DEGREES OF MSc, MSci, MEng, BEng, BSc, MA and MA (Social Sciences) TEXT AS DATA M COMPSCI 5096 Monday 24 April 2023 1. Question on Tokenisation. (Total marks: 20) (a) What is the difference between a word and a token? Why do text processing systems usually operate over tokens instead of words? [2] (b) Why do tokenisers differ across languages? Provide an example where a tokeniser for English may not be suitable for another language. [2] (c) Build a byte pair tokenisation vocabulary of size 8 for the text: “mississippi is pie”. Show all your steps. [8] (d) Name two advantages of byte pair tokenisation over traditional rule-based tokenisation, and two advantages of rule-based tokenisation over byte pair tokenisation. [4] (e) Consider a situation where you are building an automatic writing assistant. It aims to provide grammar suggestions for a human writer as they write. Would you use stemming, lemmatisation, and/or stopword removal in this application? Explain why or why not. [4] 2. Question on Language Models. (Total marks: 20) Consider the bi-gram language models, Model X and Model Y, defined as follows: aS〉) aa) ab) ac) bS〉) ba) bb) bc) cS〉) ca) cb) cc) E〉| ⟨0.00.0P(⟨a) E〉| 0.10.1P(⟨c) a, b, and c are the tokens for a particular language, and ⟨S〉and ⟨E〉indicate the start-of- sequence and end-of-sequence tokens, respectively. (a) For Model X and Model Y without smoothing, calculate the perplexity of the sequence “ ⟨S〉a b b a ⟨E〉”. Which model better represents the sequence? Show your work. [6] (b) Using Model X without smoothing, apply both greedy generation and beam search (with 2 beams), given the prefix: “⟨S〉a c”. The beam search should extend each beam using the two most likely tokens. Show your work, and provide the probability of each generated sequence. [8] (c) Why is smoothing often applied to language models? Which values of Model X and Model Y would most benefit from smoothing? What are the negative effects of too much smoothing? [3] (d) For an n-gram language model, what are the problems of a very small or very large value of n? How would you select the best n to use for a particular problem? [3] To decide an optimal value of n, an experiment should be set up with some held-back test data for the specific problem. 3. Question on the Factuality of Language Model Responses. (Total marks: 20) Recall that neural language models can generate responses to prompts (such as questions), but that they often make factual errors. We prompt GPT3 with various questions related to this course and provide the response. Critique each response, and where applicable, correct any factual errors present. (a) Question: Provide and explain the two probabilities that language models commonly measure. [3] GPT3 Response: 1. Perplexity: Perplexity is a measure of how well a language model predicts the next word in a sentence. It is calculated by taking the average log probability of the words in the sentence. A lower perplexity score indicates that the language model is more accurate. 2. Coverage: Coverage is a measure of how many words in a corpus (a collection of written or spoken language) are correctly predicted by the language model. A higher coverage score indicates that the language model is able to accurately predict more words from the corpus. (b) Question: Compare and contrast TF and IDF. [2] GPT3 Response: TF (term frequency) and IDF (inverse document frequency) are two methods of determining the importance or relevance of a given word or phrase within a document. TF is a measure of how often a given word appears in a document. It is calculated by dividing the number of times the word appears in a document by the total number of words in the document. The higher the TF value, the more important the word is in the document. IDF is a measure of how important a given word is in a collection of documents. It is calculated by taking the logarithm of the number of documents in the collection divided by the number of documents in the collection that contain the word. The higher the IDF value, the more important the word is in the collection. In summary, TF measures the importance of a word within a single document, while IDF measures the importance of a word across a collection of documents. (c) Question: What role does dimensionality reduction play when building dense word embed- dings from sparse word vectors? [4] GPT3 Response: Dimensionality reduction can help accelerate the training process of building dense word embeddings from sparse word vectors by reducing the number of dimensions of the input space, making it easier to work with and process. This can reduce the computational complexity of the training process, allowing more efficient and faster training. Additionally, by reducing the dimensions of the input space, the model can more easily discern the contextual information associated with each word, as the noise created by irrelevant dimensions is removed. (d) Question: Why are Hidden Markov Models used for Part-of-Speech Tagging? [3] GPT3 Response: Hidden Markov Models are used for Part-of-Speech Tagging because they are probabilistic models that can capture the probability of transitioning from one part-of-speech tag to another. This helps the model better estimate which tag is most likely given a certain context. Additionally, the Hidden Markov Model allows for the use of Viterbi algorithm, which further helps increase the accuracy of the tagging process. (e) Question: Give an example of a nested entity mention, and explain what the nested entity mentions are. [3] GPT3 Response: Example: ”The CEO of Apple, Tim Cook, announced...” Explanation: The nested entity mention in this sentence is ”Tim Cook”, which is comprised of two separate entities - ”CEO” and ”Apple”. The combination of these two entities forms the nested entity mention ”Tim Cook”. (f) Question: Explain how GPT3 is able to provide knowledge (such as the current CEO of Apple is Tim Cook) in its answers. What are two challenges of a language model encoding knowledge? [5] GPT3 Response: GPT3 is able to provide knowledge by using a large-scale language model that is trained on a massive amount of data. The language model is able to capture patterns in the data and use them to generate answers to questions. For example, it can look at data about Apple and its executives and use that to answer questions about the current CEO of Apple. Two challenges of a language model encoding knowledge are accuracy and generalizability. Accuracy refers to how accurately the language model can answer questions. Generalizab-ility refers to how well the language model can answer questions about topics that it has not seen before. • The inability to provide a source for the knowledge • The ethical problems that we are unable to check what knowledge is encoded, and its likely biases
INFOSYS 306 Digital Business and Innovation Tutorial 7 Objectives This tutorial aims to • deepen your understanding of platform challenges • help you evaluate the effects of the gig economy on employment Tasks 1. (a) Review the challenges that platforms often encounter covered in the lecture. (b) Examine the challenges that TradeMe may face and discuss what factors may help TradeMe tackle these challenges. 2. The gig economy is gaining momentum, and more and more people venture into the labour market, providing on-demand freelance work. In 2021, First Unions issued the first report on gig work (https://mcusercontent.com/d0c5542325e2a9c7c28f45e48/files/ed3c75ab-db49-2b31-1d03-c142c798c443/Gig_Work_in_Aotearoa_report_by_FIRST_Union_July_2021.pdf). a) Drawing upon either your own working experience as a gig worker or your interactions with gig workers, discuss (i) what benefits gig workers can enjoy from engaging in independent work and (ii) what stresses they face. b) Discuss the strategies that gig workers can use to ease the stresses. 3. Continue with group assignments.
Research Dissertation ECTS: This course carries 30 ECTS credits. Module description The objective of the project is to allow students to demonstrate and apply the techniques and knowledge acquired from the taught courses to a problem of real-world academic or managerial concern. To complete this module, which is worth 30 ECTS credits and is compulsory, students should: · demonstrate that they have a good knowledge of the relevant literature on their chosen topic · identify an interesting question associated with that topic and analyse this question using the techniques and tools learned, showing that they have a good grasp of the applicability of these techniques (statistical, numerical or theoretical); · present the results of their analysis in a clear and convincing manner, within the word limit of no more than 12,000 words; · show their ability to communicate their work to a broad audience via the creation of an executive summary which should be 1500 words or less and which should be in the form. of an academic article or managerial report. relation to degree The research dissertation and preparations leading up to its completion are designed to enable students to undertake individual research and provide them with an opportunity to specialise in contemporary topics in finance. Students will be able to integrate and apply ideas, theories and techniques learned from the MSc Programme taught modules. In addition, students are expected to integrate and synthesise learning gained outside the formal taught courses, such as previous learning, professional experience, and personal knowledge. Learning and teaching approach There will be a series of three one-hour seminars (dates and times will be communicated on Blackboard) on specific topics essential in supporting your data search, topic selection, literature reviews and completing your dissertations. Students are required to attend seminars and actively participate in Q&A sessions. learning outcomes Having successfully completed this module, the student should be able to: 1. Summarise and demonstrate an understanding of the relevant issues in the literature surrounding the research question. A chronological summary of the surrounding literature is not sufficient. 2. Explain the theory surrounding the research question. 3. Explain the methodology being applied and demonstrate that the methodology is appropriate to the question. 4. Present a coherent conclusion that follows correctly from the analysis. 5. Present arguments in a coherent manner and write in a clear style. 