Exercise no. 1 / COMFOE SUBJECT: Economic And Financial Management AIMS Financial Statements: understanding and organization DESCRIPTION OF THE ACTIVITY These are the items in your accounting books, alphabetically ordered. Provide the Balance sheet and Income Statement formally organized and understand the meaning of every account. At the end of the year (31st Dec) Account Balance This item belongs to... 1 Amortization of intangible assets 300 P&L 2 Banks (Money in the bank account) 33.563 Assets 3 Changes in inventory of raw materials (negative, expense) 200 P&L 4 Computer software (*) 1.900 Intangible assets 5 Current debt with financial institutions 12.000 liability 6 Depreciation of PPE 7.900 P&L 7 Furniture (*) 7.100 PPE 8 Independent professional services 1.700 P&L 9 Interest on loans 2.800 P&L 10 IT equipment (*) 3.000 PPE 11 Leases and royalties 9.000 P&L 12 Legal reserve 2.200 Net Equity 13 Motor vehicles 30.000 Assets 14 NC payables to suppliers of Fixed Assets 10.000 Liability 15 Other finance incomes 600 P&L 16 Other installations (*) 19.000 Assets 17 Payables for the rendering of services 14.300 Liability 18 Raw materials 1.800 Assets 19 Raw materials purchased 41.000 P&L 20 Repairs and maintenance 800 P&L 21 Revenues 100.000 P&L 22 Salaries and wages 50.000 P&L 23 Share capital 45.000 Net Equity 24 Social Security contributions paid for by the company 15.000 P&L 25 Social Security, payables 18.000 Liability 26 Suppliers (accounts payable) 45.100 Liability 27 Tax Authorities, payables 10.323 Liability 28 Trade receivables 29.560 Assets 29 Utilities 2.500 P&L 30 Volume discounts given to customers 400 P&L (*) This is the net amount once accumulated amortization or depreciation have been subtracted.
Ships in satellite images Problem description To a large degree, financial data has traditionally been numeric in format. But in recent years, non-numeric formats like image, text and audio have been introduced. Private companies have satellites orbiting the Earth taking photos and offering them to customers. A financial analyst might be able to extract information from these photos that could aid in the prediction of the future price of a stock Approximate number of customers visiting each store: count number of cars in parking lot Approximate activity in a factory by counting number of supplier trucks arriving and number of delivery trucks leaving Approximate demand for a commodity at each location: count cargo ships traveling between ports In this assignment, we will attempt to recognize ships in satellite photos. This would be a first step toward counting. As in any other domain: specific knowledge of the problem area will make you a better analyst. For this assignment, we will ignore domain-specific information and just try to use a labeled training set (photo plus a binary indicator for whether a ship is present/absent in the photo), assuming that the labels are perfect. Goal: In this notebook, you will need to create a model in sklearn to classify satellite photos. The features are images: 3 dimensional collection of pixels 2 spatial dimensions 1 dimension with 3 features for different parts of the color spectrum: Red, Green, Blue The labels are either 1 (ship is present) or 0 (ship is not present) Learning objectives Learn how to implement a model to solve a Classification task Imports modules In [ ]: ## Standard imports import numpy as np import pandas as pd import matplotlib.pyplot as plt import sklearn import os import math %matplotlib inline In [ ]: ## Load the helper module from IPython.core.interactiveshell import InteractiveShell InteractiveShell.ast_node_interactivity = "all" # Reload all modules imported with %aimport %reload_ext autoreload %autoreload 1 # Import nn_helper module import helper %aimport helper helper = helper.Helper() API for students We have defined some utility routines in a file helper.py . There is a class named Helper in it. This will simplify problem solving More importantly: it adds structure to your submission so that it may be easily graded helper = helper.Helper() getData: Get a collection of labeled images, used as follows data, labels = helper.getData() showData: Visualize labelled images, used as follows helper.showData(data, labels) model_interpretation: Visualize the model parameters helper.model_interpretation(Classifier) Get the data The first step in our Recipe is Get the Data. We have provided a utility method getData to simplify this for you In [ ]: # Get the data data, labels = helper.getData() n_samples, width, height, channel = data.shape print("Data shape: ", data.shape) print("Labels shape: ", labels.shape) print("Label values: ", np.unique(labels)) Your expected outputs should be following Date shape: (4000, 80, 80, 3) Labels shape: (4000,) Label values: [0 1] We will shuffle the examples before doing anything else. This is usually a good idea Many datasets are naturally arranged in a non-random order, e.g., examples with the sample label grouped together You want to make sure that, when you split the examples into training and test examples, each split has a similar distribution of examples In [ ]: # Shuffle the data first data, labels = sklearn.utils.shuffle(data, labels, random_state=42) Have a look at the data We will not go through all steps in the Recipe, nor in depth. But here's a peek In [ ]: # Visualize the data samples helper.showData(data[:25], labels[:25]) Eliminate the color dimension As a simplification, we will convert the image from color (RGB, with 3 "color" dimensions referred to as Red, Green and Blue) to gray scale. In [ ]: print("Original shape of data: ", data.shape) w = (.299, .587, .114) data_bw = np.sum(data *w, axis=3) print("New shape of data: ", data_bw.shape) In [ ]: # Visualize the data samples helper.showData(data_bw[:25], labels[:25], cmap="gray") Have look at the data: Examine the image/label pairs Rather than viewing the examples in random order, let's group them by label. Perhaps we will learn something about the characteristics of images that contain ships. We have loaded and shuffled our dataset, now we will take a look at image/label pairs. Feel free to explore the data using your own ideas and techniques. In [ ]: # Inspect some data (images) num_each_label = 10 for lab in np.unique(labels): # Fetch images with different labels X_lab, y_lab = data_bw[ labels == lab ], labels[ labels == lab] # Display images fig = helper.showData( X_lab[:num_each_label], [ str(label) for label in y_lab[:num_each_label] ], cmap="gray") _= fig.suptitle("Label: "+ str(lab), fontsize=14) _= fig.show() print(" ") It appears that a photo is labeled as having a ship present only if the ship is in the center of the photo. Perhaps this prevents us from double-counting. In any event: we have learned something about the examples that may help us in building models Perhaps there is some feature engineering that we can perform. to better enable classification Create a test set To train and evaluate a model, we need to split the original dataset into a training subset (in-sample) and a test subset (out of sample). Question: Split the data Set X_train, X_test, y_train and y_tests to match the description in the comment 90% will be used for training the model 10% will be used as validation (out of sample) examples Hint: Use train_test_split() from sklearn to perform. this split Set the random_state parameter of train_test_split() to be 42 We will help you by Assigning the feature vectors to X and the labels to y Flattening the two dimensional spatial dimensions of the features to a single dimension In [ ]: from sklearn.model_selection import train_test_split y = labels X = data_bw X_train = None X_test = None y_train = None y_test = None ### Flatten X X = X.reshape(X.shape[0], -1) # Split data into train and test # Create variables X_train, X_test, y_train, y_test # X_train: training examples # y_train: labels of the training examples # X_test: test examples # y_test: labels of test examples # YOUR CODE HERE raise NotImplementedError() print("X_train shape: ", X_train.shape) print("X_test shape: ", X_test.shape) print("y_train shape: ", y_train.shape) print("y_test shape: ", y_test.shape) Your expected outputs should be following X_train shape: (3600, 6400) X_test shape: (400, 6400) y_train shape: (3600,) y_test shape: (400,) In [ ]: Prepare the data and Classifier Questions: You will transform. the data and create a Classifier. The requirements are as follows: Transform. the features (i.e., the pixel grids) into standardized values (mean 0, unit standard deviation) Set a variable scaler to be your scaler Create an sklearn Classifier Set variable clf to be be your Classifier object We recommend trying Logistic Regression first sklearn 's implementation of Logistic Regression has many parameter choices We recommend starting with the single parameter solver="liblinear" You may want to use the sklearn manual to learn about the other parameters Hints: Look up StandardScaler in sklearn ; this is a transformation to create standardized values You will use transformed examples both for training and test examples So be sure that you can perform. the transformation on both sets of examples Using Pipeline in sklearn , whose last element is a model, is a very convenient way to Implement transformations and perform. model fitting/prediction In a way that ensures that all examples, both training and test, are treated consistently Enables Cross Validation without cheating In [ ]: import time from sklearn.linear_model import LogisticRegression from sklearn.model_selection import train_test_split, cross_val_score from sklearn.preprocessing import StandardScaler from sklearn.pipeline import Pipeline ## Data Scaler # Create a StandardScaler object # scaler: sklearn standard scaler scaler = None # YOUR CODE HERE raise NotImplementedError() ## Classification Model # Create a classifier # clf: sklearn classifier # name: string, name of your classifier # model_pipeline: sklearn Pipeline, if you use pipeline, please use this variable clf = None name = None # YOUR CODE HERE raise NotImplementedError() In [ ]: Train model Question: Use your Classifier or model pipeline to train your dataset and compute the in-sample accuracy Set a variable score_in_sample to store the in-sample accuracy Hint: The sklearn function accuracy_score may be helpful In [ ]: from sklearn.metrics import accuracy_score # Set variable # score_in_sample: a scalar number, score for your in-sample examples score_in_sample = None # YOUR CODE HERE raise NotImplementedError() print("Model: {m:s} in sample score={s:3.2f} ".format(m=name, s=score_in_sample)) In [ ]: Train the model using Cross Validation Since we only have one test set, we want to use 5-fold cross validation check model performance. Question: Use 5-fold Cross Validation Set cross_val_scores as your scores of k-fold results Set k as the number of folds Report the average score Hint: cross_val_score in sklearn will be useful In [ ]: # Set variable # scores: an array of scores (length 5), one for each fold that is out-of-sample during cross-validation # k: number of folds cross_val_scores = None k = 5 t0 = time.time() # YOUR CODE HERE raise NotImplementedError() print("Model: {m:s} avg cross validation score={s:3.2f} ".format(m=name, s=cross_val_scores.mean()) ) In [ ]: How many parameters in the model ? Question: Calculate the number of parameters in your model. Report only the number of non-intercept parameters. Set num_parameters to store the number of parameters Hint: The model object may have a method to help you ! Remember that Jupyter can help you find the methods that an object implements. In [ ]: # Set num_parameters equal to the number of non-intercept parameters in the model num_parameters = None # YOUR CODE HERE raise NotImplementedError() print(" Shape of intercept: {i}; shape of coefficients: {c}".format(i=clf.intercept_.shape, c=num_parameters) ) In [ ]: Evaluate the model Question: We have trained our model. We now need to evaluate the model using the test dataset created in an earlier cell. Please store the model accuracy on the test set in a variable named score_out_of_sample . Hint: If you have transformed examples for training, you must perform. the same transformation for test examples ! Remember: you fit the transformations only on the training examples, not on the test examples ! In [ ]: # Set variable to store the model accuracy on the test set score_out_of_sample = None # YOUR CODE HERE raise NotImplementedError() print("Model: {m:s} out-of-sample score={s:3.2f} ".format(m=name, s=score_out_of_sample)) In [ ]: Visualize the parameters Remember: there is a one-to-one association between parameters and input features (pixels). So we can arrange the parameters into the same two dimensional grid structure as images. This might tell us what "pattern" of features the model is trying to match. In [ ]: helper.model_interpretation(clf) Further Exploration Now you can build your own model using what you have learned from the course. Some ideas to try: Was it a good idea to drop the "color" dimension by converting the 3 color channels to a single one ? Can you interpret the coefficients of the model ? Is there a discernible "pattern" being matched ? Feature engineering ! Come up with some ideas for features that may be predictive, e.g, patterns of pixels Test them Use Error Analysis to guide your feature engineering Add a regularization penalty to your loss function How does this affect The in-sample fit ? The visualization of the parameters Hint: The sklearn LogisticRegression model has several choices for the penalty parameter has a variable value for the regularization strength parameter C Observe the effect of each change on the Loss and Accuracy. In [ ]:
COMPSCI5100 MLAI4DS Mock Exam Paper This examination paper is worth a total of 60 marks 1. Linear regression with models of the form. , is a common technique for learning real-valued functions from data . (a) Squared and absolute loss are defined as follows: Describe, with a diagram if you like, why, when optimizing the parameters with the squared loss outliers have a larger effect than with the absolute loss . [6 marks] (b) Which of the following statements is true: A) Parameter estimation with the squared loss is not analytically tractable . B) The squared loss is equivalent to assuming normally distributed noise . C) The absolute loss is a popular choice for regularization . D) The squared loss is a popular choice for regularization . [2 marks] (c) Discuss why the value of the squared loss on the training data cannot be used to choose the model complexity. [3 marks] (d) For the particular model , I optimize the parameters and end up with . What does the model predict for a test point at xnew = 3? [2 marks] (e) The radial basis function (RBF): is a popular choice for converting the original features, xn,d , into a new set of K features prior to training. Assume the value of S is given. Describe a procedure for determining the center parameter μd,k and K . [4 marks] (f) With respect to the functions they can fit, describe the difference between RBF and the basic linear model WT xn with a graph. [3 marks] 2. Classification question (a) Use a classification algorithm, describe what is meant by: (i) Generalisation [2 marks] (ii) Over-fitting [2 marks] (b) A classification algorithm has been used to make predictions on a test set, resulting in the following confusion matrix: Compute the following quantities (expressing them as fractions is fine): (i) Accuracy [2 mark] (ii) Sensitivity [2 mark] (iii) Specificity [2 mark] (c) Explain why it is not possible to compute the AUC from a confusion matrix . [4 marks] (d) Two binary classifiers are used to make predictions for the same set of six test points. These predictions are given below, along with the true labels . Compute its area under the curve (AUC) in each case . [4 marks] (e) Explain how the SVM can be extended via the kernel trick to perform. non-linear classification . [2 marks] 3. Unsupervised learning (a) Provide pseudo code for K-means (assume that the number of clusters is provided) . [5 marks] (b) Is the total Euclidean distance between data points and their cluster centers a good criterion to select number of clusters in K-means? Why? [2 marks] (c) Gaussian mixture models can be fitted to data using the expectation maximization (EM) algorithm. The EM algorithm has two steps: E-step and M-step. Describe what parameters are being estimated in each step in Gaussian mixture models . [4 marks] (e) Describe three key differences between K-means and Gaussian mixture models [4 marks] (e) K-means often converges to a local optimal solution. Describe a simple process for overcoming the local optimality of K-means . [3 marks] (g) Describe two situations (with justification) where you might choose a mixture model over K-means . [2 marks]
You will be required to analyse and benchmark the economic and financial performance of the following airlines: 1. Singapore Airlines 2. Emirates 3. Southwest Your report should not exceed 10 pages, including text, charts, tables, references, etc. The analysis should focus on the two most recent financial years: 1. Singapore Airlines – FY22/23 & FY23/24 2. Emirates – FY22/23 & FY23/24 3. Southwest – 2022 and 2023 Due date: 24 Sep 2024 by 10:55 Key performance metric calculation For each of the airlines listed, calculate and analyse the following metrics: a) Unit cost (per aircraft capacity) b) Yield (per RPK and/or RTK) c) Load factor d) Break-even load factor e) Employee productivity f) Aircraft utilisation g) EBIT margin h) Debt-to-equity ratio i) Current ratio j) P/E ratio Benchmarking analysis Based on these calculations, you should then compare these airlines with each other and discuss why the first airline (SIA) might or might not perform. well (overall and per indicator a-f) in comparison to the other two airlines and to the region it mainly operates in. Please highlight if the first airline or any of them are in any way special when compared to the average international (IATA) passenger airline. Please also discuss (briefly) the limitations of your benchmarking approach and explain all steps that you have undertaken (formulas, basis for calculations) during the benchmarking exercise.
SCHOOL OF COMPUTER SCIENCE & ENGINEERING MODULE CODE: CET333 MODULE TITLE: Product Development ASSESSMENT: 1 of 1 TITLE OF ASSESSMENT: Portfolio Report ASSESSMENT VALUE: 100% PLEASE READ ALL INSTRUCTIONS AND INFORMATION CAREFULLY. This assignment contributes 100% to your final module mark. Please make sure to keep a copy of your assignment as a backup in case your work is lost or corrupted online. THE FOLLOWING LEARNING OUTCOMES WILL BE ASSESSED: Knowledge • Have a critical awareness of a range of practitioner methods and techniques appropriate to developing a product in a specific computing context. • Understand the business and technological context in which product development and evaluation take place. Skills • Apply appropriate techniques to determine, specify, design, build and test a solution to a problem. • Critically evaluate the process and the product of development activity. IMPORTANT INFORMATION You are required to submit your work within the bounds of the University Infringement of Assessment Regulations (see your Programme Guide). Plagiarism, paraphrasing and downloading large amounts of information from external sources will not be tolerated and will be dealt with severely. The coursework submission for this module is largely based on your practice. Still, this should be duly referenced when you use material from other sources, such as an occasional short quote. It is important to note that your work WILL BE SUBJECT TO CHECKS FOR ORIGINALITY, which WILL include using an electronic plagiarism detection service. Where you are asked to submit an individual piece of work, the work must be entirely your own. The safety of your assessments is your responsibility. You must not permit another student access to your work at any time during the inception, design or development of your coursework submission and must take great care in this respect. Where referencing is required, unless otherwise stated, the Harvard referencing system must be used (see your Programme Guide or university library website). You must complete the Assignment Cover Sheet and attach it to the Portfolio Report. Tasks: • Task 1: Portfolio Report (PDF file) • Task 2: Prototype Product and Demonstration Submission Date and Time: Week 13, at 23:59. Detailed in the CANVAS assignment area Submission Location: Electronic submission link to the CANVAS assignment area Assessment (Portfolio Report– 100%) Assessment Scenario AI-Solution is a fictitious start-up company based in Sunderland. AI-Solutions leverages AI to assist various industries with software solutions to rapidly and proactively address issues that can impact the digital employee experience, thus speeding up design, engineering, and innovation. AI-Solutions has distinguished itself by integrating an AI-powered virtual assistant that responds to users' inquiries and provides AI-based, affordable prototyping solutions. This unique selling point sets them apart from their competitors and is sure to pique customer's interest. The company's mission is to innovate, promote, and deliver the future of the digital employee experience, with a strong focus on supporting people at work. This commitment is at the core of the global expansion and aims to make a worldwide impact. Analysis, Design and Development Task During client meetings, you will receive additional information to help you complete the design and development task. These meetings will demonstrate the knowledge and skills relevant to your program of study. You will not need to present the entire solution – more details are provided below: Each top-up degree program has its own specific requirements. You only need to fulfil the requirements for your program. • Applied Business Computing The company requires an online web system or presence to showcase their expertise globally, and you will assist them in this case. You need to develop a short form for Scheduled Demo where customers can fill out the form with their name, email address, phone number, company name, country, and the information they are interested in for an AI-powered virtual assistant or to schedule a personalised solution demo. There should be additional options for participating in promotional events (Join our Events), where customers can view technical solutions demonstrated to promote and advertise the company's product solutions. The company needs a password-protected admin area to access data on the number of customer inquiries. The company requires accurate analysis of customer information to understand customer satisfaction and predict company success, including the number of interested customers for scheduled demos or joining promotional events and the number of jobs placed for virtual assistance and prototyping solutions. • Computer Systems Engineering The website will provide details of the software solutions offered to customers, highlights of past solutions provided to industries, customer feedback with ratings, articles promoting the company, photo galleries of promotional events and upcoming events. In order to gauge the demand for the services, the company need a system that promotes its software solutions and allows potential customers to reach out to them with their specific job requirements. Customers will be asked to provide their name, email address, phone number, company name, country, job title, and job details using a Contact Us form. They won't need to create accounts or provide passwords. Additionally, we need a password-protected admin area that allows them to access data on the number of customer inquiries. • Network Systems Engineering You have been assigned to develop appropriate network design methodologies and use them to create and implement a Secured Network Solution for the new office building. Please take note of the following information: The building is planned to have four floors, with two departments on each floor, as follows: • 1st floor: Sales and Marketing Department (100 users), HR Department (100 users) • 2nd floor: Finance and Accounts Department (100 users), Administration Department (100 users) • 3rd floor: Technical Communication Department (100 users), Server Room (10 devices) • Ground floor: Design and Development Department (200 users) Please remember the following: It is important to develop a network simulation along with documentation of the addressing scheme. You will need to evaluate different methodologies, justify your choice, and use them to implement a secure network solution. It's crucial to use a hierarchical model that provides redundancy at every layer to ensure network reliability. Each department is required to have a wireless network for the users. All devices in the network are expected to obtain an IP address dynamically from the dedicated DHCP servers located in the server room. Devices in the server room are to be allocated IP addresses statically. You should evaluate the design, implementation, and security. • Mobile and Web Technologies The company aim to develop a Mobile App that will enable industry customers to interact easily with an AI-powered virtual assistant. The app will be smart enough to recognise when a conversation needs to be transferred to a human staff member, ensuring a seamless and efficient user experience. Customers will need to provide their name, email address, phone number, company name, country, and the type of information they are interested in, such as software assistance, scheduling a demo, inquiring about events, or chatting with a sales representative. • Business Intelligence and Data Analytics & Information and communication technology You are responsible for Product Sales Data Analysis as a sales team member. In general, you will need to develop tools that allow the company to analyse the effectiveness of its software solutions. The company aims to use the analysis tools to assist in marketing and advertising the proposed software solutions on the website. The company needs you to create test data for web server logs. Assume the web server is Internet Information Server, and the log format is as follows. Save the web server log file in an Excel file (CSV or similar format). Then, develop tools using R or Python to analyse the web server logs and report information such as country, number of jobs placed, types of jobs requested, requests for schedule demos or promotional events, and requests for AI-powered virtual assistant, etc. The company expects a comprehensive analysis, including diagrams such as bar charts, scatterplots, or pie charts, to represent the best results. You can also use basic statistics to provide summaries, such as means and standard deviations. This analysis will help you assess your sales team's performance and evaluate the overall effectiveness of the company's sales strategy. Web server log file example: 02:49:36 128.1.0.0 GET /index.html 200 03:01:07 155.55.0.24 GET /images/events.jpg 304 01:20:04 157.20.5.0 GET /event.php 200 03:54:36 157.20.20.10 GET /scheduledemo.php 200 04:17:04 157.20.30.10 GET /prototype.php 200 Assessment Tasks Your role is to act as a consultant, analyse your client's needs, develop and test a prototype solution, and deliver and evaluate this solution with your client. In parallel, you must document the project professionally and ethically, just as you would if you were an analyst working for a software house or service provider. You must consult with your client to determine and agree on the exact requirements. Your module tutor (or someone appointed for this by the module tutor) will act as your client. You are to interview your client to determine the exact requirements and to develop your solution using suitable technologies. To carry out the project professionally, you must carefully consider the appropriate development methodology and the choice of implementation technologies. This should be based on the client s needs and the nature of the project. You will need to document the choice of these methods and technologies and any alternatives in your report and justify your choices. The application should be developed based upon a sound software engineering or networking/telecoms approach, which should cover the requirements elicitation, implementation, testing and evaluation phases where you verify the solution and critically evaluate the overall result. You also need to plan your project and generate a project schedule with task breakdown, effort allocation, and task sequencing. You are then required to demonstrate the use of this documentation, including any updates/adjustments that reflect the true development history, including any rescheduling, and provide a critical reflection on the history of your project. You must evaluate your system with your client and confirm that it meets the requirements originally negotiated and satisfies the client s needs. This evaluation activity must be reflective and show that the development process and the product were properly tested and evaluated. How well you report all these aspects will affect the mark you receive; please view the marking criteria. Your module mark is derived both from your ability to provide a technical solution for a client AND from the portfolio, which documents the planning and conduct of the project in total; compliance with portfolio requirements is, therefore, very important. Task 1: Portfolio Report You are required to produce a portfolio report that documents the project's development. This MUST be submitted as a single PDF file that is well structured, coherent and contains the following sections: Front Cover: This should include the module code, your project title, your student’s name and your ID. Contents: Your work MUST include page numbers throughout and in its contents. 