason to believe that µ < 40 if 64 mice that are placed on this diet have an average life of 38 months? Assume a population standard deviation of 5.8 months. Use a p-value in your conclusion. 3. It is claimed that automobiles are driven on average no more than 20,000 km/yr. To test this claim, 100 randomly selected automobile owners are asked to keep a record of the distance they travel. Would you agree with this claim if the random sample shows an average of 23,5000 km? Assume a population standard deviation of 3900 km. Use a p-value in your conclusion. 4. A study was made to determine if the subject matter in a physics course is better understood when a lab constitutes part of the course. Students were randomly selected to participate in either a 3-semester-hour course without labs or a 4-semester-hour course with labs. In the section with labs, 11 students made an average grade of 85 with a standard deviation of 4.7, and in the section without labs, 17 students made an average grade of 79 with a standard deviation of 6.1 . It is claimed that the laboratory course increases the average grade by at least 8 points. Carry out hypothesis testing using p-value to conclude. Assume the populations to be normally distributed with equal variances. 5. A study was conducted to determine if the “strength” of a wound from surgical incision is affected by the temperature of the knife. Eight dogs were used in the experiment. “Hot” and “cold” incisions were made on the abdomen of each dog, and the strength was measured. The resulting data is given below. Dog Knife Strength 1 Hot 5120 1 Cold 8200 2 Hot 10,000 2 Cold 8600 3 Hot 10,000 3 Cold 9200 4 Hot 10,000 4 Cold 6200 5 Hot 10,000 5 Cold 10,000 6 Hot 7900 6 Cold 5200 7 Hot 510 7 Cold 885 8 Hot 1020 8 Cold 460 (i) Write an appropriate hypothesis to determine if there is a significant di↵erence in strength between the hot and cold incisions. (ii) Test the hypothesis using a paired t-test. Use a p-value in your conclusion. 6. Aflotoxins produced by mold on peanut crops in Virginia are to be monitored. A sample of 64 batches of peanuts reveal levels of 24.17 ppm, on average, with a variance of 4.25 ppm. Test the hypothesis that σ2 = 4.2 ppm against the alternative that σ2 = 4.2 ppm. Use a p-value in your conclusion. 7. A study is conducted to compare the lengths of time required by men and women to assemble a certain product. Past experience indicates that the distribution of times for both men and women is normal but the variance of the times for women is less than that for men. A random sample of times for 11 men and 14 women produced the following data: Men Women n1 = 11 n2 = 14 s1 = 6.1 s2 = 5.3 Test the hypothesis that σ1 2 = σ2 2 against the alternative that σ1 2 > σ2 2. Use 5% level of significance in your conclusion. 8. A die is tossed 180 times with the following results: x 1 2 3 4 5 6 frequency 28 36 36 30 27 23 Is this a fair die? Use a 1% level of significance. 9. In an experiment to study the dependence of hypertension on smoking habits, the following data were taken on 180 individuals: Non-smokers Moderate Smokers Heavy Smokers Hypertension 21 36 30 No Hypertension 48 26 19 Test if hypertension is associated with smoking habits. Use a 5% level of significance. 10. Suppose that an allergist wishes to test the hypothesis that more than 30% of the public is allergic to some cheese products. Explain how the allergist could commit (i) a Type I error; (ii) a Type II error. 11. A sociologist is concerned about the e↵ectiveness of a training course designed to get more drivers to use seat belts in automobiles. What are the null and alternative hypotheses if she commits a (i) Type I error by erroneously concluding that the training course is ine↵ective? (ii) Type II error by erroneously concluding that the training course is e↵ective? 12. A random variable has a normal distribution with mean µ and a known variance, σ2 = 9. The null hypothesis H0 : µ = 20 is tested against the alternative hypothesis H1 : µ > 20 using a random sample of size n = 25. It is decided the null hypothesis will be rejected if the sample mean is more than 21.4 . (i) Obtain the probability of Type I error. (ii) Obtain the probability of Type II error and the corresponding power of this test when, in fact, µ = 21. (iii) If we require the probability of Type I error to be 0.05 maximum, what is the new rejection rule? (iv) What would be the probability of Type II error with the rule given in (iii) above when, in fact, µ = 21? 13. A drug for relieving nervous tension is claimed to be 60% e↵ective. However, experimental results in which the drug is administered to a random sample of 100 adults suffering from nervous tension show that only 50 received relief. Is there sufficient evidence to conclude that drug effectiveness is not 60%? Use a 5% level of significance. State the underlying assumption you use when carrying out your hypothesis test. 14. In a study to estimate the proportion of residents in a certain city and its suburbs who favor the construction of a nuclear power plant, it is found that 63 of 100 urban residents favor the construction while only 59 of 125 suburban residents are in favor. Is there a significant difference between the proportions of urban and suburban residents who favor construction of the nuclear plant? Use of a p-value to conclude.
CE 314/887 Assignment 2 Text classification December 2024 Deadline: Please follow deadline on FASER Build a text classifier on the Emotions sentiment classification dataset - a collection of English Twitter messages meticulously annotated with six fundamental emotions: anger, fear, joy, love, sadness, and surprise. You can use any classification method except the Naïve Bayes method and rule based method, but you must training your model on the first 90% instances and testing your model on the last 10% instances. The Emotions dataset will be uploaded on the moodle page for you to download. Dataset Homepages: https://www.kaggle.com/datasets/nelgiriyewithana/emotions Some tutorials can be found here: https://www.kaggle.com/datasets/nelgiriyewithana/emotions/code?datasetId=4403839&sortB y=voteCount Your code should include: 1: Read the file, incorporate the instances into the training set and testing set. 2: Pre-processing the text, you can choose whether you need stemming, removing stop words, removing non-alphabetical words. (Not all classification models need this step, it is OK if you think your model can perform. better without this step, and you can give some justification in the report.) 3: Analysing the feature of the training set, report the linguistic features of the training dataset. 4: Build a text classification model, train your model on the training set and test your model on the test set. 5: Summarize the performance of your model (You can gain additional marks if you have some graph visualization). 6: (Optional) You can speculate how you can improve your works based on your proposed model. 7. You need to include a ‘readme ’ file in your submission, which you need to tell: - What’s you python version and third-libs used in you assignment (also give the version). - How to run you code. After you build such a model and test on the test set, you should write a report (no longer than three pages in A4,with Arial 11 fonts) to summarize your work. (You can use the existing algorithms on github or kaggle (please include the references on you codes), but you must not directly copy and paste their code! However,you are not allowed to use the Naïve Bayes algorithm and VADER classifier, which practiced in Lab 4) Suggestion: some bonus points: Have necessary comments on your code Have proper reference on your report Have graph visualization on your report Investigate more evaluation methods, like not only show the P R F score, but also run multiple times and show the standard derivation on P R F (I am sure you can find more evaluation methods.) Write your report like a mini-conference paper (you can learn from this paper: • Zichao Yang, Diyi Yang, Chris Dyer, Xiaodong He, Alex Smola, and Eduard Hovy. 2016.Hierarchical Attention Networks for Document Classification. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 1480-1489, San Diego, California. Association for Computational Linguistics. )