6. Present research in a logical structure, i.e. does not omit relevant material or include irrelevant material. Textbook, readings & Lectures structure Suggested readings: Relevant to All Research Dissertation Types · Babbie, E. (2010). The Practice of Social Research, 12th Edition, Wadsworth/Cengage Learning, Belmont, CA · Beins, B.C., and McCarthy, M.A. (2012). Research Methods and Statistics, Boston, Pearson · Dillman, D, A., Smyth, J.D. and Chritian, L.M. (2008). Internet, Mail and Mixed-Mode Surveys: The Tailored Design Method, 3rd Edition. London, England: Wiley. · De Fusco, R., McLeavey, D., Pinto, J. and Runkle D. (2007). Quantitative Investment Analysis, 2nd Edition, CFA Institute Investment Series, John Wiley and Sons, New York # · Flick, U. (2013). The SAGE Handbook of Qualitative Data Analysis, Sage Publications, London · Hart, C. (1998). Doing a Literature Review: Releasing the Social Science Research Imagination, London: Sage Publications Ltd. · Marschan-Piekkari, R. and Welch, C. (2004) Handbook of Qualitative Research Methods for International Business, Edward Elgar, Cheltenham · McMillan, K (2011). How to Write Dissertations and Project Reports, Prentice Hall, Harlow · Pallant, J. SPSS (2012). Survival Manual, 5th Edition, Buckingham, Philadelphia: Open University Press. · Pears, R. and Shields, G. (2010). Cite Them Right: The Essential Referencing Guide, 8th Edition. Houndmills, Basingstoke, Hampshire England: Palgrave Macmillan. · Saunders, M., Lewis, P. and Thornhill, A. (2012). Research Methods for Business Students, 6th Edition, Harlow, England, UK, Financial Times/Prentice Hall Assessment What is acceptable? The dissertation must pose and attempt to answer a specific research question. This typically takes the form. of posing and testing a hypothesis. Projects typically, but not exclusively, involve a discussion of recent literature in the chosen area around the research question, the collection and description of data, a description and application of a testing methodology, the presentation, discussion and interpretation of results and a conclusion. Other formats such as surveys and case studies are also acceptable. The replication of (not plagiarism of) other studies applied in a different geographic area, asset class etc. and the updating of previous studies are also acceptable. Analysis of a problem facing a company or industry which allows for the demonstration of the achievement of the learning objectives is also permitted. Sometimes a combination of forms may be appropriate. One type of research is not more valuable or ‘better’ than the other; choosing the research style. depends on the research question, your interests and skills. Note, the following are not acceptable types of MSc projects: · Pure literature surveys · Mere evidence that the candidate has learned a new subject · A synthesis of other writing or a piece of journalism · A mere compendium of facts and statistics · Descriptive essays · Projects unrelated to the relevant academic discipline and literature. Purely institutional topics are not permitted; by purely institutional we mean particular solutions to particular problems of relevance to a particular firm at a particular time. Topics should be chosen to ensure that they will be amenable to a broad interest. If a student is working with a firm, they must ensure that the material they submit as part of the project is suitable for public dissemination via the library or other means and is not solely of relevance to that firm at that time but rather that it addresses a more general issue. On data, it is important not to be too ambitious and to spend just enough time to collect those data which are required for the task in hand. Spend more time on analysis than on data collection and manipulation. An exception to this may be where the creation of a particular dataset, index, or measure is itself the topic chosen. It is important that you time work realistically. Things always take longer than you think. Schedule and stick to the schedule. Schedule Your draft proposal for the finance dissertation is due in by 5pm on Friday 24th January 2025. This will not be graded but provides an opportunity for you to suggest a supervisor and a topic that you are interested in and to help allocate supervisors. However, this will form. the basis of your first meeting with your supervisor and will be the grounds for a supervisor to agree to take you on, so they should be of as good quality as possible. You will not be tied to this proposal, upon meeting your supervisor you may make substantial changes to the topic before you submit your final proposal that will be graded at the end of March. The Director will allocate a supervisor to guide your research by the end of February. The choice of a supervisor depends on the availability and interests of faculty members. Students should establish contact with their allocated supervisors at an early stage, to discuss supervision arrangements. Schedule of meetings with your supervisor (minimum of 4 meetings): After submission of the draft research proposal by 5pm 24th January 2025, formal supervision starts in February, and runs until July. Some supervisors may choose to use group supervision rather than individual supervision. This increases peer learning opportunities, and has been found to be very beneficial for student learning. You should contact your supervisor to arrange these meetings. Detailed feedback will be provided once on your research proposal and each chapter of your dissertation. · 1st meeting in February: draft research proposals submitted end January to be discussed, guidance provided by supervisor. · 2nd meeting in March: refine specific research questions based on feedback received in 1st meeting, research questions should be informed by the literature review. Supervisor to give feedback on draft of dissertation proposal. Dissertation Proposal worth 20% to be submitted by 5pm Friday 28th March 2025. The proposal should be between 2000-2500 words, with a maximum of 3000 words allowed, providing a broad outline of your research question and how you propose to address it. This proposal should state the basic idea or question to be addressed in the project, what data and computing facilities will be required, whether or not these are available within the school and university, and if not available how they will be sourced, and also include an indicative timetable. The proposal should include a brief overview of the relevant literature, referenced in the author (date) format. Research Proposal Marking Guidelines (Part I) Aims and Objectives: · Research aim (a statement of what the research sets out to achieve) · Research objective(s) (the more specific research aims) · Possible research questions and/or hypotheses (each research question could have one or more hypotheses. However, there should not be more hypotheses than research questions). (Part II) Rationale and Contribution · Description of Topic · A rationale for the choice of Topic · Literature Review (Primary source: Original research from journals, articles or conferences, original materials such as historical documents; Secondary source: Evaluations, reviews or syntheses of original work; Tertiary source: Broadly scoped material put together usually from secondary sources to provide an overview, e.g. a textbook) · The contribution of the research to financial/management/public policy practice (Part III) Methodology · Research methodology · Primary (interviews, questionnaires) or secondary data (publicly available databases, such as Datastream, Bloomberg, Reuters, Worldscope) collection · Proposed subject population and sample · Data analysis (qualitative, multiple regression, cross-sectional/panel data etc) techniques · Resources, confidentiality, ethics issues etc. · Plan for Project Completion (Part IV) References The following marking scheme for the Research Proposal will be applied by supervisors: · 25% is allocated for Aims and Objectives · 30% is allocated for Rationale and Contribution · 25% is allocated for a description of the Methodology to be used · 10% is allocated for the Proposal Structure (logical, well-argued case, good references) · 10% is allocated for Style. (spelling, grammar, clarity, conciseness) Supervisors will mark these proposals on a scale 1-100, which will be converted to 20% of the final dissertation mark (out of 30 ECTS allocated for the dissertation). Between April and July you should complete the remainder of the work, the Dissertation. Note that many supervisors are away in July, so communication via email alone may be required in the final weeks. · 3rd meeting early April: supervisor to provide feedback on final research proposal submitted at the end of March and provide guidance for carrying out the proposed research and writing the dissertation. · 4th meeting sometime in May: report on progress with dissertation, receive feedback on any parts of dissertation submitted to supervisor. A first full draft of all sections of your thesis should ideally be submitted at the latest by the end of June to give time for feedback and incorporation of feedback by student before submission deadline on Friday 11th July 2025. SUBMISSION The Dissertation has a limit of 12,000 words (not including references), and must be submitted by 5pm on the 11th July 2025. In addition to the main document, an executive summary not exceeding 1,500 words must also be included. Students must keep a diary of their meetings or substantive discussions with the supervisor. This diary should not be extensive, but should record where and when the meeting took place, the main issues agreed for work, and action items arising. Submission of all parts of the dissertation, the draft proposal in January, the final proposal in March and the final thesis in July will be via blackboard on the Finance Dissertation page. The penalty for late submission is as follows; 10% for each day that the submission is late. An extension on medical grounds will only be granted after discussion with the Programme Director. Only medical certificates for the four week period prior to submission date will be considered and an extension on medical grounds will only be given if there have been valid attempts to meet the deadline as supported by the dissertation supervisor and diary of meetings. EXAMINATION The Course Director, in collaboration with the Director of Postgraduate Teaching and Learning, will assign a reviewer. This person may or may not be the supervisor. The project will be marked on the basis of the following schedule: 20% for the Research Proposal submitted on Friday 28th March 2025. 80% for the finalised project submitted on Friday 11th July 2025. The following components of the final thesis will be evaluated: · 10% is allocated for presentation and professionalism of the documents · 10% is allocated for process quality via incorporation of supervisor feedback · 10% is allocated for industrial, managerial, academic or social relevance of the work · 70% is allocated for the intellectual quality and rigour of the material
DEGREES OF MSc, MSci, MEng, BEng, BSc, MA and MA (Social Sciences) Information Retrieval (M) COMPSCI5011 Wednesday 10 May 2023 1. (a) The following corpus of documents has been processed by an IR system where stemming is not applied: Doc1: Real estate speculation is of interest. Doc2: Interest rates are increasing interest in home costs. Doc3: Students have no real interest in interest rates. Doc4: As interest rates fall, the real estate market is heating up. Doc5: The government is considering increasing interest rates. (i) Assume that the following terms are stopwords: an, as, and, are, do, in, is, of, not, the, up. Give the vector space model representations of documents Doc1 and Doc2, assuming that you are using (raw) term frequency to weight the terms. Show clearly the terms of your vectors as well as their weights. [2] (ii) Consider the following query Q: Q= interest rates Provide the vector space model representation of Q, showing both the terms as well as their weights. [1] Compute the cosine similarity between the query Q and Doc1 as well as the cosine similarity between Q and Doc2. Show your working. [2] (iii) Assume the same list of stopwords as (i) above. Construct an inverted file for all the documents of the corpus, showing clearly the dictionary and posting list components. Your inverted file needs to store sufficient information for computing a simple tf*idf term weight, where wij = tfij *log2(N/dfi). [5] (iv) Assuming the use of a best match ranking algorithm, rank all documents of the corpus using their relevance scores for the following query: real estate interest Show your working. Note that log2(1.5)= 0.3219, log2(1.6666)= 0.7369, log2(2.5)= 1.3219 and log2(5)= 2.3219 (you may not need all of these). [3] (b) Consider the following non-interpolated recall-precision graph, showing the performance of an IR system on a given query Q. For this query Q, the IR system has returned 20 documents. Assume that Q has 16 relevant documents in the ground truth, not all of which have been retrieved by the system for the query. (i) Compute the interpolated precision values for this query Q. Show your working. [4] (ii) The IR system has returned 20 documents ranked from rank=1 to rank=20. In the tables below, indicate if the corresponding document at that rank was relevant (R) or non-relevant (X). [2] 121314151617181920
