LUBS2910 Management Research and Analysis Assessed Coursework (Semester 1, 2024/2025) 50% Assignment MANAGEMENT RESEARCH AND ANALYSIS (LUBS2910) COURSEWORK ASSIGNMENT: SEMESTER 1 (2024-2025) There are two tasks in this assignment: Task 1 and Task 2. You must complete both tasks. Each task is worth equal marks. The overall word limit for both tasks together is 2000 words. You should spend around 1200 words on Task 1 and 800 words on Task 2. For Task 1, you should describe and evaluate the good practice of questionnaire design and a plan for a quantitative data analysis. For Task 2, you should conduct some statistical analyses on the SPSS dataset provided and report the results of your analyses. The full details of each task are provided below. TASK 1 Imagine that you have been commissioned to undertake a research project by Leeds City Council. The Chief Executive Officer (CEO) of LCC is concerned that the employees are dissatisfied. There have been many anonymous complaints on social media, and the level of staff absenteeism and turnover is on the rise. The CEO would like to find out about the level of job satisfaction and about the factors that might influence the satisfaction of the employees. You have two tasks to complete for the CEO. First, design a questionnaire and develop a plan describing how you would like to distribute the questionnaire. Second, develop a plan describing how you would analyse the data collected and what steps you would undertake in the analysis process. Further guidance on these tasks is provide below. 1. Describe and critically evaluate how you would design the questionnaire (around 900 words). 1.1. What key points would you cover in the introduction? 1.2. What demographic and study variables would you include? 1.3. What hypotheses would you develop using these variables? 1.4. What are the key considerations when selecting measures for the variables? 1.5. How would you structure the questionnaire? 1.6. How would you measure your variables? Include references (if applicable), example items and rating scales. 1.7. How would you distribute the questionnaire? Please include the questionnaire in the appendix in a word format (does not count in the overall word count). 2. Describe the steps you would take to perform. the data analysis and test your hypotheses, including the statistical techniques you would use (around 300 words) TASK 2 British universities are some of the finest in the world. Their excellent reputations are underpinned by their historical traditions, their successful teaching and research, and their international social media presence. The British university sector therefore represents a relevant and interesting topic for business research and has therefore been selected for your coursework assignment. Indicative of the excellent reputations of British universities is their high standing in international university league tables. In the latest league table, produced byQuacquarelli Symonds (QS) for 2025there are 15 British universities ranked among the world’stop 100 universities. The quantitative data for this coursework assignment concerns the overall performance of these 15 British universities, their student and faculty numbers, sustainability, research power (citation/faculty), and social media presence. Number of Facebook followers was recorded on the 8th of October. The quantitative SPSS dataset accompanying this assignment that is drawn from these sources is called: University_data_2025.sav. Please do not change this dataset. Use it in its original format. You should use SPSS software to conduct the following statistical analyses on the dataset and then report your analyses in the form of the Results section of a journal paper from the field of Business and Management. Conduct the following statistical analyses on the quantitative data in the SPSS dataset: 1. Calculate the mean and standard deviation for each continuous data variable. 2. Calculate the correlations between each continuous data variable. 3. Report the means, standard deviations, and correlations in a correlation matrix. 4. Note, in particular, the correlations between overall performance in 2025 and each of the following three variables: employment outcomes in 2024, citations/faculty in 2024, and number of Facebook followers in 2024. 5. Draw a line graph of the mean overall performance for the British Universities over the three years 2023, 2024, and 2025. 6. Conduct and report a one-way Analysis of Variance (ANOVA) to examine any changes in overall performance over the three years 2023, 2024, and 2025. 7. Draw a bar graph showing the mean number of total students in 2024 and total staff in 2024. 8. Conduct and report at-test between the number of total students in 2024 and total staff in 2024. 9. Conduct and report a regression analysis with overall performance rating in 2025 as the dependent variable and the following as independent variables: citations/faculty in 2024, employment outcomes in 2024, and number of Facebook followers in 2024. Assignments should be a maximum of 2000 words in length. All coursework assignments that contribute to the assessment of a module are subject to a word limit, as specified on the assessment brief. The word limit is an extremely important aspect of good academic practice, and must be adhered to. Unless stated otherwise in the relevant module handbook (if one has been provided), the word count includes EVERYTHING (i.e. all text in the main body of the assignment including summaries, subtitles, contents pages, tables, supportive material whether in footnotes or in-text references) except the main title, reference list and/or bibliography and any appendices. It is not acceptable to present matters of substance, which should be included in the main body of the text, in the appendices (“appendix abuse”). It is not acceptable to attempt to hide words in graphs and diagrams; only text which is strictly necessary should be included in graphs and diagrams. You are required to adhere to the word limit specified and state an accurate word count on the cover page of your assignment brief. Your declared word count must be accurate, and should not mislead. Making a fraudulent statement concerning the work submitted for assessment could be considered academic malpractice and investigated as such. If the amount of work submitted is higher than that specified by the word limit or that declared on your word count, this may be reflected in the mark awarded and noted through individual feedback given to you. The deadline date for this assignment is 12:00:00 noon on Tuesday 7th of January 2025. An electronic copy of the assignment must be submitted to the Assignment Submission area within the module resource on the Blackboard MINERVA website no later than 12:00:00 noon prompt on the deadline date. Faxed, emailed or hard copies of the assignment will not be accepted. Failure to meet this initial deadline will result in a reduction of marks, details of which can be found at the following place: https://students.business.leeds.ac.uk/assessment/code-of-practice-on-assessment/ SUBMISSION Please ensure that you leave sufficient time to complete the online submission process, as upload times can vary. Accessing the submission link before the deadline does NOT constitute completion of submission. You MUST click the ‘CONFIRM’ button before 12:00:00 noon for your assignment to be classed as submitted on time, if not you will need to submit to the Late Area and your assignment will be marked as late. It is your responsibility to ensure you upload the correct file to the MINERVA, and that it has uploaded successfully. It is important that any file submitted follows the conventions stated below: FILE NAME The name of the file that you upload must be your student ID only. ASSIGNMENT TITLE During the submission process the system will ask you to enter the title of your submission. This should also be your student ID only. FRONT COVER The first page of your assignment should always be the Assessed Coursework Coversheet (individual), which is available to download from the following location: https://students.business.leeds.ac.uk/forms-guidance-and-coversheets/ STUDENT NAME You should NOT include your name anywhere on your assignment
COM6115 Assessment: Sentiment Analysis Changelog Ver 1.1 26/11/2024 In dark red font: ● Added clarifications (same as the announcement made); ● Fixed typo in the marks for Step 2.1. Ver 1.0 15/11/2024 Initial Release. Quick Summary To better understand the strengths and limitations of Bayesian text classification, in this assignment, you are going to investigate Sentiment Analysis using two sentiment datasets you will be provided. You will also be provided with a Python script. that implements Naïve Bayes. You will need to write a report (nomorethan1500words) to describe your results and findings. Note: This assessment accounts for 30% of your total mark for the course. Your report may be submitted for a plagiarism check (e.g., Turnitin). For any clarification on this assessment, please use the Discussion Board on Blackboard. Assessment Tasks STEP1 Download the data from Blackboard. This contains the following: 1. A dataset with snippets of movie reviews from the Rotten Tomatoes website (one text file for positive reviews and one text file for negative reviews): a. rt-polarity.pos b. rt-polarity.neg 2. A smaller dataset with snippets of reviews for Nokia phones (again, 2 files): a. nokia-pos.txt b. nokia-neg.txt 3. A sentiment dictionary of positive and negative sentiment words: a. negative-words.txt contains 4783 negative-sentiment words b. positive-words.txt contains 2006 positive-sentiment words 4. A Python script. called Sentiment.py (you will need Python 3 to run it). This includes: an implementation of Naïve Bayes, a knowledge-based classifier using the sentiment dictionary, and some helper functions. STEP2 Run Naïve Bayes on Rotten Tomatoes Data The code splits the Rotten Tomatoes Data into a training and test set in readFiles(), then builds the p(word|sentiment) model on the training data in trainBayes(), and finally applies Naïve Bayes to the test data in testBayes(). 1. Observe the data in the positive and negative word dictionaries (positive-words.txt and negative-words.txt). Make any necessary changes in the code lines 24-28 to store the words in lists. Explain your decisions in the report. [3pt] 2. Write a function which will print out Accuracy, Precision, Recall and F-measure for the test data. [5pt] 3. Run the code and report the classification results. [5pt] STEP3 Run Naïve Bayes on Nokia Data 1. In the Python script, towards the end of the file (lines 276 and 278), uncomment out the other two calls to testBayes(). These run Naïve Bayes on the training data and on Nokia product reviews. 2. What do you observe? Why are the results so different? [10pt] STEP4 What is being learnt by the model? 1. Which are the most useful words for predicting sentiment? The code you have downloaded contains another function mostUseful() that prints the most useful words for deciding sentiment. Uncomment the call to mostUseful(pWordPos, pWordNeg, pWord, 100) at the bottom of the program, and run the code again. This prints the words with the highest predictive value. Add these words (for both positive and negative sentiment) to your report. You can add them in an Appendix (these do not count to the total word count). [5pt] 2. Are the words selected by the model good sentiment terms? How many of them are in the sentiment dictionary? [5pt] STEP5 How does a rule-based system compare to Naïve Bayes? 1. Add some code for the function testDictionary() which will print out Accuracy, Precision, Recall and F-measure for the test data. [2pt] Uncomment out the three lines towards the end of the program that call the function testDictionary() and run the program again. All this code does is add up the number of negative and positive words present in a review and predict the larger class. 2. How does the dictionary-based approach compare to Naïve Bayes on the two domains? What conclusions do you draw about statistical and rule-based approaches from your observations? [5pt] 3. Write a new function to improve the rule-based system, e.g., to take into account negation, diminisher rules, etc and justify your decisions. Run the program again and analyse the results on both datasets. [25pt] STEP6 Error Analysis 1. Comment out all but one of the testBayes and testDictionary calls (one for each, two in total). 2. At the top of the program, set PRINT_ERRORS=1. Make any necessary changes to the definition of testDictionary so that errors are printed when called, similarly to testBayes. 3. Run the program again for each of the test calls, and it will print out the mistakes made. List the mistakes in the report. [5pt] 4. Please explain why the model is making mistakes (e.g., analyse the errors and report any patterns or generalisations for each of the two test calls). [15pt] Marking Criteria Along with your code, you should submit a report to describe your results and findings by following the tasks detailed in the 6 steps above. You should also submit any additional files you may have used for implementing the improved rule-based system. Here is a summary of the marking criteria: 1. Quality of the report, including structure and clarity. No more than 1500 words. [15pt] 2. Step 2 [13pt] 3. Step 3 [10pt] 4. Step 4 [10pt] 5. Step 5 [32pt] 6. Step 6 [20pt]