M2 Project Progress Report - Virtual Hair Style. Fit October 17, 2024 1 Project Objective As Figure 1 shows, this project aims to change hair color with hairstyle fitting, including: • Shape Transform.: Transform. the hair outline in target images to fit with reference im- ages as precisely as possible. • Color Change: Map hair color into expectation directly. Figure 1: Shape Transform. 2 Entities and Relationships So far, we have identified the following components: • Element: Pixels of images, atomic units processed for desired appearance. • Entity: All images used in the project are collections of entities. Besides the reference image, a target image is necessary to transform. the hair shape. • Attribute: Positions and colors of pixels in images are attributes we focus on, especially hair part(it meas that we supposed we have identified the position of hair in images). In an image, a pixel has a value to represent the color, and pixels with specified positions can make up the outline and shape of contents. Let us draw a conclusion about relationships among attributes, elements and entities. Figure 2: Entity Relation In summary, the entities and elements in this problem are followed: • The reference image R hasn pixels for hair part. The pixel at position i with value Ri , for i = 1,...,n. • Supposed we have already identified the hair positions. Therefore, the target image T has m pixels constituting hairs. The pixel at position j with value Tj , for j = 1,...,n. • The resultant image R* has same n pixels with origin one. The pixel at position i with content Ri(*), for i = 1,...,n. The relationship between the entities and elements has following points: • Pixels at non-hair positions should keep unchanged as possible, like human face, back- ground. However, it is possible to change these parts little for better fit. • Give known hair position of images, hair pixels are primary elements we focus on. Generally, we can abstract this task as a function F to transform. pixels of hair parts in target image to fit with reference image for the least error: where i is same position of pixels among reference and resultant images 3 Problem Definition • Objective: Given a target image with known position pixels of hairs, our design F trans- forms hair pixels to fit with reference image with least error. • Inputs: – Reference image R: Contains n pixels, where each pixel at position i has value Ri , for j = 1,..., n. – Target image T: Contains n pixels for hair part, where pixel at position j with value Tj , for j = 1,...,n. • Outputs: You seem to assume correspondence is known. If so, you can't have known correspondence between any image pixel, You can only have known correspondence between some identified landmark points. Where are these landmark points? – Transform. function F : F represents the transformation including shape and color change: Rj(*) = F(Rj ) = C(T(Rj )) (2) T is shape transformation function, and C is color change function. – Resultant target image R* : The transformed image that displays the modified hair on the target image, it contains n pixels, each is transformed by Rj(*) = F(Rj ) • Optimization Objective: Minimize the difference between the resultant image and the origin image: Minimize ||F(Ri ) - Ri || 2 (3) • Constraints: – Shape Constraint: The constraint using landmark points ensure that hair does not extend below the top of the eyebrows or beyond the sides of the face. – Color Consistency Constraint: Average color α is used to ensure color transfor- mations maintain consistency with surrounding pixels. 4 Algorithm Design Algorithm 1 Hair Style Fit 1: Initialize R* = R, i.e., set each pixel Ri(*) = Ri for all i 2: Initialize error E = ∞, threshold τ > 0 3: Identify landmark points in R* : • Set leyebrow top as the highest pixel point where hair should not extend below (e.g., above the eyebrows). • Set lface left and lface right as the outermost pixels on the left and right sides of the face, respectively. • Map corresponding landmark points between T and R. 4: Apply Thin Plate Spline (TPS) Transformation: • Utilize TPS to perform. a smooth transformation of T based on the mapped landmark points. • Adjust T to achieve a natural transition towards the reference hair shape in R* . 5: Smooth and refine T: • Use the TPS transformed positions to further smooth the edges of the hair region. • Adjust details to correct potential artifacts introduced by transformation. 6: Adjust hair from T to fit the reference image: 7: *Note: This step involves the correction of hair position to avoid unnatural breaks and to ensure it aligns correctly with the facial landmarks, as illustrated in Figure. 3. 8: for each pixel pi in T do 9: if pixel pi is below leyebrow top then 10: Trim T by removing pixels below leyebrow top 11: end if 12: if pixel pi is beyond lface left or lface right then 13: Trim T by adjusting pixels beyond lface left or lface right 14: end if 15: end for 16: Color Adjustment of Hair: • Compute the average color of hair in R* as CR = AverageColor(R* ). • Compute the average color of target color(the color which we want to transform to) as CT = AverageColor(T). • Calculate the scaling factors for color adjustment: – scaler = CR(*)/CT for each color channel (e.g., RGB). • Apply pi = pi × scaler on each pixels pi in T: 17: Global Transformation: 18: Initialize transformation parameters: scales = 1, rotation θ = 0, translation t = [0, 0] 19: repeat 20: Set E′ = E 21: for each pixel pi in T do 22: Set d = ∞ 23: for each pixel qj in R* do 24: if ||qj − pi || < d then 25: Set c(pi ) = qj , d = ||qj − pi || 26: end if 27: end for 28: Compute local transformation Ti from pi to c(pi ) 29: Update global transformation: s = s + α × (scale of Ti − s) θ = θ + α × (rotation of Ti − θ) t = t + α × (translation of Ti − t) 30: where α is an coefficient to adjust the step (e.g., 0.1) 31: end for 32: Apply global transformation to T: T′ = Transform(T,s,θ, t) 33: Compute new error E = Error(T′ , R) 34: until |E − E′ | < τ 35: Output final transformed hair mask T′ 36: Overlay T′ onto R* to generate final image Figure 3: Landmark Points 5 Justification 5.1 Correctness The correctness of the algorithm can be justified in the following aspects: • Landmark Detection: The algorithm identifies facial landmarks, like the top of the eyebrows and the edges of the face, to enforce constraints that hair cannot extend above these points. This prevents the hairstyle. from obscuring the face and maintains a natural appearance. If hair from the target image falls below the eyebrow line, the algorithm trims it for a more visually appealing look. • Transformation: TPS ensures a seamless transformation and natural appearance after changing hairstyle. • Color Change: During the color-changing step, the algorithm calculates the average hair color from both images and applies scaling factors, ensuring the final image looks natural and visually appealing. How does the algorithm satisfies optimization objective and constraints? 5.2 Convergence The convergence of the algorithm is established through iteration and error minimization: • Error Reduction: The algorithm initializes an error metric E to quantify the difference between the transformed hairstyle and the reference hairstyle. In each iteration, it updates the hair pixels to minimize the error until it sufficiently converges to the threshold. • Finite Iterations: The iterations are finite because the algorithm alters a limited number of pixels within defined masks, adhering to constraints set by facial landmarks. This ensures that the algorithm converges to a solution instead of entering an infinite loop.