Machine Learning & Artificial Intelligence for Data Scientists COMPSCI5100 Thursday 12 December 2024 Question 1: Regression (Total marks: 20) Consider using regression to predict the birth rate in the US using the data shown in the following figure: Figure 1.1 Birth rate (per 1000)from 1909 to 2008 (a) Consider fitting the data with a polynomial regression of order 10. Identify the numerical issue with model fitting and propose a solution with sufficient details [4 marks] (b) Consider fitting the data with a polynomial regression of order 2, identify the two regions of most likely poorly fitted data points and explain why. [6 marks] (c) Consider fitting the data in Figure 1.1 with a liner regression model with the sigmoid basis function: Explain the choice of hyperparameter μk (mu_k) and s that could lead to the following fitted model [4 marks] Figure 1.2 A liner regression model using sigmoid basis function fitted to the data (d) We used two fitting strategies, namely ridge regression and lasso, and obtained the following fitting models in Figure 1.3 A and B. Identify which fitting strategy is used in each figure and explain why and how the chosen fitting method could have generated the result. (note, each method is used only once). [6 marks] Figure 1.3 A Figure 1.3 B Question 2: Classification (Total marks: 20) a) Assume the following training data in the two-dimensional plane of x1 and x2 is available (Figure 1). The target variables for the points in the red and blue are +1 and -1. We summarise the data as the following tuples: , , , and , respectively. Figure 2 i. Design a k-NN classifier with k=1 and use it to determine the class variables C1 through C4 for the following test data points: , , , and : [4 marks] ii. What would be the class variable C4, if we had used k=3? [2 marks] iii. Write down the equations that specify the decision boundary between the two classes. [4 marks] b) In the same data set in Figure 1, we apply a linear SVM model with the predictor y(x1,x2) for classification. i. Which data points are the support vectors? Write down the equation for y(x1,x2). (Hint: First visually assess the data to determine the decision boundary and the support vectors. Observe the constraints for the margin and SVM classifier.) [ 6 marks] ii. Specify the Lagrange multipliers α1, α2, α3, α4 for each of the data points in the training data (2,0), (0,2), (-2,0), and (0,-2), respectively. [4 marks] Question 3: Unsupervised learning (Total marks 20) Consider using the K-means algorithm to perform. clustering on the following scenario Figure 3.1 A. We expect to form. two clusters as shown in Figure 3.1 B. Figure 3. 1 A: Original Data Figure 3.1 B Expected Clusters (a) Outline what would happen if we directly apply K-means with Euclidean distance to this data. Can it achieve the clustering objective? How will it split/group the data and why? [3 marks] (b) An alternative approach is to use Kernel K–means. Would kernel K-means could help in this dataset and why? [2 marks] (c) An alternative approach is to use mixture models. Would mixture models help to better classify this dataset than K-means and why? [3 marks] (d) The plot in Figure 3.2 shows some 2D data. PCA is applied to this data. Explain how the first principal component would look if it is overlaid on the plot. Explain your reasoning. (Note: there is no need to make a drawing. You can provide a description of the shape based on the coordinate system provided in the original figure.) Figure 3.2 2D Points [2 marks] (e) Similar to the previous question, explain what the second principal component would look like and why. (Note: there is no need to make a drawing. You can provide a description of the shape based on the coordinate system provided in the original figure.) [2 marks] (f) Describe the four-step process you should use to determine the number of clusters in Kernel K-Means. (Hint: Each step gets a mark.) [4 marks] (g) Describe two approaches you could take to managing the curse of dimensionality in, for example, genetic data. For example, how would you overcome this if you had a high-dimensional dataset with thousands of genetic features but only hundreds of subjects? [4 marks]
404050: Integration and Infinite Series MATH-XL 31B Summer 2025 Section 1 4 Credits 06/26/2025 to 08/28/2025 Modified 02/24/2025 Description This course covers transcendental functions, methods, applications of integration, sequences and series. Transferable for UC credit. Institutional Policies Student Conduct Students are subject to disciplinary action for several types of misconduct or attempted misconduct, including but not limited to academic dishonesty, such as cheating, multiple submission, plagiarism, or knowingly furnishing false information to the University; or behavioral misconduct, such as theft or misuse of the intellectual property of others, harassment, or disruption of the learning environment. Students are encouraged to familiarize themselves with the Student Rights & Responsibilities Policy (https://www.uclaextension.edu/student-services/rights-and-responsibilities) and to report concerns. 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Mobile Cultures MDIA2091 Assessment One: Essay & dateFeedbackFPCDHD
Project Part A Single Player Freckers COMP30024 Artificial Intelligence March 2025 1 Overview In this first part of the project, you will write a program to play a simplified “single player” variant of Freckers. Before you read this specification, please make sure you have carefully read the entire ‘Rules for the Game of Freckers’ document on the LMS. Although you won’t be writing an agent to play against an opponent just yet, you should be aiming to become familiar with the game rules, board layout and corresponding coordinate system. The aims for Project Part A are for you and your project partner to (1) refresh and extend your Python programming skills, (2) explore some of the algorithms you have encountered in lectures, and (3) become familiar with the Freckers game environment and coordinate system. This is also a chance for you to develop fundamental Python tools for working with the game: Some of the functions and classes you create now may be helpful later (when you are building your full game-playing program for Part B of the project). We will be using Gradescope as the testing environment when assessing your code. You can (and should) regularly submit to Gradescope as a means to get immediate feedback on how you are progressing. The autograder is already equipped with a couple of “visible” test cases for this purpose. See the Submission section at the end of this document for details. Both you and your partner are expected to read this specification in full before commencing the project, then at your earliest convenience, you should both meet up and come up with an action plan for tackling it together (see Section 4.2 for our expectations regarding teamwork). 1.1 Single player Freckers Single player Freckers is a simplified version of the two-player game whereby you play as Red. The game starts with an arbitrary configuration of lily pads and Blue frogs (Blue does not move). The goal for Red is to get a single frog to the final row of the board (r = 7) in as few actions as possible. There are a number of assumptions you should make when solving this problem: 1. In the given initial board state, there are six Blue frogs arbitrarily placed on the board, as well as zero or more lily pads. A single Red frog is placed in the first row (r = 0). 2. The cost of a “solution” to this problem is defined as the number of actions that must be played by Red to get their frog to the other side of the board. 3. If there is a tie, that is, there are multiple sequences of actions that attain a win for the same minimum cost, then any such sequence is considered an optimal solution. 4. There is no need to worry about turn limits or draws. Optimal solutions for the single player variant of the game should not come anywhere near 150 actions. 5. GROW actions are prohibited – that is, all actions that comprise a minimal cost solution must be MOVE actions. This means it is possible for there to be no solution to certain instances of the problem (i.e., if there are no lily pads that facilitate a path to the other side). 1.2 Your tasks Using a search strategy discussed in lectures, your tasks are to: 1. Develop and implement a program that consistently and efficiently identifies the shortest sequence of actions required to win, given an arbitrary (valid) board state as its “input”. 2. Write a brief report discussing and analysing the strategy your program uses to solve this search problem. These tasks are described in detail in the following sections. 2 The program You have been given a template Python 3.12 program in the form. of a module called search. Your task is to complete the search() function in program.py. You may write any additional functions/classes as needed, just ensure any additional .py files are kept within the same module. When completed, search() should return a list of MOVE actions denoting the lowest cost win sequence, given an initial board state as input. Note that we have already supplied the necessary input/output code (in __main__.py), so you don’t need to worry about this. To streamline the running of test cases, you can use < to redirect a .csv file to the program via the standard input stream. More information regarding the test cases is provided in Section 2.5. Before continuing, download the template and follow the “Running the template code” guide on the assignment LMS page. Once your local development environment is set up, try running the command python -m search < test-vis1.csv 2.1 Program inputs The search(...) function is given a Python dictionary as input, denoting the initial state of the board. Dictionary entries have the form. Coord(r, c): CellState, where: • r and c denote the coordinate on the board for a cell, (r, c) • CellState is an enum which can be either: – CellState.RED (cell contains a Red frog), OR; – CellState.BLUE (cell contains a Blue frog), OR; – CellState.LILY_PAD (cell contains a lily pad) This allows you to retrieve the state of a cell using a coordinate structure instance Coord(r, c) as a key. Just keep in mind that not all cells are necessarily occupied (the dictionary is a sparse representation), so check that the key exists before using it. The Coord and CellState types are defined in the provided core.py file. You are welcome to reuse types defined in this file anywhere in your solution, but please don’t modify them. For the purposes of assessment, you may assume that any given input coordinates and cell states will be valid (i.e., within valid bounds and taking one of the states listed above). 2.2 Program outputs The search(...) function must return a sequence of MOVE actions that form. an optimal (minimal cost) solution to the given problem. This should be represented by a list of structures, each item having the form. MoveAction(Coord(r1, c1), [Direction.d1, ...]). Notice that a MoveAction accepts an arbitrary number of Direction objects via its constructor. This is used to describe a series of jumps, if needed. A move to a neighbouring cell (or a single jump) can be represented by a single Direction. The skeleton code comes with a hardcoded example illustrating these scenarios (it is also a solution to the first provided test case). If there is no way for Red to win for a given input scenario, you should instead return None from the function, not an empty list. Please take care when printing anything to “standard output” as this is used for printing the actual result. All lines beginning with $SOLUTION will be taken to be part of the final action sequence (see __main__.py in the template). 2.3 Efficiency While correctness and optimality matters first and foremost, you should also consider efficiency when designing your solution (think about different input scenarios as discussed in Section 2.5). Consider profiling your solution and compare approaches in practice before assuming a theoretical optimisation actually provides a noteworthy efficiency improvement. Similarly, be sure to consider the performance and memory usage of data structures you utilise. Constant factors can and do matter in some instances. You are welcome to use any data structures that come with the template code as part of your solution, but do so critically and ensure they are appropriate for what you are trying to achieve. 