CS-230 Software Engineering Functional Specification (2024/2025) 1 Introduction You have been tasked with creating a digital version of the 1984 Boulder Bash game. There are many versions of this game from various ports to modern remakes. This is a game of strategy, quick response ... and maybe a little luck! You can read a bit about Boulder Dash (optional) at https://en.wikipedia.org/wiki/Boulder_Da sh_(video_game). or watch a YouTube video at https://www.youtube.com/watch?v=FiEVfa1OK_o. There is even an online version of the original available at https://boulder-dash.com/online-free-game/. The game’s rules are detailed in this document. They are not too complex, but not too simple either. Various design decisions have been taken to keep the complexity at a reasonable level and therefore may deviate from the original game. The rules specified here are a little loose. If they specify something then it must be followed, but otherwise you have creative freedom. It is up to you to design the classes, algorithms and GUI involved in the development of this game. The gameplay specified in this document mostly aligns with (a subset of) the original game, but does differ in places and functionality. 2 Game Title and Theme You can come up with the title and theme of the game. You may stick with the theme of Boulder Dash or you may choose your own by putting some thought into this and making something unique and exciting! This document describes the overall game idea, the gameplay and the rules. However, you may (and maybe should) substitute game elements (i.e., their names and graphics) to align with your own theme. For exam-ple, you could create a game around robots, dinosaurs, or whatever you like. The tiles and enemies (see Sec-tions 3.1 and 3.2) may be substituted according to your theme as long as they behave as described in this doc-ument. The graphics used in this document are fairly simple. You can and should produce graphics of equal or nicer quality if you are able to. Even simple image files, when tiled, can look very nice. 3 Overall Idea, Components, and Gameplay The game comprises multiple levels (i.e., a collection of maps). A player plays one level at a time and, upon completion of a level, progresses onto the next level. A level is made up of square tiles that form. a 2-dimensional rectangular map in which the player and enemies travel. Your job as the player is to reach the exit. Each level is an underground cave where the ver-tical axis represents elevation (i.e., gravity pulls some things down). Many of the details below leave certain quantities and durations open. The level files will specify these values (see Section 5). The tiles that make up the level are all square and the same size. They are split into 3 categories: Basic Tiles, Collectable Item Tiles, and Actors. 3.1 Basic Tiles The tiles that make up the level are all square and the same size. They are: • Path: A Path is the most “normal” of floors. It can be walked on by the player and enemies. • Dirt: Dirt can be walked on by the player. However, when the player walks on dirt, it is compacted and becomes a normal path. Enemies cannot travel over dirt. • Normal walls: Normal walls block all movement. Neither the player nor the enemies can move over walls. • Titanium walls: Like the normal wall, titanium walls block all movement. Neither the player nor the enemies can move over walls. Titanium walls cannot be destroyed by explosions (see Section 3.7). • Magic walls: Like the normal wall, magic walls block all move-ment. Neither the player nor the enemies can move over walls. They interact with falling dia-monds and boulders though (see Section 3.6). • Exit: The level is won if the player walks on an exit tile after collecting the required number of diamonds but before the level time elapses (see Section 3.10). • Key: Keys come in four variations: red, green, yellow, or blue. Keys are collected when the player moves onto them. • Locked Door: Locked doors come in four variations: red, green, yellow, and blue. A locked door acts as a wall, except in one situation. If a player attempts to move onto a locked door and has a key of the cor-responding colour then the locked door opens and is replaced by a path. A key can only be used to open a single locked door (i.e., the key is consumed when opening a locked door). 3.2 Actors The following tiles are actors (i.e., things which can move): • Player: The player that is controlled using the keyboard. • Boulder: Boulders block the path but they can be pushed (see Section 3.3. They also roll and fall (See Sec-tions 3.4 and 3.5), and will kill enemies and the player (see Section 3.7). • Diamond: Diamonds can be collected by the player by mov-ing onto them. In fact this is required to win the level (see Section 3.10). The also roll, fall, and kill like boulders (see Sections 3.4, 3.5, and 3.7). • Butterfly (a type of enemy): Butterflies can only travel over path tiles, and only if these tiles do not contain another enemy. A butterfly tries to follow the edge of tiles it can travel on. This would be like following a maze keeping your hand on the wall to the left/right of you at all times. Each butterfly is initially set to either follow the left edge or the right edge when the level is loaded. For example, a left-edge following butterfly would behave as follows: Butterflies drop diamonds when destroyed by falling boulders and diamonds (see Section 3.7). • Firefly (a type of enemy): Fireflies behave the same as butterflies but do not drop diamonds when destroyed by falling boulders and diamonds (see Section 3.7). • Frog (a type of monster): Frogs can only travel over paths and only if these tiles to do not contain another enemy. Frogs are the smartest of all the monsters and their mission is to seek out the player and kill them. They move rather slowly compared to the player. They move towards the player but do so by finding the shortest path through the level. If no path is possible for the frog to move then the frog moves in a random (but valid) direction. Note: This is one of the more difficult parts to imple-ment. • Amoeba: Amoeba is an organism that spreads (see Sec-tion 3.8). Enemies refer to butterflies, and fireflies, and frogs. If an enemy finds itself directly next to the player in one of the four cardinal directions (directly above, directly below, directly left of, or directly right of) the player, the player is killed. 3.3 Pushing Boulders A boulder can be pushed by the player if, and only if, it is being pushed onto a path which is free of any enemies. For example: Here, the player moves right and pushes the block onto an empty path tile. 3.4 The Falling of Boulders and Dia-monds Both boulders and diamonds will fall if they are not supported by a tile underneath them. If the tile below a boulder or diamond is a path then it will fall. For example: Note that the boulder could have rolled to the left or the right. The player can move under a diamond or boulder with-out it falling. For example, Here, the player is supporting the boulder. If the player moves to the left or right then the boulder will start to fall. If the player moves down then the boulder will also start to fall, but will kill them (see Section 3.7). 3.5 The rolling of Boulders and Dia-monds Boulders and diamonds can also roll sideways if space allows. If a boulder or diamond is on top of another curved tile (that is, a boulder, diamond, or wall) and the tile to the left or right, let’s assume left, is an empty path with an empty path below it, then the boulder/-diamond will move to the left. It will then start to fall (see Section 3.4). For example: Examples of situations where the boulder/diamond would not roll are: 3.6 Magic Walls Magic walls allow boulders to be transformed into dia-monds and diamonds to be transformed into boulders. Should a diamond fall into the top of a magic wall, it will fall through it and emerge as a boulder. Similarly, should a boulder fall into the top of a magic wall, then it will fall through it and emerge as a diamond. For example: 3.7 The Dangers of Falling Boulders and Diamonds Falling boulders and diamonds can do serious damage. If a falling boulder or diamond hits an enemy then the enemy explodes. Explosions happen in a 3x3 square with the hit enemy at the centre. The explosion lasts only briefly. Explosions affect tiles in different ways. Any dirt, normal walls, magic walls, locked doors, keys, boulders, diamonds, or amoebae caught up in an ex-plosion are destroyed. Any enemy caught up in an ex-plosion is also destroyed (but this does not generate a secondary explosion). Paths replace anything that is destroyed. Finally, if a butterfly is hit by a diamond or boulder, then after the 3x3 explosion dissipates dia-monds are left in place of the explosions unless some-thing could not be destroyed. For example (assuming the enemies were not moving for simplicity): If a falling boulder or diamond hits a player then the player dies. As a side note, in the original game, the player dies with an explosion that also drops diamonds, but this is only for aesthetics as the player is dead. 3.8 Amoeba and Its Spreading Amoebae are harmless, except they spread. Each group of connected amoebae acts as one. Periodically, each group will spread to a random neighbouring cell (left, right, above or below) if possible. The rate of growth is specified in the level file. Amoebae can grow over empty path tiles and Dirt. If a connected group of amoebae cannot grow, because growth is blocked, then the entire group turns to dia-monds. If the connected group reaches a predefined size, specified in the level file, then they all turn to boulders (this acts as a sort of time delay which will probably block a large part of the level). An amoeba kills any enemy it directly touches in one of the four cardinal directions (directly above, directly below, directly left of, or directly right of). Finally, amoebae can be destroyed by explosions. 3.9 Game Ticks This section describes one approach of achieving easy movement of actors. There would be other approaches, but this is the recommended approach. At a regular interval a game tick occurs (say every 200ms). All movement happens via the game ticks. For example, the player might move on every 3rd game tick; while the frog might move on every 5th game tick (hence is slower than the player); and the diamonds and boul-ders might roll and fall on every game tick (hence be quite fast). This allows different actors to behave/move at different speeds. When the user presses a direction key, the game regis-ters that direction and will attempt to move the player in that direction the next time the player moves in a game tick. Note: The act of pressing the key does not move the player (else, tapping the key quickly would cause the player to move faster than intended). 3.10 Winning and Losing the Level In order to win the level, the player must reach the exit before the level time elapses and after collecting the re-quired number of diamonds. The score is based on the number of collected diamonds and any remaining level time. The exact calculation is left open as a design choice. You lose the level if you die or if the level time elapses. 4 Simplifications from the Origi-nal Game The gameplay specified in this document mostly aligns to the original game, but does differ in places and func-tionality. Here is a non-exhaustive list of some of the more major aspects: • Only a subset of tiles, items, and enemies are used in this document. • While the game should be designed and imple-mented to have good-looking graphics, it does not need to have nice/fancy/smooth animations. Thus, a jumpy form. of animation is fine. Of course, if you really want to you can implement a smooth style. but this will be much much more difficult. • Scrolling the board is not necessary (you can cre-ate the levels so that they fit in the window). That said, it would be a great extra feature. If you don’t allow scrolling, make sure a large enough level can be accommodated so that it is interesting. 5 Levels The game comprises of multiple levels. Upon complet-ing a level the next level will be unlocked. Your player profile will keep track of the maximum level you have unlocked (see Section 6). You can replay all unlocked levels. Each level is stored in its own file. It is up to you how you design and structure these files. However, they must be simple ASCII based files. This will make it easy for you to design multiple levels. Each level must store (at least) the following informa-tion (not necessarily in this particular order): • Size of the level (width and height) - the level need not be square. • All the tiles and their locations. • All related data of enemies, etc. For example, – For each butterfly and firefly, if it is left-edge following or right-edge following. • The number of seconds that the player has to com-plete the level in (see Section 3.10). • The number of diamonds needed to win (see Sec-tion 3.10). • The rate that amoeba groups spread (see Sec-tion 3.8). • The size limit for amoeba groups at which they transform. into boulders (see Section 3.8). A starting point of your design might be as follows: 1 7 3 2 PPPPPD * 3 PWWPPD@ 4 PTTPPDD The above specifies the tiles of the level shown below. The first two numbers specify the width and height of the level (which will make parsing the file easier). Fol-lowing this, each row of the file represents one row of tiles of the level. With each tile being separated by a space. Here, P represents a path, W represents a normal wall, T represents a titanium wall, D represents dirt, * repre-sents a diamond, and @ represents a boulder. Note that not all tiles are shown in this example. Many tiles/en-emies will need extra data to be specified. Following this could come the rest of the data such as the time to complete the level, enemies, as well as all related data, etc. There are many ways to design this. The important thing is to think about how you will parse the data. For example, by reading the width and height of the level first, it makes it much easier to read the tiles that follow as you know how many will appear on each line of the file and also how many lines of tiles there will be). 6 Player Profiles The application should support player profiles. Player profiles are used to record game stats for different play-ers. Player profiles can be created and deleted via ap-propriate menu items or buttons within the application. Each player profile: • Has a player name. • Keeps track of the maximum level that has been unlocked. 7 High Score Tables The application keeps track of a high score table per level and also allows these to be displayed (per level). Each time a player completes a level, their profile name is recorded on the high score table for that level along with their score. Only the top 10 scores are kept (if a player scores less than all the top 10 for that level then they do not get added to the high score table). 8 Data Persistence The player profile data is persisted across runs of the application. That is if the user quits the application, then upon reopening the application, the data is not lost. 9 Save/Load Games in Progress The user shall have the ability to save an ongoing game. This will save the current game state to a file. The user can later load the saved game and resume play. All game data shall be in the same state as they were when the game was saved. 10 Extra Features This section is not part of A1, but it will be part of A2. It will be helpful for you to plan for ex-tensibility and hence know about this section. To achieve top marks in A2, you will need to be creative. At a minimum, all the functionality of the Functional Specification should be completed to a high standard. All features should adhere strictly to the specification. You need to get all this working well in order to get a low First Class mark (in A2). In order to get higher marks, you are required to extend the implementation in novel ways. All extensions that do not violate the specification will be considered. Substantial extensions to the software, extra reading and learning, will be re-quired to achieve a high First Class mark (in A2). 11 Forbidden Features There are forbidden features which you must not design nor implement. These features are re-served for the assessment of CS-235. Do not design nor implement any of the follow-ing features for CS-230: • A level editor that allows users to create and de-sign their own custom levels via a nice editor. • Allowing multiple difficulties per level (where for example, the same level at a higher difficulty would have less time, etc.). 12 Libraries and Frameworks You must program this game in Java using JavaFX. It is strongly recommended using JavaFX’s Canvas class to draw the game. You may use any classes and packages that are part of the standard Java SDK. You may not use any other libraries or frameworks with-out first seeking approval. Please use the Canvas dis-cussion board to ask such questions. Game frameworks will not be allowed, this includes physics engines.