CSE 101 Introduction to Data Structures and Algorithms Programming Assignment 3 In this assignment you will build a Graph module in C that implements the Depth First Search (DFS) algorithm. You will use your Graph module to find the strongly connected components of a digraph. Read the handout on graph algorithms, and sections 22.3-22.5 of the text. Also see the pseudo-code posted on the class webpage at Examples/Pseudo-Code/GraphAlgorithms. A digraph G = (V, E) is said to be strongly connected iff for every pair of vertices u, v ∈ V, vertex u is reachable from v, and vertex v is reachable from u. Most directed graphs are not strongly connected. In general, we say a subset X ⊆ V is strongly connected iff every vertex in X is reachable from every other vertex in X. A strongly connected subset that is maximal with respect to this property is called a strongly connected component of G. In other words, X ⊆ V isa strongly connected component of G, if and only if (i) X is strongly connected, and (ii) the addition of one more vertex to X would create a subset that is not strongly connected. Example We can see that this digraph contains 4 strongly connected components: C1 = {1, 2, 5}, C2 = {3, 4}, C3 = {6, 7}, and C4 = {8}. To find the strong components of a digraph G call DFS(G). As vertices are finished, place them onto a stack. When DFS is complete the stack will store the vertices ordered by decreasing finish times. Next, compute the transpose GT of G. (The digraph GT is obtained by reversing directions on all edges of G.) Finally run DFS(GT ), but in the main loop (lines 5-7) of DFS, process vertices in order of decreasing finish times from the first call to DFS. This is accomplished by popping vertices off the stack. When the whole process is complete, the trees in the resulting DFS forest span the strong components of G. Note the strongly connected components of G are identical to the strong components of GT . See the algorithm Strongly- Connected-Components in section 22.5 (p.617) of the text. Your graph module will again use the adjacency list representation. It will, among other things, provide the capability of running DFS, and computing the transpose of a directed graph. DFS requires that vertices possess attributes for color (white, black, grey), discover time, finish time, and parent. Below is a catalog of required functions and prototypes, constituting the majority of Graph.h. // Constructors-Destructors Graph newGraph (int n); void freeGraph (Graph* pG); // Access functions int getOrder(Graph G); int getSize(Graph G); int getParent(Graph G, int u); /* Pre: 1
CET313 - Artificial Intelligence Assessment Brief Intelligent Prototype Development 1. Specification This assignment is weighted at 100% of the overall module and will be marked out of 100. This assessment requires approximately 40 hours to complete. The aim of this assessment is to provide you with an opportunity to demonstrate your understanding and practical skills in Artificial Intelligence. You must propose your own project concept, subject to approval by the Module Leader. The assessment is designed to assess your ability to develop a small prototype, evaluate its performance, and compile a comprehensive report. Additionally, you are required to submit a portfolio of evidence from practical exercises undertaken during the course. 1.1. Learning Outcomes LO1. Demonstrate comprehension of a range of AI techniques and their application to problem solving within society, industry, and research. LO2. Articulate awareness of contemporary developments in the field of AI and their application and potential implications. LO3. Critically assess real-world problems and determine which AI approaches are suitable for their solutions. 1.2. Deadlines Files submitted via Canvas. Deadline Thursday, 09th January 2025 13:59 pm 2. Important Information All work is to be completed individually, except where explicitly stated, and you will only be able to receive Marks for your own work. You are responsible for the security and integrity of your own files, and you must not permit others access to your assignment work. Plagiarism or paraphrasing without due accreditation will be dealt with severely asset out in the University Infringement of Assessment Regulations and detailed in the Programme Handbook. You can also refer to the library guidebook on plagiarism such as Avoiding plagiarism - University Library Services (sunderland.ac.uk) Students are permitted to use AI tools used in an assistive role within the assessment. However, the student must declare in the submission the used tool(s) and how did you use it.Examples of where AI might be used in an assistive category include: • Drafting and structure content. • Supporting the writing process in a limited manner. • As a support tutor. • Supporting a particular process such as translating content. • Giving feedback on content or proofreading content However, students cannot use AI tools to do the project for you as the work must be completely done by the students. All AI generated content must be validated by the student. You are expected to submit work in the file formats requested. Submitting links to files saved elsewhere in the cloud will not be considered and will result in a zero mark. The actual files must be loaded to Canvas and readily available to the assessor. After uploading and submitting your files, you must check that you can also retrieve and open them. It is your responsibility to ensure files are not corrupted at the time of submission and to report any issues immediately to the help desk, copying in your lecturer and to seek alternative arrangements when required. 3. Tasks You are required to complete three main tasks; the tasks details can be found below: 1. Development of Prototype (30%): Develop a prototype that relates to the selected project. The prototype should showcase your practical skills and knowledge in AI. Ensure the prototype is functional and aligns with the project objectives. For details about the prototype please refer to the scenario. 2. Evaluative Report (40%): Write an evaluative report that documents your development process, the performance and functionality of the prototype, and the extent to which it meets the project objectives. Your report should critically assess the strengths and weaknesses of your prototype, propose potential improvements, and discuss the implications of your findings. The report must include a link to the e-portfolio that has your weekly workshop notebook. 3. Portfolio of Evidence (30%): Compile a portfolio of evidence from practical exercises completed during the course. This may include code samples, design documents, project notes, or any relevant material that demonstrates your practical engagement with the course material. 4. Deliverables: 1. Jupyter notebook with the prototype script. (for the used datasets, you can cite it in the report or upload a zip file that contains the Jupyter notebook and any other required files). The notebook should have comments and must show all the results. 2. A report explaining each step of the development and containing the link to the e portfolio. The report structure is shown in the next section. 3. E-portfolio link showing your weekly work. The portfolio must be hosted on university hosting service (no external services are acceptable) and must be accessible to the module delivery teams, 5. Report Structure The evaluative report must include the following sections: • Cover sheet the cover sheet must be upload filled for the report. • Abstract (less than 150 words): This should provide a high-level overview of the project, including its goals, objectives, and outcomes. • Introduction (1-2 pages): This should provide more detailed information about the project, including its background, motivation, and scope. • Literature Review (1-2 pages): Short literature review of the most relevant research papers on the project. • Methodology (2-3 pages): This should describe the methods and techniques that were used to complete the project. • Results and Discussion (2-3 pages): This should present the findings of the project in a clear and concise manner. The results should be interpreted and discuss their implications. • References: This should list all the sources that were cited in the report. 6. Scenario Imagine you are attending a job interview at a charitable organisation and have been asked to prepare a project that demonstrates your skills and professionalism. The focus of the project is to showcase how artificial intelligence can be applied to support Alzheimer’s disease research or diagnosis. For instance, you could develop a machine learning model that classifies individuals as likely or unlikely to have Alzheimer’s. Alternatively, you might predict MMSE (Mini-Mental State Examination) scores using machine learning techniques. You are free to choose your dataset format, whether tabular, image, or audio, as all are acceptable for this project. 7. Marking Criteria Task 1 - Prototype (30 Marks) Mark Range Level Description 0-3 Some Trying The project is incomplete or unrelated to the required work. Minimal effort is evident. 3-6 Beginner Some work has been attempted, but the project lacks completeness and coherence. Key elements are missing. 6-9 Basic A basic code implementation is present, but parts may be incomplete or non-functional. The project demonstrates foundational understanding but limited progress. 9-12 Developing A simple prototype has been created, similar to an in-lab exercise. Basic functionality is achieved, but there’s limited development beyond essentials. 12-15 Good A working prototype is evident, with most development steps completed. There is some demonstration of understanding, though analysis may be minimal. 15-18 Merit A well-developed prototype with clear comparisons between different models or methods. The project demonstrates a good level of understanding and effective analysis. 18-21 Very Good A robust prototype with detailed comparisons and evaluations of models/methods. The work shows a thorough understanding and accurate analysis. 21-24 Excellent A professionally executed project with all development steps completed. The project includes comprehensive and detailed comparisons with other models/methods. 24-27 Excellent and Thorough A complete project with all steps professionally executed, including extensive comparisons with other models/methods and relevant literature. 27-30 Expert An expertly developed project that meets all requirements to a high standard, with detailed, professional comparisons of models/methods and integration of relevant literature for a well-rounded analysis.