2.4 Example See Figure 1 for an example solution to the test-vis1.csv test case which comes with the template code. You might notice there are alternative action sequences Red could have taken to win in the same number of moves. If your solution happens to compute a different sequence of the same (minimal) cost, that’s perfectly fine. Take a look at the given template code and you’ll see this same solution currently “hardcoded” into the search() function. Obviously you should write code to solve the actual problem yourself but the given example should provide clarity around the structure of the output. (a) MOVE(0, 5) [DOWN] (b) MOVE(1, 5) [DOWN-LEFT] (c) MOVE(3, 3) [LEFT] (d) MOVE(3, 2) [DOWN, RIGHT] (e) MOVE(5, 4) [DOWN] (f) MOVE(6, 4) [DOWN] Figure 1: Example solution visualisation for test-vis1.csv. In this example, it is possible for Red to win in six moves. 2.5 Testing Thorough testing of your work is critical in order to ensure that your program functions as expected across a wide array of possible inputs. Testing also helps identify bugs early on during development. To help get you started, we have provided two sample test cases in the template, which are also live on Gradescope: • test-vis1.csv – a test case with an optimal solution comprising three actions. A solution to this test has already been illustrated in Figure 1. • test-vis2.csv – a test case where there is no possible solution. If you open these files, you will see that the initial 8 × 8 board state is represented as 8 lines of 8 comma-separated characters. Each character is either a “blank” (an empty cell), an R (Red frog), a B (Blue frog) or a * (a lily pad). This file format is already parsed for you in the __main__.py file that comes in the template. It is a good idea to study this file to see how this works, but it should not be modified. You can and should write your own tests. The given two cases cover distinct input scenarios but are not “exhaustive” in their own right. 2.6 Gradescope To help you check that your work is compatible with our marking environment, both of the supplied tests are also live on Gradescope (where you’ll submit your project) and can be run at any point before the deadline. Whenever you make a submission, both tests are automatically run on the spot, and the resulting outputs are compared against our sample solution outputs. You should periodically submit to Gradescope to confirm that your work is compatible with our marking environment. There is no penalty for multiple submissions prior to the deadline, nor is there any hard limit to how many you can make. By default, only the most recent submission will be marked. When we mark your work, a number of “hidden” tests of varying difficulty will be run in the exact same environment on Gradescope. Three of these (in addition to the two provided/visible cases) will be semi-visible before the deadline, that is, you will be able to see an estimated mark for each but without specific feedback. Each test case (hidden or not) will be given a maximum execution time of 30 seconds before being forcibly terminated. 3 The report You must also briefly discuss your approach to solving this problem in a separate file called report.pdf, submitted alongside your program. Your discussion should address the following: 1. With reference to the lectures, which search strategy did you use? Discuss implementation details, including choice of relevant data structures, and analyse the time/space complexity of your solution. 2. If you used a heuristic as part of your approach, clearly state how it is computed and show that it speeds up the search (in general) without compromising optimality. If you did not use a heuristic based approach, justify this choice. 3. Imagine that all six Red frogs had to be moved to the other side of the board to secure a win (not just one). Discuss how this impacts the nature of the search problem, and how your team’s solution would need to be modified in order to accommodate it. Your report can be written using any means but must be submitted as a PDF document. Your report should be between 0.5 and 2 pages in length, and must not be longer than 2 pages (excluding references, if any). The quality and readability of your report matters, and marks won’t be given where discussion is vague or irrelevant to topics discussed in the subject. 4 Assessment Your team’s Project Part A submission will be assessed out of 8 marks, and contribute 8% to your final score for the subject. Of these 8 marks: • 5 marks will be for the correctness of your program, based on running your program through a collection of automated test cases. The tests will run with Python 3.12 on Gradescope. Programs that do not run in this environment will be considered incorrect. There will be a 30 second time limit per test case, and credit will not be awarded for tests where a timeout occurs. All test cases will be solvable by our sample solution well within this time limit. You can minimise the risk of incompatibilities by submitting to Gradescope early and often. You may re-submit as many times as you like, so make sure you take advantage of this. As discussed previously, you should write your own tests in addition to the two given tests, as they don’t cover all input scenarios. • 3 marks will be for the clarity and accuracy of the discussion in your report.pdf file, with 1 mark allocated to each of the three points listed above. A mark will be deducted if the report is longer than 2 pages or not a PDF document. Your program should use only standard Python libraries, plus the optional third-party library NumPy (this is just for extra flexibility – use of NumPy is not required). With acknowledgement, you may also include code from the AIMA textbook’s Python library, where it is compatible with Python 3.12 and the above limited dependencies. 4.1 Code style/project organisation While marks are not dedicated to code style. and project organisation, you should write readable code in case the marker of your project needs to cross-check discussion in your report with your im-plementation. In particular, avoid including code that is unused. Report marks may be indirectly lost if it’s difficult to ascertain what’s going on in your implementation as a result of such issues. 4.2 Teamwork This project is to be completed in teams of two. Both you and your partner are expected to contribute an equal amount of work throughout the entire duration of the project. While each person may focus on different aspects of the project, both should understand each other’s work in full before submission (including all code). Both partners are also expected to be proactive in communicating with each other, including meet-ing up early in the process and planning ahead. There will inevitably be deadlines in other subjects for one or both of you, and you’ll need to plan around this (extensions won’t be granted on this basis). Ensure that you set up regular ongoing meetings so that you don’t lose track of what each person is doing. We recommend using a code repository (e.g., on GitHub) to collaborate on the coding portion of the project. For the report, you may wish to use cloud based document editing software such as Google docs. This not only assists with keeping your work in sync and backed up, but also makes “auditing” easier from our end if there ends up being a dispute over contributions. Where there is clear evidence that one person hasn’t contributed adequately, despite their partner acting in good faith to collaborate with them as equals, individual marks will be awarded to better reflect each person’s work. In the event that there are teamwork issues, please first discuss your concerns with your partner in writing comfortably before the deadline. If the situation does not improve promptly, please notify us as soon as possible so that we can attempt to mediate while there is still time remaining (an email to the lecturers mailbox will suffice). 4.3 Academic integrity Unfortunately, we regularly detect and investigate potential academic misconduct and sometimes this leads to formal disciplinary action from the university. Below are some guidelines on academic integrity for this project. Please refer to the university’s academic integrity website or ask the teaching team, if you need further clarification. 1. You are encouraged to discuss ideas with your fellow students, but it is not acceptable to share code between teams, nor to use code written by anyone else. Do not show your code to another team or ask to see another team’s code. 2. You are encouraged to use code-sharing/collaboration services, such as GitHub, within your team. However, you must ensure that your code is never visible to students outside your team. Set your online repository to ‘private’ mode, so that only your team members can access it. 3. You are encouraged to study additional resources to improve your Python skills. However, any code adapted or included from an external source must be clearly acknowledged. If you use code from a website, you should include a link to the source alongside the code. When you submit your assignment, you are claiming that the work is your own, except where explicitly acknowledged. 4. If you use LLM tools such as ChatGPT, these must be attributed like any other external source – you should state exactly how you’ve used them in your report (under “References”). Technology to detect use of such tools is constantly evolving, and we will endeavour to use what is available come marking (or even retrospectively) to detect dishonest use of it. We do, however, believe such tools can be useful when used in the context of proper understanding of a subject area – in short, use them responsibly, ethically, and be aware of their limitations! 5 Submission The deadline is 11:00PM on Monday 7 April, Melbourne time (AEST). You may submit multiple times, but only the latest submission will be marked. The procedure for submission via Gradescope is similar to that of the “Project Team Member Nominations” submission, however in this case you are submitting multiple files. You must include your (unmodified) team.py file in the top level directory of your submission so that your team can be correctly identified. Note that only one team member needs to submit the final work. Once submitted, they must then add their partner to the submission within the Gradescope interface (top right corner). If both team members submit individually we will randomly pick one of the submissions to mark and link team members based on information in the team.py file. Projects won’t be remarked if the wrong one was picked in such a scenario (i.e., if one was an old submission). Here’s how the file tree for your submission should look: / team.py .............................. The exact same file as the original you submitted report.pdf ............................ Your report for this assignment (must be a PDF) search ......................................... Your Python module for this assignment main .py...........................The original file from the template unmodified core.py..............................The original file from the template unmodified program.py ....................................... You may have other .py source files (optional) ......................................You can include other files like test cases (optional) If you create a .zip archive with this structure internally, you should be able to “drag and drop” it into the Gradescope “upload” box, and this will preserve the directory structure. Alternatively, if you have set up a GitHub repository to collaborate with your project partner, this can be an efficient way to streamline submissions via Gradescope – again, ensure the same directory structure is used inside the code repository as shown above. You may submit multiple times on Gradescope, and you are in fact strongly encouraged to do this early and often in order to test your work in the assessment environment. If you do make multiple submissions, we will mark the latest submission made. Late submissions will incur a penalty of one mark per working day (out of the 8 total marks allocated for this part of the project). Extensions If you require an extension, please email the lecturers using the subject ‘COMP30024 Extension Request’ at the earliest possible opportunity. If you have a medical reason for your request, you will be asked to provide a medical certificate. Requests for extensions received after the deadline will usually be declined.