1. Requirements Specification: A mandatory statement of your proposed solution's functional, non-functional, or technical requirements and expected deliverables using the template provided. Your client must approve and sign off on this statement as a basis for the development. 2. Planning Documentation: A Project Schedule that identifies the tasks, effort allocation, timescales and deliverables required during the project to successfully generate the proposed solution and systems documentation by the specified deadline. This must also reflect upon any revisions to scheduling where applicable during the project 3. Client Contact Record Sheet: Mandatory record of 3 client meetings. This should be completed and signed off by your client and yourself at set points in the project, then scanned and inserted into your e-portfolio illustrating your regular engagement with the client with key bulleted Action Points. 4. Methodology: A report made with direct reference to your Planning Documentation, which explains and justifies the main approaches, methods and tools you have built into your planning cycle to ensure that you deliver the specified solution to your client in the agreed- upon timescales. Note this report must be written in your own words about your professional practice. This is NOT a research review, so you are not required to reference academic papers. However, you need to investigate the approaches you intend to implement and apply in your practice to write about them critically. 5. Solution Design Documentation: Present the design documentation you have created relevant to your field of study. The section includes the design of the product prototype, documentation that describes the system, and evidence. 6. Testing and Evaluation: This section should detail how you tested your project against the functional and non-functional requirements. You should provide details of the testing methodologies, protocols, frameworks, tools, etc., and provide your testing results. 7. Technical Deployment of the Solution: This section describes the solution's technical requirements, including a summary of any installation and/or deployment procedures in the proposed production environment. It is highly recommended that a screencast be also included. 8. Critical Reflection: Regarding your Planning Documentation and Practitioner Statement, critically review the effectiveness of implementing the methods and tools adopted during the entire planning and development cycle and how this will inform and adapt your approach to client projects in the future. Task 2: Prototype Product and Demonstration You are required to produce a working solution and demonstrate this to your client. This demonstration will be done via a pre-recorded video, which shows the functionality of the solution you have created. Your solution must: 1. Conform. to the agreed requirements 2. Be functional and largely error-free You should carefully plan your demonstration before you begin recording it to ensure that you fully demonstrate how you have met each requirement. You will only be graded on the functionality you demonstrate in the video. Submission Requirements Task 1: For this task, you must submit a single PDF file containing all the sections outlined in the Assessment Tasks section of this document. Task 2: For this task, you are required to submit a video demonstration of your solution; you will only be graded on the functionality that you demonstrate in this video. Help with Referencing Whenever you need to refer the reader to the source of some information, e.g., a book/journal/academic paper/WWW address, provide a citation at that point within the main body of your report. Example 1: ... as we are all now aware, referencing is not trivial (Kendal, 2017) Provide a reference list towards the end of your research paper (after your conclusions section but before any appendices) that contains: • References, a list of books/journals/academic papers/URLs, etc., that have been directly cited from within the report (see example citation above). • Any material from which text, diagrams or specific ideas have been used, even if this has been presented in your own words, must be cited within the main body of the paper and listed in the reference list. It is not enough to list this material in a bibliography. Example 2: For Example 1 (using the Harvard system), the reference list would contain the following: Kendal S., 2017, Referencing standards, lnternational Student Journal, Vol 55, Pages 25 30, Scotts Pub., lSBN 1–243567–89 This shows the authors, the date published, the title of the paper (in single quotes), the title of the journal or conference (in italics), volume, page numbers, and publisher (ISBN desirable but not essential). For further help, see the following book, which is available in the library: • Cite Them Right: The Essential Guide to Referencing and Plagiarism by Richard Pears and Graham Shields An interactive online version of this guide is available by logging into My Sunderland with your User ID and password and then clicking on Me and Library Resources.
FASH 303 // The Business of Fashion Project 2: Business Plan 25% of final grade WHY (PURPOSE) Understanding the complexity and strategic direction required to establish a start-up business is key to developing a purposeful approach to design process and meaningful appreciation of how products, services brands, and consumers interconnect. This project will require in-depth business analysis, making use of a diverse range of sources and materials, which will enhance students’ research skills and strengthen their preparedness for employment. This group project also requires the development of core interpersonal, communication, and project management skills. WHAT (OVERVIEW) Using an original fashion/style/luxury business idea and the business plan template provided in class, complete a full business plan for a start-up business of your own. Complete each section of the plan based on the tips given by the template, and your personal research on each topic. You should aim at breaking down your initial idea in its core components and investigate the viability of each of these by researching things like manufacturing, retail costs, suppliers, promotional strategies etc… Your written information must be supported by visual documentation (photographs, diagrams, charts etc…). ALL information must be cited academically following either MLA, APA, or Chicago Turabian citation formatting standards. This includes both written and visual sources. The financial section should include a cashflow chart for the first year of operation accompanied by a cashflow forecast for the first year of operations (broken down by month) and a cashflow projection for the first 5 years (broken down quarterly). Remember that the purpose of a business plan is to convince a banker or investor to loan you money or make an investment, so be sure to clearly identify what information would be more important for them to know and present it clearly using appropriate language and visual material. SUBMISSION REQUIREMENTS // 25% of final grade - One PDF file of your full business plan, to be submitted through Blackboard. - Prepare and deliver a 3-minute “elevator pitch” presentation (supported by a Powerpoint or PDF slide deck) on your plan. The slide deck must be saved on the class dropbox prior to the beginning of class 13. The complete report must be submitted to Blackboard as a single PDF file, no larger than 10MB, using the appropriate link in the “Submissions” section of Blackboard. Formats other than PDF will not be accepted. All necessary files must be uploaded to blackboard BEFORE the beginning of class presentations. FEEDBACK SCHEDULE: · Work-in-Progress Feedback Feedback will be offered throughout the development of this project through regular in-class reviews. These reviews will include individual progress discussions with faculty, as well as peer-to-peer group discussions. Please refer to schedule of in-class work-in-progress review activities posted in the course syllabus. Students are welcome to email drafts of work-in-progress to Faculty for one-on-one guidance. When emailing work-in-progress for faculty review, please allow 48hrs for reply. · Critique and post-submission feedback Peer and Faculty feedback will be shared verbally during in-class presentations. Particular focus will be drawn to highlight both positive reinforcement and constructive guidance. All students are expected to participate and engage during this process to maximize the learning value of the formal presentation and discussion process. Assignment rubric score and written feedback from Faculty will be posted to Blackboard following submission of the completed project. · The rubric used for the scoring of this project, is accessible under the “Submissions” section on Blackboard.
CRIM 1161 A02 202135: Assignment 5: Public Law - Administrative and Criminal Law (12%) Assignment 5 has 100 marks,is based on Units 12 and 13,and is worth twelve percent of your course grade.You should allow sufficient time to benefit from feedback prior to your final exam.Please submit the assignment once you have completed Unit 13. Part A. Problem Solving(40 marks) Instruction For Part A, you will be presented with a fact pattern.Applying what you have learned about administrative and criminal law,you will analyze the fact pattern by answering five questions. Read the following scenario Lenka Marbella and Jules Chow had been living in Canada as common law spouses for two years when Lenka becamel seriously ill and had to quit her job. Her doctor prescribed opioid drugs for pain control. At the same time, Jules was on stress leave from his job as a mortuary attendant. According to his therapist, he had seen one too many victims of serious crime and was suffering from PTSD. The couple found it difficult to survive on Jules disability pay. So, when Lenka gradually began to feel better, instead of telling her doctor, she started selling her prescription drugs on the street. A year later, one of her customers, Tyson, followed her home and, before she could open her door, grabbed the bag she used for carrying her prescriptions. When Lenka tried to pull it back, Tyson threw her against her front doorl and punched her in the stomach. All this happened in sight of an undercover police car that had been tailing Tyson who was a known drug dealer. One officer grabbed Tyson and hustled him into the car. The other, instead of helping Lenka, yelled, "You' re under arrest" and wrestled her to the ground, banging her head off the sidewalk in the process. Meanwhile Jules, alerted by the racket, raced out of the house and found Lenka lying on the ground motionless in a pool of blood. When paramedics arrived, they pronounced her dead at the scene. No charges were ever filed against the police. All of this was so disturbing for Jules that he had to be hospitalized and sedated. His PTSD returned in full force and he was unable to return to work. When he tried to apply for criminal injuries compensation, the Chair of the Board refused to send him the application form. on the ground that no charges had been laid against the police and no crime proven. Jules found a lawyer willing to take the case to judicial review. The court held that the Board had denied him procedural fairness by raising the application evidentiary threshold so high that it precluded a chance to be heard atl all. The court sent the case back to the Board with a direction to issue Jules with an application package. When Jules's case was eventually heard, the Chair of the Board was on the three person panel. Jules' lawyer argued that he should be eligible for compensation because, as a result of criminal acts by the police and Tyson, he was suffering from severe mental and nervous shock. The panel in its decision, focused on the criminal acts of Lenka and reasoned that, because she was participating in crime herself, she could not be classified as a "victim" under applicable legislation. Lenka would not have been eligible for compensation had she lived. Thus, the reasoning of the panel went, Jules, in his role as her surviving spouse, could not be eligible either. And anyway, Jules must have known what was going on. The panel refused to allow Jules to testify about his illness on the ground that it was irrelevant. Nor would they allow a neighbor who saw the incident to testify as a witness on Jules' behalf. Now Jules wants to return to the court for another judicial review. Answer the questions Applying what you have learned about the rules of natural justice and assuming you are acting for Jules, answer the following questions. 