Arch1102_T3 2024: Architectural Design Studio Two Project Two: Bicycle Parking and Workshop + Kiosk in Courtyard with flexibility Week 6 (16 Oct): no studio class PROGRAM Program for the Kiosk is as for Project One Program for Bicycle Parking and Workshop A New Entrance Has Been Created for Bicycles (see site map on page 4) A. Parking: Provide free-standing, self-supporting, floor or wall-mounted bike parking stands for 30 bikes (min.). (see: ‘FlatTop’ or ‘Anaconda’ models as shown in The Bike Parking Handbook, p. 11,12 – document available on Moodle resources. Please note these examples are only cited as a guide for dimensioning. The exact design of the bike parking stands is up to individual students.) B. Workshop: Staffed by one person. Free to use. Provide three interconnected spaces: 1. Store (min. 20 meters squared) 2. WC (for use by staff only – one toilet and one basin) 3. Workshop (min. 60 meters squared) (Provide: 10 meters long x 600mm wide, 900mm high workbench, 3 bike repair stands, wall hung tool rack/board; a tire pump zone.) * - note: bench can be continuous (10 meters) or split into smaller bench elements, for example, 3 x 3.3 meters elements. Think of the project as a series of architectural situations: An outdoor court for people and bikes A workshop-front A sequence of working benches/stands Consider goods delivery and rubbish removal. Workshop operating hours are 7 am to 5 pm (they are only closed on public holidays). The building must be closed and secured after operating hours. Users: Art school students/staff and the general public who are visiting the Gallery and the Library. Rules/Limits • The ground has to be excavated. Consider ramp and subterranean architecture. The area and location depend on the design within the given site boundary. • You may, retain, revise, integrate, add to or entirely redesign the kiosk from project one. • As your site, you must add Zone B to Zone A, assuming that the two long walls bordering Zone B, including the upper floors to the roof, are removed. No change in Zone C is allowed. However, you can choose to connect with the existing stairs in B27. • Nominated minimum areas do not include external walls. • Use entirely orthogonal geometry - no curves or diagonals. The roof plane may be angled. • Assemble the building using planes, folded planes, and linear elements only. See the definition included below. • Masonry and timber will be the primary materials used, and concrete slabs will be used for the floor. Timber can also be used for joinery, doors, and windows. Lintels may be used over door and window openings, and bike racks may be metal. Continuous flat planes: These are elements that have two of their dimensions of relatively equivalent size compared to their substantially smaller third dimension. The planes must be flat - not curved or folded. They must maintain their surface continuity as much as possible, which means that openings such as doorways, windows, voids, etc. must be subservient to this continuity. Examples are floors, walls, ceilings, roofs, landings, benchtops, screens and deep sills. Continuous folded planes: Each of these elements is made from a single flat plane that has been folded at least once. The planes must maintain their surface continuity as much as possible, which means that openings such as doorways, windows, voids, etc. must be subservient to this continuity. Examples of folds are extruded L-, U-, Z-, O- shapes. The elements can be floors, walls, ceilings, roofs, stairs, awnings, balustrades, staircases and combinations of these. Linear elements: Those in which one of the three dimensions is substantially longer than the other two. Examples are: columns, handrails, stair treads, sills, architraves, picture rails, skirtings, fascias, shelves, pipes and the individual elements in screens. Associated Precedents (see Moodle) Mies van der Rohe, Barcelona Pavilion (1929) Le Corbusier: a selection of 2 housing projects. Villa Baizeau, 1928, built (Carthage, Tunisia) and Résidence Peyrissac, 1942, unbuilt (domaine agricole, Cherchell, Algeria). Tasks + Submission Requirements General Notes for the 3 Tasks: Task 1: In all projects, Task 1 requires you to represent the nominated precedent building(s). This may involve preparing two- or three-dimensional drawings or constructing three-dimensional studies. You will be asked to prepare this material based on available drawings and photographs. These exercises are designed to develop and improve your skills in: • Analysing spatial strategies in architectural precedents. • Considering the relationship between two-dimensional drawings and three-dimensional space. • Considering differences in various modes of architectural representations – photographs, models, perspectives, axonometric, digital models, etc. In representing these works, bear in mind that redrawing and model-making involve a process of interpretation and abstraction. Models and drawings, depending on their scale, medium, materials, methods of construction, etc., can carry different ideas and express various material and spatial sensibilities. Task 2: In all projects, Task 2 requires you to prepare two design options. The two options need to be developed with reference to your precedent study (the assigned one and your inspiration drawn from all six) and your interpretation of the site, as well as specific requirements stated in the brief. Each studio tutor will promote a specific attitude to the analysis of the precedents. It will unpack the relevance of this architectural model to your site and project brief. In preparing your two options we also encourage you to explore the relationship between your two proposals, making clear their points of distinction. For example, you may choose to explore two distinct siting options. Exactly the same process as in Project One applies: a series of study models, diagrams involving siting strategies and spatial relations, sections, and plans in 1/100 scale must accompany your early stage of design development and communication with your tutor. Task 3: In all projects, Task 3 requires you to prepare one design scheme. This is to be developed based on discussion emerging from Task 2. For instance, the development of this scheme can emerge from the reworking of one of your previous options or result in a hybrid model that attempts to bring aspects of the two together. In developing this option, you are expected to repeat the processes discussed above. The relationship between the precedents, development of the themes, enquiries into the site, etc., are to be constantly scrutinised and reworked. It is through this process of critical reflection that your designs can develop and gain subtle complexities. Weekly Activities WK 5 – Submit the completed site modelling in 2 scales: 1/500 and 1/100. WK 5 – Task 1: Precedent exercise work in teams of 3 to 5 (follow your tutor’s guidance) and complete in the studio. 4 to 5pm: Submit for discussion (each tutor will finesse the schedule) Requirements: 1:500 sketch model situated in the context model 1:100 sketch plans/sections WK 6 – Flexibility Week (no studio class) Task 2 (independent work): Sketch Design_2 Options (post them on Conceptboard for feedback. Send the link to your tutor by 5pm on 15 Oct if you want feedback from them. Requirements: 1:500 sketch model situated in the context model 1:100 plans and sections 1:100 working model WK 7 – Submit for discussion Task 3: Developed Sketch Design 1:500 sketch model situated in the context model 1:100 plans and sections 1:100 working model WK 8 – Project Two Final Submission (20%, Studio Review from 1 to 4:30 pm, 30 Oct, with collective feedback from 4:30 to 6 pm - follow your tutor’s instruction). Submission requirements: Model/Modelling: Massing model @ 1:500 (situated in context model or 3D modelling of the site): a series of them Final model @ 1:100. The model must be refined in material detail and finish. The roof of the model must be removable, and the interior clearly represented. Show Zone C and adjacent buildings (the architectural characters of the context). Construct the selected details of the walls adjacent to the site (B14, B15 & B16). Printed drawings - Use scale bar. (min requirements) A site plan @ 1:500 showing the surrounding context and boundaries of adjoining buildings. Circulation for bicycle paths and footpaths must be resolved and represented in the drawing. A plan @1:100 A long section* @1:100 A short section* @1:100 (* - Notes: minimum one section through excavated part of the final design showing its relationship with the courtyard) Format: Use A2 size as the basic modular combining different modes of representation, e.g., plan mix with section or rendering mix with sectional perspective. Avoid one drawing per slide/sheet. Medium: SketchUp, Rhino, Illustrator, and Photoshop only. Fine line weight and high-quality drawings + renderings. Other than black and white, only two colours can be employed (You will lose marks if Lumion is used to create hyper-real images instead of “render”). Text: Include course name, project title, your name, tutor’s name, project name and orientation (North Point). No additional text. PDF: Submit all your drawings and photographs/snapshots of your models/3D modelling as a single PDF file to Moodle. Upload by 10 pm 29 Oct. You will lose marks if the submission is a list of jpeg files. Criteria for Review & Assessment: Project 2 assessment is worth 20% of the total course assessment. You will be assessed on the following criteria: • Compliance with the prescribed limits: three interconnected spaces and excavated ground; • Clarity of architectural strategies as they have been drawn from precedents and their translations into relevant design strategies and spatial tactics; • Clarity of the relationship between the topics of siting, enclosure and materials in reference to the program; • Understanding of methods of assembly and construction, including section(s) through excavated areas; • Precision and clarity of architectural representation. Site Boundary for Project Two:
CEGE0057 Advanced Civil Engineering Materials: Individual Coursework Brief Introduction Reinforced concrete is the most widely used modern construction material in the world, where concrete composed of cement, water and aggregates provides the compressive strength, while steel reinforcement improves the tensile and shear properties and provides a degree of crack resistance. Nowadays, one of the major challenges facing civil engineers and construction industry is to provide sustainable and durable infrastructures to society. Increasing emphasis has been placed on finding advanced materials that can fulfil such requirements to reduce the environmental impact, repair and maintenance costs and extend the lifetime of concrete infrastructure. The Brief In this coursework, you will briefly explain the meaning of sustainability and durability and make a comprehensive analysis and an in-depth discussion on the effective ways to improve sustainability and durability of reinforced concrete infrastructure, with particular focus on the use of advanced civil engineering materials as alternatives to conventional reinforced concrete considering its constituents. The discussion should cover (but not limited to), the main contents of this course, including sustainability and construction materials, fibre reinforced composites, advanced concrete, and advanced steel to demonstrate an understanding of these advanced civil engineering materials and material selection for engineering applications. The relevant concepts or mechanisms can be explained with the help of simple but clear diagrams. You are expected to complete this coursework within 5 days. The submission is in the form of a report that can be typed or hand-written and should not exceed 7 sides of A4 including relevant, correctly labelled and captioned graphs and tables of data (not including reference list). If it is typed, please use margins of 2 cm in all directions, 12-point Times New Roman font, single line spacing, and one column, where possible. The mark scheme (below) takes into account the range of information sources used. All sources of references included in the report should be correctly acknowledged in a standard citation format (preferably using Harvard style of referencing). The report must be submitted in PDF format with a file name of CEGE0057_YourFirstName (e.g. CEGE0057_Richard) to Turnitin via Moodle (submission link under ‘Coursework - Individual Report’), which can be used to check for similarity and plagiarism. The deadline for submission is 5pm on 17th January 2025 (GMT). Marking Scheme The following mark scheme will be adopted for the coursework. The coursework mark is worth 80% of the total course mark: Mark Description 80% or more An outstanding submission into which exceptional effort has gone, shows significant insight into advanced materials and material selection for sustainable and durable concrete infrastructure, either extending beyond the topics covered in lectures, or, if limited to those topics, applying them in novel ways. Sources of information are extensive and well-documented. Presentation is of a professional standard. Communication is very clear and shows imagination in using the limited space available. 70-79% An excellent report shows good understanding of advanced materials and material selection for sustainable and durable concrete infrastructure. The purpose, limitations and key arguments of the report have been clearly articulated. A varied range of information sources has been used. Presentation is generally clear and precise. 60-69% A good report shows some understanding of material selection for sustainable and durable concrete infrastructure but misconceptions or gaps in knowledge in key areas are also apparent. Information sources are sound but limited in scope. Communication merely adequate. Possibly some misunderstandings are present, or the depth of knowledge is limited. 50-59% A satisfactory report that has been adequately put together. Some basic understanding of advanced materials and material selection for sustainable and durable concrete infrastructure is shown. There is some treatment of the theoretical concepts and evidence of background reading, but topic is not explored in any depth and information sources are limited. Little ability at engineering communication is shown. 40-49% An unsatisfactory report with very limited submission deficient in insight, evidence of research and evidence of understanding. Very poorly communicated. 39% or less A poor report. The work is superficial and poorly thought through. There is no evidence of theory or background reading. Poor use of language, grammar and absence of referencing.
BUSM137 Introduction to Marketing Management 2022/2023 Assessment instruction: Students are asked to choose a particular brand within the market that they segmented in their group project and evaluate their chosen brand on three elements: targeting strategy, pricing strategy, and branding strategy. For each of these elements, students are required to discuss the current strategy taken by the company and provide suggestions for future improvements. Targeting, pricing, and branding strategies will be discussed in the class over the semester. Students will be provided with appropriate frameworks to these strategies. Multiple case studies discussed on each topic will also help students to evaluate strategies and provide actionable recommendations. The report should include: 1. section A- Brief overview of the brand (Desk research using SWOT or 5Cs) 2. section B- Targeting strategy (current strategy, your recommendations) 3. section C- Pricing strategy (current strategy, your recommendations) 4. section D- Branding strategy (current strategy, your recommendations) General notes on written submissions: A. Word count • You are allowed to exceed by 10% for each assessment. • Tables, diagrams, and figures that you copy from other sources will not count in word limit and can go to the appendix section; however, if you use multiple sources and make your own summary (e.g., a summary table/diagram), then it will count towards the word limit and you should add them in the main text. • References are not included in the word count. B. Grading Criteria. Here are some pointers as to what I am looking for in your essays: • Evidence of reading the assigned literature and thinking about what kinds of theories or concepts might be useful in answering the questions. In other words: Evidence of understanding and ability to apply course concepts • Logic and justification of choices • And PLEASE reference throughout the text, even if you are just using the assigned material. Assessment Criterion/Coursework Rubric Row Module learning outcome Program learning outcome Section A (15%). Providing an overview of the company using SWOT or 5Cs framework B1. Assess marketing concepts in different business contexts and recommend plans for improvement 2.4. Supports argument with relevant data* Section B (25%). Examining the targeting strategy(s) of the company A1. Understand the basic concepts and theories in marketing B1. Assess marketing concepts in different business contexts and recommend plans for improvement 2.4. Supports argument with relevant data* 2.8. Evaluate contemporary issues in business management/society (focus on targeting) 2.10. Recommends solutions that could be applied in practice Section C (25%). Examining the pricing strategy(s) of the company A1. Understand the basic concepts and theories in marketing B1. Assess marketing concepts in different business contexts and recommend plans for improvement 2.4. Supports argument with relevant data* 2.8. Evaluate contemporary issues in business management/society (focus on pricing) 2.10. Recommends solutions that could be applied in practice Section D (25%). Examining the branding strategy(s) of the company A1. Understand the basic concepts and theories in marketing B1. Assess marketing concepts in different business contexts and recommend plans for improvement 2.4. Supports argument with relevant data* 2.8. Evaluate contemporary issues in business management/society (focus on branding) 2.10. Recommends solutions that could be applied in practice Overall (10%): Quality of writing, coherence of the arguments, both in each section and as a whole (e.g., logical connection between recommended actions in sections B, C, and D) C1. Write clearly and report information in a professional and effective manner A2. Critique how different aspects of marketing integrate to deliver a coherent plan 2.1. Selects credible sources of data 3.2. Expresses arguments coherently through writing 3.3. Uses appropriate language, grammar and spelling suitable for scholarly writing 3.4. Displays good structure, formatting, style and presentation of writing 3.5. Cites sources of information and data using consistent and a recognised referencing style Submission Information Please observe the following style. guide. Unless otherwise specified, • All work must be typed and submitted in MS Word or Adobe PDF format • Font size should be 12 point (unless otherwise specified) • Font style. should be Arial • Lines should be double-spaced • Leave margins for comment Insert page numbers • Use a header containing your student ID number, the module code to which your work applies, and the date. And Please: • Always spell-check and proof read your work before handing it in (once you have submitted your work you will not be permitted to retrieve it) • Keep your own electronic back-up copy of your work and if possible save on two devices Avoid plagiarism • Submit your work on time Guidelines and Late-Work Policy 1. Coursework submitted late (and there are no extenuating circumstances) will incur a late penalty. Five per 2. cent of the total marks available shall be deducted for every period of 24 hours, or part thereof, that an assignment is overdue there shall be a deduction of five per cent of the total marks available (i.e. five marks for an assessment marked out of 100). After seven calendar days (168 hours or more late) the mark shall be reduced to zero, and recorded as 0FL (zero, fail, late). 