Module: MC3632 The Cultural Politics of Contemporary Hollywood Assignment: Assignment 2 – Research essay Contribution to final mark: 60% Deadline: 10th December 2024 (3:00pm) Module Learning Aims and Outcomes assessed in this assignment MLA a: Interrogation of the relationship between text and context via the relationship between Hollywood films and cultural politics. MLA b: Employment of textual analysis and ideological criticism in the discussion of Hollywood films. MLA c: Demonstration of applied understanding of key concepts in the study of the cultural politics of identity to examples from contemporary Hollywood. MLO a: An in-depth and applied understanding of major issues, concepts and debates in the cultural politics of identity. MLO b: The ability to identify, discern between and engage with the discourses that arise from representational cultures of contemporary Hollywood. MLO c: A historically located and contextually informed understanding of Hollywood film culture. MLO d: An advanced ability to analyse and discuss Hollywood films in terms of the cultural politics of identity. MLO e: The ability to synthesise, evaluate and apply key concepts in the study of representation and identity to pre-selected and self-selected examples of Hollywood cinema. MLO f: The ability to conduct in-depth research into and execute a study of 21st century Hollywood cinema that engages with issues of cultural representation across intersecting axes of identity. MLO g: The ability to generate original research questions [this part of MLO g is optional], and make a coherent argument about the cultural politics of identity in relation to contemporary Hollywood. Brief description of the assignment (including length or wordcount) TASK: Compare and/or contrast Bridesmaids (2011) class case study films with a contemporary Hollywood film (from the year 2000 to the present) that you have selected yourself, discussing aspects of its cultural politics of your own choosing. For this essay you are asked to critically interrogate the relationship between cultural politics and contemporary Hollywood in relation to a case study text (or texts) of your choice. Word count: 2000 (plus or minus 10%, i.e. shoot for a total word count, including the bibliography this time, of between 1800 and 2200 words). What are we looking for? You should make an evidence-based argument using primary sources (i.e. the films themselves and/or their marketing materials such as trailers or posters) and secondary sources (e.g. quoted or paraphrased material from scholarship on your topic that you have researched and read, and/or relevant industry data that you have researched), and with reference to pertinent course readings. In addition to engaging with relevant course materials you should also demonstrate evidence of wider reading, and wider research that is specific to your topic of study and your film/s of choice. The essay should convey an informed understanding of your topic and should demonstrate close and detailed engagement with any key films that you are dealing with. It should incorporate discussion of specific scenes and instances that you determine are significant in communicating or negotiating the film’s cultural politics. ADVICE: Choose your texts carefully. Think about what this module is asking you to do. Refer to the handbook for a reminder of the learning objectives and outcomes and make sure there is scope to meet them in the topic that you choose and the films that you write on. Take care to use the correct terminology as it pertains to the topic in question If in doubt look up the meaning of a term or concept that you are not sure of. Proof-read your work very thoroughly It is extremely important that you say what you want to say as clearly as possible. Good points cannot be rewarded if the marker cannot discern them within unclear writing. Cite all your sources fully and accurately Use the same referencing style. consistently throughout your work. Make sure you attribute all quoted material to the proper author. Include a full bibliography at the end of the essay. Examples from feedback provided in previous years Fail: This essay has manifestly misunderstood the assignment instruction to compare one of the listed films to another example of contemporary Hollywood by comparing Get Out to the Korean film Parasite. This severely limits the possibility of being able to demonstrate relevant understanding of module aims and content, and of being able to demonstrate that the relevant module learning outcomes have been met. Despite your efforts to emphasise the connections that Parasite has with Hollywood film culture via its Academy Award win for Best Picture and corresponding impact that it made among audiences internationally, there are still no grounds offered up to understand Parasite as an example of contemporary Hollywood, which is unsurprising because by most available measures this Korean film is manifestly not an example of contemporary Hollywood. There is some evidence of engagement with some relevant course content and material (e.g. racial appropriation and erasure which we addressed in Week 3, is identified as relevant to the cultural politics of race in Get Out and applied to your understanding of the characterisation of the characters of Georgina and Walter accordingly. Some effort has been made to consider how specific filmmaking techniques and cinematic devices have been used to convey and negotiate Get Out's cultural politics of race, e.g. the observation about use of close-ups to convey Chris's experience of racial subjugation. This is good to see. Some relevant wider reading around the topic has been undertaken (e.g. hooks and Yancey), but this might have been balanced by inclusion of reference to relevant course readings. Many of the secondary sources listed in the bibliography are not relevant to the cultural politics of contemporary Hollywood in line with the approach that has been taken here following misunderstanding of the assignment instructions. Disregarding all of the material here that is not relevant to the assignment (i.e. all of the material on Parasite) the assignment reads as disjointed and unfinished. There are some good and relevant observations made about Get Out that view in terms of the cultural politics of race which make some of the material of a passable standard, but not enough in the context of an assignment which is, in effect, incomplete. 40% - 49%: In some respects Love Actually is very well chosen as a point of comparison with Crazy Stupid Love given that they are both relationship comedies with some noteworthy points of overlap in terms of their respective cultural politics of gender. However, there are other respects in which it is less well chosen, e.g. it is a British film and not a Hollywood film (notwithstanding its funding and distribution arrangements), which you should have acknowledged and accounted for in your explanation of your decision to pair these two films together or else chosen a more straightforward example of a Hollywood film, as per the assignment instructions. Many aspects of the presentation are inconsistent, in particular the capitalisation and use of italics for film titles - this makes the essay look messy and suggests the need for much more careful proof reading. There is far too much in the way of speculative assertion and too little in the way of evidence-based critical analysis. Engagement with the films themselves is also lacking, and there needs to be better fact checking and clearer writing, because it reads at one point as though you think Crazy Stupid Love is a film from the early 2000s, when we know it was released in 2011. You are very wedded to the idea of explaining the politics of femininity in these films by viewing it and certain characters through the lends of the figure of the 'modern day femme fatale'. This is an interesting position to take up, but this cultural figure has not been explained or contextualised, nor has it been substantiated via textual analysis, so this reading is unpersuasive. Scholars like Katherine Farrimond and Samantha Lindop have done very interesting work on the notion of 'postfeminist noir' and thereby of the figure of the modern day femme fatale, so scholarly support for your reading was available had you carried out wider research into your chosen topic. Some pertinent points about the universality of whiteness as a subject position are made towards the end, although not using critical academic language or terms encountered in class. Some demonstration of understanding of the cultural politics of race in the chosen films is therefore displayed, although demonstration of understanding is limited. Research is poor. No academic sources have been cited, and no course materials (other than the film Crazy Stupid Love) have been engaged with. 50% - 59%: The Sisterhood of the Travelling Pants is well chosen for the purposes of this assignment as a point of comparison with Bridesmaids. You do well to demonstrate the extent to which both films can be understood as 'chick flicks' according to how different scholars characterise this type of film. And you have drawn from a good combination of secondary sources encompassing both relevant course readings and also wider reading around relevant topics that you have done. However, there is a lot of unclear writing, some of your points don't stand up to scrutiny and/or lack illustrative evidence from the case study texts to back them up, and you don't show that you understand how the points you make relate directly to the cultural politics of gender as we studied it on the module. Symptomatic of this is the fact that in your conclusion you are unable to make concluding remarks that go beyond having identified that the cultural politics of gender is something that is in both films. 60% - 69%: This is a very clearly expressed essay with a clear line of argument that references a really good range of scholarship encompassing both relevant course materials and also well sourced relevant wider reading. There is good applied use of relevant terminology and you've engaged well with relevant examples from both films to evidence a number of well made points. Bridesmaids and Charlie's Angels are very well paired for the purposes of this comparative analysis, although you might have also considered some of the ways in which they contrast, to enable more nuanced understanding of the similarities under discussion here. Also, more could have been done throughout to indicate the ideological stakes of the points being made (e.g. in relation to the Bridesmaids example where the man next to Annie is mistaken for her romantic partner). 70% - 79%: Excellent essay. Lone Survivor and American Sniper are very well paired for the purposes of this assignment. You've done some great and on point wider reading around the topic area and brought it to bear successfully on your discussion. You've used and interpreted relevant industry data which is great to see, and for the most part you've done so very effectively (that said, some of the conclusions that you draw from your references to box-office data could use a little more thought and reflection). You demonstrate a strong understanding of the ideological stakes of the cultural politics of nationhood in both of these films and you do a great job of situating the production and release of these films in relation to relevant contextual factors, which you also do very thoughtfully. Your writing is fluent and engaging. Great work. 80% and above: Outstanding work. Immensely engaging and impressively comprehensive, especially given the limitations of the word count. You demonstrate extremely sophisticated understanding of many key concepts encountered on the module including hegemony, patriarchy and postfeminism. And you demonstrate great critical confidence in reading the films of the Star Wars franchise through these lenses and in these terms. Your argument is extremely compelling throughout, your writing style. is beautiful, and your attention to detail and your ability to interpret meaning and draw out the ideological stakes of such detail is second to none. Your also seem to be just as comfortable taking a broad overview approach as you do taking a detailed micro-analytical approach. You've done a great range of reading, and you've dealt with a really impressive number of primary texts, encompassing both films and their marketing materials. My only question is about the retrospective application of postfeminism to second wave feminist era texts (which you might have acknowledged that you were doing), but you've done it extremely persuasively, demonstrating the original Star Wars trilogy to be 'protopostfeminist' in a way. It was such a pleasure to read your extremely impressive work. What happens if you miss the deadline? You will be penalised if you do not have extenuating circumstances and you miss the deadline for this assignment. If you submit your assignment in the 24 hours following the deadline, your mark will be capped at 40%. If you submit your assignment after 24 hours have passed, your assignment will receive a mark of 0%. What happens if you fail this assignment? If you fail this assignment but you obtain a passing mark overall in this module, you will not be required to resubmit this assignment. If you fail this assignment and your overall mark for this module is not a pass, the examining board may invite you to resubmit this assignment during the re-sit period in August. Please wait for instructions from the examining board if you find yourself in this situation.
DEPARTMENT OF ELECTRONIC, ELECTRICAL AND SYSTEMS ENGINEERING, SCHOOL OF ENGINEERING LH POWER ELECTRONICS AND POWER SYSTEMS (30037) Power Systems Assignment: Power Flow Studies using MATLAB Consider a two-bus system with the single-line diagram shown in Figure 1. In the two bus system, Bus 1 is slack bus where voltage is given in Fig. 1. Bus 2 is a PQ bus where load power is given. The impedance of the transmission between bus 1 and bus 2 is Z12 = 0.0 + j0.15 p.u. Please code computer programme using Newton-Raphson method to calculate power flow solution, using (1) a voltage convergence tolerance of εp = 10 −6 : max |vik+1−vik |
MATH 421 Assignment # 4 Fall 2024 Date Due: 2024/12/03 Exercise 5.2.9: Answer the following questions. (a) Is the function in L1(R>0;R)? (b) Show that Hint: Use Example 5.2.19–2. Exercise 5.3.5: Consider four functions f1 , f2 , f3 , f4 : R → R satisfying Answer the following questions. (a) For each of the transforms FCC(f1 ), FCC(f2 ), FCC(f3 ), and FCC(f4 ), indicate whether it exists in the sense that for a ∈ {1, 2, 3, 4}. (b) For each of the transforms FCC(f1 ), FCC(f2 ), FCC(f3 ), and FCC(f4 ), indicate whether it is continuous. (c) For each of the transforms FCC(f1 ), FCC(f2 ), FCC(f3 ), and FCC(f4 ), indicate whether it is differentiable. (d) For each of the transforms FCC(f1 ), FCC(f2 ), FCC(f3 ), and FCC(f4 ), indicate whether it is in L1(R;R). (e) For each of the transforms FCC(f1 ), FCC(f2 ), FCC(f3 ), and FCC(f4 ), indicate whether it is in L2(R;R). Table 1. Exercise 6.1.1: In Table 1 are given plots of three discrete-time functions defined on Z and their DCFT’s. You are not told which function goes with which DCFT. Without doing any computations, indicate which function in the left column goes with which DCFT in the right column. Exercise 6.1.2: Prove Proposition 6.1.9. Exercise 6.1.5: Find a function f ∈ ℓ2 (Z(∆);C) such that the function FDC(f) is not continuous.
MATH3772/5772 Multivariate Analysis Practical A data file athlrecs .txt containing country record times for men’s track events for 55 coun- tries immediately prior to the 1984 Olympic Games can be found in Minerva. It contains the following variables: Country: Name of country m100: Record time for 100m race in seconds m200: Record time for 200m race in seconds m400: Record time for 400m race in seconds m800: Record time for 800m race in minutes m1500: Record time for 1500m race in minutes km5: Record time for 5000m race in minutes km10: Record time for 10000m race in minutes mara: Record time for the Marathon (approx. 26 miles) in minutes status: 1 for developed countries; 3 for third world countries For the purposes of this practical, just concentrate on the 4 races m100, m200, m400, m800. Simultaneous confidence intervals may be helpful for parts 2 and 3. 1. Examine whether it is reasonable to assume that the data can be described as multivariate normal. 2. For the whole set of 55 countries, investigate the hypothesis μ800 = 2μ400 = 4μ200 = 8μ100 . This hypothesis says that the speed of the record runs over that range of distances is constant (after first ensuring the units of time are the same for all races). To carry out this test you may find it convenient to make a linear transformation of the data. Let X denote a 55 × 4 data matrix for the races of interest. Find a matrix A(3 × 4) such that if the above hypothesis holds, then the mean of the data matrix Y = XAT is 0. 3. The countries have been split (somewhat arbitrarily) into developed countries (status = 1) and third world countries (status = 3). Next investigate the hypothesis that the 4-dimensional mean vector for race times is the same for the two groups of countries. 4. [Level 5 only.] Carry out a kmeans clustering of the data into k = 2 clusters. Compare the resulting clusters to the partitioning of the data by the status variable. Some useful commands in R ath=read.table("athlrecs.txt",header=T) attach(ath) x=cbind(m100, m200, m400, m800) # create a data matrix for the 4 races # using all 55 countries x1=x[status==1,] # define a 23 x 4 submatrix of developed countries x2=x[status==3,] # define a 32 x 4 submatrix of third world countries General assessment information This practical is included in the assessment for MATH3772/5772. It comprises 20% of your overall mark for the module. Your report should be submitted by 5pm on Monday 2 December 2024 in Gradescope (in Min- erva go to Assessment and Feedback - > Submit My Work - > Gradescope, and look for “Practical”). You are encouraged to collaborate with other students, but the work that you submit must be done independently. Furthermore, the use of artificial intelligence (e.g. ChatGPT) is prohibited. Serious consequences will result if copying or use of AI is detected. There will be two available practical sessions to give opportunity for you to ask me questions. This will be on Thursday 21 November 2024 at 9-11am in the Psychology Computer Cluster 1.43, and on the same day at 12noon-2pm in the EC Stoner Computer Cluster 6.61. I anticipate that students will bring questions about both statistical questions (what they are supposed to be doing) and computing problems (how to get an R program to work). The analyses in the practical should be performed using the statistics program R. Writing up Write a short report (in Word or Latex, must be typed) outlining the analyses you have per- formed, including discussions of how appropriate the techniques were and explaining the results. The report should be aimed at someone who has a basic knowledge of statistics and hypothesis tests. The report should contain relevant plots (which should be explained in the report) that can be copied from R. The report should not contain any R commands or output directly copied and pasted from the R console. The aim of this practical is to explain the analyses that you have performed and giving R commands does not do this. The R commands and relevant outputs should be put in the appendix (below). The report should be word processed and should not exceed 6 pages of A4 in total, plus two extra pages for Level 5, including any plots that you wish to show. The texts in the report should be single spaced with at least 11 pt font size. In addition to the report, attach an appendix that includes all of the R commands that you have used and the associated output. There is no need to reproduce any plots in the appendix. You also should include an Academic Integrity Form (available in Minerva) before submitting your report. The appendix and the academic integrity form do not count towards the number of pages in your report.