1Assessment brief Overall Context: As a cloud and virtualization security expert, you're responsible for assessing and strengthening the security of a prototype containerized cloud system. You have been provided with prototype that is intended for deployment within the company's existing Docker production setup. The development team has expertise in system development but lacks experience in virtualization security. Your task is to create a detailed report presenting your evaluation results and security recommendations for the client, a company that develops cloud-based solutions. This report will: •Provide recommendations on how they could improve the security of a system, using a technical case study of their work as an example. TASKS: 1.Define up to 10 test cases to verify the functionality and security of the containers and application. 2.Refine supplied Dockerfiles and any custom files, excluding those from official repositories, to create hardened images capable of running the application. 3.Generate hardened images from the refined Dockerfiles. 4.Determine runtime commands for running containers from the images on the production team's chosen system, ensuring maximum security. Consider commands for: a.one-time configuration b.running the container each time 5.Verify the application's correct functionality against the test cases when applying the proposed runtime security to the containers.
GGRA30: GIS and Empirical Reasoning Creating Portable GIS Projects Technical Memo Fall 2024 1 Introduction: GIS Project Components Unlike software with which you may be more familiar, such as Microsoft Word and Excel, typical use of GIS does not store all of your data in one file. Instead, specific geographic data sets, such as informa-tion about roads in your area, are often useful for many purposes and you would not want to have to extract them from an earlier project every time you needed road information for a new map. Because of this flexibility in the use of geographic data, the difficulty involved in maintaining accurate data, and the cost that comes from the effort involved in maintaining and storing geographic information, GIS software tools have been designed to place high value on being able to connect a variety of data sources and formats together. As shown in Figure 1, a typical GIS project can often connect and orga-nize data from a number of sources and can produce data that will be stored in equally diverse output data locations. Once you realize this, an obvious implication is that it is important to develop some understanding of where data is stored and what you are updating when you work on your GIS project. If you do not understand this then you may find yourself wondering “where did my data go?” after you have been working on a project for a while, especially if you find yourself using multiple computers while working on GIS course assignments (e.g., both at home and at campus or using different campus lab computers). In university GIS courses, you will often be provided with data as a starting point for your assign-ments and you may well find that you use a particular data set only for one assignment (unlike what I Figure 1: GIS organize a number of information sources and outputs as configured by the settings for a particular project. A flexible approach that requires the user to be organized and keep track of where and how data is stored. wrote about the frequent usefulness of a regional road layer above, for example). Therefore, to make your studies as simple as possible, I propose a method of organizing your work on each of your GIS assignments, tutorials, and projects to keep all of your project data and configuration information to-gether. This should help you to avoid losing your data and having to start a project or assignment over. With this objective in mind, first, what are the project components shown in Figure 1? GIS Software The computer application that you use to display, edit, organize, print and manage ge-ographic data about all kinds of things that interest you in the world. For this week’s tutorial and your current assignment, this is QGIS. Project Configuration The combination of geographic data about a region and how you are using a particular set of software applications to organize, edit, and display that data is commonly called a project. When you carry out such a project, you make a dizzying number of decisions and those must be remembered or else you will have to make them again and again and again. . . These decisions (often referred to as configurations or properties) are stored in a file that details what data your project is using, where that data is stored, and how you want that data to be organized and displayed in your maps or other visual summaries. Typical project configuration decisions could include: • What region does your map show? • What data layers are included in your map? • Where is the data stored for each of your layers and in what format? • What colour(s) do you want to use to display each data layer (e.g., the roads)? • How are roads labelled on my map? • What size page am I planning to print my map on? • What is my map title? As you can see, many of these decisions / properties / configurations describe how geographic data is being used within this project. The answers to these questions will change for other map-ping projects without changing the fundamental properties or locations of geographic phenomena such as a particular road’s location or its name. Geographic data These are data about the location and characteristics of phenomena within the world. For example, the location of a road along with it’s name, address ranges, traffic rules (i.e., is it one-way or two-way), speed limit, etc. These can be any attributes of the particular phenomena being modelled and the exact set of attributes recorded may depend to some extent on the objectives of the people or organization that created the data. Although you may be more concerned about some of the attributes stored for these real world entities than others (i.e., perhaps you care about address ranges but not the direction of traffic flow for these roads), your use of the data in a par-ticular project would not require you to change roads attributes or locations stored in the data unless you were actually changing the roads in the real world. Geographic data are then information about the world that can be used for multiple purposes and are generally expected to reflect the situation of some phenomena in the world at a particular time, independent of how you might choose to use that data. Section 2 describes a way to organize a GIS project to keep the configuration and the geographic data together, well organized, and portable. By portable, I mean that your project when organized as suggested can be moved from one computer to another (e.g., from a campus computer to your laptop or home computer) without all of your geographic data “disappearing”. 2 Setting Up a Portable QGIS Project The procedure outlined below will create a folder for working on your current project and named ap-propriately so that, as a general guideline, a few weeks from now you could look at the folder name and be able to tell what that folder contains. Examples could be GGRA30_Assign1 or GGRA30_Practi-cal6. To reiterate, the purpose of the recommended folder structure is that you will have one folder that contains everything for this work, it will be easy for you to move this folder around with you (e.g., copy to USB key or cloud storage) and continue working without losing data with which you have been working, and later you’ll be able to recognize why you have this folder. 1. Create a new folder. If you are working on a campus computer, creating a folder in Downloads or on the Desktop works well. Name it appropriately, as described above. For the remainder of these instructions, I will refer to this as “GGRA30_SampleProject”. 2. Create a new folder called data inside GGRA30_SamplePro-ject. This will hold any data provided for you and any new geographic data or other files (e.g., exported PDFs of maps) you create while working on your project. 3. Start QGIS. Once the program has started, select Project | Save as. . . (yes, immediately before you do anything else). With the file browser presented, navigate to your GGRA30_- SampleProject folder, assign an appropriate name and save the project file. The file just created with the “.qgs” or “.qgz” exten-sion is the project config-uration file described in Section 1 and shown in Figure 1. 4. As you create data layers while you are working on your project, ensure that you save them back into the data folder created inside GGRA30_SampleProject. This will keep all of the project data together, while not cluttering the top-level project file so you can easily find and reopen the main QGIS project file when you need to. Generally, you want to access the data files created by a GIS project using the GIS software so the project configuration file is your usual starting point for working with the data as well. • Note that the first time you save a data file while working on a project, QGIS may default to its installation location as the first suggested folder (and you’ll have to navigate to where you really want to save the file). But once you save a file it should then offer the last folder you used as the starting point for saving other items. Whatever you do and whatever the software does, pay attention to where you save your project and data to make sure you can find it later. • The folder structure described here is just that: a structure. To ensure that all of your data is kept together, you need to pay attention to how you store data and results and develop the habit of using the storage structure to reduce confusion in your work. 5. You should now have a project folder organized as shown in Figure 2. Work on your project, saving the main project file periodically when you think of it and when things seem to be going well. 6. At the end of your working session, remember to save a copy of the GGRA30_SampleProject folder to a safe storage location (USB key, Dropbox, Google Drive, etc.). As you become more practiced with this procedure, you should use your safe storage location as a backup for your current work and as a known working checkpoint copy. • Only copy over your backup version of the project if you know that you are replacing it with a new version that represents “progress” toward your completion of the project. • If you have had some problem while working and the current version you are working on seems to be “broken” somehow, consider not keeping it and reverting to the previously stored version of the project that is hopefully not broken. • Even though the decision to not save a new backup probably means you will be redoing some work, hopefully from a known good checkpoint, replacing a good backup with a bro-ken update is almost never a good idea. Figure 2: QGIS project file and folder organization. Although this memo purports to offer you a useful GIS project organization scheme, it really offers you one of many such possibilities along with advice to develop work practices that will allow you to take advantage of any reasonable organization scheme. The clarification between geographic informa-tion and GIS project configuration is also very useful and you would do well to ensure you understand the difference.