3. Each module has word limits for coursework assignments; however, the decision about whether to impose a penalty mark for exceeding the word limit is made by each module organiser. You must check the module handbook and the assignment briefing documents to see whether the particular module organiser has adopted a penalty system. It is your responsibility to read the handbook and assignment briefing carefully. If no penalty is specified then the module organiser will take into account the word length under the standard marking conventions. For example, if you have exceeded the word limit then it might be that you have not been sufficiently succinct or focused in your assignment and therefore might be penalised for these weaknesses. Please note that word limits do NOT include references or appendices. However if excessive material is included on appendices then this too will be judged accordingly and you may be awarded a lower mark. 4. You should ensure that your submission is in either Microsoft Word or PDF format. 5. Failure to submit in either one of these formats will result in a mark of 0 being awarded for the particular assessment. It is therefore your responsibility to ensure that the file format is correct and it can be opened by the receiving party. 6. You should ensure that the correct piece of assignment is uploaded as the document downloaded on the due date by the module organiser will be marked regardless of content. You will not have another opportunity to submit the work again if you mistakenly uploaded the wrong document. 7. ALLOW YOURSELF PLENTY OF TIME TO SUBMIT YOUR COURSEWORK. DO NOT LEAVE IT UNTIL THE LAST MINUTE 8. Computer problems, such as computer viruses, failure to make a back-up copy or temporary internet access problems, will NOT be viewed as a valid reason for late submission. 9. Check that your assignment submission has been successful, and print a copy of the confirmation screen. 10. If you submit your assignment after the deadline, you will still be able to submit your coursework via QMplus however you will be penalised for late submission, the only exception to this is if you have an approved extension due to extenuating circumstances.
2024/25 ASSIGNMENT REMIT – LIVE FACE TO FACE PRESENTATION Programme Title BABE, BAITM, BAHTM, Module Title Financial Strategy Module Code 001 ASSIGNMENT TITLE Presentation on the financial performance of a listed company Level 6 Weighting 50% W/C Hand Out Date 11/11/2024 Due Date By 17:00 on 16/01/2025 Cut-off Date for Late Submissions (10 working days after the due date) By 17:00 on 30/01/2025 Feedback Post Date ASSIGNMENT FORMAT LIVE PRESENTATION (FACE TO FACE) Presentation Duration 15 Minutes Submission Format Live presentation Individual Permitted use of Artificial Intelligence within this assessment For this presentation, Artificial Intelligence can ONLY be used as stated within the Using AI Ethically within Assignments document on the next page. ASSIGNMENT TASK: “You are an advisor for a client looking to invest £10million within the Online food ordering / Food delivery sector. You need to choose one of the following companies who you believe might be the right investment opportunity for your client – Deliveroo or Just Eat. To fulfil this role you are required to: 1. Provide an Introduction / background to your chosen company. 2. Evaluate the working capital management of your chosen company, incorporating financial ratios. Complete a financial analysis comparing the company’s current financial performance (liquidity, profitability, and efficiency) to two of its primary competitors for the last two years of historical data. 3. Discuss and analyse the strategic future aims of the company and the financial impacts. 4. Synthesis from your analysis whether the investor should invest in your chosen company. MARKING CRITERIA: ● Below are the marking criteria that align with both the task(s) set and the quality of your presentation. Clear weightings/marks will be noted for each criterion. ● The overall mark awarded for this assignment will explicitly show how the mark was calculated based on your performance against each criterion. The following is an indicative marking criterion for your presentation: Your clarity of communication and flow of your presentation will equate for 10%. 1. Your Introduction / background to the chosen company will equate for 5% 2. Your evaluation and analysis of the working capital management of the chosen company, incorporating financial ratios will equate to 20% 3. Your financial analysis of the company’s current financial performance (liquidity, profitability and efficiency) in comparison to previous years (at least 2 years) and two of their main competitors will equate to 30%. 4. Your discussion and analysis of the strategic future aims of the company and the financial impacts will equate to 15%. 5. Your analysis whether the investor should invest in the company will equate to 10% References – quality and range of references (at least 10 - 15 references across different sources are expected) – The currency of the data (how recent is the data) is important. Marks for references will also be embedded with the sections above, but the extent, presentation, currency and quality of the references will equate to 10%. TASK GUIDANCE: ● Focus on attention to detail, quality of work and overall academic standards. ● For additional guidance on this assignment, please access the assignment vodcast available on Canvas. GUIDANCE FOR LIVE FACE-TO-FACE PRESENTATIONS: ● All students must verify their identity with their student ID card before the live session starts. If you do not have your ID card (which is a mandatory requirement across all UCB campuses), please speak to the assessor about an alternative way to verify your identity. ● Please note that all sessions will be recorded by the assessor, as this will be required for examiner observation. Specific guidance on presentations for this assignment Presentation time, no longer than 15 minutes, creating effective engaging slides, and showcasing lists of references within the presentation. LEARNING OUTCOMES: ● Learning Outcomes are what the student needs to demonstrate after completing a module. An assessment is a way in which students can demonstrate their achievement of these Learning Outcomes. Learning Outcomes are NOT the same as the assignment task. i. Evaluate and analyse the significance of Working Capital Management for a Business iii. Evaluate in depth published financial statements and demonstrate a high level of analytical skills concerning current performance and future strategies. ACADEMIC SKILLS OUTCOMES: ● The Academic Skills Outcomes to be developed by completing this assignment can be found here. ● For Apprenticeship programmes, identify the applicable Knowledge, Skills and Behaviours the assignment seeks to test. GENERAL ASSIGNMENT GUIDANCE: Using Artificial Intelligence (AI) ethically within your assessments Please read the information below on how to use AI ethically within your assignments, including Grammarly. Please ask your lecturer or CASE if you are unclear about any information within this link. If a marker has questions about how you have used AI within your assignment, you may be invited to a meeting to discuss your work. https://rise.articulate.com/share/4h250QmyY8hNnsQ9HS7B702dJedhlsM6 Teamwork Assessment Should this assignment require you to work as part of a team, you will receive an individual grade based on your performance and personalised feedback. The assignment brief will provide clear details on how your individual grade and feedback will be determined against the task and marking criteria. Importance of Presentation Timings Timings must be observed for all assessed presentations/practical exams. For live sessions, the assessor will stop the assessment once the time has been reached. Cut-off date for late work The cut-off time for late submissions is 10 working days (UCB working days) after the original assessment hand-in/due date. After this time, you cannot submit any late assessments, even if you have Extenuating Circumstances to cover them. https://www.ucb.ac.uk/higher-education-student-handbook/assessment-issues/ (See Additional Information Section, ‘Assignments and how to Submit them’) Students with Support Plans may have additional time to submit their work after the formal submission date. Generic Grading Criteria The Generic Grading Criteria (GGC) are the generic features and expectations of work