Assignment One: Implementation of Autokey Cipher and Row Transposition Cipher CE235 Assignment One2024-2025University of Essex1. Task of Assignment OneThe aim of Assignment One is to write a Python program, which will implement the Autokey cipher and the Row transposition cipher. The introduction of the two ciphers can be found from the lecture notes.Specifically, this assignment task is expected to do the following:§ Design (with flowcharts) and develop a Python program with encryption and decryption functions for Autokeycipher and the Row transposition cipher.§ For the Row transposition cipher, if the length of the plaintext message (number of letters in message) is notmultiple times of the key length, you can add several letters to the end of the message before encryption, and delete these added letters after decryption to recover the original message.A sample Python program caesar.py for Caesar cipher is provided for reference. The sample Python program can be run with default demo message and key from the command line in Terminal window like this:python caesar_ciphyer.pyThis reference program caesar_ciphyer.py encrypts a given message and then performs decryption. After the caesar_ciphyer.py program is run, it will display the plaintext, ciphertext, and the decrypted plaintext. If the cipher works correctly, the plain text and decrypted text should be the same.Your program for these two ciphers should have a name like ak_registrationnumber.py (replace registrationnumber with your own registration number). Your program must run from command line like this (with a registration number 123456) :python ak_123456.pyThe outputs of ak_registrationnumber.py (including the key, plaintext, ciphertext and decrypted text) are required to bedisplayed, following the display format given in the reference program caesar_ciphyer.py. 2. How to submitSubmit one python program file to Faser with the following filename (replace registration number by your own one):ak_registrationnumber.pySubmission deadline is Wednesday, 04/12/2024 (UK time: 23:59); Beijing time: Thursday, 7:59, 05/12/2024.3. Marking SchemeThere are 12 marks for this assignment (out of 100 marks for the overall module marks).1) Flowcharts for the Autokey cipher and the Row transposition cipher take 1 mark each. The functions for the Autokeycipher and the Row transposition cipher take 5 marks and 5 marks, respectively.2) You will be asked by Professor He or the teaching assistants at NWU to demonstrate your work (including flowchartsand program) and answer some questions to ensure it is your own work. Marks will be given according to the quality of your work and the demonstration. If you are asked to but you don’t demonstrate your work, no mark will be given to your assignment work.3) Apart from demonstration of your work to the teaching staff members, it is mandatory for you to submit your program file to Faser (flowcharts are not to be submitted to Faser). You will not get mark for your work on the assignment if your program is not submitted to Faser. If your submission is late, there will penalty on your mark according to the university policy.4) Extra check of your submitted program may be conducted by Professor He. If any programming or plagiarism problems are found from the check, your marks will be deducted.4. PlagiarismYou should work individually on this assignment. Anything you submit is assumed to be entirely your own work. The usual Essex policy on plagiarism applies: http://www.essex.ac.uk/plagiarism/.Appendix A. Sample Python Program for Caesar Cipher import sys #------------------------------------------------------------------------------ ### Note: Allow the program to be run from the command line:## You can simply use the default message and key given in the program # python caesar_cipher.py## You can also use message and key given in command line in Terminal (not required), ## where, atestmessage is the message (no space!)# python caesar_cipher.py atestmessage# the alphabet with all symbols in the set can be encryptedLETTERS = 'abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ'def caesar_cipher(message, mode, key):# stores the encrypted/decrypted form of the message ciphertext = ''# run the encryption/decryption code on each symbol in the message string for symbol in message:if symbol in LETTERS:# get the encrypted (or decrypted) index of this symbol in the LETTERS num = LETTERS.find(symbol) # index of the uncoded symbol in LETTERS if mode == 'encrypt':num = num + key elif mode == 'decrypt': num = num - keyelse:print('Correct operation mode is needed') exit()# handle the wrap-around if num is larger than the length of LETTERS or less than 0 num = num % len(LETTERS)# add encrypted/decrypted number's symbol at the end of translated ciphertext = ciphertext + LETTERS[num]else:# just add the symbol without encrypting/decrypting if it is not in the set LETTERS ciphertext= ciphertext + symbolreturn ciphertext def row_cipher(message, mode, key):# You need to comment out the following statement and complete the function body for your own python program output_text = messagereturn output_text def autokey_cipher(message, mode, key):# You need to comment out the following statement and complete the function body for your own python program output_text = messagereturn output_textif __name__ == '__main__':# Determine the number of arguments in the command line numArgv = len(sys.argv)# the default string to be encrypted/decrypted message = 'a secret message.'# the default key used by the caesar cipher key_caesar = 3# the default key used by the autokey cipher key_autokey = 'abcde' # the default key used by the row transpose cipherkey_row = '3214' # note: you can change the key_row with other orders of the columns being read out.if numArgv == 2:# get the message from the input given in the command line message = sys.argv[1]elif numArgv == 3:# get the message and key for autokey cipher specified the input given in the command line message = sys.argv[1]key_autokey = sys.argv[2]elif numArgv == 4:# get the message and key specified the input given in the command line message = sys.argv[1]key_autokey = sys.argv[2]key_row = sys.argv[3]elif numArgv > 4:# Instruction on providing message and key from command lineprint('+++Please input optional message and keys for autokey and row transpose cipyers with correct format') print('+++python caesar_cipher.py')print('+++For example: ')print('+++python caesar_cipher.py testmessage abcd') # here the string "testmessage" is the plaintext,exit()# change the mode to encrypt or decryptmode = 'encrypt' # set to 'encrypt' or 'decrypt'ciphertext_caesar = caesar_cipher(message, mode, key_caesar)mode = 'decrypt'plaintext_caesar = caesar_cipher(ciphertext_caesar, mode, key_caesar)# Note: you can use two separate functions for encryption and decryption instead of using one function # for both encryption and decryption with different inputs.mode = 'encrypt' # set to 'encrypt' or 'decrypt'ciphertext_autokey = autokey_cipher(message, mode, key_autokey)mode = 'decrypt'plaintext_autokey = autokey_cipher(ciphertext_autokey, mode, key_autokey)mode = 'encrypt' # set to 'encrypt' or 'decrypt' ciphertext_row = row_cipher(message, mode, key_row) mode = 'decrypt'plaintext_row = row_cipher(ciphertext_row, mode, key_row)### Note: don't change the following code in your own program for displaying program outputs print('##############################################')print('Caesar Cipher with key: ', key_caesar) print('##############################################')print('Plain message: ', message) print('Ciphertext: ', ciphertext_caesar) print('Decrypted text: ', plaintext_caesar)print(' ') print('##############################################') print('Autokey Cipher with key: ', key_autokey) print('##############################################') print('Plain message: ', message)print('Ciphertext: ', ciphertext_autokey)print('Decrypted text: ', plaintext_autokey)print(' ') print('##############################################') print('Row Transposition Cipher with key: ', key_row) print('##############################################') print('Plain message: ', message)print('Ciphertext: ', ciphertext_row)print('Decrypted text: ', plaintext_row)
Security, Privacy and Ethics Coursework 2Learning outcomes assessed:B: Evaluating the potential risks and benefits of AI technologies on privacy and personal dataC: Understanding the importance of fairness in AI systems and its implicationsOverviewArtificial intelligence has great effect on modern lives. In this coursework, the theme on framework design of new medical image-based bias-mitigated and fair computer-aided diagnosis system will be investigated and explored. The coursework consists of two parts. In Part 1, you need to complete a report based given theme. In Part 2, you need to explain your design via video presentation.Part 1 (Individual Report: 70 marks)Healthcare industry is rich of electronic (digital) medical data from different modalities. Deep learning has revolutionized the use of machine learning in healthcare industry by leveraging on the model’s automatic feature extraction and learning. To date, deep models have been applied for numerous computer-aided diagnosis tasks such as prediction, detection and classification. Despite its promising outlook, deep learning- based computer-aided diagnosis models still fail to earn the trust of medical doctors. In fact, there are reports of inaccurate missed diagnoses due to bias error, lack of understanding about the underlying mechanism of deep learning, and miscalibration in real practice. Therefore, there is an open call for fair and transparent computer-aided diagnosis model for trustworthy smart healthcare.The aim of this task is to empirically assess the current status of computer-aided diagnosis, technologies, challenges and solutions in smart healthcare, and research questions in AI fairness to design a towards innovative bias-mitigated and fair deep learning medical image-based computer-aided diagnosis model framework design with predefined traits. Hence, you are required to equip yourselves with the understanding about the specific domain of medical imaging. Besides, you need to apply the knowledge acquired from the lectures and tutorials to complete this coursework. You also need to do literature review to identify further relevant information that is helpful to develop your report content.Task Instructions:(1) You are required to study the medical image-based artificial intelligence computer-aided diagnosis by using deep learning in smart healthcare domain. Therefore, literature review is needed. For beginner, you can refer to the suggested review paper to understand the domain of smart healthcare using AI:Most Nilufa Yeasmin, Md Al Amin, Tasmim Jamal Jati, Zeyar Aung & Mohammad Abdul Azim. 2024. Advanced of AI in Image-Based Computer-Aided Diagnosis: A Review. Array. 23(2024) 100357. Available Online: https://doi.org/10.1016/j.array.2024.10035Moreover, you are required to study extra learning materials to familiarize yourselves with image- based computer-aided diagnosis by using deep learning. Please note that no mark will be given to the literature review nor content extract from the given review paper. However, this effort shall serve as your first steep for your proposed towards innovative bias-mitigated and fair deep learning medical image-based computer-aided diagnosis model framework design.(2) Write a report on your proposed towards innovative bias-mitigated and fair deep learning medical image-based computer-aided diagnosis model framework design. The report should be written in a clear and concise manner with no more than 1,500 words+/-5%. in total length. Your final report should be detailed, relevant and rationale in addressing the following sections: SectionDescriptionApproximate word count Motivation You are required to select your project domain from one of the following areas of interest:• COVID-19 detection• Thorax abnormality detection• Late age-related macular degeneration detectionYou are required to explain the motivations associated with your selected area of interest and how AI-based computer-aided diagnosis can help to improve the diagnosis delivery in your selected area of interest. 150 Case Study 1Privacy and ethical challenges associated with large-scale 450 medical image datasets for Artificial Intelligence-based computer-aided diagnosis models You are required to identify an open access large-scale dataset in your selected area of interest and study the dataset details. Then, you are required to:• Explain the relevant ethical issues associated with medical image acquisition and sharing for open development of computer-aided diagnosis • Evaluate the relevant privacy risks associated with medical image data sharing of your selected area of interest in accordance to the Health Insurance Portability and Accountability Act (HIPAA)• Describe the innovative and rationale data governance measures to achieve better transparency, communication security and sensitive digital medical data protection of your selected area of interest. Case Study 2 Bias and Fairness in Artificial Intelligence-based computer-aided 450 diagnosis models You are required to perform analytics approach on existing computer-aided models to help address this case study. Please include necessary evidence of analysis such as detection results, graphs and plots from the python programming codes to support your justification. Please note that no mark will be given to the evidence of analytics approach but it is compulsory to demonstrate your effort in this coursework.You are required to design a new computer-aided diagnosis model associated with your selected area of interest. The new model should critically cover these traits:• Innovative bias-mitigation strategy in new AI-based computer-aided diagnosis model’s algorithm setting• Innovative fairness practice in new AI-based computer-aided diagnosis model’s social equality setting• Incorporation of innovative transparency approach in your computer-aided diagnosis model’s algorithm andcohort criterion. Framework Design You are required to propose and explained a new design framework based on the motivation, case study 1 and 2. The new design framework should be illustrated in a detailed overall model diagram. The design framework should encompass the following characteristics:• Selection of most appropriate AI backbone models for maximum bias-mitigation compared to other state-of- art• Use of relevant explainable AI for transparent algorithmic mechanism• Use of relevant evaluation metrics to evaluate the AI fairness improvement 450 Important: Do not repeat existing information that is in the research papers. This will only contribute to low mark. Instead, you need to synthesize your own ideas/opinions based on your understanding and present them in your own words.Part 2 (Individual Presentation: 30 marks) Task Instructions:(1) Prepare and record a short individual presentation video of 5 minutes+/-5%. Your presentation should be clear, should be in no more than 10 Powerpoint slides and should not take beyond 5 minutes+/-5%. The presentation should address the followings:i. To introduce and explain the significance of your proposed towards innovative bias-mitigated and fair deep learning medical image-based computer-aideddiagnosis model framework design.ii. To explain how your proposed model design can effectively become General DataProtection Regulation (GDPR) and IEEE “Human Standards” with Implications for AI compliance in order to promote your design to overseas healthcare market successfully.Report Format:Cover Page: This should include the Assessment Number, Assessment Title, Student Name, Student ID and Student EmailBody of the report: This should include all the relevant section headings to address each section as indicated above and marking rubrics.References: Both your in-text and the references included in the “References” section at the end of the report should adhere strictly to the IEEE reference style.Formatting requirement:• Use multiple spacing: 1.08 and spacing after: 8pt;• Use a standard 12-point font, font type: Tahoma• Use “Justify” body text• Put your page numbers at the top right (except the cover page)• Most importantly, always run a spelling and grammar check; however, remember, such checksmay not pick up all errors. You should still edit your work manually and carefully.Referencing:It is compulsory to use IEEE reference style for citing and referencing research. Reference list is excluded from the imposed word limit.Presentation Format:Students are not requested to submit their presentation slides to the submission system. However, they must present their Powerpoint presentation slides clearly throughout the video presentation period. Otherwise, the presentation will not be evaluated and ZERO marks will be given.All video presentation must be uploaded to the Mediasite and attach the video presentation link at the last page of report. It is student responsibility to attach the link properly and apply the right accessibility setting in Mediasite to ensure examiners can access to the video presentation link in their computers during marking.Please note that during marking, lecturers are not responsible for any inaccessible video presentation link at their computers due to any kind of reason or under any kind of circumstances, and has the right to give ZERO mark for the inaccessible video presentation link attached in the report. Marking CriteriaPart 1: Individual Report (70 Marks)The following criteria will be used to assess the assignment. This report is marked for the whole group.Outstanding: Report format is well-organized throughout including heading styles, fonts, and margins, figure/table/diagram are critically interpreted and discussed, writing flows extremely fluently from one own idea to another, information is critically deciphered and elaborated in convincing and perfect way with impactful evidence that reflects highly insightful and unique own idea expression, all information is located in the appropriate section. Reference style must be precisely correct and citation arranged sequentially in main text.Very Good: Report format is properly organized throughout including heading styles, fonts, and margins, figure/table/diagram are effectively interpreted and discussed, writing flows smoothly from one idea to another, information is well explained in a logical, rationale and systematic way with comprehensive evidence that reflects insightful own idea expression, all information is located in the appropriate section. Reference style must be correct and citation arranged sequentially in main text.Appropriate: Report format is organized and proper, figure/table/diagram are properly interpreted and discussed, sentences are well-structured and words are chosen to communicate ideas clearly that reflects correct own idea expression, information is presented in logical, rationale and systematic way with substantial evidence, information is located in the appropriate section. Reference style must be correct and citation arranged sequentially in main text.Needs Improvement: Report format is inconsistent and hard to read, figure/table/diagram are poorly interpreted and discussed, sentence structure and/or word choice sometimes interfere with clarity in explaining own idea, information is hard to follow as there is very little continuity, and many items are in the wrong section. Reference style must be correct.Hard to Understand: Report format is inconsistent and hard to read, figure/table/diagram are not used effectively, sentence structure and word choice make reading and understanding difficult, sequence of information is difficult to follow without much own idea expression, lack of appropriate sections and many items are in the wrong section. Reference style might be incorrect.No Submission or Missing Section: No submission or missing section of the discussion in the report.Section Marking criteria Total Marks Motivation • The motivations associated with your selected area of interest and how AI-based computer-aided diagnosis can help to improve the diagnosis delivery in your selected area of interest is precisely described.Marks• Outstanding: 9 - 10• VeryGood:7-8• Appropriate: 5 - 6• Needs improvement: 3 - 4• Hard to understand: 1 - 2• No submission or missing section: 0 /10 Case Study 1 Marksin the Task Instruction.• Details associated with Case Study 1 are precisely explained to fulfil the task description as per highlighted• Outstanding: 16 - 20• Very Good: 13 - 16• Appropriate: 9 - 12• Needs improvement: 5 - 8• Hard to understand: 1 – 4• No submission or missing section: 0 /20 Case Study 2 Marksin the Task Instruction.• Details associated with Case Study 2 are precisely explained to fulfil the task description as per highlighted• Outstanding: 16 - 20• Very Good: 13 - 16• Appropriate: 9 - 12• Needs improvement: 5 - 8• Hard to understand: 1 – 4• No submission or missing section: 0/20 Framework Design Marksin the Task Instruction.• Details associated with Framework Design are precisely explained to fulfil the task description as per highlighted• Outstanding: 16 - 20• Very Good: 13 - 16• Appropriate: 9 - 12• Needs improvement: 5 - 8• Hard to understand: 1 – 4• No submission or missing section: 0 /20 Total marks /70 Part 2: Individual Presentation (30 Marks)You are required to submit a video presentation of 5 minutes+/-5% to the Mediasite and attach theaccessible presentation link. The content of video presentation should reflect the following specifications:1. Significance of your proposed towards innovative bias-mitigated and fair deep learning medical image-based computer-aided diagnosis model framework design.2. How your proposed model design can become General Data Protection Regulation (GDPR) and Global Initiative on Ethics of Autonomous and Intelligence System compliance3. How your compliance model can be promoted successfully at overseas healthcare market.The overall video presentation rubrics are provided below.Marks 5 4 3 2 1 Significance of proposed framework design Content is unique, impactful and insightful, and outperform the specification Content is concise, informative, factful and relevant, and exceed the specification Content is comprehensive, logical and relevant, and meet the specification Content is good, relevant and meet specification but lacks informativeness Content is irrelevant and does not meet specification Model compliance strategy Content is unique, impactful and insightful, and outperform the specification Content is concise, informative, factful and relevant, and exceed the specification Content is comprehensive, logical and relevant, and meet the specification Content is good, relevant and meet specification but lacks informativeness Content is irrelevant and does not meet specification Promotion strategy Content is unique, impactful and insightful, and outperform the specification Content is concise, informative, factful and relevant, and exceed the specification Content is comprehensive, logical and relevant, and meet the specification Content is good, relevant and meet specification but lacks informativeness Content is irrelevant and does not meet specification Content organization Content is impactfully- organized and fluent for audience to follow, Use of native-level English without any error, and time control is perfect. Content is systematically- organised and smooth, Use of fluent English with minimal error pronunciation, time control is superb. Content is well- organized and acceptable, Use normal English with some unclear pronunciation, time control is properly- managed. Content is average level and somehow hard to follow, Use of poor English with unclear pronunciation, time control is poor. Content is messy and very hard to follow, Use of poor English with not understandable pronunciation, time control is unacceptable. Presentation Delivery mode Delivery mode Delivery mode Delivery mode Delivery mode skills is precisely is highly is confident, lacks is in disarray poised, confident, flow flow is properly confidence and without any charmingly confident, flow is accurately controlled and fluent, interaction is creative, skillful and attractive. is well- controlled and smooth, interaction is well managed, skillful and attractive. managed and smooth, interaction is properly managed and average. smoothness, flow is mediocrely managed, interaction is boring. sense of confident, flow is poorly managed and interaction is very boring.