ELEC4310: Power System Analysis Assignment 1 Transmission Line Characteristics & Line Compensations Submission due date: 4 pm Friday 21st March 2025 A1-Transmission line calculation (25 Marks) Consider a 345kV, 150km, 50Hz three phase transposed overhead lossless transmission line, which is made up of three phase conductors with configuration shown in Figure 1. The sub-conductor diameter and GMR is 1.5cm and 1.2cm respectively. Considering a long transmission line, determine the followings. [Note: Please refer to Lecture 2 Materials to find theories related to calculations] Figure 1. Line configuration for A1 (1) Phase constant β, surge impedance Zc, velocity of propagation vp, and the wavelength λ of the line.[Note: Ideally vp should be closer to the speed of light ] (8 marks) (2) The ABCD parameters of the line. (5 marks) (3) Current, real and reactive power delivered at the sending end, the percent voltage regulation of the line, and the power factor at the receiving end if the receiving end absorbs 1200 Mw (|Vs| = 1.0 p.u., |VR| = 0.95 p.u.). (10 marks) (4) Load impedance to maintain |Vs| |VR|. (2 marks) P1 PSSE simulation (45 Marks) Write a brief report for the following questions. You MUST show your graphic results clearly in your report. Make sure you have indicated the necessary parameters. We will check both your PSSE screenshots and PSSE*.sav and *.sld files to verify the results. If the results do not match, you will not get credit for your submission. Based on the line parameter in A1: (1) Use PSSE to verify your calculation in A1 (4). (Hint: Consider Surge impedance loading) (10 marks) (2) For the original uncompensated line in A1 (Assume IVsl = 345 kV), answer the following questions using PSSE, if the load is 1200 MW at unity power factor: (a) What is the magnitude of the receiving end voltage? (3 marks) (b) What can we do to maintain IVRl=0.98 p. u.? What kind of compensation is needed? What is the value of the compensation in MVar? Use PSSE to justify your results. (6 marks) (c) In the original uncompensated network, increase the load, and investigate the maximum real power that can be delivered to pure resistive load. Validate your results using theoretical calculation and PSSE results. (6 marks) (d) Design a series compensation scheme in PSSE to enhance the readability of the original uncompensated line in A1. Explain with justification in relation to P1 (2c). (10 marks) (3) For the original uncompensated system in A1, if the load is 900 MW at 0.95 power factor lagging. Design a shunt compensation scheme (valued in MVar) in PSSE to improve the power factor to above 0.98, while maintain the receiving end voltage within the range of 0.96 p.u. ≤ |VR| ≤ 1.02 p. u.. (10 marks) P2 PSSE-Python API investigation (30 Marks) [Note: Since PSSE Xplore 34 is a 32-bit software version, you need to use a Python version, which should be a 32-bit version, too. Therefore, using Python 2.7 (32-bit) (default installation with PsSE Xplore 34) as your default Python IDLE throughout this course is recommended.] For P2(1), please provide P2_1.py, P2_1.sav, and P2_1.sld. For P2(2), please provide P2_2.py and P2_2.sav. The functions used in psspy API include and are not limited to: psseinit, newcase_2, case, save, bus_data_4, branch_data_3, machine_data_2, load_data_5, switched_shunt_data_3, plant_data_3, fnsl, abusreal, aswshreal. (1) Build a system with a Python script. and run power flow solution to verify your calculation in A1 (4). Save the built case in P2_1.sav by coding in the Python script. P2_1.py. After running the script. file P2_1.py, open P2_1.sav with PSSE and develop the network in a slider file P2_1.sld via the AutoDraw function in PSSE. Arrange the buses and branches in a readable manner, take a screenshot and include it in your report. (20 Marks) (2) Write a Python script. P2_2.py to open P2_1.sav from P2(1) and change the load to 900MW at unity power factor. Add a shunt compensation scheme through Python codes to keep the receiving end voltage at 1.00. Design a fully automated process to determine and display the MVar value. (10 Marks) IMPORTANT SUBMISSION INFORMATION (a) Case files (.sav), the corresponding diagram files (.sld) and Python script. files (.py) should be submitted in order to achieve a valid submission. Each case file should be named appropriately, with names identical to their corresponding diagram file. For the files to be provided, please read P1 & P2 requirements detailed below. (b) All case and diagram files should be compressed into a single zip file, named "########_PSSE_files', where ######## is your 8-digit student number. (c) Your PSS/E report must be submitted to Blackboard directly in PDF version. The report itself should NOT be included in the zip file. (d) Make sure you have answered all the questions. (e) This report should be no more than 15 pages (one sided only, Times New Roman font size 12, single line spacing with 2cm margin on all sides). Appendix does not count. You should place tables and graphs into the body of the report. All figures, tables and codes presented in the report are counted in the page limit. (f) Report must include concise summary of results for each section, along with comments and dis cussions. It could be as short as one sentence or long as a paragraph. (g) Place figures as close as possible to where you refer them to in the context.
ARTHIS 151C: FINAL STUDY GUIDE (Complete!) Wednesday, December 11, 8:00-10:00am: please show up on time! You will need an exam booklet. The final will consist of three sections: 1. slide identification section: in which you must identify five images by artist, title, date, medium (10 minutes, 10%) 2. Two slide comparison essays, in which you identify and write a brief essay comparing two images in response to a prompt (20 minutes each, 50%) 3. One long essay question on a major theme of the class (drawn from readings and lecture): I will provide 3 essay topics a week before the exam, and one topic will appear on the exam (30 minutes, 40%) 4. Extra-credit question (worth three points if fully answered): What art work that we’ve seen in class has the most meaning for you? Write a short paragraph (5-6 sentences) on why. This is not a research question and your response may include personal reasons! II. Long essay topics: Prepare the three essay topics below; one will appear on the exam. You should base your exam on your knowledge of the material from the lectures and readings. For each essay, you must have a thesis, define your terms, and discuss at least three examples of art in some detail. The essay should be of a reasonable length for 30 minutes 1. Discuss how and why Mao Zedong is represented in art works after 1949. Choose three examples of representations of Mao, and examine the choices made in and functions of these portraits and depictions of Mao. At least one of your example should date to post-1976. 2. Discuss ideas of authorship and the identity of the artist after 1949. Choose three and consider who the maker of the art work is and their part in defining the meaning of the art work. What is the relationship that they establish with the audience? 3. Discuss the emergence of new forms of experimental art post 1976. Choose three examples and explain how they break away from forms of art produced earlier in subject? Style? Format and medium? Influence? Audience? The images for which you are responsible are listed below. You will find the actual images on the Powerpoints on Canvas; I also include a few phrases or terms that relate to each image. Chairman Mao and the Cultural Revolution 1. Sun Zixi (b.1929), In Front of Tiananmen, 1964, oil on canvas Great Leap Forward 2. Fu Baoshi (1904-65) and Guan Shanyue (1912-2000), This Land So Rich in Beauty, 1959, ink and color on paper Great Hall of the People, Beijing, 18 x 29 feet, Mao, “Ode to Snow” poem 3. Zhang Zhengshi, Portrait of Mao, 1952, oil on canvas “Standard portrait,” Tiananmen gate, magnifying, substituting, masking, detachment 4. Liu Chunhua, Chairman Mao Goes to Anyuan, 1967, oil on canvas “model work,” “Mao Zedong’s Thought Illuminates the Anyuan Workers’ Movement” exhibition, Liu Shaoqi, collective art 5. Lin Yong (b.1942), The Spirit of Yan’an Shines Forever, 1971, ink and color on paper Three Prominences, hong-guang-liang: red, bright, shining; Jiang Qing, Yan’an, cult of personality 6. Ye Yushan (b.1935) and others, Rent Collection Courtyard, clay, 1965 “model work,” Sichuan Academy of Fine Arts, Liu Wencai, collectivism, Landlord Manor Museum 7. Anonymous, Destroy the Old World, Forge the New World, 1969, poster Four Olds, Red Guard, Cultural Revolution 8. Shen Jiawei (b.1948), Standing Guard for Our Great Motherland, 1974, oil on canvas Sent down youths/rusticated youths, Wusuli River, Heilongjiang, worker-peasant-solider art 1970s 9. Chen Yifei (1946-2005) and Wei Jingshan (b.1943), The Taking of the Presidential Palace, 1977, oil on canvas 1942 People’s Liberation Army victory; Museum of Military Affairs; realism, history painting; Shanghai Academy of Painting 10. Cheng Conglin (b.1954), X Day X Month 1968/A Snowy Day in 1968, 1979, oil on canvas New Realism/Scar Art [1978 Scar, short story by Lu Xinhua], Sichuan Academy of Fine Arts, October 1979, Chengdu 11. Luo Zhongli (b.1948), Father, 1980, oil on canvas Stream of Life painting/Rustic Realism, “My Father”, National Youth Exhibition, Sichuan Academy of Fine Arts 12. Zhang Wei, Hall of Supreme Harmony, 1976, oil on pasteboard, 19 x25 cm. Wuming (No Name) Society, underground art 13. Wang Keping, Idol, 1979, wood The Stars (Xingxing), 5th National Art Exhibition, National Art Museum of China [NAMOC], dissident art 14. Zhang Qun and Meng Luding, A New Era: Revelation from Adam and Eve, 1985, oil on canvas Zhejiang Academy of Fine Arts, Adam and Eve, New Era, mise-en-abyme, Surrealism (Salvador Dali) 1980s: 85 Art New Wave, Experimental and Avant-garde Art 15. Huang Yong Ping, “The History of Chinese Art” and “A Concise History of Modern Art” After Two Minutes In the Washing Machine, December 1, 1987, 1987, installation (paper pulp, glass, wood) Xiamen Dada, Anti-Art, installation art, conceptual art, iconoclasm, censorship, authorship? 