at a given level on your programme. The GCC per level is used in conjunction with the assignment marking criteria to determine the mark for your assignment. For more information on the GCCs for Levels 4-7, please click on this link. https://www.ucb.ac.uk/higher-education-student-handbook/assessment-fairness-and-marking/ Plagiarism and Academic Misconduct Please read the policy on Plagiarism and Academic Misconduct below. UCB will be robust in ensuring that marks awarded for assignments are based on fair and ethical assessment and referencing practices by students. https://www.ucb.ac.uk/higher-education-student-handbook/essay-writing/plagiarism/ Extenuating Circumstances Extenuating Circumstances (ECs) are significant personal difficulties which adversely impact your ability to complete your assignment. Please read the supporting information below, if you have ECs impacting your ability to complete this assignment. https://www.ucb.ac.uk/higher-education-student-handbook/assessments-if-things-go-wrong/ UCB Referencing Guide You are required to reference your sources within your assignments appropriately. Please click on the link below to learn how to reference various sources of information. This Guide also includes how to acknowledge all Generative AI used within your assignment, ensuring that you comply with the Using AI Ethically within your Assignments policy (as covered above). https://portal.ucb.ac.uk/download/referencing/referencing-guide.pdf Access the Assignment Life Cycle The Assignment Life Cycle offers you additional support at each stage of the assignment process. Please click on the link below: https://ucbirmingham.instructure.com/courses/26756
Assignment Remit Programme Title MSc Money, Banking and Finance Module Title LM Applied Financial Econometrics Module Code 07 40055 Assignment Title Assignment Level Masters level Weighting 50% Hand Out Date 28/10/2024 Deadline Date & Time 08/01/2025 12pm Feedback Post Date 16th working day after the deadline date Assignment Format Report Assignment Length 1500 words Submission Format Online Individual Module Learning Outcomes: This assignment is designed to assess the following module learning outcomes. Your submission will be marked using the Grading Criteria given in the section below. LO 1. Apply and evaluate econometric procedures employed to analyse financial data. LO 2. Critically evaluate the results and the approaches adopted. LO 3. Critically analyse financial databases and create dynamic models using appropriate software, thereby developing key transferrable skills for banking and finance graduates. LO 4. Develop complex problem solving, digital, analytical and advanced numeracy skills. General information • This coursework accounts for 50% of your final mark. • The coursework is an individual piece of work. You are required to write your own answers and cannot work jointly. All submissions will be routinely checked for plagiarism. [Plagiarism Policy] • The coursework in either part should not be longer than 750 words, for a maximum of 1500 words in total. • Figures, tables and codes do not count towards the word limit. • Please make sure that you fill in the cover page when submitting your report. Assignment: Please read the following requirements carefully before you start your project. The Assignment will be in two parts, Part A and Part B. Each part is worth 50% of marks. Students must answer both parts of the questions. Students shall submit two pdf documents, one for each part, clearly declaring whether the document is for part A or part B. Part A [50%] In Part A, you shall • Follow the assignment remit and answer all the questions as required. • Perform. the appropriate quantitative analysis and make sure your codes work well. If you receive any error messages, try to correct it. Otherwise, delete or hashtag (#) the code that caused the problem so that you can compile the codes. • Perform. statistical tests to support your results and empirical approach if needed. Explain the tests hypothesis and interpret the results clearly. • Present your tables and graphs clearly. • Explain your procedure (of selecting the best fit model) clearly and provide a detailed and thorough analysis on your results (without going over the word limit) Bear in mind that • This is NOT an essay. There is no requirement on the format. But your work needs to be readable and follow the requirements in this remit. • The coursework MUST be compiled in RStudio and submitted via Canvas in PDF format. You can compile your script. to a Word document first and then add the cover page. Please check if your document is clearly readable. If everything looks good to you, you can save it as a PDF file for submission using Microsoft Word. • The answers to each question MUST be clearly stated. Your comments and answers to the questions could be written following “#” in your R codes, which will be compiled. Assignment Questions You are required to select a listed company in either S&P500 or FTSE100 indices. Download its daily close price data from 1st January 2024 to the date that you start this project (this can be any date between the release date of this remit and the deadline) and then perform. the following analysis. 1. Write a very brief description on the data you study (no more than 100 words). You need to introduce the company, the sample period, and the data source. You also need to show that the sample is in line with the requirement. [5%] 2. Create a time series plot on the price data and comment on its trends. Then take the log differences on the prices and generate the daily return rates (Returns). Report and comment on the summary statistics including mean, median and standard errors. Plot the time series plot and the histogram of the Returns. Comment on your results. [5%] 3. Plot the ACF and PACF of the Returns and comment on your graphs. Is the daily return series a white noise process? Is it stationary? [5%] 4. Test the stationarity of the Returns with appropriate ADF and KPSS tests. Explain the null hypothesis and the specific format of the tests. [5%] 5. Estimate an appropriate ARIMA model for the Returns. You need to present the estimation results in a well-organised table, explain why you select such a model, and comment on the estimated coefficients. [10%] 6. Based on the ARIMA model of your choice, forecast the stock prices in the next five trading days? You need to explain the details of your forecasting procedure. Comment on your forecast if you know the realised prices. [5%] 7. Based on the ARIMA model of your choice, check if there is an ARCH effect. You must explain your procedure and results clearly. If yes, estimate an appropriate ARMA-GARCH model and explain why you select such a model. Otherwise, estimate a GARCH(1,1) model on the Returns. Based on your model, what is the forecasted volatility of the Returns in the next trading day? [10%] 8. Reflect on your work, assuming you are a line manager and you read such a report. Comment on the work in terms of its strengths and weaknesses. Identify the areas that need to improve. Please be honest in your reflection. You are welcome to discuss your feelings about this assignment (no more than 100 words). [5%] Part B [50%] Computer Project Aim Use the dataset Bankdata.dta, which has been extracted from BankFocus, to undertake econometric analysis on bank profitability, measured by either return on average assets, return on average equity or net interest margins. BankFocus is a global database of banks and financial institutions previously known as Bankscope. The information is sourced by Bureau van Dijk from a combination of annual reports, information providers and regulatory sources. BankFocus offers detailed, standardized reports and data for over 55,000 banks across the globe (30,000 US and 25,000 non-US). Due to the difficulties accessing data, you are given the dataset. Bankdata.dta, which can be found under the “Assignment” section on Canvas, contains variables from 11,485 banks over the sample period 2005 to 2019. A list of the variables and the mnemonics is given in the appendix. Please note that there are a considerable number of missing observations for some variables. You must select: · a specific research question · the dependent variable · the group of countries to analyse · the sample period · a number of estimation techniques Since some articles use macroeconomic variables as control variables, you can add macroeconomic variables to the dataset but need to submit the data in an Excel file. The best places to get the macroeconomic variables are:- OECD: https://stats.oecd.org/ IMF: https://data.imf.org/?sk=4c514d48-b6ba-49ed-8ab9-52b0c1a0179b&sId=1409151240976 World Bank: https://databank.worldbank.org/home.aspx You are expected to write an academic report, containing a literature review, presentation/description of data and econometric model, discussion/interpretation of econometric results and analysis plus a conclusion. Please submit the do file along with the report (compulsory). A list of suggested references will be provided in Lecture notes and in the assessment support period. Grading Criteria / Marking Rubric Your submission will be graded according to the following criteria: 1. Perform. the appropriate quantitative analysis to answer each of the questions. 2. Provide a detailed and thorough analysis on the results, without going over the word limit. 3. Submit a well organised and detailed report. Submit a piece of work that is well written, with no typos or grammatical mistakes, organised and well formatted.