CMPEN 431: Programming Project 2 Branch Predictor Simulator Overview The Branch Predictor Simulator is a Python-based simulation tool to evaluate the performance of different branch prediction algorithms. This document will guide you through the steps needed to run the simulator, generate branch traces, and understand how to implement each branch predictor. This simulator helps to gain insights into branch prediction mechanisms used in modern computer architecture, suitable for educational purposes. Running the Simulator The process of running the simulator involves the following steps: 1. Generating the Branch Trace The branch_trace_generator.py script. generates synthetic branch traces that are used by the simulator to evaluate each branch predictor. To generate a branch trace, you can use the command below: python branch_trace_generator.py --trace --branches --seed --trace (optional): path to the branch trace file (default to branch_trace.csv) --branches (optional): Specifies the number of branches to generate. Default is 10,000. --seed (compulsory): Specifies the random seed as your PSU ID for reproducibility. The generated trace is stored in a file called branch_trace.csv and contains two columns: BranchAddress: The address of the branch instruction. Outcome: The actual outcome (taken or not taken). 2. Running the Branch Predictor Simulator Once the trace file has been generated, you can run the main simulator using the main_simulator.py script. python branch_simulator.py --trace --x -- fast --trace (optional): path to the branch trace file (default to branch_trace.csv) --x (optional): Specifies the number of branches for calculating interval-based accuracy (default is 10). --fast (optional): Skips the 2-second pause between intervals, making the simulation run faster. The simulator reads the branch_trace.csv (unless a different name was provided) file and runs each of the implemented branch predictors, providing cumulative accuracy statistics during and after the simulation. Logs and Output Data Real-Time Statistics The simulator logs real-time statistics in a file named realtime_stats.txt. This file contains cumulative accuracy information for each branch predictor during the simulation, formatted as follows: Predictor, Branches Processed, Cumulative Accuracy (%) Predictor-Specific Logs Each predictor generates a detailed log of predictions during the simulation. These logs are stored in the logs directory, with one file per predictor, e.g., logs/One_Bit_log.txt. Each file contains information in the format: Branch: , Correct: Branch History Table (BHT) Logs The simulator also saves the state of the Branch History Table (BHT) for applicable predictors in the bht_logs directory. Each predictor's BHT log provides insight into the internal state of the predictor after the simulation. Analysis You can inspect the generated log files to plot the data using external tools like Python, MATLAB, or spreadsheet software for more detailed analysis. Branch Predictors Explained 1. Static Predictors Static Taken / Not Taken: These predictors always predict the branch will be taken (or not taken). No learning occurs. 2. One-Bit Branch Predictor Maintains a Branch History Table (BHT) that stores a single bit for each branch address. This bit represents whether the branch was previously taken or not. The predictor simply repeats the last outcome. 3. Two-Bit Branch Predictor Utilizes a two-bit saturating counter for each branch address. The counter ranges from 00 (strongly not taken) to 11 (strongly taken). Prediction is considered taken if the counter value is 10 or higher. The counter should be incremented or decremented based on the actual outcome. 4. Bimodal Branch Predictor Uses a fixed-size BHT indexed by the lower bits of the branch address. Each entry in the BHT has a two-bit counter similar to the Two-Bit Predictor. The prediction accuracy is improved by reducing aliasing in the prediction table. 5. GShare Branch Predictor Employs global branch history to determine prediction outcomes. It should XOR the global history register with the branch address to generate an index into the BHT. This approach helps to correlate predictions across different branches. 6. Hybrid Branch Predictor Combines the GShare and Bimodal predictors. A choice table determines which predictor (GShare or Bimodal) should be trusted for each branch. The choice table is updated to improve the accuracy of prediction based on which predictor was correct for each branch. Anticipated Steps These steps can serve as a high-level guideline to aid you during the project: 1. Run branch_trace_generator.py script. to generate the trace file given your PSU ID as the seed parameter. 2. Implement branch predictors: static, 1-bit, 2-bit, bimodal, gshare, and hybrid branch predictors 3. Run branch_simulator.py script. to test out the different branch predictors using the trace file generated in step 1. 4. Complete the report. Submission Requirements 1. Project Report 2. Code Implementations of the branch predictors mentioned above. Code Implementations You need to implement various branch predictors in branch_predictors.py file. For each predictor, you need to implement three functions __init__(self): constructor for the predictor. You can utilize this function to initialize the predictor predict(self, address): given an address return a prediction (either 0 for not-taken, or 1 for taken) update(self, address, actual_outcome): this function is utilized to update the state of the predictor with the actual outcome of the branch instruction. Report Minimum Requirements 1. Describe in 100 words or less how the provided simulator enable testing various branch predictions. 2. Table with the overall accuracy of each predictor of the generated trace file. 3. Plots that show the branch predictor accuracy over time. The x-axis should be the Number of Branches and the y-axis should be the Prediction Accuracy (%). 4. Elaborate on the results of the predictors and why some predictors performed better than others. Directory Structure The simulator organizes its files and logs as follows: . ├── branch_predictors.py # Branch predictor implementations ├── branch_trace_generator.py # Generates branch trace files ├── branch_simulator.py # Main branch predictor simulator ├── branch_trace.csv # Generated branch trace file ├── logs/ # Logs for each branch predictor │ ├── One_Bit_log.txt # Detailed logs for the One-Bit predictor │ └── ... ├── bht_logs/ # Logs for BHT states │ ├── GShare_bht.txt # GShare BHT state │ └── ... └── realtime_stats.txt # Real-time statistics log System Requirements Python 3.x tabulate for tabular progress display To install the dependencies, run: pip install tabulate
MU1629a Final Project: Exploring Compositional Craft due December 11*. As we near the completion of your Introduction to Composition course, you will demonstrate the tools and devices you have learned as part of your studies. As part of this final assignment, you will compose a 2-3 minute composition for 3-5 instruments of your choice relying on techniques introduced to you through observation and study of the relevant repertoire (50% of the final grade). You are expected to utilize some of the concepts discussed in the materials, including but not limited to Þ Pitches Þ Intervals Þ Scales Þ Chords: triads, seventh chords, chord construction Þ Rhythms Þ Counterpoint Þ Harmony: neo-Riemannian transformations, tonal relationships, dissonance, neotonality Þ Compositional Techniques: fragmentation, liquidation, convolution, repurpose, juxtaposition, repetition, expansion, cadence. Þ Minimalism Þ Form. ABA, AB, throughcomposed, theme and variations, contrasts. Þ Graphic notation Þ Extended techniques The assignment will comprise of several stages that will consist of different deadlines. You must meet all requirements and deadlines for the chance to receive full marks. *Assignment Stages and Deadlines 1. Idea & Sketch individual discussion: During the week of November 18th, you will meet with the instructor for 10-minute online Zoom lessons to discuss and assess your musical sketches, instrumentation and plan. You must email a pdf file to the instructor by 12PM noon on November 18th of the opening of your piece or ideas of your composition and be sure of your instrumentation, length and scope. 2. Composition Readings & Recordings (20%): You will find your own performers to present your finished compositions during 15-minute sessions on November 25&27, December 2&4 in Studio 242. The time will be assigned for the performers but the students of the class must be present for the entirety of each session, as observation of this process is an integral part of the learning process. It is your responsibility to ensure the performers are there for the assigned time. If your piece cannot be performed, you will lose 20% of your final grade. You must have (2) legible physical scores for the instructor and one set of parts for the musicians. 3. Final Score Submission (30%): by 12PM noon on December 11th, you must electronically (PDF format only) submit a publisher-ready and corrected score, a set of parts and a 1-page verbal description of how the work relates to the course material. No late submissions accepted. You will be graded on your Coherency (25%), Preparedness for the readings (25%), Notation (25%) and Relevance to class syllabus (25%).
MODULE HANDBOOK [Academic Year 2024/2025] Module title and code Mobile Communication 1 ENGD3105 Level UG Award BEng Section 1: Introduction Welcome to ENGD3105. This handbook presents the module. It gives module aims, characteristics and learning outcomes, module assessment description and weightings, module calendar, assignment submission dates, feedback date, prerequisites, and references. Professor Raouf Hamzaoui School of Engineering and Sustainable Development Section 2: Aims, characteristics and learning outcomes Mobile communication is as much a part of everyday life as TV and radio. However, mobile communications is a rapidly changing technology. This module focusses on these changes, particularly on how the technology is evolving to satisfy new needs and the shortcomings of prior art. This is a technical course in that it unpicks these technological developments by analysing past, current and future mobile technologies, including channel allocation, digital modulation, and channel coding. This module has a strong student-led focus. Coursework is undertaken as a research report, where students have to research, define and carry out their own experimental investigations. Traditional lectures are used to present and explain technical information. Tutorials are used to develop understanding and to investigate wider implications of theory and practice. Coursework is undertaken in the form. of research investigations with students being given 'briefs' and they are responsible (under the guidance of the tutor acting as 'consultant') for the design of experiments, the practical work and presentation of this. Learning outcomes: 1) Students should be able to demonstrate an understanding of the key technological components of historical, contemporary and future mobile communication systems, 2) Students should be able to demonstrate skills in research and development according to a variety of briefs. Syllabus: Characteristics of the mobile radio environment (path loss, shadow fading, multipath fading, Doppler frequency shift). Digital modulation techniques (ASK, FSK, PSK, QPSK, M-QAM, MSK, GMSK). Channel coding (Turbo coding, LDPC coding). Cell structure and traffic handling (how to partition a geographical area, SIR computation, call blocking probability). The full module template is available in the Module Information folder. Section 3: Module assessment description and weightings · Phase test (weighting: 50%). Choose 4 out of 5 questions. 3 hours. Anonymous marking. Date: 15/01/25 15:00-18:00 in Q0.15. Previous papers and solutions are available in the Revision folder. · Coursework assignment (weighting: 50%). One report based on work in MATLAB. Start date: 16/10/24. Due date: 13/12/24 noon. Feedback date: 08/01/25. Anonymous marking. · University generic undergraduate mark descriptors: see https://www.dmu.ac.uk/documents/about-dmu-documents/quality-management-and-policy/academic-quality/learning-teaching-assessment/ug-mark-descriptors.pdf Section 4: Module calendar The module calendar can be found in the Module Information folder. Section 5: References The main references for this module are · Mobile Wireless Communications, Mischa Schwartz, Cambridge University Press, 2005. · Introduction to Wireless Systems, Bruce Black et al., Prentice Hall, 2008. Section 6: Prerequisites A good background in the following topics is required. · Trigonometry · Complex numbers · Calculus (integrals, derivation) · Probability and random variables
Computer Graphics Final Project Introduction This project demonstrates a comprehensive application of computer graphics techniques learned during the module. The implemented application showcases: • Basic geometry rendering. • Texture mapping for enhanced realism. • Lighting and shadow mapping using Phong shading. • Animation and user interaction (camera control and object movement). • An advanced feature: Screen-Space Ambient Occlusion (SSAO) for realistic soft shadows. The infinite scene effect adds depth to the user experience by simulating a boundless environment. These features are integrated to create a visually appealing and interactive application. Progress Report This section illustrates the development stages of the project with accompanying screenshots. Stage 1: Basic Geometry Rendering The initial step was to render basic geometries, such as cubes and planes, using Vertex Array Objects (VAOs) and Vertex Buffer Objects (VBOs). Figure 1: Basic geometry rendering (cubes on a plane). Stage 2: Texture Mapping Textures were applied to the rendered geometries using STB image loading to enhance realism. Figure 2: Texture mapping applied to the cube. Stage 3: Lighting and Shadows Phong shading was implemented to simulate realistic lighting, and shadow mapping was added to create dynamic shadows. Figure 3: Lighting and shadow mapping. Stage 4: Animation and User Interaction Objects were animated using transformation matrices, and user interaction (camera control) was imple- mented for an interactive experience. Figure 4: Object animation and camera interaction. Stage 5: Advanced Feature (SSAO) Screen-Space Ambient Occlusion (SSAO) was added to enhance the realism of soft shadows and depth perception in the scene. Figure 5: SSAO effect applied to the scene. Quality and Robustness Quality - The application maintains a stable frame rate of 15 FPS on compatible hardware. - Features such as shadow mapping and SSAO significantly enhance the visual quality. Robustness - OpenGL error checking ensures stability during runtime. - Graceful handling of missing resources (e.g., textures) with clear error messages. - Efficient memory management avoids resource leaks. Limitations and Future Work Limitations • Minor artifacts in shadow edges due to resolution constraints of the shadow map. • SSAO noise in areas with insufficient samples. Future Work • Extend the application with dynamic weather effects, such as rain or fog. • Improve lighting by implementing real-time global illumination. • Enhance compatibility for WebGL and mobile platforms. Acknowledgements This project utilizes the following resources: • GLFW for window and input management. • GLEW for OpenGL extensions. • GLM for mathematical operations. • Tutorials from LearnOpenGL. Special thanks to the instructor and peers for their guidance and support.