1. Explain what breaches of natural justice may have occurred here? (10 marks) 2. Explain what error of law may have occurred with respect to Jules' status before the panel? (5 marks) 3. Find at least two court (not tribunal) cases in CanLIl that may help your arguments before the court. You may narrow your search by typing "criminal injuries compensation" as one of the terms in the document text field. Discuss how you apply these cases to Jules'judicial review. (15 marks) 4. Keeping in mind that this is a judicial review, not an appeal, if the court decides in Jules' favour, what would be the likely outcome? (5 marks) 5. Is there any information missing from the scenario that might affect the ultimate result of this case? (5 marks) Part B. Essay Question (60 marks) Background information for your essay The entrenchment of the Charter of Rights and Freedoms in 1982 has had a dramatic effect on the criminal justice system. For centuries before the Charter, the right to be presumed innocent until proven guilty was a core tenet of many criminal law jurisdictions. But that right did not preclude tactics that placed overwhelming power in the hands of the state through police and prosecutors. The legal rights set out in sections 7 through 14 of the Charteremphasize individual rights of the accused. The question is how far should these individual rights go? Where should the balance lie between protection of an accused and protection of the community from criminal activities, especially those involving crimes of violence? Section 11(b) of the Charterstates that: "Any person charged with an offence has the right to be tried within a reasonable time." This right is related to section 7: Everyone has the right to life, liberty and security of the person and the right not to be deprived thereof except in accordance with the principles of fundamental justice. But what is a "reasonable time" ? What is an "unreasonable delay" ? The Supreme Court of Canada (SCC) has wrestled with this issue for over 30 years. In Chapter 2 of your text, Boyd describes three cases that deal with section 11(b): R.v. Askoy. [19901 2 S.C.R. 1199: 1990 CanLIl 45 (SCC)-conspiracy to commit extortion and weapons charges-delay of 34 months R.v. Morin. 199211S.C.R.771: 1992 CanLII 89 (SCC)- driving while impaired- delay of 14 2 months R.v. Jordan. 20161 S.C.R. 631; 2016 SCC 27 (CanLII)- trafficking in hard drugs- delay of 49 months Following Askov, where Cory J, stated that a reasonable delay from committal to trial would be in the range of no more than 6 to 8 months, thousands of cases were stayed and accused walked free. Prosecutors treated these times as if they were fixed limitation periods rather than guidelines. Two years later, in the case of Morin, the Court modified their reasoning. As Sopika J stated: The purpose of the suggested period was not therefore that it was to be treated as a limitation period and inflexible.....The Court must acknowledge that a guideline is not the result of any precise legal or scientific formula. It is the result of the exercise of a judicial discretion based on experience and taking into account the evidence of the limitations on resources...... Gradually, the application of the guidelines in Morin became more flexible, unpredictable and longer than the guidelines in Askov. Meanwhile the criminal justice systems in most provinces became bogged down and it could be months, if not years, before a trial could be scheduled. Jordan, who had created a lucrative "dial-a-dope" business waited almost 50 months from charge to trial. When his case reached the SCC, a slim majority (5:4) of judges overruled Morin and created hard time limits - much longer than in Askov-with little room for judicial discretion (18 months for cases in provincial courts and 30 months for those inl superior courts). Despite these longer time limits, there have been hundreds of stays following Jordan, including charges of murder. As you have learned from your readings, there have been centuries of tension between rigidity and predictability on the one hand, and flexibility and judicial discretion on the other. For example, the development of the laws of equity gave discretion to judges whereas the rigid rules of common law put them in a straight jacket. More recently, legislated mandatory minimum sentences have prevented judges from providing alternative sentences when an individual case might warrant it. It remains to be seen how much discretion may be open to judges if they think justice requires a bending of the time limits in Jordan. Instructions Use the general instructions for essay writing set out at the beginning of your course materials, including an introduction, discussion and conclusion. Various academics, lawyers and columnists have written about Jordan and its consequences. For your essay, you will: 1. Read the majority judgment by Sopinka J in Morin and the additional concurring judgment by McLachlin J. Although not necessary for vour essay, you may supplement your understanding of references to the Askov case by reading its headnote (summary at the beginning of the judgment) and the complete short judgment of McLachlin J. 2. Read the headnote in Jordan. (You may wish to read the entire judgment if you feel it is necessary to fully understand the issues.) 3. Read the following two articles written after Jordan: 1. Drew Yewchuck, "Justice in a timely manner: The new framework for trial within a reasonable time" 2. Sean Fine, "Courts shaken by search for solutions for delays" in the Globe & Mail, March 2017 4. Carry out your own independent research for more discussion of Jordan. Your sources (no more than four) must come from reputable internet sites or academic journals; for example, CanLIl Connects, Canadian Lawyer Magazine, Canadian Criminal Law Review. You may access journal sites with the assistance of the TRU library. You must provide full citations for all your sources. For legal references, use the Canadian Guide to Uniform. Legal Citation as a guide to correctly formatting citations and references to legislation and case law. 5. Write your essay by comparing the approaches taken by the five majority judges and those taken by the four minority judges in Jordan. Based on your own research and the above readings, write your conclusion about the best way to deal with unreasonable delays in bringing cases to trial. In doing so you should consider why the majority judges chose to create ceilings. For example, see paragraph 50 of Jordan. A presumptive ceiling is required in order to give meaningful direction to the state on its constitutional obligations and to those who play an important role in ensuring that the trial concludes within a reasonable time: court administration, the police, Crown prosecutors, accused persons and their counsel, and judges. It is also intended to provide some assurance to accused persons, to victims and their families, to witnesses, and to the public that s. 11(b) is not a hollow promise. And why the minority favoured a modification of the approach in Morin as expressed by Cromwell J: [147] My colleagues would define reasonableness by assigning a number of months of delay — "ceiling[s]" (para. 5)— that will be taken to be reasonable unless the accused establishes not only that the case took markedly longer that it reasonably should have, but also that he or she took meaningful steps that demonstrate a sustained effort to expedite the proceedings. As I see it, this is not an appropriate approach to interpreting and applying the s. 11(b) right for several reasons. First, reasonableness cannot be captured by a number; the ceilings substitute a right for "trial under the ceiling[s]" (para. 74) for the constitutional right to be tried within a reasonable time. Second, creating these types of ceilings is a task better left to legislation. Third, the ceilings are not supported by the record or by my colleagues' analysis of the last 10 years of s. 11(b) jurisprudence and have not been the subject of adversarial debate. Fourth, there is a serious risk that the introduction of these ceilings will put thousands of cases at. risk of being judicially stayed. Fifth, the ceilings are unlikely to achieve the simplicity that is claimed for them. Finally, setting aside 30 years of jurisprudence and striking out in this new direction is unnecessary. Address the following questions in the discussion part of your essay by turning them into sections with appropriate section headings: 1. Should there ever be a stay for people charged with extreme violence such as murder? Or for complex crimes like money laundering that involve disclosure of thousands of complicated documents? 2. Keeping in mind the doctrine of supremacy of Parliament and recent controversy about "judge made law" under the Charter, should creation of time limits be left to legislators rather than judges? 3. What remedies other than a stay might you choose when there has been unreasonable delay? For example, inl Europe it is possible to award financial compensation if the accused is acquitted or a reduction in sentence if convicted. 4. Based on your discussions of A. through C., reach a conclusion, when applying section 11(b) of the Charter, about the best way to balance the rights of an accused with the right of society to be protected from criminal activity.
Questions 1. The Climate Dice are Loaded. Now, a New Frontier? 【ClimateDice.13July2023.pdf】 a. What does Hansen mean by “Climate Dice,” and using his analysis, are they more “loaded?” b. What does Hansen mean by “Earth’s energy imbalance?” c. Explain the logic of using 1880 as the start date for comparing global temperature. 2. Carbon Atlas Exercise a. Figures and Graphs i. Select five countries: two advanced economies, two developing, and one underdeveloped country. ii. Plot the total territorial, consumption, and transfer fossil fuel emissions measured in MtCO2 for each country. Also, define each type of emission and how it is different. iii. Comment on trends and theorize which emission (territorial, consumption, or transfer) captures that country’s emissions. b. Interpretation i. Repeat the same exercise for one, but change the unit to kgCO2/GDP. ii. Comment on trends and theorize which emission (territorial, consumption, or transfer) captures that country’s emissions. iii. What unit better illustrates the emissions of each country? 3. Over or Underpopulation Exercise a. Figure and Graphs i. Select five countries: two advanced economies, two developing, and one underdeveloped country. ii. Find each country's fertility and fatality rates using World Bank data—plot figures. iii. Plot all five countries “Food Production Index” 1. What is a “Food Production Index.” i.e., what are we measuring, and explain the concept of an index. b. Using the above arguments, agree or disagree with the following Malthusian quote, “I SAID THAT POPULATION, WHEN UNCHECKED, increased in a geometrical ratio, and subsistence for man in an arithmetical ratio (p. 6).” c. Interpretation 4. Go to the Monthly Energy Review a. Select “Energy consumption by sector.” b. Plot data from “Transportation sector energy consumption” Table 2.5. annually from the years 1949-2023 of the following categories: Coal Consumed by the Transportation Sector Natural Gas Consumed by the Transportation Sector (Excluding Supplemental Gaseous Fuels) Petroleum Consumed by the Transportation Sector (Excluding Biofuels) Total Fossil Fuels Consumed by the Transportation Sector Biomass Energy Consumed by the Transportation Sector Total Primary Energy Consumed by the Transportation Sector Electricity Sales to Ultimate Customers in the Transportation Sector End-Use Energy Consumed by the Transportation Sector Electrical System Energy Losses Proportioned to the Transportation Sector (Trillion Btu) (Trillion Btu) (Trillion Btu) (Trillion Btu) (Trillion Btu) (Trillion Btu) (Trillion Btu) (Trillion Btu) (Trillion Btu) c. What trends do you notice? d. What sector is the largest energy consumer, which is the least? 5. Using the same starting point, Monthly Energy Review, a. Select “Renewable Energy” b. Select 10.1 - “Production and consumption by source” c. Plot, annually the following indicators for the years 1981-2023 for production and consumption (2 different graphs: one for production, one for consumption. Wood Energy Production Biofuels Production Total Biomass Energy Production Total Renewable Energy Production Hydroelectric Power Consumption Geothermal Energy Consumption Solar Energy Consumption Wind Energy Consumption Wood Energy Consumption Waste Energy Consumption Biofuels Consumption Total Biomass Energy Consumption d. For both graphs, what trends do you notice? e. For both graphs, what is the largest producer (consumer) and smallest producer (consumer) f. Please explain why you think the period is much shorter compared to fossil fuel exercise in part 4? Grading Rubric The table below details how your instructor will be grading your assignments. Grading Question Possible Points 1 10 2 10 3 10 4 10 5 10 Total 50 Points per Question Logos 2.5 Pathos 2.5 Ethos 2.5 Climate Literacy 2.5 Total 10 Grading Scale 50 100 = A+ 47.5 95 = A 45 90 = A- 42.5 85 = B 40 80 = B- 37.5 75 = C 35 70 = C- Definitions Climate Literacy – Does the student identify each climate indicator (units, value, etc.) in their analysis of each question. Pathos – Emotional and Sympathy in your argument. Logos – Structure of your argument. Introduction, Body paragraph, and Conclusion for each answer, and is it at least 500 words, not including quotes. Ethos – Credibility of your argument. Students will create a Zotero account and then make a bibliography using APA citations. Please start a group called “ENRE PS1” and share the link in your final submission.