16. Zhang Peili, X?, 1987, oil on canvas Zhejiang Academy of Fine Arts, Hangzhou, conceptual art, Exhibition proposal: Procedure for “Act First and Shoot Later: About ‘X?’”, Meishu magazine, anti-art 17. Geng Jianyi, The Second State, 1987, oil on canvas Song Baoguo, Zhejiang Academy of Fine Arts, grisaille, emotions 18. Xu Bing (b. 1955), Book from the Sky, 1987-1991, installation National Art Gallery of China, conceptual art, string-bound book, audience 19. Fang Lijun, Series 2, No. 2, 1991-92, oil on canvas China/Avant-Garde exhibition, Cynical Realism, “hooligan [liumang] aesthetic” 20. Liu Wei (b.1963), The New Generation, 1991, oil on canvas Revolutionary Family series, memory 21. Li Shan (b.1942), Rouge Series No. 8, 1990, oil on canvas Political Pop, rouge 22. Zhu Fadong, This Person is for Sale, 1994, performance, 1994 Migrant labor and migration, dagong, underclasses, commercialism, Marx, Beijing 23. Zhang Huan, Twelve Square Meters, performance, 1994 Dashan Village [Beijing East Village], masochism, annihilation, Chris Burden, Vito Acconci, fish oil, latrine, anti-commercial 24. Zhang Huan, To Add One Meter to an Unknown Mountain, performance at Miaofeng Mountain, Beijing, May 22, 1995 Landscape, collectivity, community, Foolish Old Man Who Moved a Mountain, “There are always higher mountains beyond a high mountain and there are always more capable people beyond a capable person.” 25. Song Dong (b.1966), Breathing, performance, 1996 Tiananmen Square, Back Sea, tuqi= to exhale, express opinion; apartment art, water/ice 26. Yin Xiuzhen, Suitcase, 1995, performance and installation, wooden box, concrete, clothes, copper plate Apartment Art, memory 27. Lin Tianmiao (b. 1961), Braiding, 1999, mixed media installation Cotton thread, women’s work, identity, gender 28. Cai Guoqiang, Project to Extend the Great Wall of China by 10,000 Meters (Projects For Extraterrestrials No. 10), installation/performance, 1993 Shanghai Drama Academy, gunpowder, alchemy (Daoism) and transformation, “big bang,” Quanzhou 29. Ai Weiwei, Sunflower Seeds, 2010, installation Tate Modern Turbine Hall; Jingdezhen, 8 million porcelain seeds (10 tons) 1600 workers/ 5 years, craftsmanship
MATH375: Tutorial 2 Tutorial 2 1. Let r, σ, S0,K, M,T, be given positive numbers. The random variable S(T) is defined as: where W (T) ∼ N(0,T). Calculate: where I(·) is the indicator function. The values inside the square brackets above are the terminal values (payoffs) of European digital (binary) options: the payoff in (i) is that of a cash-or-nothing call, whereas the payoff in (ii) is that of an asset-or-nothing call. 2. Let (Ω, F, P) be a probability space on which the random variable X is defined. Also let the σ-algebras D and G be such that D ⊂ G ⊂ F. If X is integrable, prove that: 3. Let (W (t),t ≥ 0) be a standard Brownian motion. Calculate E[X(t)] for each of the following cases:
Economics 201 Microeconomics Tutorial 5 Question 1: The following table shows what happens to a firm in terms of additional output (denoted Q) per day for each addition worker (denoted L) that the firm hires. Each additional unit of output can be sold at a flat market price of $20 per unit. Suppose this firm needs to pay all workers a flat payment of $256 a day or $32 per hour for a 8 hour workday. Calculate the average and marginal product for workers at this firm. How many workers should this firm hire? What output is produced? Assuming away all other costs and assuming that labour is the only cost for this firm, what is the firm’s profit per day? L Q 0 0 1 15 2 32 3 52 4 70 5 85 6 94 7 100 8 104 Question 2: In 2021, Kane Williamson played 8 games for the IPL franchise Sunrisers Hyderabad. The following table shows the cumulative runs scored over these 8 games. Calculate Williamson’s Average Runs and Marginal Run per game. Graph these to convince yourself that they follow the usual relationship between Average and Marginal Product (marginal is above average when average is rising; marginal is below average when average is falling) Games Runs 1 20 2 48 3 79 4 106 5 125 6 140 7 150 8 155 Question 3: Suppose a firm’s production function is given by What kind of returns to scale does this production function exhibit? What if Q = L*K? Question 4: A firm buys labour at $50 per unit and capital at $150 per unit. The firm wishes to invest $800,000 in the production process. Suppose the firm’s production function is of the fixed proportions type such that the firm must combine labour and capital in a 1:5 ratio. This implies L:K = 1:5 or L/K = 1/5. This means that the firm will always use 5 times the amount of capital as the amount of labour to produce a particular level of output that is commensurate with the $800,000 outlay. What amount of L and K will this firm choose to utilize? Question 5: Suppose the production function for a particular output is given by Q = L*K where L denotes labour and K denotes capital. L costs $50 per unit while K costs $1000 per unit. What is the cost minimizing combination of inputs to produce 1620 units of output?
Rules for the Game of Freckers COMP30024 Artificial Intelligence February, 2025 Freckers is a captivating two-player strategy game that brings a pond to your tabletop. Navigate your team of six frogs across an 8x8 grid, outsmarting your opponent with every hop and jump. With each turn, the pond transforms, presenting new challenges and opportunities. Will you be the first to reach the other side and claim victory? Freckers offers a perfect blend of simple rules and deep strategy, ensuring a fresh and exciting experience with every game! Overview Freckers is a two-player perfect information game played on a 8 × 8 board. The players (Red and Blue) take turns to move six frogs from one side of the board to the other. Figure 1: An example (in-progress) game of Freckers. Game Board We use a two-dimensional coordinate system to describe the positions of cells on the game board (Figure 2). Formally, a valid board coordinate is an integer pair (r, c), 0 ≤ r ≤ 7, 0 ≤ c ≤ 7, where r is the row on the board and c is the column. A given coordinate may be directly adjacent to up to eight other coordinates. For example, (1, 2) is adjacent to eight coordinates: • (1, 3) - RIGHT • (1, 1) - LEFT • (0, 2) - UP • (2, 2) - DOWN • (0, 1) - UP-LEFT • (0, 3) - UP-RIGHT • (2, 1) - DOWN-LEFT • (2, 3) - DOWN-RIGHT Those on the edges or corners of the board naturally have fewer adjacent coordinates. For example, the coordinate (7, 7) only has three: (6, 6) (UP-LEFT), (6, 7) (UP), and (7, 6) (LEFT). Figure 2: The coordinate system used on a Freckers game board. Gameplay Below we outline the high-level “sequence” for a typical game of Freckers. The subsequent sections then describe the individual components of this sequence in detail. • The game begins with the configuration shown in Figure 3 and proceeds sequentially. Note that in our illustrations, the green circles represent “lily pads”. Initially there are 28 lily pads on the board with frogs occupying 12 of these lily pads. • By convention, Red starts. Throughout the game Red and Blue take turns to play actions: – A MOVE action involves moving a frog to an unoccupied lily pad, either in a directly adjacent cell, or, via multiple jumps over occupied lily pads (similar to jump moves in classic Checkers). – A GROW action generates lily pads around all six frogs of the current player in the directly adjacent cells. • The game ends when a player moves all their frogs such that they occupy the opposite row of the game board; or, a turn limit of 150 turns is reached. Figure 3: Initial game board configuration. Actions MOVE On a given turn, the current player may choose to MOVE one frog “forwards” or sideways to an unoccupied destination lily pad. They may optionally jump over other frogs (of either colour) one or more times along the way to the destination cell. Specifically, the definition of “forwards” depends on the current player’s colour: • Red may only move RIGHT, LEFT, DOWN, DOWN-LEFT and DOWN-RIGHT • Blue may only move RIGHT, LEFT, UP, UP-LEFT and UP-RIGHT Figure 4 illustrates the valid moves for each respective player according to the rules above. Figure 4: Valid MOVE directions for Red and Blue respectively (assuming there is just one frog on the game board). Notice that there is an unoccupied lily pad in the possible destination cells. Regardless of whether the player moves to an adjacent cell or jumps over multiple frogs along the way, the destination cell must always contain an unoccupied lily pad. After performing a MOVE action, the lily pad in the source cell (from which the frog leaves) disappears and can only be regenerated via a subsequent GROW action (described next). Figure 6 illustrates two example jump sequences. Notice how in (b) directions change during a multi-jump sequence. Importantly, all these jumps are in one of the five valid directions for the respective turn player (Blue). Figure 5: Red plays MOVE action (3, 7) [DOWN-LEFT]. The board state before (left) and after (right) playing the action is shown. (a) Red plays MOVE action (2, 4) [DOWN-RIGHT]. (b) Blue then plays MOVE action (4, 2) [RIGHT, UP-RIGHT, UP]. Figure 6: An example showing a sequence of two MOVE actions, the first involving a single jump, and the second involving three successive jumps. GROW On a given turn, a player may alternatively choose to play a GROW action. This results in lily pads being generated in the cells adjacent to all six of their frogs. In cells where there is already a lily pad (occupied or not), no updates are made. Similarly, a lily pad may be generated in the same cell more than once, for example, if two of the player’s frogs neighbor each other. The end result is ultimately no different - a single unoccupied lily pad is generated in the cell. Figure 7 shows an example GROW action played by Red. Figure 7: Red plays a GROW action. The board state before (left) and after (right) is shown. Ending the Game A game of Freckers ends if one of the following two conditions is met: 1. A player moves all of their frogs to the opposite row of the board (r = 7 for Red and r = 0 for Blue). This player is declared the winner. 2. There have been 150 actions played (across both players) with no winner declared. The player with more frogs in the opposite row is declared the winner, or, if this is a tie, the game ends in a draw. Figure 8 shows an example board configuration where Red has won the game. Notice how r = 7 for all six frogs. Figure 8: Red plays a final MOVE (6, 0) DOWN-RIGHT action to claim victory. v1.1 Fixed Figure 5, which should have had a missing “from” lilypad in the resultant state.