DEPARTMENT of MUSIC RESEARCH and COMPOSITION MU1629a 2024-2025 INTRODUCTION to COMPOSITION Fall Term Course Prerequisites: None, but restricted to students enrolled in the Don Wright Faculty of Music. Please note that prerequisites are no longer automatically checked prior to course registration. It is the responsibility of each student to ensure that they have the specified prerequisites. Unless you either have the requisites for this course or special permission from the Dean to enroll in it, you will be removed from the course and it will be deleted from your record. This decision may not be appealed. You will receive no adjustment to your fees in the event that you are dropped from a course for failing to have the necessary prerequisites. Course Description: Introduction to the elementary components of compositional technique involving pitch and contrapuntal relationships, harmonic interactions, rhythmic properties, texture, timbre, architecture and basics of orchestration. Topic discussions will involve a survey of musical examples drawn from the 20th and 21st century literature, as well as modern texts on compositional, contrapuntal and harmonic technique and their manipulation. Course Outcomes: Students can expect to gain the understanding of basic compositional concepts and become acquainted with the elements of an individual musical language. Students will be introduced to the appropriate compositional vocabulary and become familiar with the skills and abilities of describing musical material. They will learn to manage compositional tools and apply them to the creation of their own short-scale compositions and learn how to notate their music and organize musical matter. Assignments: Assignments are due at the beginning of class on the assigned due date. Late assignments will be accepted up to one week past the deadline with a 5% penalty each day. Assignments will not be accepted one week past the deadline. This policy will be strictly enforced. Only medical emergencies will be considered as a valid excuse. Academic Offences: Any infraction on academic honesty and plagiarism will not be tolerated. With the ubiquitous use of AI and ChatGPT, the course instructor will be on the lookout for any work completed by students through the use of dishonest means. The objective of this course is to help students develop their own artistic voice, language and vocabulary – skills only obtainable through hard work and academic integrity. Suspected plagiarism and/or use of virtual assistance will be immediately flagged and forwarded to the appropriate academic integrity committee. Submission of work with which you have received help from someone else (other than the course instructor or TA) is an example of plagiarism, which is considered a major academic offence. Scholastic offences are taken seriously and students are directed to read the appropriate policy, specifically, the definition of what constitutes a Scholastic Offence, as found at: http://www.uwo.ca/univsec/pdf/academic_policies/appeals/scholastic_discipline_undergrad.pdf Required Course Materials: • A minimum of 12-stave high-grade manuscript paper. • Pencils. • Erasers. • Binder to collect handouts and course materials. • Access to digital notation software. Professional software such as Sibelius or Dorico is recommended, but the use of free online tools such as MuseScore and NoteFlight will be acceptable. • A method of taking a scan of your work to upload to the instructor (Genius Scan is an excellent free app). • A method of viewing online instruction and joining Zoom meetings. Although assignments may be submitted in PDF format, some will be accepted in hand-written form. only. Recommended Reference Texts: • Notation and Rudiments: Barbara Wharram, Elementary Rudiments of Music, Second Edition, The Frederick Harris Music Co., Limited. • Orchestration: Samuel Adler, The Study of Orchestration, Third Edition, New York: Norton. ISBN 039397572X. • Literature: J. Peter Burkholder & Claude V. Palisca, Norton Anthology of Western Music, Volume 2: Classic to Twentieth Century, New York, W. W. Norton & Co. ISBN 0-393- 92562-5 (pbk.). • Composition: Arnold Schoenberg (edited by Gerald Strang & Leonard Stein), Fundamentals of Musical Composition, Belmont Music Publishers. Toby Young, ed, The Cambridge Companion to Composition, Cambridge: Cambridge University Press. ISBN9781108831697. Olivier Messiaen, The Technique of My Musical Language, Alphonse Leduc, Éditions Musicales. ISBN 2-85689-058-X. Evaluation: Evaluation of assignments will be based upon the following criteria: • Coherency: how well the students expresses their musical ideas and organizes material. This can include but no be limited to pitch material, harmony, rhythm, texture & counterpoint. • Notation: how clearly the student expresses their ideas through the use of notation. • Orchestration: how well the student is able to use orchestration tools when writing for individual instruments and ensemble. • Vocabulary: how well the student understood and implemented course material. Grading Scale: A+=90-100%, A=80-89%, B=70-79%, C=60-69%, D=50-59%, F=0-49%. • A+: One could scarcely expect better from a student at this level. • A: Superior work which is clearly above average. • B: Good work, meeting all requirements, and eminently satisfactory. • C: Competent work, meeting requirements. • D: Fair work, minimally acceptable. • F: Fail. Grade Distribution: Assignments: o Assignment 1: Rudiments exercises and hand-writing (5%), due September 16th. o Assignment 2: Creation of Musical Ideas (10%), due September 23rd. o Assignment 3: Enhancing Musical Patterns and the Practical Application of Musical Ideas and First Composition Creation (15%), due October 11th. o Assignment 4: Testing Public Speaking Skills and Oral Coherency – Group Presentations (10%), November 4&6. o Assignment 5: Building Musical Vocabulary and Eloquence – a concert review paper (10%), due November 11th. o Reading Sessions/Presentations: Presentation of Final Composition Drafts, including score and parts – November 25&27, December 2&4 (20%). o Final: Second and Final Composition Creation showcasing skills and tools learned throughout the course with publisher-ready score and detailed programme notes (30%), due December 8. Please note it is a course requirement that the final assignment is performed in class. You may choose to participate in the performance yourself, ask your classmates or find players within the school. Examinations & Attendance: Any student who, in the opinion of the instructor, is absent too frequently from class or laboratory periods in any course will be reported to the Dean of the Faculty offering the course (after due warning has been given). On the recommendation of the department concerned, and with the permission of the Dean of that Faculty, the student will be debarred from taking the regular examination in the course. The Dean of the Faculty offering the course will communicate that decision to the Dean of the Faculty of registration. Compulsory First Year Exam Exemption: The Dean's office has granted this course an exemption from the Senate policy that requires each first-year course (1000-1999) to administer a common, compulsory, final examination scheduled during the examination period worth not less than 30% of the final grade. MUSIC 1629A: Introduction to Composition Timetable: Week of September 9 o Notation. o Intervals. o Chords. o Scales. o Time. o Reference texts include Barbara Wharram, Elementary Rudiments of Music, Second Edition, The Frederick Harris Music Co., Limited: Chapters 1-5 & 7. Assignment 1 distributed on September 9. Week of September 16 Assignment 1 collected on September 16. o The birth of musical idea. Reference scores include, but not limited to G. Ligeti (1923- 2006): Musica Ricercata; S. Reich (b. 1936): Piano Phase; P. Glass (b. 1937): Music in Fifths; M. Monk (b. 1942): Change. A. R. Thomas (b. 1964): Traces. D. Rakowski (b. 1958): Blue Horizon. A. Pärt (b. 1935): Pari intervallo. o Reference texts include Arnold Schoenberg (edited by Gerald Strang & Leonard Stein), Fundamentals of Musical Composition, Belmont Music Publishers: Chapters I-VIII. Philip Lasser, An Inquiry into the Contrapuntal Fabric of Music, Volume I, Rassel Editions: Part I. Toby Young, ed, The Cambridge Companion to Composition, Cambridge: Cambridge University Press: Part I, Creative Processes. Assignment 2 distributed on September 16. Week of September 23 Assignment 2 collected on September 23. o Enhancing the musical pattern. Reference scores include, but not limited to A. Ginastera (1916-1983): Piano Sonata no. 1. Terry Riley (b. 1935): In C. A. R. Thomas (b. 1964): Dancing Helix Rituals. P. Boulez (1925-2016): Douze Notations. G. Scelsi (1905- 1988): Preludi, No. XV. A. Louie (b. 1949): Small Beautiful Things. o Reference texts include Paul Hindemith, The Craft of Musical Composition, Book 1, Fourth Edition, Schott: Chapter II. Arnold Schoenberg (edited by Gerald Strang & Leonard Stein), Fundamentals of Musical Composition, Belmont Music Publishers: Chapters IX-XII. Assignment 3 distributed on September 25. Week of September 30 National Day for Truth and Reconciliation. No class on September 30th only. o Discussion of complex instrumental combinations, instrumentation, musical nuance and vocabulary. Reference scores include, but not limited to P. Boulez (1925-2016): Dérive I. M. Lindberg (b. 1958): Piano Trio. P. Glass (b. 1937): Piano Quintet “Announciation”. B. Sheng (b. 1955): Clearwater Rhapsody. S. Ran (b. 1949): Glitter, Doom, Shards, Memory, String Quartet no. 3. o Reference texts include Olivier Messiaen, The Technique of My Musical Language, Alphonse Leduc, Éditions Musicales: Chapter XIII. Week of October 7 o Practical application of music ideas and introduction to orchestration. Reference scores include, but not limited to E. Astapov (b. 1988): Hear My Voice. C. Debussy (1862- 1918). I. Stravinsky (1882-1971): The Rite of Spring. A. Schnittke (1934-1998): Piano Quintet & In Memoriam. o Reference texts include Samuel Adler, The Study of Orchestration, Third Edition, New York: Norton. Assignment 3 is due October 11 at 11:59PM by email. Week of October 14 Fall Reading Week, NO CLASSES Week of October 21 Assignment 4 distributed (Group presentations). o Group lessons (masterclass style) on assignment 3. Week of October 28 o Group lessons (masterclass style) on assignment 3. Assignment 5 distributed on October 30. Week of November 4 o Group presentations. Week of November 11 Assignment 5 collected on November 11. o Modes. o Harmony. o Counterpoint. o Extended techniques. o Experimentation. o Multimedia. o Reference Scores include, but not limited to A. Schoenberg (1874-1951): Pierrot Lunaire. I. Stravinsky (1882-1971): Symphony of Psalms, mvt. I. O. Messiaen (1908- 1992): Quartet for the End of Time, mvt. I. K. Penderecki (1933-2022): Threnody to the Victims of Hiroshima. C. Rouse (1949-2019): Gorgon. S. Gubaidulina (b. 1931): In Tempus Praesens, Concerto for Violin and Orchestra. C. Vivier (1948-1983): Orion. C. McPhee (1900-1964): Tabuh-Tabuhan. o Reference texts include J. Peter Burkholder & Claude V. Palisca, Norton Anthology of Western Music, Volume 2: Classic to Twentieth Century, New York, W. W. Norton & Co: Chapters 141, 146, 160 & 165. Final assignment distributed on November 11. Week of November 18 No classes. Week of November 25 Reading sessions. Week of December 2 Reading sessions. Final assignment is due electronically on December 6 at 11:59 PM. The assignment must include a publisher-ready score, a set of parts, as well as a descriptive program note.