UNIT CODE: ACFIM0015 UNIT NAME: Algorithmic Trading DEADLINE: Monday 28 April 2025 before 13:00 BST SUBMIT TO BLACKBOARD UNIT SUBMISSION POINT Overview • Your summative coursework represents 100% of the final mark for the unit. • The coursework is in the form of a written report, in the style of an academic research paper. • Your report should describe the design, implementation, and testing/evaluation of an algorithmic trading system. The evaluation will require you to run a suitably large number of computer simulation experiments which are likely to take many hours (or days) of continuous computation: plan your time accordingly and do not leave this assessment to the last minute! • Penalties will apply if the coursework is submitted late. • Penalties will apply if the coursework is submitted in a format other than the one specified. • The coursework is an individual piece of work – you should work on this yourself and not as a group. You will be required to make a plagiarism statement and your submission will be tested for originality. • Use of AI text generators such as ChatGPT is prohibited. The text of your report should be written by you, in your own words. If we find that you have used ChatGPT, that will be treated with the same seriousness/severity as if you had been found guilty of plagiarism. Coursework Requirement Eight-page research paper on algorithmic trading strategies The Brief: For this coursework submission you will be given pre-existing Python code for an algorithmic trading system known as PT2, which operates as an intra-day proprietary trader (often abbreviated to a “prop trader”) of a cryptocurrency, i.e. a trader that starts each day with a sum of money and then buys and sells cryptocoins on a financial exchange, trading for its own profit, rather than on behalf of a third-party client, and at the end of the day liquidates any holdings into cash. Your assignment for this coursework is to modify, and potentially adapt, extend, or replace, the code for PT2 in an attempt at improving its performance relative to another prop trader algorithm known as PT1, which you will also be given the Python code for. You should compare the performance of PT1 and your version of PT2 in a market simulator where the dynamics of the price for the cryptocoin is modelled on time-series data from one or more cryptocurrencies. For this coursework you should use the Bristol Stock Exchange (BSE) market simulator, which is freely available on GitHub. BSE is a minimal simulation of the core mechanism within most technology-enabled financial markets: the Limit Order Book (LOB) and its associated Matching Engine. BSE is written in Python, and includes code for six different types of algorithmic trading “robots” called Giveaway, ZIC, Shaver, Sniper, ZIP, and PRZI, each of which trades according to some simple algorithm, and several of which were introduced in Lecture 3. ZIC is an implementation of the Zero-Intelligence Constrained strategy introduced by Gode & Sunder (1993); Sniper is inspired by, but not identical to, Kaplan’s Sniper (Rust et al., 1992). Giveaway, Shaver, ZIP, and PRZI are all by Cliff (Cliff, 1997, 2018, 2023). In keeping with the tradition for trading algorithms being referred to by a short sequence of letters reminiscent of a stock-market ticker-code, within BSE the Giveaway algorithm is referred to as GVWY, and the Shaver algorithm is referred to as SHVR. The source-code for BSE (a single file, BSE.py) is freely available under the MIT Open Source License, and can be downloaded from the GitHub repository: https://github.com/davecliff/BristolStockExchange. Documentation for BSE is in the repository’s Wiki: https://github.com/davecliff/BristolStockExchange/wiki. In the Python code for BSE you will see that PT2 is a verbatim clone of the simple prop trader algorithm called PT1. For this assessment, you should alter/extend/replace the PT2 code and then explore the behaviour of your new PT2 trader by running a series of sensibly structured comparative experiments, and then analyse the results from those experiments using appropriate visualization and statistical methods. You may want to adapt and extend the existing PT2 algorithm code, or you might want to instead implement someone else’s trading algorithm, such as “GD” by Gjerstad & Dickhaut (1998), which, like ZIP, was demonstrated by Das et al. (2001) to outperform human traders, or one of its extensions such as MGD (Tesauro & Das, 2001) or GDX (Tesauro & Bredin, 2002) – see also (De Luca & D. Cliff, 2011); or you may want to try your hand at developing your own novel trading algorithm from scratch – maybe involving contemporary machine learning methods such as XGBoost (Chen & Guestrin, 2016; Chen, 2023), or LSTM Deep Learning (Hochreiter & Schmidhuber, 1997; Wray, Meades, & Cliff, 2020; Cismaru 2024). Finally, you should write a brief report in the style of an academic research paper, as if you were going to submit it to an international conference for peer-review. It should clearly explain how your PT2 trader robot works, the design of the experiments to evaluate the performance of PT2, their outcomes, and your analysis of the results and whatever conclusions you draw in response to your analysis. You are free to choose how to structure your paper, but it should at least include a clear introduction and explanation of: 1) your PT2 algorithm, citing any publications that influenced your design; 2) your choice of performance evaluation metric; 3) your design of experiments to evaluate PT2; 4) statistical analysis of the performance of your PT2; and 5) the conclusions you draw from the analysis of your experiment data. Your report should be formatted according to the Springer Lecture Notes in Computer Science (LNCS) conference-proceedings format, details of which (including templates for Microsoft Word and for LaTeX) are available here: https://www.springer.com/gp/computer-science/lncs/conference-proceedings-guidelines The target word-count for your report is approximately 2000 words of text, but the hard length-limit is that it should be no more than eight pages in the required format (specified below) – in this format an entire page full of written English is about 500 words, so 2000 words of pure text would be only four pages, but to do a good job of this you will need to have figures/graphs/tables etc as well as the written narrative. should be no longer than eight pages in LNCS format, including all figures, references, equations, tables, and any snippets of code or pseudocode that you include to explain your PT2 algorithm. The full code for your Trader_PT2 algorithm should then be included as an appendix. The additional pages for appendices showing your code are not counted in the 8-page limit. Your paper That is, you can write eight pages of content explaining your work, and then the content on Page 9 and onwards should be just the code for your PT2, along with details of any edits you made elsewhere in BSE.py. Background: All of the robot trader strategies available in BSE other than PT1 and PT2 have been implemented to act in a manner inspired by the job of sales traders in real financial markets. A sales trader receives orders to buy and/or orders to sell from her clients, and then tries to best execute the order in the market: the client supplies the limit price, and the sales trader tries to get a deal at a price better than the limit. In all the published research literature that we looked at in the lectures, the robot trading algorithms such as GD/MGD/GDX and ZIP have been tested in their capacity as sales traders: the robot is given an order with a limit price, and then the robot tries to get a deal at a price better than the limit. However, in most markets there are not just sales traders. Another type of trader that a financial firm might employ is a proprietary trader, or “prop trader” . Prop traders buy and sell using the firm’s own money, or their own personal money: the job of the prop trader is to make a profit on these transactions, so basically the name of the game is to buy things when their price is low and sell them when their price has risen. Sometimes it may be necessary to sell for a lower price (e.g., if the market it is crashing, it is better to sell at a small loss now than wait and then have to sell for a larger loss later). Prop traders are usually allowed to buy and sell any type of financial instrument that they can make money on, although usually they will specialize in various asset classes: one person might be a prop trader in currencies; another might prop trade in tech stocks only; and so on. The most specialized a prop trader can be is to buy and sell just a single financial instrument, and BSE has only one tradable instrument, so you are being asked to develop a single-instrument intra-day prop trader for your PT2. Recent intra-day cryptocurrency price time-series, binned at 5-minute time-resolution, are freely available for download from finance.yahoo.com. CSV-format data files for three example intraday cryptocurrency price time-series are available on the Blackboard page for this assessment, along with instructions for how to download more from finance.yahoo.com. BSE.py (version 1.95) has been edited and extended to make it as simple as possible for you to complete this coursework assignment. PT1 and PT2 are initially identical, and the PT1 algorithm is explained in its class-definition docstring (to find this in the code, search for TraderPT1 (Trader) -- the explanatory “docstring” text follows immediately after this). In the code you download from GitHub, PT2 is a verbatim copy of PT1: your task for this coursework is to edit PT2, changing it to use whatever signals and algorithm you decide to implement; please only edit the definition of PT2, you should leave all the other trader-types as they are. It's important that you leave PT1 unchanged because that way you can compare the profitability of your revised/extended PT2 to that of the original PT1. BSE.py offers two ways by which to specify the intra-day price-changes that are used to set the pattern of movements in the prices of the "customer order" assignments that the non-prop-trader agents execute in their role as automated sales traders, which in turn affect the bid and offer prices on the LOB and thereby influence the series of transaction prices seen in the market: the price changes can be specified by calling a price-offset function that returns a time-varying offset value determined by an equation; or the price-changes can be specified via an external data-file that is read in by the if name == "__main__" code at the end of the BSE.py file. In the __main__ code you can see instructions and three local function definitions that implement this functionality. The three local functions are: schedule_offsetfn_read_file(...) -- this reads in intraday prices from a .csv file and returns the sequence of [time, price] pairs in a single list. schedule_offsetfn_from_eventlist(time, params) -- this returns the price offset for the current time by reading it from the list of [time, price] pairs. schedule_offsetfn_increasing_sinusoid(t, params) -- this returns the price offset for the current time by calculating it from a sinusoidal equation that increases in amplitude and frequency over time. To switch between offsets coming from a file or from an equation, you can edit the __main__ code that sets up range1 and range2 for the supply_schedule and demand_schedule respectively. In BSE.py, the supply and demand schedules are set to read their price-offsets from a file. Immediately after the opening test for if name == "__main__" come three lines of code that deal with assigning a value to the variable price_offset_filename, which is the name of the data-file that will be read in. The first is a simple assignment: price_offset_filename = 'offset-BTC-USD-20250210.csv' -- this reads in a CSV file of intra-day BTC-USD prices recorded at 5-minute time resolution on 10th February 2025. If you press "run" in your integrated development environment (IDE, such as PyCharm or Idle) or if you execute from your PC's terminal command-line interface (CLI) via a command such as: python3 BSE.py then the BSE market session(s) you run will use the movements of BTC-USD stock on 10/02/25 to generate the price offsets. Thus, one way of altering what data-file is read from is to just edit that assignment of price_offset_filename to some other filename. However, another way of altering the name of the price data file is available if you're using the CLI: if you supply an additional argument to the call of BSEv.1.95.py then that filename overrules the default BTC-USD filename. For example, if you enter on the CLI: python3 BSE.py offset_BTC_USD_20250211 then your BSE price-offsets will be based on the data from that CSV file (which is a time-series of movements in the USD price of BTC on 11th February 2015) instead. As an example, Figure 1 shows the sinusoidal offset function produced when using a positive value as the scale parameter for schedule_offsetfn_increasing_sinusoid:the prices offsets get progressively larger and the average price steadily rises (if scale is negative, the price offsets get progressively more negative and the average price declines over the session). Figure 1: Sinusoidal price offset function with scale>0, resulting in peak prices progressively rising. And Figure 2 then shows the sequence of transaction prices in BSE when that offset function is used. Figure 2: Transaction prices for a single 7.5hour BSE market simulation using the equilibrium price-offset function shown in Figure 1. Horizontal axis is time measured in hours; vertical