Q1 Consider the flexural vibration of a beam, of length l, cross section area A, density ρ, second moment of area I and Young’s Modulus E, that is simply-supported at both ends but with a torsional spring, of stiffness kt attached at the right-hand end, as shown in figure Q1. (a) The general solution for the flexural vibration of a beam is given by where are constants and Clearly stat-ing the boundary conditions, derive the frequency equation for the beam in terms of kt and αl. (7 marks) (b) It is proposed that an appropriate function for a guessed modeshape to be used in Rayleigh’s method is x 4 + ax3 + bx2 + cx + d. For the case where kt = , using the boundary conditions suggest appropriate values for a, b, c and d. Comment on whether this would also be an appropriate guess for the beam if a lumped mass was added to the righthand end of it. Note that you are not expected to apply Rayleigh’s method. (4 marks) (c) For the case where kt =, using a single element FE model, calculate an estimate of the first natural frequency. Suggest how this could be used alongside the answer to part (a) to give an accurate estimate for the first natural frequency of the beam. Comment on the likely accuracy of this estimated natural frequency compared to the Rayleigh’s method estimate that could be calculated using the guessed modeshape in part (b). (9 marks) The mass and stiffness matrices for an element of beam of length L subject to flexural vibration are: Q2 Consider the following open loop transfer function (OLTF) where a plant Gp (s) is controlled by a proportional controller Gp (s) = K. (a) For K = 1 sketch the polar plot of the OLTF and determine whether the closed loop is stable by applying the Nyquist criterion (note: consider s = 0 a stable pole). In addition, estimate, using your sketch and/or calculations, the gain margin and phase margin of the closed loop. For full marks you need to include the expressions of the OLTF gain and phase and fill in the table below: (10 marks) (b) Discuss how the phase margin and gain margin will evolve if the chosen K is greater than 1. Reflect on the possibility of achieving non-oscillatory response for any value of K and suggest, if needed, changes in the controller that could deliver over-damped behaviour. (10 marks)
Week 6: Accounting Analysis 2 Dell, Inc 8-K dated 13 August 2007 The investigation raised questions relating to numerous accounting issues, most of which involved adjustments to various reserve and accrued liability accounts, and identified evidence that certain adjustments appear to have been motivated by the objective of attaining financial targets. According to the investigation, these activities typically occurred in the days immediately following the end of a quarter, when the accounting books were being closed and the results of the quarter were being compiled. The investigation found evidence that, in that timeframe, account balances were reviewed, sometimes at the request or with the knowledge of senior executives, with the goal of seeking adjustments so that quarterly performance objectives could be met. The investigation concluded that a number of these adjustments were improper, including the creation and release of accruals and reserves that appear to have been made for the purpose of enhancing internal performance measures or reported results, as well as the transfer of excess accruals from one liability account to another and the use of the excess balances to offset unrelated expenses in later periods. The investigation found that sometimes business unit personnel did not provide complete information to corporate headquarters and, in a number of instances, purposefully incorrect or incomplete information about these activities was provided to internal or external auditors. The investigation identified evidence that accounting adjustments were viewed at times as an acceptable device to compensate for earnings shortfalls that could not be closed through operational means. Often, these adjustments were several hundred thousand or several million dollars, in the context of a company with annual revenue of between $35 billion and $56 billion and annual net income of between $2.1 billion and $3.6 billion for the periods in question. Nevertheless, the errors and irregularities identified in the course of the investigation revealed deficiencies in our accounting and financial control environment that require corrective and remedial actions. For a description of the control deficiencies identified by management as a result of the investigation and our internal reviews described below, as well as management’s plan to remediate those deficiencies, see “Control Deficiencies” and “Remediation Plan” below. Question 1 How do the above accounting adjustments affect Dell’s reported income? What is the effect on cash flow from operations? Question 2: Home Depot Comment on the change in Cash Flow from Operations between 2000 and 2001? Is earnings management likely? What kind of earnings management might the management of Home Depot have engaged in? Question 3: Is there a red flag? ABC Ltd, JFK Ltd, and IJK Ltd are the three major retail businesses in Imaginaryland. Their market shares have been constant for the past decade. Below is some information about their sales and inventory balances expressed in I$ million, the reporting currency, for the past 5 years. There is steady economic growth in Imaginaryland and the retail business is stable. A group of investors is concerned about the health of ABC Ltd because it took an inventory writedown of I$1.815 million in 2021. There was no further writedown of inventory in 2022 and 2023. There is no incremental growth opportunity in the retail business. Do you think there is reason for concern? Question 4: Understated expenses ABC Ltd is listed on the Imaginary Stock Exchange. Its reporting currency is Imaginary Dollars (I$). ABC Ltd has been experiencing some financial troubles lately. However, management believes that this is a temporary phase, and that it will soon return to normal times. Cash flow has been declining for the past two years, and ABC Ltd has been consistently falling behind on contractual payments to suppliers for raw materials. Consequently, the suppliers have sued ABC Ltd over unpaid bills. The court is expected to rule in favour of the suppliers, and ABC Ltd would be liable to pay I$40 million in fines. The management of ABC Ltd argue that there is a low probability of such a ruling, and have not recognised this liability in the books. Assume that the tax rate is 30%. Do you agree with ABC Ltd’s financial reporting choice? If not, what adjustments will you make to the financial statements? Question 5: Overstated Asset Imagine you're a financial analyst following StellarTech Inc., a cutting-edge technology company known for its innovative products in the field of artificial intelligence and robotics. Recently, you've discovered a significant error in the company's financial statements. During your analysis, you discover that the company had overvalued its inventory by $5 million in its most recent annual report. Assume a tax rate of 30%. What adjustments would you make to StellarTech’s reported financials? Question 6 Read the article titled “Adjusted EBITDA Is in the Eye of the Beholder” What are some advantages of using non-GAAP earnings measures like adjusted EBITDA? What are some disadvantages of such measures?
ASSIGNMENT QUESTION BSE1074 CHILD PROTECTION AND SAFETY IN PRACTICE Assignment I (Individual) Course Learning Outcomes: CLO1: Analyse the ethics and professionalism of safety and protection on children’s learning, development and behaviour (C3, PLO1) 1. For this individual assignment, you are requested to do a thorough review from varieties of resources (textbooks, journal articles, proceeding/conferences/seminars/forums papers, online documents etc.) on ethics and professionalism related safety and protection of children in educational settings. 2. Critically analyse the role of ethics and professionalism in safeguarding young children’s learning, development, and behaviour. Support your discussion with at least two relevant examples. 3. Prepare a slide presentation of not more than 10 slides to summarise your assignment. Refer to the rubric provided to assist you in completing your assignment. [Total: 20 marks] Note: Your assignment should be typed using 12 point Times New Roman font and 1.5 line spacing. Your assignment should be between 3500 to 4500 words excluding references. Do not copy the assignment question and instructions to your answer. Only assignment answer developed within the approximate word limit will be assessed. The number of words should be shown at the end of your assignment. Your assignment must be submitted between 30th June – 04th July 2025. Submission after 04th July 2025 will NOT be accepted. Your assignment should be prepared individually. You should not copy another person’s assignment. You should also not plagiarise another person’s work as your own. This assignment accounts for 20% of the total marks for the course and shall be assessed based on the Rubrics or Answer Scheme attached. Academic honesty is highly valued at UCSI University Malaysia. Turnitin is a tool that helps your lecturers and tutors to detect instances of academic dishonesty. It is important to understand that Turnitin does not itself detect plagiarism – that is a judgment made by your lecturers and tutors. Turnitin is just one tool among several that assists them in making their decision. You are required to submit your final copy of your assignment via turnitin prior to the due date of the assignment. Students are also advised to keep the latest version of all submitted assignments (soft-copy and hard copy) to prevent any loss of work through accidents or error. Assignment Rubrics BSE1074 CHILD PROTECTION AND SAFETY IN PRACTICE / MAY 2025 SEMESTER Criteria Missing Item Fair Excellent Max Marks critically analyse the role of ethics and professionalism in safeguarding young children’s learning, development, and behaviour. 3.0 There is no analysis on the role of ethics and professionalism in safeguarding young children’s learning, development, and behaviour. No example presented to support the analysis. Analysis on the role of ethics and professionalism in safeguarding young children’s learning, development, and behaviour were vague. No example presented to support the analysis. NO references referred. Analysis on the role of ethics and professionalism in safeguarding young children’s learning, development, and behaviour were brief but clear. Only ONE (1) example presented to support the analysis. Less than THREE (3) references referred. Analysis on the role of ethics and professionalism in safeguarding young children’s learning, development, and behaviour were clear and acceptable but not critical enough. TWO (2) examples presented to support the analysis. FOUR (4) to SIX (6) references referred. Analysis on the role of ethics and professionalism in safeguarding young children’s learning, development, and behaviour were clear, detailed and critical. TWO (2) or more examples presented to support the analysis. SIX (6) or more references referred. 12.0 Preparation of slide presentation and presentation 0.5 There is no slide presentation prepared. Slide presentation plain and dull. Less than FOUR (4) slides prepared. Slide presentation is acceptable. FIVE (5) to SIX (6) slides prepared. Slide presentation is clear and interesting. SEVEN (7) to EIGHT (8) slides prepared. Slide presentation is detailed and interesting supported with graphics/images. NINE (9) to TEN (10) slides prepared. 2.0 Conclusion 0.25 No conclusion. There is no clear conclusion, the paper just ends. The conclusion is recognisable but does not tie up introduction and content. The conclusion brings together the introduction and contents. Well summarised. The conclusion is strong and brings together the introduction and contents. Well summarised. 1.0 TOTAL MARKS / JUMLAH MARKAH 20