ITAO2009 Data Analytics for Business Academic Year 2024-2025 Module Description Increasingly, organisations are relying on data analysis to interpret corporate information when making business decisions. Indeed, timely and appropriate use of data analytics is considered a crucial component among organisations that are committed to achieving business success. This module explores basic methods and concepts in data analytics for analysing and interpreting data. The module takes both a theoretical and practical approach to the use of data analytics in practice. A highlight of the module is the use of KNIME software to analyse data for decision making and evaluative purposes. Students who successfully complete the module will be able to signal to potential employers that they have the theoretical, practical plus industry-standard software skills to compete. Module Content The module is taught in two types of lectures, class lectures and computer sessions. The first type (i.e., class lectures) will take place in class where the theoretical background on data analytics will be covered. It takes a holistic approach to understanding data analytics - the maturity of data analytics in industry; uncover where, when, and how it is being used; and identify whether or not its use results in greater effectiveness, efficiency and performance returns. Indicative class lectures contents include: • Small and Big Data - Case study (e.g., Netflix, Facebook etc.) • Descriptive and inferential analytics • Applications of data analytics in business • The concept of confidence intervals and hypothesis testing • Simple and multiple linear regression analysis Computer sessions focus on data analysis, covering both descriptive and predictive analytics, emphasising on methods, such as correlation analysis and regression analysis. Computer sessions will be taught through instructor led computer workshops using KNIME software. Indicative computer sessions contents include: • Introduction to KNIME • Descriptive analytics and visualisation • Correlation analysis • Performance of linear regressions Learning Outcomes On successful completion of this module students will be able to: Subject Specific 1. Demonstrate an understanding of the role and impact of data analytics in dealing with a variety of business problems. 2. Demonstrate an ability to summarise, analyse and present data effectively to others. 3. Employ statistical techniques to draw well founded inferences from quantitative data. 4. Demonstrate an ability to use appropriate software. 5. Demonstrate an ability to understand the scope and limitations of quantitative methods. 6. Identify sources of published analytics, understand their context and report on their wider relevance. 7. Interpret and disseminate research results and findings. General 1. Apply critical analytical skills and problem-solving skills to a variety of different situations. 2. Synthesize, analyse, interpret and critically evaluate information from a variety of different sources. 3. Work effectively as an individual and as part of a team. Course Schedule This module is taught in class lectures and computer sessions, and it will include group work, lectures, and computer practical. Classes will be a combination of the traditional lecture, discussion, and interactive student-led sessions. It is imperative that students undertake preparatory work before coming to each class. The itinerary for each session is provided in Table 1 of this document. Computer sessions will focus on the practical implementation of marketing analytics using KNIME software. TABLE 1: ITAO2009 DATA ANALYTICS FOR BUSINESS SCHEDULE 2024/25 Lecture 1 Topic / Activity • Introductions • Discussing the module’s Outline and Assessments • Explain how the module’s assessments are meticulously aligned with the module’s learning outcomes • A brief introduction to Data Analytics for Business Main Textbook • Albright, S. C., & Winston, W. L. (2020). Business analytics: Data analysis and decision making. Cengage Learning, Inc. (Chapter 1). Lecture 2 Topic / Activity • Introduction to Business Analytics Main Textbook • Albright, S. C., & Winston, W. L. (2020). Business analytics: Data analysis and decision making. Cengage Learning, Inc. (Chapter 1). • Koole, G. (2019). An Introduction to Business Analytics. Lulu. com. (Chapter 1). Other Suggested Reading • Chahal, H., Jyoti, J., & Wirtz, J. (2019). Business analytics: Concept and applications. Understanding the Role of Business Analytics: Some Applications, 1-8. • Power, D. J., Heavin, C., McDermott, J., & Daly, M. (2018). Defining business analytics: an empirical approach. Journal of Business Analytics, 1(1), 40-53. • Delen, D., & Ram, S. (2018). Research challenges and opportunities in business analytics. Journal of Business Analytics, 1(1), 2-12. • Schläfke, M., Silvi, R., & Möller, K. (2012). A framework for business analytics in performance management. International Journal of Productivity and Performance Management, 62(1), 110-122. • Yin, J., & Fernandez, V. (2020). A systematic review on business analytics. Journal of Industrial Engineering and Management (JIEM), 13(2), 283-295. • Duan, Y., Cao, G., & Edwards, J. S. (2020). Understanding the impact of business analytics on innovation. European Journal of Operational Research, 281(3), 673-686. Computer Session 1 Topic / Activity • Introduction to Knime Main Textbook • Knime training manual and lecture slides issued by course instructors. Other Suggested Reading • Acito, F. (2023). Introduction to KNIME. In Predictive Analytics with KNIME: Analytics for Citizen Data Scientists (pp. 21-52). Cham: Springer Nature Switzerland. Lecture 3 Topic / Activity • CRISP-DM and Data (Small and Big) Main Textbook • Albright, S. C., & Winston, W. L. (2020). Business analytics: Data analysis and decision making. Cengage Learning, Inc. (Chapter 2 & 3). • Zikopoulos, P., & Eaton, C. (2011). Understanding big data: Analytics for enterprise class hadoop and streaming data. McGraw-Hill Osborne Media. (Chapter 1 & 2). Other Suggested Reading • Schröer, C., Kruse, F., & Gómez, J. M. (2021). A systematic literature review on applying CRISP-DM process model. Procedia Computer Science, 181, 526-534. • Martínez-Plumed, F., Contreras-Ochando, L., Ferri, C., Hernández-Orallo, J., Kull, M., Lachiche, N., ... & Flach, P. (2019). CRISP-DM twenty years later: From data mining processes to data science trajectories. IEEE transactions on knowledge and data engineering, 33(8), 3048-3061. • Kitchin, R., & Lauriault, T. P. (2015). Small data in the era of big data. GeoJournal, 80, 463-475. • Hand, D. J., Daly, F., McConway, K., Lunn, D., & Ostrowski, E. (1993). A handbook of small data sets. cRc Press. • Sagiroglu, S., & Sinanc, D. (2013, May). Big data: A review. In 2013 international conference on collaboration technologies and systems (CTS) (pp. 42-47). IEEE. • Fan, J., Han, F., & Liu, H. (2014). Challenges of big data analysis. National science review, 1(2), 293-314. Computer Session 2 Topic / Activity • Visualisation in Knime Main Textbook • Knime training manual and lecture slides issued by course instructors. Other Suggested Reading • https://www.knime.com/blog/visual-data-exploration-in-three-steps • https://www.knime.com/blog/data-visualizaton-101-five-easy-plots-to-get-to- know-your-data
School of Economics Undergraduate assessment brief Unit: EFIM10016 Economic Data Assessment’s contribution to unit: 100% Release Date: 27/11/2024 Submission Date: 04/12/2024 at 11:00 am Feedback Released: 13/01/2025 Students are strongly advised to submit their work ahead of the deadline. Should you have a problem with submission to Blackboard you should email [email protected] for guidance immediately. • Your answer(s) to Part 2 should be at least 400 words. Your answer to Part 3 should not exceed 300 words. Answers much longer than this are unlikely to be sufficiently concise, whilst answers shorter than this are likely to be missing key details, and are likely to gain lower intellectual marks. You should include an accurate word count on the front cover of the assignment. • Assignments handed in after the deadline, without a pre-arranged extension, will be subject to late penalties. Details relating to penalties are at the end of this document. • A reference list/bibliography is recommended. This list does not contribute to the word count. Information on referencing can be found via ourlibrary. • Please use Arial or Calibri font at 12-point. • Your assignment should be combined into a single document and submitted in pdf format with a document name containing your student number. • You may include photographs or scans of your own hand-drawn, labelled diagrams or calculations. We would advise you to generate your own diagrams but if you include diagrams or pictures that you have not produced yourself, or are modified versions of existing images, you should ensure you reference them appropriately. Figures and tables should normally be included inline in the text. • Your answer will be assessed using theUniversity Marking Criteria. This is a piece of COURSEWORK that contributes to your Unit mark and you can: • Use resources to support you in completing your answer. • Draw upon a range of accepted resources including, your own notes, lecture slides/recordings, course material, textbooks, journal articles, online resources. ALL work should be written in your own words. • Ask for help from your personal tutors or academic lecturers if you do not understand an aspect of the coursework. • Broad discussion with your tutors, fellow students, friends and family on the assessment topic and your ideas/approach may help you to further your knowledge and understanding. • Use your network of family and friends to gain support and encouragement during the assessment period. Please remember this is a formal assessment and you should behave in a manner consistent with our values. This means you cannot: • Allow others to directly contribute to your written answer by revising or adding to the academic content. This is collusion and is against University Regulations. • Share your assessment with others or ask others to share their work with you. • Copy and paste any material (text, images, coding, calculations) from other sources, including teaching material and shared revision notes directly into your answer without appropriate acknowledgement. This is plagiarism and is against University Regulations. • Pay another person or company to complete the assessment for you. This is contract cheating and is against University Regulations. Economic Data 2024 - Final Coursework 1 Part 1 (10%) Question 1 Is consumption a flow or a stock variable? Question 2 On January 1,1987, 56, 743, 897 people were living in the United Kingdom. One year later, on January 1, 1988, 56, 860, 203 people were living in the United Kingdom. From January 1, 1987, to December 31, 1987, 775, 405 were born and 644, 342 died in the United Kingdom. From January 1, 1988, to December 31, 1988, 787, 303 were born and 649, 185 died in the United Kingdom. What is the sum of migration flows and the statistical adjustment/discrepancy in the United Kingdom, from January 1., 1987 to January 1, 1988? Question 3 Consider the dataset in Table 1 that includes the number of women and live births by age groups (for brevity we only consider ages 31-35). What is the Total Fertility Rate? Table 1: Births by age Age Women Births 31 402,796 23,879 32 430,794 20,549 33 445,650 23,422 34 361,045 23,902 35 377,328 22,207 Question 4 Consider the dataset on mice in Table 2. What is the period life expectancy at birth? Table 2: Mortality in mice Time Initial population Mice dying in this period 0-1y 100 20 1-2y 80 14 2-3y 66 47 3-4y 19 8 4-5y 11 1 Question 5 Consider the following small economy: In this economy, we have one farm that produces flour. The farm sells the flour to a bakery for £6. The farm employs workers who receive £1 in wages. The farm pays £3 in taxes to the Government. Using the flour from the farm, the bakery workers create scones. The bakery sells scones for £21 to the local inhabitants and exports scones for £9. The bakery also imports clotted cream from the neighbouring economy for £5. The bakery workers receive £12 in wages, and the bakery pays £ 10 in taxes. The public sector provides a health service and a school for all inhabitants. The Government pays the health workers and teachers £22 in wages. The public sector has no expenses beyond these wages. The inhabitants of this economy pay £9 in taxes. They also buy clotted cream from the bakery for £5. The bakery is owned by a woman in the neighbouring economy. What is the sum of all profits generated in this economy (the operating surplus)? Question 6 Consider the following small economy: In this economy, we have one farm that produces flour. The farm sells the flour to a bakery for £6. The farm employs workers who receive £1 in wages. The farm pays £3 in taxes to the Government. Using the flour from the farm, the bakery workers create scones. The bakery sells scones for £21 to the local inhabitants and exports scones for £9. The bakery also imports clotted cream from the neighbouring economy for £5. The bakery workers receive £12 in wages, and the bakery pays £ 10 in taxes. The public sector provides a health service and a school for all inhabitants. The Government pays the health workers and teachers £22 in wages. The public sector has no expenses beyond these wages. The inhabitants of this economy pay £9 in taxes. They also buy clotted cream from the bakery for £5. The bakery is owned by a woman in the neighbouring economy. What is the Gross Domestic Product (GDP) of this economy? Question 7 Consider the following country. In year 2004 the GDP was £1,724,583 and in year 2017 the GDP was £2,483,266. What was the average annual percentage growth in GDP over this period? Question 8 Consider the following country. In year 2004 the GDP was £1,624,583 and in year 2017 the GDP was £2,883,266. What was the total percentage growth in GDP over this period? Question 9 What would earnings of 68,638 measured in current prices in the year 2010 correspond to in 2012 prices,using the consumer price index in Table 3? Table 3: CPI Year CPI 2008 98 2009 104 2010 104 2011 106 2012 107 2013 112 2014 113 2015 113 2016 118 2017 118 2018 118 2019 123 Question 10 Using the consumer price index in Table 4, what was average annual inflation from 2009 to 2011? Table 4: CPI Year CPI 2008 99 2009 102 2010 102 2011 106 2012 107 2013 108 2014 114 2015 112 2016 116 2017 117 2018 122 2019 125 Question 11 Provide the derivation of the formula for contributions to growth based on the expenditure approach to GDP. Question 12 Table 5 shows the GDP per capita (GDPPC) and Purchasing Power Standards (EU27_2020=1) (PPS). What was the GDP per capita in Poland in the price level and currency of the Netherlands in the year, 2021? Table 5: GDP Country GDPPC PPS Year Iceland 8,728,110 225.71 2021 France 36,660 1.08676 2021 Poland 68,760 2.75272 2021 Estonia 22,580 0.819114 2021 Malta 28,310 0.882986 2021 Czechia 571,020 19.3578 2021 Netherlands 48,840 1.15346 2021 Lithuania 20,000 0.695785 2021 Question 13 Consider the following economy. The total population of this economy is 1,816,102 people. There are 587,022 elderly and 428,644 children. Moreover, 207,492 people of the working age population are out of the labour force, and 35,904 people are unemployed. How big is the labour force of this economy? Question 14 Consider the following economy. The total population of this economy is 1,916,102 people. There are 597,022 elderly and 418,644 children. Moreover, 217,492 people of the working age population are out of the labour force, and 35,904 people are unemployed. What is the unemployment rate in this economy? Question 15 A household has a total income of 60 thousand pounds. The household consists of 3 adults and 3 children (younger than 14 years). What is the equivalised household income, using the OECD equivalence scale? Question 16 Based on the data on equivalised disposable income after social transfers in Table 6, calculate the number of households below the risk-of-poverty-rate according to the Eurostat definition. Table 6: Equivalised income Household Equivalised Income 1 13,121 2 12,138 3 10,936 4 2,254 5 6,858 6 13,176 7 1,832 8 3,796 9 13,533 10 7,238 11 1,526 12 4,482 13 3,877 14 825 15 13,230 16 9,012 17 9,035 18 3,599 19 6,041 Question 17 If we had an exchange rate of 1.21 US Dollars to the British Pound yesterday, and an exchange rate of 1.42 US Dollars to the British Pound today, did the British Pound then appreciate, depreciate, or stay constant compared to the US Dollar? Question 18 Consider the following small economy: In this economy, we have one farm that produces flour. The farm sells the flour to a bakery for £83. The farm employs workers who receive £15 in wages. The farm pays £57 in taxes to the Government. Using the flour from the farm, the bakery workers create scones. The bakery sells scones for £71 to the local inhabitants and exports scones for £96. The bakery also imports clotted cream from the neighbouring economy for £65. The bakery workers receive £42 in wages, and the bakery pays £22 in taxes. The public sector provides a health service and a school for all inhabitants. The Government pays the health workers and teachers £109 in wages. The public sector has no expenses beyond these wages. The inhabitants of this economy pay £30 in taxes. They also buy clotted cream from the bakery for £65. The bakery is owned by a woman in the neighbouring economy. What is the income inequality in this economy in terms of the wage share, that is the share of income generated (wages and profits) that goes to wages? Question 19 Consider the following economy. In the year 2013, the economy had 213,779 unemployed women and 420,085 unemployed men. The year after, the number of unemployed women was 235,502 and the year of unemployed men was 505,221. In year 2014, there were 4,597,147 employed men and 1,799,085 employed women, compared to 4,321,923 employed men and 1,691,377 employed women in year 2013. What was the total percentage change in the unemployment rate from, 2013 to 2014? Question 20 Consider the following economy. In the year 2013, the economy had 213,779 unemployed women and 450,085 unemployed men. The year after, the number of unemployed women was 237,044 and the year of unemployed men was 493,500. In the year 2014, there were 4,390,729 employed men and 1,718,304 employed women, compared to 4,321,923 employed men and 1,691,377 employed women in the year 2013. How much did the change in the number of unemployed women contribute to the overall change in the number of unemployed from 2013 to 2014? 2 Part 2 (80%) Your assigned country depends on the timing of your lab session: • Group 1: Tuesday 9:00 to 11:00: Greece • Group 2: Tuesday 11:00 to 13:00: Finland • Group 3: Tuesday 16:00 to 18:00: Spain • Group 4: Wednesday 09:00 to 11:00: Slovenia • Group 5: Wednesday 11:00 to 13:00: France Task 1 Your task is to analyse the change in the number of people in your assigned country since the 1980s. Your answer could cover topics such as population stocks and flows, changes in fertility, and life expectancy. The task description is intentionally vague. You decide on the precise time periods and the exact focus of your assignment. Use at least 400 words and 2 visualisations for this exercise. All visualisations should be made from scratch using R. You can use data from Eurostat, OECD, or the World Bank. Task 2 Your task is to describe the development in unemployment, inequality, or poverty in your assigned country. You decide the exact period to study. This could be a historical period, the current period, or both. You can look at overall unemployment, inequality, and poverty, or you can look at specific subgroups (for example by age or gender). You can also study whether unemployment and inequality are correlated. Use at least 400 words and 2 visualisations for this exercise. All visualisations should be made from scratch using R. You can use data from Eurostat, OECD, or the World Bank. Task 3 Describe the growth in GDP per capita for your assigned country. You decide the exact period to study. The task description is intentionally vague. Use at least 400 words and 2 visualisations for this exercise. All visualisations should be made from scratch using R. You can use data from Eurostat, OECD, or the World Bank. Task 4 Describe the development of prices in your assigned country. You decide the exact period to study. The task description is intentionally vague. Use at least 400 words and 2 visualisations for this exercise. All visualisations should be made from scratch using R. You can use data from Eurostat, OECD, or the World Bank. 3 Part 3 (10%) You should identify all mistakes in the following visual and verbal description of inequality. • Your answer can be in bullet points, or you can provide a coherent paragraph describing all mistakes that you identified. • The word limit is 300 words. Inequality in the United Kingdom 50 40 30 20 1970 1980 1990 2000 2010 2020 The figure depicts inequality in the United Kingdom since the 1970s. We use the Gini index because it is the most suited to understand changes in inequality over time. We use data provided by the World Bank. As seen by a rising Gini index, it is clear that inequality in the UK has decreased since the 1970s. This shows that as economies grow, inequality tends to decrease.