axis is transaction price measured in cents. Each blue data-point marker on the graph is an individual transaction. As can be seen from Figure 2, the time series of transaction prices in BSE follows the shape traced by the offset function of Figure 1, but there is quite a lot of noise/variance in the actual transaction prices because of the presence of "noise traders" such as ZIC in this market. Figure 3 shows the accumulated bank-balance of a single PT1 trader operating in the market experiment of Figure 2. Figure 3: Account balance of a single PT1 trader operating in the market session for which transaction prices were illustrated in Figure 2. Horizontal axis is time measured in seconds; vertical axis is account balance in cents. Figure 4 shows the 24-hour time series for intraday price movements in the example offset data file for BTC- USD on 11th February 2025. Figure 4: 24-hour time-series for USD price of Bitcoin at 5-minute intervals over 11th February 2025. And Figure 5 then shows the transaction-price time series for a one-day BSE session using that 11/2/25 BTC- USD data-file for the offset function: the pale blue markers are the transaction prices; the red markers show the simple moving average (SMA) of the most recent 100 transaction prices. As you can see, the SMA matches the movements of the BTC-USD input data very well. Figure 5: Transaction-price time-series in a market session for which the price offset function was illustrated in Figure 4. Format as for Figure 2. Blue markers are individual transaction prices; red markers are the simple moving average (SMA) of transaction prices over the preceding five minutes (300sec). As can be seen from Figure 5, the rises and falls in the SMA(300) very closely match those of the original BTC-USD time-series, and the difference in absolute values of prices (i.e., the BTC prices are all in the $90,000 range, while the BSE prices are in the range $1-$2) is merely a result of a constant scaling coefficient applied within BSE. Figure 3 shows that the PT1 trader has periods (when the market price is rising) where it is trading profitably and its bank-balance is growing, and periods (when the market price is falling) where it is no longer making a profit but is not actually losing money either. Your job for this assessment is to try to come up with something more profitable than PT1: good luck! You are certainly not limited to using only the three one-day BTC-USD price time-series data available on Blackboard: similar intra-day price time-series for very many cryptocurrencies are available online, and you should use as many one-day price series as you think appropriate. If you are using a data-intensive machine- learning approach such as Deep Learning neural networks, you may find that you need a lot of training data, more than is available from sources of real-world intraday price series. In that case you may want to write a synthetic data generator (SDG) that uses a known/accepted mathematical model of financial asset price changes such as Geometric Brownian Motion with Jumps, to create plausibly realistic fictional intra-day data: see, for example: https://mtns.math.nd.edu/papers/19046_4.pdf . You may initially want to place a hard limit on the size of any one trader’s inventory: this should help keep the market liquid, as it will prevent one trader amassing a huge inventory. For example, if the limit on inventory size is three, any AMM trader can buy stock until it is holding three items, at which point its only allowable action is to sell. Similarly, if an AMM trader sells an item and thereby reduces its inventory to zero items, its only allowable action will be to buy (that is, the AMM trader cannot “short” the stock, selling items it does not currently own: so technically we are asking you to implement a long-only AMM trader). Traders holding one or two items in their inventory would be free to sell or to buy (or to simply wait), depending on what their strategy indicates is best to do, given the current market conditions. It is fine if you want to start by using a previously-published algorithm like ZIP or MGD and then alter or extend it – that’s how a lot of progress in science and engineering is made. But if you want to start from scratch, that is absolutely fine too. We’re not requiring you to write a new (or revised) algorithm, but you can probably pick up some extra marks by at least trying to do so: your algorithm certainly doesn’t have to be world-beating, but you are expected to explain the design choices you made and to show that you know how to experimentally evaluate a new trading algorithm in the relatively simple context of BSE. Also, your PT2 is not required to be better than PT1: it could be that you decide to implement some interesting-looking new machine learning method, Method X, in your PT2 but then in evaluation testing you realised that for some reason Method X does not work well in the context of intra-day crypto trading. You will not lose marks for negative results such as this, so long as the design decisions you make in creating your PT2 are well explained and justified, and so long as your implementation and evaluation/testing analysis is appropriately designed and rigorous. You should write your PT2 code using the current stable release of Python (Version 3.11, as of Feb. 2025). Along with your paper, we would like you to submit the source-code for your trader class, as an appendix. If you make any changes to other parts of BSE (e.g. to produce additional output files from a market session), please also explain those in an appendix to your paper,but note that we expect your PT2 to run in the GutHub version of BSE.py. Please do not include all of your version of BSE.py: we are not interested in the code you didn’t edit: all that matters is the code for your new PT2 trader, and specific details of any changes you made elsewhere in BSE.py. Please add the trader code as an appendix to your paper, and also any code snippets for edits elsewhere in BSE.py, so that everything required is all in one PDF file. Please only submit a single PDF file and nothing else (that is, no source-code other than your Trader_AMM, no data files, no gzip files, no tarballs). We will randomly select some of your submissions for test-runs: we’ll take your trader code and run it in the GitHub release of BSE.py, to check that the results in your paper are independently replicable. If we can’t replicate your results within reasonable error margins, then the final grade you get is likely to be severely reduced. The bottom line is: (a) to be safe, don’t make any substantive changes to BSE.py except for the Trader sub-class that you edit; (b) don’t fake or edit your results, because falsifying results is academic misconduct on the same level as plagiarism or cheating in an exam. Marks will be awarded for: • Quality of Experiment Writeup: 25% How well is the paper structured? How clearly does it explain the background to your work, and what you have done? • Quality and Presentation of Results: 25% How thoroughly is the experimental evaluation carried out? How clearly are the results presented? • Quality of Statistical Analysis and Conclusions: 25% Are appropriate statistical analyses chosen and conducted correctly? Are correct conclusions clearly drawn from the analysis, and explained well? • Challenge and Originality: 25% How challenging is the task you set yourself? How comprehensive are your PT2 and the experimental setup? How extensive is the analysis? Marks will be deducted for: • Your paper is not submitted in LNCS format. • The text of your paper is longer than 8 pages. The 8-page limit includes everything: title, abstract, main text, all figures/graphs/diagrams/tables, the References/Bibliography, all footnotes and endnotes, and any appendices other than the source-code appendix. The source-code appendix showing your Python code should start no later than at the top of page 9. You can use as many pages as you need to print your Python code. • You have set a smaller font-size than the LNCS standards, and/or you have set the margins to be thinner, and/or you format graphs/diagrams/figures/tables to occupy ridiculously small amounts of page-area, all of which are common (but idiotic) attempts to fit more content on each page, in the hope that we don’t notice that actually you’ve written much more than the 8-page maximum in standard LNCS format. • You have set a larger font-size than the LNCS standards, and/or you have set the margins to be wider, and/or you format graphs/diagrams/figures/tables to occupy disproportionately large amounts of page- area, all of which are common (but idiotic) attempts made to fit less content on each page, in the hope that we don’t notice that actually you’ve written much less than the 8-page maximum in standard LNCS format. Please note that the LNCS format sets specific margin widths on all four edges of the page and uses a 9- point (9pt) font for the text of the Abstract, for Footnote text, and for the References, and a 10pt font or the main text of the paper. The conventional advice for the labelling in figures/diagrams/graphs/tables is that the font-size used for the labels should be no smaller than the font used for footnotes, so please ensure that the font size on all label-text in your figures/diagrams/graphs/tables, when sized to print in your paper, is no less than 9-point. References H. Ashton (2022), Law-breaking trading algorithms: Emergence and Deterrence. PhD thesis, Department of Computer Science, University College London. https://discovery.ucl.ac.uk/id/eprint/10155509/3/Ashton_10155509_Thesis_id_removed.pdf. T. Chen, (2023), XGBoost Documentation. https://xgboost.readthedocs.io/en/stable/index.html. T. Chen & C. Guestrin (2016), XGBoost: A Scalable Tree Boosting System. In Proceedings 22nd ACM International Conference on Knowledge Discovery & Data Mining (KDDM2016) pp.785-794. https://arxiv.org/abs/1603.02754. A. Cismaru, (2024) DeepTraderX: Challenging Conventional Trading Strategies with Deep Learning in Multi-Threaded Market Simulations. Proceedings of the 16th International Conference on Agents and Artificial Intelligence (ICAART2024). SSRN: https://ssrn.com/abstract=4692622. D. Cliff (1997), Minimal-Intelligence Agents for Bargaining Behaviours in Market-Based Environments. Hewlett- Packard Labs Technical Report HPL-97-91. https://www.researchgate.net/publication/235963415_Minimal- Intelligence_Agents_for_Bargaining_Behaviors_in_Market-Based_Environments#fullTextFileContent. D. Cliff (2018), BSE: A Minimal Simulation of a Limit-Order-Book Stock Exchange. In: Bruzzone F (ed) Proceedings of 30th European Modeling and Simulation Symposium (EMSS2018), pp 194-203. https://arxiv.org/pdf/1809.06027. D. Cliff (2023), Parameterized Response Zero Intelligence Traders. Journal of Economic Interaction and Coordination, 19:439-492. SSRN: https://ssrn.com/abstract=3823317. R. Das, J. Hanson, J. Kephart, & G. Tesauro (2001), Agent-Human Interactions in the Continuous Double Auction. In: Proceedings IJCAI-2001, pp. 1169-1176. https://dl.acm.org/doi/10.5555/1642194.1642251. M. De Luca & D. Cliff (2011), Agent-Human Interactions in the Continuous Double Auction, Redux: Using the OpEx Lab-in-a-Box to explore ZIP and GDX. In Proceedings of the 3rd International Conference on Agents and Artificial Intelligence (ICAART-2011), pp. 351-358. https://www.scitepress.org/papers/2011/32939/32939.pdf. S. Gjerstad & J. Dickhaut (1998), Price Formation in Double Auctions. Games and Economic Behavior, 22(1): 1-29. https://www.sciencedirect.com/science/article/abs/pii/S0899825697905765 . D. Gode & S. Sunder (1993), Allocative efficiency of markets with zero-intelligence traders. Journal of Political Economy, 101(1): 119-137. https://www.jstor.org/stable/2138676. A. Guarino, L. Grilli, D. Santoro, F. Messina, & R. Zaccagnino (2022), To learn or not to learn? Evaluating autonomous, adaptive, automated traders in cryptocurrencies financial bubbles. Neural Computing and Applications, 34: 20715-20756. https://dl.acm.org/doi/abs/10.1007/s00521-022-07543-4. S. Hochreiter & J. Schmidhuber (1997), Long Short-Term Memory. Neural Computation, 9(8): 1735-1780. https://direct.mit.edu/neco/article-abstract/9/8/1735/6109/Long-Short-Term-Memory. J. Rust, J. Miller, & R. Palmer (1992), Behavior of trading automata in a computerized double auction market. In: D. Friedman & J. Rust (eds.) The Double Auction Market: Institutions, Theories, & Evidence , pp.155-198. Addison- Wesley. https://www.santafe.edu/research/results/working-papers/behavior-of-trading-automata-in-a-computerized-dou. P. Shinde, I. Boukas, D. Radu, M. de Villena, & M. Amelin (2021), Analyzing Trade in Continuous Intra-Day Electricity Market: An Agent-Based Modeling Approach. Energies 14, 3860. https://www.mdpi.com/1996-1073/14/13/3860. G. Tesauro & R. Das (2001), High-Performance Bidding Agents for the Continuous Double Auction. In: Proceedings of the 3rd ACM Conference on Electronic Commerce, pp.206-209. https://dl.acm.org/doi/10.1145/501158.501183. G. Tesauro & J. Bredin (2002), Sequential Strategic Bidding in Auctions using Dynamic Programming. In: Proceedings AAMAS 2002. https://dl.acm.org/doi/pdf/10.1145/544862.544885. C. Van Oort, E. Ratliff-Crain, B. Tivnan, & S. Wshah (2023), Adaptive Agents and Data Quality in Agent-Based Financial Markets. Arxiv: https://arxiv.org/pdf/2311.15974. A. Wray, M. Meades, & D. Cliff (2020), Automated Creation of a High-Performing Algorithmic Trader via Deep Learning on Level-2 Limit Order Book Data. In Proceedings of the 2020 IEEE Symposium Series on Computational Intelligence (SSCI). Arxiv: https://arxiv.org/pdf/2012.00821.