Assessment 2: Project Report This assignment contributes to 70% of the overall module mark for COMP3003 and is an individual assignment. Task (1): Literature Review (25% of the total mark of assessment 2) You've previously studied supervised and unsupervised learning. For this assignment, delve into self-supervised learning. Examine how it differs from supervised and unsupervised methods. Detail the advantages of self-supervised learning and provide an in-depth exploration of its real-world applications, showcasing how it's been leveraged across various industries and scenarios. Task (2) Reinforcement Learning (35% of the total mark of assessment 2) Consider the following model in Figure (2) with 5 states and Right and Left actions, and γ = 0.9. Figure (2): Markov Decision Process (chosen action happens with probability 1) The figure shows that the model, at S1, can only go to S2, and at S5, can only go to S4. The reward for taking action from S4 to S5 is r = 10, while being r = 1 otherwise. 1. Describe the optimal policy for the MDP. 2. Describe V∗(S3), V∗(S4), and V∗(S5)? (in terms of γ and not state values). 3. Consider executing Q-learning on this MDP. Assume that the Q values for all (state, action) pairs are initialized to 0, that α = 0.5, and that Q-learning uses a greedy exploration policy, meaning that it always chooses the action with the maximum Q value. The algorithm breaks ties by choosing Left. What are the first 15 (state, action) pairs if our robot learns using Q-learning and starts in state S3 (e.g., (S3,Left), (S2,Right), (S3,Right),...)? In all the above points, please justify your answers in much detail with the necessary explanation and analysis. Task (3): Classification (40% of the total mark of assessment 2) a) Find an appropriate dataset for the task (e.g., using Google Dataset Search or any other dataset repository) in .csv format or any other similar format. Explain the content and structure of the dataset. b) Load the data you have been provided with into your program, and prepare it through normalization whenever necessary. c) Divide the dataset into training and test datasets. Use cross-validation, and report - with justification - the best value of k-folds for the size of the dataset (discuss this point in light of accuracy scores). d) Create your neural network model and train your model. Evaluate the performance of the model using different classification metrics. A detailed discussion is required (use plots whenever you can). e) Repeat the above steps (a-d), and train a naïve Bayes classifier. You should clearly justify which probabilistic distribution you choose for the classifier. What are the other probabilistic distributions that might be used with the naïve Bayes model in this task? Why (Justify your answer using the plot of data)? Compare its performance to that of the neural network model in much detail. f) From your observation and analysis, are there any limitations or drawbacks of the neural network and the naïve Bayes classification models? Deliverables & Assessment Criteria (All submissions should be in one ZIP file) • Task (1) requires writing an academic essay and the style. should reflect that by including references (Harvard style). References should be peer-reviewed journal papers and conference papers. The structure of the essay (in PDF format) should be structured including sections for Introduction (10% of the task mark), literature review (20% of the task mark), applications (30% of the task mark), discussion (20% of the task mark), conclusion (10% of the task mark), and references (10% of the task mark). The essay should be no more than 2,000 words. • For tasks (2 and 3), please justify your answers in one PDF file with mathematical explanations and details to demonstrate your level of understanding of the different topics of the tasks. • For task (2), Part (2-1) is worthy (10% of the task mark), Part (2-2) is worthy (30% of the task mark), and Part (2-3) is worthy (60% of the task mark). • For task (3), your answer should not be more than 2,800 words (excluding diagrams, images, tables, Matlab code/comments, and references). Any references should be appropriately cited in the report using the Harvard referencing style. The report should be organized as follows: - Abstract (5% of the task mark): about 150 words - Introduction (10% of the task mark): supervised learning and neural network and naïve Bayes models about 650 words - Implementation (60% of the task mark): implementation of neural network and naïve Bayes models (including implementation steps, Matlab code, results/screenshots, the performance of classification, and explanations) – about 1000 words - Discussions and Conclusions (10% of the task mark) (including drawbacks of neural network and naïve Bayes models) – about 1000 words - References (5% of the task mark) - Appendix (including all your Matlab code + “attach the source code files to your submission”) (this code is worthy of 50% of the marks of the “Implementation” and “Discussions and Conclusions” parts. • Besides, please submit a video of 5 minutes describing briefly your contribution and showing clearly a demo of the code working (10% of the task mark). Threshold Criteria (these are indicative only): < 40% Little or no analysis, and answers are largely incorrect. Little understanding of the subject. Almost no evidence of investigation and research on answering the questions. The report and essay are not clear and are not well-written or structured. 40–49% Brief discussion and little analysis for the different tasks. Answers are partially correct and/or complete and missing elaborate details and explanations. There is little evidence of investigation and research on answering the questions. The content and structure of the report and essay are moderately appropriate. 50–59% Adequate discussion and analysis for the different tasks. Answers are mostly correct and complete, with an acceptable level of detail providing some explanation of how results are obtained. There is some evidence of investigation and research in answering the questions. The content and structure of the report and essay meet basic standards of quality. 60–69% Detailed discussion and analysis for the different tasks. A significant majority of answers are correct and complete with a good level of detail explaining clearly how results are obtained. There is good evidence of investigation and research on answering the questions. The report and essay are of good standards and quality. > 70% The different tasks are very well discussed in detail supported by excellent argument. Answers are correct and complete, especially with clear and well-justified analysis and description. There is strong evidence of investigation and research on answering the questions (e.g., through deep analysis and full investigation). The report and essay are of high standards and quality (focused and concise).