CPT111: Java Programming Semester 1, 2024-25 Coursework 3: Programming Project – A Simple Quiz System Assignment type This is a group programming assignment. You are required to work as a group of 4 to 5 students, which will be allocated randomly. (You should be able to find your group members’ information from Learning Mall (LM), and should contact the instructors if there is any problem.) Weighing Total marks available: 100, accounting for 30% of overall module marks. Release date 1pm CST Wednesday 6 November 2023 Due date 11:59pm CST Sunday 1 December 2024 Learning outcomes (A) Understand and appreciate the principles of using object-oriented programming techniques for the construction of professional robust, maintainable programs deployed to meet real world business goals; (B) Design, write, compile, test, debug and execute object-oriented program using an Integrated Development Environment (IDE); (C) Effectively develop object-oriented programs as a member or a leader of a software development team with continuous development strategy supported by AI technology; (D) Implement object-oriented programming to represent, display, and manipulate data while mitigating security risks; (E) Evaluate legal, social, environmental, ethical, diversity, inclusion, and intellectual property issues related to object oriented program development; (F) Apply knowledge of engineering management principles, commercial context, project and change management in object-oriented program. Submission platform Each group is required to submit an electronic copy of your assignment via LM. You are allowed ONE submission only. It is your responsibility to upload the correct document. Late submissions Penalties will apply for any work submitted after the due date unless you have obtained a formal extension prior to the date for submission. The penalty applied will be 5% of the available marks for the assignment for each working day or part thereof that the assignment is late. The penalty will be capped at five working days (120 hours) from the assignment deadline. Work submitted after five working days will receive a grade of zero. Submission confirmation We strongly advise you to double check that you have submitted the correct document / final version of your answer. Submission of incorrect file If you have submitted the incorrect file, you should email the correct file to the instructors prior to the deadline. Submitting the incorrect file can result in failure. Special consideration Requests for an extension due to illness, misadventure, or other extenuating circumstances beyond your control will only be considered via a formal application for special consideration through e-Bridge. Report format ALL answers must be written in English. The report must: • be submitted as a .doc, .docx (do not submit PDF or Apple Pages) • contain headings and subheadings • have 3 cm margins • use a legible font (e.g., Calibri, Arial or Times New Roman) • be presented in 11 point font size with 1.5 line spacing • be paginated Use of Generative Artificial Intelligence (AI) Simple Editing Assistance For this assessment task, you may use standard editing software but not generative AI. You are permitted to use the full capabilities of the standard editing software, such as Microsoft Office suite, Grammarly, and Integrated Development Environments (IDEs) for coding. If the use of generative AI such as ChatGPT is detected, it will be regarded as serious academic misconduct and subject to the penalties mentioned above. Tips • Read the questions carefully. • Write succinctly, and avoid repetition. • Avoid being overly descriptive. • Remember to save/back up your work regularly. XJTLU provides all students free access to XJTLU Box. It may be prudent to save your work to your XJTLU account so that you can access it from multiple devices in case you encounter hardware issues. • You are encouraged to post administrative/procedural questions about the assignment on the LM Q&A Forum. The instructors will answer for the benefit of all students. Coursework 3: Programming Project – A Simple Quiz System The purpose of this assignment is to design and develop an application that can be used to facilitate educational quizzes, allowing users to select topics of interests, take quizzes related to those topics, and view their quiz scores on a personal dashboard. The primary aim is to create a user-friendly and interactive platform. that enhances the learning experience through topic selection and question assignment. 1 Functional requirements As this is a programming assignment, not a software design one, here is a skeleton to guide you in the creative part. 1.1 At startup • The program should load data, such as user information, questions, etc., from selected files, and store it in memory (using relevant variables and data structure). – You may assume that all user information are stored in a comma-separated values (CSV) file, as illustrated in resources user.csv, where the 1st, 2nd, and 3rd columns of the file represent the user id, user name, and password of the user, respectively. – You can assume that all questions are stored in some XML files, as illustrated in folder: resources questionsBank. A Java class ReadQuestions has been provided to you in the src folder demonstrating how the questions can be read from the XML files into the memory, and the Javadoc of the classes used can be found under the folder repositories javadoc xjtlu.cpt111.assignment.quiz. Note: You need to add the library: repositories xjtlu cpt111 xjtlu.cpt111.assignment.quiz 0.0.1 xjtlu.cpt111.assignment.quiz-0.0.1.jar to the class path (of your IDE) first before running the ReadQuestion class. • The program should: – show information about data loaded. – validate the questions read. That is, in our case, a question is valid if it (i) falls into a topic (either new or an existing one) (ii) has a question statement, (iii) has more than one answer available for selection, and (iv) has one-and-only-one answer. – show a menu that present a list of interactions possible. • Besides, you may need to add more questions (under different topics) to the question bank yourself. 1.2 Menu — General behaviour The application should support, at least, the following functions. • User registration and authentication • Topic selection – The topics available should be based on the input from the questions. • Quiz taking – You can decide the number of questions in a quiz. However, each quiz should contains questions at different level of difficulties. – The questions should be shown to the user one-by-one! – The order of answers available for selection should be shuffled every time before a question is shown to the user. • The score of the quiz should show to the user immediately after the quiz has been finished and the results should be saved into a score file – You should design the structure (and format) of the score file on your own, and make sure that you can read its content back when the program start! • User dashboard for viewing quiz results of each topic and history of the previous 3 tests attempted. • Leaderboard for showing the names of the users with the highest score in each topic. 1.3 Technical requirements The objective of this assignment is for you to program a simple quiz system so it should be interactive in the form. of showing text to the user and requesting their answer and data update through user input as well as reading data from different file types. If you fail to do so, your submission will automatically attract a penalty of 50%. 1.4 Concept requirements Your code must feature and make use of the following elements. • All your code must be divided in relevant/meaningful functions and classes. • Declare/define and use variables with the appropriate data types and meaningful names. • Input will always be converted into proper data type. • The program should prevent crash in any situation, thus make sure you test your program properly. 1.5 Constraints Dependencies Using libraries or modules that are NOT covered in this course is strictly prohibited and will result in zero (0) marks automatically in this assignment. Internet connectivity The application is assumed to be run locally on a computer; hence no internet connection is required. 2 Important reminder Documentation is also a critically important part of your software engineering. Your use of comment (in Java source file) will be graded. You must write the final version of the program totally on your own. Sophisticated plagiarism detection system are in operation, and they are pretty good at catching copying! Re-read the policy on the university home page, and note the University’s tougher policy this year regarding cheating. Using libraries or modules that are NOT covered in this course is strictly prohibited and will result in zero (0) marks automatically in this assignment. Your programming style. (how clearly and how well you speak a programming language, i.e., Java in this course) is what will be graded. Correct functioning of your program is necessary but not sufficient; you must use the concepts covered in class and meet all requirements stated in this assignment and as detailed in the marking rubrics (Section 4). 3 Submission requirements Your assignment has to submit to Learning Mall (LM). You should submit the following: A. A report (written using Microsoft Word) that includes the following items: 1. A cover sheet stating the student number of your team members. 2. A description of your project 3. Information and explanation of how you add, store and handle users’ data (including the data structure that you used to store users’ data in memory), the algorithm that you used to identify the user names in the leaderboard, how to handle problems that appear in the questions (e.g., questions with more than one correct answers) and other parts of the program, etc. 4. Tests performed to verify the correctness of the program. 5. Printouts/screen capture of your program’s execution and tests. (This can be incorporated into Items 3 and 4 above) 6. Peer evaluation form. (Section A). (This will be used to adjust the final mark of individual students according to his/her contributions to the project.) B. ALL Java source codes and resource files (e.g., images, questions and users files, etc.) that are required to run the application. You should compresses all files into a single “.zip” file before submission. You should NOT include any files from the repositories folder or any files that are NOT relevant to the application into the submission file. Failure to follow this requirement will result in mark deduction.
ENGL3002 Research Proposal Assignment Sheet and Rubric Fall Semester, 2024 You will submit a 1500 to 2000-word research proposal on any topic related to language and communication. • Proposals outside this range (not counting references) may receive a deduction. N.b., the word length range means that there is no ±10% applicable to this assignment. • Given the scope of the subject, you are expected to choose a method that has been covered in class and to propose a research study that meets these criteria. • You should adopt a research paradigm and work clearly within that paradigm. It does not matter which one you choose as long as there is consistency between the proposed research study, research questions, and methodology, and your chosen paradigm. • The proposal can be written with your Capstone Project in mind. You will not be bound to do this proposal for your capstone but your research proposal needs to be a feasible project of the scope of a capstone. This proposal must include the following 5 sections: Abstract • A summary of the research proposal in 150 words, similar to published article abstracts. Introduction For this section, you are strongly advised to follow the structure we have used in class from the work of Swales (i.e., Moves 1 – 3)—consult the files ENGL3002_Week04_PlanningResProposal.pptx and ConstructingLitReview_Territory.pdf, from Week 4. These are from the seminar class on planning your research proposal. Follow the steps in those documents • Begin with a clear statement of the topic of your proposal and why it is important • Move onto the research that has been done in this area and suggest where the gaps are in this literature. • Important point: Do not simply say that a particular type of study has not been done in Hong Kong. It probably has! But, that is not reason enough. Tell us why the study needs to be replicated here. What is important about the Hong Kong context that means we cannot simply take the results of studies done elsewhere at face value? • Finally, occupy the niche! Point out how your research question flows from the arguments that you have made above—make sure your research question/s is/are clearly stated. • Engagement with existing theory/literature • You need to cite at least 6 appropriate academic sources, of which at least 2 need to have been published in 2023 or 2024) • in your reference list, you will need to provide hyperlinks through the DOIs to the actual research papers for all references Method • a clear and appropriate method that relates directly to the RQ • including a sound and specific rationale for the chosen research method vs others Expected findings • believable expected findings References • All cited references to be presented in APA7 format Proposals are due via Blackboard on December 2nd at 11:59pm • All proposals will be scanned via Turnitin at submission. There is no single cutoff number that is used to determine possible plagiarism. This judgement is made via the report that is produced. • You must submit using a format such as Microsoft Word that can be read by Turnitin. You will not be deemed to have submitted until your file has been received and successfully scanned by Turnitin via Blackboard. • Please make use of office hours to make appointments to ask any questions about the proposals or other assessment items.