Assessment 3-Group Business Proposal- 3200 to 4000 words Learning Outcomes Learning Outcome 1 Demonstrate an understanding of core theoretical frameworks that underpin the fields of entrepreneurship and marketing Learning Outcome 2 Evaluate and critique proposed entrepreneurial endeavours and marketing activities through an examination of their theoretical and market foundations Learning Outcome 3 Explain innovation and critique the innovativeness of proposed entrepreneurial endeavors and marketing activities Assessment Task: As a team write a Business Proposal for a new business startup. Your proposal needs to cover the following areas: Executive Summary (not counted in word count) Table of Contents (not counted in word count) 1. Introduction - Who are you and what is your idea? 2. Problem - What problem are you trying to solve? Is it really a problem? 3. Solution - Describe how are you planning to solve the problem. 4. Advantages - What makes your solution special? How are you different from others? 5. Product - How does your product or service actually work? Show some examples. 6. Traction-Traction means having a measurable set of customers that serves to prove a potential. 7. Market-Know, or at least attempt to predict, the size of your target market. 8. Competition - What are the alternative solutions to the problem you are trying to solve? 9. Business model - How are you planning to make money? Show a schedule (at least 1 year) when you expect revenues to pour in. 10. Investing - What is your planned budget? What kind of money are you looking for? Include a detailed budgeting plan for the first year. References-a minimum of 20 references required (40+ recommended) (not counted in word count)
EEE 203: Signals and Systems I Course Description: Introduces continuous and discrete time signal and system analysis, linear systems ourier, an z-ransforms. Course Overview In this course, you will learn continuous-time (CT) and discrete-time (DT) signals CT and DT system impulse response linearity, time-invariance, causality and block diagrams of systems CT and DT convolution Fourier transform. and its properties frequency response and frequency-domain analysis of CT systems Laplace transform. and its properties Z-transform. and its properties introduction of frequency-domain analysis of DT systems sampling theorem BIBO stability Course Learning Outcomes At the completion of this course, students will be able to: state and apply time-domain properties of continuous-time (CT) and discrete-time (DT) linear time-invariant (LTI) systems. apply the CT Fourier transform. in signal analysis. understand and can use fundamental frequency-domain properties of CT LTI systems. Textbooks Required Textbook: Signals and Systems by Alan V. Oppenheim, 2nd edition, Prentice Hall 1997 (ISBN: 0138147574). Reference Book: Signals and Systems Made Ridiculously Simple by Zoher Z. Karu, 1st edition, Zizi Pr, 1995 (ISBN: 0964375214). Short and concise introduction to Signals and Systems, Appendix has good review topics on complex number, trigonometric identities, partial fraction expansion, sequences and series and etc. Schaum's Outlines Signals and Systems by Hwei P. Hsu, McGraw-Hill 1995 (ISBN: 0070306419). More examples for you to practice Software You will need MATLAB to complete lab assignments and run examples. You can download MATLAB from My ASU (http://my.asu.edu) My Apps (the 6th item on the left column). You may then search for MATLAB and install the latest version. If you have trouble installing MATLAB, please send email to [email protected] and describe the specifics of your error. You need to install the following Toolboxes: Signal Processing Toolbox (butter command, Week 4 Lab 3) Communications Toolbox (amdemod/ammod commands, Week 5 Lab 4) Control Systems Toolbox (feedback, tf, stepinfo commands, Week 6 Lab 5) Simulink (step command, Week 6 Lab 5) Two useful MATLAB tutorials are listed below: Interactive Online Tutorial: "MATLAB Onramp" at https://matlabacademy.mathworks.com/Links to an external site.. You will need a Mathworks account to sign in. A Beginner's Guide to MATLAB: matlab_guide.pdf
Cash Is King: Master Budgets to Inform. a Credit Decision INTRODUCTION Early one morning in March, Jordan Buford was preparing his daily work when his boss, Olivia Anton, approached him and announced, “Little Annin Flagmakers (LAF) has submitted an application for a line of credit (LOC) for April through June. I want you to prepare budgeted financial statements similar to the ones you prepared for our last LOC applicant. I need this by 3 p.m. today for the 4 p.m. credit committee meeting. Be prepared to make a loan recommendation and to address questions from the credit committee. I have cleared your schedule. Let me know if you need anything.” Kent Bank is a state bank with multiple branches that offers a variety of services for personal and commercial needs. The bank has been serving the local community for more than 110 years and prides itself on its personalized approach to provide financial services, local management, long-term stability, and a full range of deposit and lending products and services. Commercial credit decisions at Kent Bank are made by the Commercial Credit Committee, which consists of the senior commercial credit analyst and two vice presidents. Buford was recently hired by Kent Bank as a commercial credit analyst to provide analysis for commercial loan applications. During his undergraduate studies, he studied accounting and finance, and shortly after graduation passed the CMA® (Certified Management Accountant) examination. Buford reports directly to Anton, the senior commercial credit analyst who has been with Kent Bank for 10 years. As Buford began the work, he recalled his last LOC analysis and how well received it was. He had taken the information provided by the company and developed master budgets in Excel that used an input section with numbers that could be changed for assessing different scenarios. The committee had specifically asked about the effect of a sales reduction of 2%, 5%, and 10% on the applicant’s cash needs. He wanted to be prepared for these types of questions. LITTLE ANNIN FLAGMAKERS BACKGROUND LAF manufactures one product, a large durable 8’ × 12’ American flag, which it sells for US$120. Because of the large size of the flag, this product is not sold in stores; rather it is sold through a relatively small number of online retailers. Each quarter, retailers estimate sales for the upcoming five months, revising proximate sales as necessary. In general, the retailers are reasonably good at estimating their sales needs, but some variation in demand does occur, and the retailers expect to be able to adjust orders as needed. LAF allows retailers to adjust each month’s purchases to 80% to 120% of the estimated sales levels. Flags are shipped to retail customers using JIT distribution so that the online retailers do not have to store inventory. Typical sales for the flag are 1,800 units per month with seasonal increases in April through August. Sales estimates are 2,500 units in April, 6,000 units in May, 3,000 units in June, 2,500 units in July, and 2,000 units in August. Customers historically have paid 40% of their purchases in the month of the sale, 55% in the following month, and the remaining 5% is uncollectible. MANUFACTURING AND SG&A COSTS The flags are made in one plant, which has a capacity of 6,200 units per month. LAF budgets have 20% of next month’s sales in finished goods inventory at the end of each month. There is plenty of storage space for finished goods. Fabric is the only direct material and each flag requires five pounds of fabric at US$7 per pound. LAF plans to have 40% of next month’s fabric needs on hand at the end of the month. Fabric is purchased on credit with 40% paid in the month of purchase and 60% paid the next month. The standard direct labor hours to manufacture one flag is 0.50 hours at US$40 per hour. For simplicity, direct labor costs are budgeted as if they were paid when incurred. Manufacturing overhead rates are computed quarterly and applied based on direct labor hours. Fixed manufacturing overhead costs are estimated to be US$57,950 per month, of which US$20,000 is property, plant, and equipment (PPE) depreciation. Variable manufacturing overhead, including indirect materials, indirect labor, and other costs, is estimated at US$10 per direct labor hour. The selling and administrative expenses include variable selling costs (primarily shipping) of US$1.25 per unit and fixed costs of US$63,000 per month, of which US$10,000 is depreciation of the administrative office building and equipment. FINANCIAL STATEMENT DETAILS AND CASH PLANNING LAF uses first in, first out (FIFO) inventory valuation. As of March 31, the expected finished goods inventory is 410 units, valued at US$75 per unit. The company expects to have 4,600 pounds of fabric on hand, valued at US$7 per pound. Other expected account balances include accounts payable at US$55,000, accounts receivable at 132,000, cash at US$37,745, land at US$520,000, and building and equipment at US$1,800,000 with accumulated depreciation of US$750,000. LAF has no long-term debt; common stock is valued at US$500,000 and is not expected to change during the quarter; expected retained earnings as of March 31 are US$1,247,695. LAF budgets for US$30,000 ending cash balance each month and is requesting a line of credit that will allow it to adjust for its cash needs. The dividends of US$15,000 are paid each month. During the quarter, LAF planned to purchase equipment in May and June for US$47,820 and US$154,600, respectively. This equipment is being purchased to increase capacity and is not expected to come on line until after the quarter, thus not affecting the manufacturing overhead costs. LOAN DETAILS LAF has requested a line of credit of US$60,000 to cover production costs during the seasonal increase in business. Kent Bank uses the following terms on its lines of credit. All borrowing is done at the beginning of the month in whole dollar increments. All repayments are made at the end of the month in whole dollar increments. The full line of credit is expected to be paid off by the end of the quarter with all the interest repaid at the end of the quarter. The interest rate on this loan is 16% per year. REQUIRED 1. Using the data input provided (Exhibit 1), prepare LAF’s master budgets in Excel. Do not hard-code numbers into the spreadsheet, except in the financing section of the cash budget. 2. Conduct a sensitivity analysis, decreasing sales 2%, 5%, and 10% for April through August. New sales levels are provided in Exhibit 2. Adjust the financing and cash needs at these new sales levels. 3. Determine a credit recommendation for Kent Bank, to lend or not. Be prepared to justify your credit decision. 4. Explain why the cash budget is more important to a bank than the accounting net income when determining a credit decision. 5. Explain why decreases in sales is examined in a sensitivity analysis for a credit decision.
Individual Assessment Date Due: 22 Oct 2024 @ 11:59 p.m. Submit your completed Assignment to the blackboard under the Assessment Folder. This assignment is worth 30% of your final grade. Question 1: · Why the project management concept is essential and core for any business nowadays. Ø Please add four (4) references to support your discussion. Ø Whenever you need to quote ideas and/or the quotes of any other author, you should acknowledge this using the Chicago referencing method (17th ed.). Ø Be approximately 600 to 700 words in length, excluding diagrams and references. (12 Marks) Question 2: 1. Develop a WBS (Work Breakdown Structure – using the five phases) to write and publish a research study in a book with an international publisher; be sure to identify the deliverables and organizational units (People) responsible. Develop a corresponding OBS (Organization Breakdown Structure) which identifies who is responsible for what. a. Please use ONLY MS word application for this question b. For each WBS task, please list the resource(s) c. No need to add the time and cost for each WBS task. (12 Marks) Question 3: 2. Given the network plan that follows, compute the early, late, and slack activity times; determine the planned project duration; and identify the critical path. (6 Marks)
SEMT20001-R: Principles of Computational Modelling COURSEWORK REASSESSMENT: QUESTION 4 Question 4: Equation-based modelling (a) (i) Write Python code that numerically solves an ODE system (1) given a right-hand-side function f, timespan [t0, tf], initial condition y0, and number of steps N / step-size h = (tf - t0)/N, using the time-stepping algorithm （2) where (2 marks) (ii) Using both the forward Euler method, and the method defined in part (a)(i), solve the ODE initial value problem (3) for t ∈ [1, 3], with step-size h = 0.5. Plot a graph of the numerical solutions, together with the exact solution (4) and comment on the results. (4 marks) (iii) Plot a graph of the absolute error of the numerical solution of equation (3), using the method given in part (a)(i), versus step-size h, for a suitably-chosen range of h. You should use the Euclidean distance between the numerical solution (5) to define the error. Use the graph to find the order of the truncation error as h → 0. (4 marks) (b) The ODE system dt/dy = f(t, y), written in terms of components y = (S, E, I, R), represents an equation-based model of the transmission of a non-fatal disease with four states: in which the population is divided into four groups: • Susceptible: healthy and not yet infected with the disease, • Exposed: infected with the disease, but not yet infectious, • Infectious: both infected and infectious (i.e., able to pass on the disease to others), • Recovered: healthy, with long-lasting immunity. The variables in the model, S(t), E(t), I(t), and R(t), are the (non-dimensional) proportions of the total population in the four states at any time t. The parameters are b the birth rate, d the natural-causes death rate (assumed equal across the population groups), β the infection rate, γ the recovery rate, and α the infected to infectious rate (so 1/α is the average latency period). All the parameters are assumed to be constant and positive, and measured per simulated day. (i) Use solve_ivp to solve the SEIR model (6), with parameter values b = d = 0.01, α = 0.25, β = 0.2 and γ = 0.1, for t ∈ [0, 3 × 365], with initial conditions S(0) = 0.99, E(0) = 0.01, I(0) = 0 and R(0) = 0, and plot a graph of the solution versus time. Explain what the results mean in the context of the infection in the population. How long does it take for the solution to reach a steady state, where the magnitude of the rate of change of the solution, |dt/dy|, is less than 10-5 ? (4 marks) (ii) Plot a graph of the steady-state solution of the SEIR model (6) versus β, for β ∈ [0, 0.5], by using solve_ivp to solve the SEIR model (6) until the dynamics reach a steady state for a range of values of β . The other parameters should be fixed to the values given in part (b)(i). Explain the graph you have obtained, in the context of the infection in the population. (4 marks) (c) As described in the lecture notes, the Robertson system is an equation-based model of a reaction between three chemicals X , Y , Z, with concentrations x(t), y(t), z(t): where the reaction rates are k1 = 0.04, k2 = 3 × 107 , k3 = 104 . Write a Python function that measures the performance of a solve_ivp solver method, when solving the Robertson system (7) for t ∈ [0, 5] with initial conditions x(0) = 1, y(0) = z(0) = 0, by returning: • the number of right-hand-side function evaluations of solve_ivp, • the average run time of solve_ivp, given a solve_ivp method (the lectures used method= ' RK45 ' and method= ' Radau ' ), and desired relative and absolute tolerances, rtol and atol. Use your code to plot graphs of • the number of right-hand-side function evaluations of solve_ivp, • the average run time of solve_ivp, versus relative error tolerance rtol, for at least four solve_ivp solver methods, with values of rtol between 10-4 and 10-8 and atol=1e-3*rtol. Interpret the results, and explain how they might influence your choice of method when using solve_ivp. (7 marks)
Q1. News about Security Incident [10 Marks] Write a summary for a recent piece of news about an Information Security or Network Security incident, and discuss the following points: - How was it done? - What was the technical issue behind? - Why is the incident important? - What is the lesson learnt? - How can it be prevented? - … Write NO MORE THAN 300 words, and please also submit a copy of the news or the corresponding URL. Q2. Security Implications of RC4 on Wi-Fi Security [20 Marks] The security of Wi-Fi can be broken due to RC4. Investigate the issue and explain why it is the case in your own words. Notes: - Before diving into the “meat”, include a section titled Problem Statement, to clearly define the problem, clarify relevant terms and concepts, and explain “what can go ” wrong . - Provide at least two reasons and cite the referenced material. - Go beyond a high-level overview; do include technical details. Q3. Modes of Operation for AES [30 Marks] Alice uses AES-128 and one of the modes of operation to encrypt 12 blocks/units of plaintexts, P1 P2 P3 … P12, to get the corresponding blocks/units of ciphertexts, C1 C2 C3 …C12. The IV together with the cipher blocks are then transmitted to Bob through an unreliable channel. In the following cases, what will Bob get when he decrypts the received cipher blocks (i.e., recovering P1’ P2’ P3’ … P12’) with the same mode of operation as Alice uses? * (a) The CBC mode is used; IV, C5, C6, C9 and C10 are corrupted. (b) The CFB-16 (a.k.a. 16-bit CFB) mode is used; the leftmost (most significant) 10 bits of C2 are corrupted. (c) The CFB-8 mode is used; the entire C2 (8-bit of it) is lost, and Bob treats the received C3 as C2, C4 as C3, … (d) The OFB (a.k.a. OFB-128) mode is used; the rightmost (least significant) 32 bits of C4 are corrupted. (e) The CTR mode is used; C2 is corrupted. Your answer should include at least the following: - Among P1’ to P12’ , Bob will decrypt which one(s) correctly and which one(s) incorrectly. - For those blocks/units that cannot be decrypted correctly, what’s the reason? Please use their respective encryption/decryption equations to illustrate your point. References: - Chapter 20.5 “Cipher Block Modes of Operation” of the book Computer Security: Principles and Practice. - Chapter 7.2 – 7.6 of the book Cryptography and Network Security: Principles and Practice. * For simplicity, we assume that the actual data sent over the channel may vary across the 5 cases, but all are labeled P1 to P12 and C1 to C12. For example, the length and actual content of P1 in (a) (a 128-bit block) may differ from P1 in (b) (a 16-bit unit). Q4. Meet-in-the-middle Attack [30 Marks] Consider the Meet-in-the-Middle attack of Double DES (the weakened, exportable version), with 64-bit data blocks and two 40-bit keys. If a pair of keys (K1, K2) can satisfy EK1(P1) = DK2(C1), i.e. lead to the same output in the middle stage, we call it a collision. The corresponding pair of keys is referred to as a colliding key-pair, which may or may not be the pair of authentic keys. (a) For a random pair of 40-bit keys, what is the probability that they will produce a collision at the middle stage? (b) If one enumerates all possible key-pairs (40-bit per key), on average, how many colliding key-pairs do we expect to find if we only check each key-pair against one given pair of plaintext and ciphertext, say (P1, C1)? (c) For a random pair of 40-bit keys, what is the probability that it will lead to a collision at the middle stage for 2 different pairs of plaintext and ciphertext, i.e. (P1, C1) and (P2, C2)? That is, the probability that (K1, K2) will satisfy: EK1(P1) = DK2(C1) and EK1(P2) = DK2(C2). (d) To follow up on part (c), if we enumerate over all possible key-pairs, what is the expected number of key-pairs that can satisfy EK1(P1) = DK2(C1) and EK1(P2) = DK2(C2) simultaneously? Q5. Simple Key Exchange [10 Marks] Dr. X suggests the following way to confirm that you and your friend are both in possession of the same (secret) key: You can create a random bit string with a length equal to that of the key, XOR it with the key, and send the result over an open and vulnerable communication channel to your friend. Your friend XORs the incoming block with his key (which should be the same as your key) and sends the result back. You check, and if what you receive is your original random string, you have verified that your friend has the same secret key. Neither of you has ever transmitted the key, so it remains secret. Is this scheme secure? Why or why not?
SEMT20001-R: Principles of Computational Modelling COURSEWORK REASSESSMENT: QUESTION 3 Question 3: Discrete vs continuous (a) Consider the map xk+1 = r sin(πxk) , k = 1, 2, . . . , (1) where r is a parameter from the interval r ∈ [0, 1]. (i) For parameter value r = 0.5 find all fixed points, for parameter value r = 0.8 find all period-two orbits, and for parameter value r = 0.96 find all period-three orbits of map (1) within the interval [-1, 1]. Use a combination of interval bisection and interval Newton’s method. (3 marks) (ii) Using indicator functions, calculate the transition matrix K for map (1) and for parameter values r = 0.5, r = 0.8 and r = 0.96. Find the stationary distribution for the three parameter values. Are there more than one ways to calculate stationary distributions from a given transition matrix? (3 marks) (iii) Explain what would happen if instead of indicator functions, one used Chebyshev collocation to approximate the transition matrix and then calculate the stationary distribution. (2 marks) (b) Consider the function f(t) = exp(2cos(2t)) , t ∈ [-π,π]. (2) (i) Approximate function f, using orthogonal collocation with a library of functions ϕn,j(t), con- structed by appropriately scaling and shifting the order-n Dirichlet kernel For each n there are 2n + 1 collocation points, defined by The library of functions is Illustrate the convergence of your approximation as a function of n on a diagram. (2 marks) (ii) Now use the constant, sine, and cosine functions as your library functions to carry out (non- orthogonal) collocation on the grid given by equation (3). (2 marks) (iii) The truncated Fourier series of order n of a function f can be written as Show that the collocation scheme using the Dirchlet kernel Dn is orthogonal and that it is equivalent to the non-orthogonal scheme using constant, sine, and cosine functions. (3 marks) (iv) Now use Chebyshev approximation to approximate function f and illustrate the covergence in a diagram. (2 marks) (c) Consider the van der Pol oscillator and its linearised version (5) about the equilibrium at x = 0. (i) A solutions of equation (5) is Set the boundary conditions to x(-1) = f(-1) and x(1) = f(1) and compute a numerical so- lution of equation (5) using Chebyshev approximation with the optimal Chebyshev polynomial coefficients. (2 marks) (ii) Now use the grid to compute a numerical solution of equation (5) and compare the accuracy of the two results. (3 marks) (iii) Rewrite the nonlinear equation (4) into first order form and compute its numerical solution as an initial value problem on the interval [-1, 1] using Chebyshev collocation. Use initial conditions x(-1) = 1, x.(-1) = 0. Illustrate and explain the convergence properties of the numerical solution as the number of grid points increases. Use the numerical solution with n = 256 grid points as the reference. (3 marks)
Exercise no. 3 / COMFOE SUBJECT: Economic and Financial Management AIMS Financial Statements: understanding and organization DESCRIPTION OF THE ACTIVITY These are the accounts of the Financial Statements of your company, alphabetically organized, at December of last year. Please draw up the Balance sheet and Income Statement formally organized and understand the meaning of every account. PL E Amortization of intangible assets 360 CA Receivables 16.200 CA Banks 26.300 NET Reserves 10.000 NCA Buildings 31.800 PL E Salaries and wages 10.720 PL ? Changes in inventory (+) 10.200 PL R Sales 125.000 PL E Depreciation on PPE 570 NET Share capital 35.000 PL Income from debt securities 3.500 PL Social Security contributions by the co. 3.200 PL Independent professional services 1.000 CL Social Security payables 2.250 PL Interests on loans 4.000 CL ST debts with financial institutions 40.700 NCA Land 60.000 CL Suppliers 31.000 CA Merchandises 38.000 CL ST debts with suppliers of fixed assets 35.000 PL E? Merchandises purchased 85.750 CL Tax office payables 11.500 PPE-NCA Motor vehicles 23.000 PL Transport 600 CL Payables for the rendering of services 7.500 PL Utilities 150 PL Corporate taxes 10.000
BUSBIS 1600 Technology-Enabled Business Transformation Individual Assignment 1: Qualtrics Tutorial You work for Henry Evers Inc. The company does an onboarding process for new hires that is divided into Onboarding Sessions for 4 departments – Finance, HR, Operations and IT spread over the first 2 years after joining the company. The onboarding sessions rotate the new hires through a training program on the different IS systems used at different locations of the company. The purpose of the survey is to gather information on whether the onboarding program has been useful for the new hire employees (
