Assignment III The assignment requires to perform a simple Monte Carlo experiment. The physics of the process we are studying imposes that the real-valued observations we are measuring, given the classes, follow Laplace distributions. These distributions are characterized by the probability density function where λ > 0 is the scale parameter, and μ ∈ R is the location parameter. Denote this situation as X ~ Lap(μ, λ). A deeper investigation into the phenomenon and how the measurements are obtained results in further assuming that all scale parameters are equal to 1, i.e., different classes are characterized only by different location parameters. Assume that we have two classes, A and B. The observations, given the classes, follow Lap(μξ , 1) distributions, where ξ ∈ {A, B}. We want to understand the effect of the distribution of the observations on the classification performance of the Bayes classifier. To that aim, design a Monte Carlo experiment to estimate the overall accuracy. Your response to this assignment must be organised as a research report, i.e., in four sections: Intro- duction (your wording of the problem, including your scientific question), Methodology (what you did), Results (what you observed: the plot), and Discussion (your answer to the scientific question). The following tasks split the problem in a logical sequence of steps that, if well executed, will result in all the material you need to produce the research report. Optionally, your report may refer to the step number. 1. [No marks:] Familiarise yourself with the Laplace distribution. Browse Chapter 24 from John- son et al. (1995) (this book is available at our library). In particular, take note that if X = (X1 , X2, … , Xn ) is a random sample from the Lap(μ, λ) model, then the maximum likelihood estimator of μ is ^μ = q1/2 (X), the median of X. 2. [2 marks:] Make plots of densities in linear and semilogarithmic scales obeying the recommen- dations stated in the “Presentations” part of this course. See how they change depending on the scale and/or location parameters. Discuss the plots (no discussions will result in losing the marks for this task). 3. [2 marks:] Obtain the Bayes classifier that discriminates observations between classes A and B. This classifier is defined by the point x* that satisfies Pr(Y = A)fx (x* ; μA , 1) = Pr(Y = B)fx (x* ; μB , 1), where fx (x; μ, λ) is the density that characterises the Lam(μ, λ) model, and Pr(Y = A) is the prior probability of class A. Show your working; failure to do so will result in a loss of marks for this task. 4. [6 marks:] Make a simulation study with the following parameters: • A unique seed for the pseudorandom number generator, fixed before the replications loop begins. • nA = 1000, the sample size of class A; • nB = 300, the sample size of class B; • λA = λB = 1, the scales of models A and B; • μA = 0, the location parameter of model A; • μB ∈ {—1, —0.5, 3}, the location parameters of model B. • For each μB , replicate R = 500 times the following experiment: o Simulate a sample of size nA from the Lap(μA , λA ) model (you may use the rlaplace function from the extraDistr package in R). o Simulate a sample of size nB from the Lap(μB , λB ) model. o Randomly select the training samples from each class with probability P = 4/5, and use the remaining observations as test samples. o Using the training samples, compute ^pA , ^μA , ^μB , the maximum likelihood estimators of the unknown parameters PA , μA , and μB . The maximum likelihood estimator of a probability, in our case, is the sample proportion. o Find x* using ^pA , ^μA , and ^μB . o Apply x* to the test sample, and compute the overall accuracy attained in this replica- tion. o Compute the average overall accuracy using the R values. Also compute the sample standard deviation of these R values. o Make a plot of the average overall accuracy as a function of μB . Add a measure of accuracy to these average values. Justify your choice. This plot must follow the recom- mendations stated in the “Presentations” part of this course. • Discuss your findings (no discussions will result in losing the marks for this task). References Johnson, N. L., Kotz, S., & Balakrishnan, N. (1995). Continuous univariate distributions (2nd ed., Vol. 2). John Wiley & Sons.
ME 3456: Dynamics Fall 2024 Laboratory Experiment #1: Projectile Trajectories with Aerodynamic Drag Abstract In introductory physics courses, you learned the mathematical theory behind 1- dimensional and 2-dimensional projectile motion under ideal conditions. However, in real world applications, conditions are rarely ideal, and much more complex measurements and calculations are required to accurately predict projectile motion. In this experiment, you will learn numerical methods for predicting and calculating motion behavior in the presence of air resistance. To do this, measurements of the motion of a foam ball are taken and compared with different mathematical models with and without air resistance. This allows students to experimentally determine drag force. Learning Objectives • Dynamics of Ideal Motion • Theory of Aerodynamic Drag force • Numerical Evaluation of Data • Comparison of Analytical, Numerical, and Experimental Data I. Problem Statement The purpose of this laboratory is to study the effect of air resistance on the motion of a small foam ball. We will use air resistance information extracted from a vertical fall of the ball to predict the trajectory of the ball when undergoing projectile motion. We will also quantify the air resistance effect by comparing with the ideal motion predicted when there is no resistance. The findings will also be used to predict the flight of a baseball in Fenway Park with and without the effect of air resistance. We start by looking at the pure vertical fall case. The motion of the ball can be predicted analytically by considering that the air resistance introduces a negative acceleration component (upwards, opposed to the direction of motion) that is proportional to the square of the falling speed. II. Introduction a. Aerodynamic Drag Force: In introductory physics, equations of motion for projectile motion are derived assuming that aerodynamic drag is negligible. In reality, there will be a drag force acting on the ball, in a direction exactly opposite to its velocity vector. This force can be estimated via the following results from fluid mechanics. When a body moves “sufficiently quickly” through a fluid, the drag is not caused by the fluid viscosity, but rather by its mass, i.e., a force is needed to accelerate the fluid "intersected" by the path of the ball. This force is proportional to the square of the speed. In that case, the drag force (FD) may be approximated by: (1) where ρf is the air density, is the area of the projection of the ball on a plane perpendicular to the direction of motion, v is the velocity vector of the ball (with respect to the air), and CD is the drag coefficient, an experimentally determined quantity. The negative sign in Eq. (1) signifies that the drag force acts in the direction opposite to that of the velocity vector. For a smooth sphere, CD ≈ 0.47. However, a foam ball does not have a smooth surface, and roughness (like the dimples on a golf ball) can change the drag coefficient. For a spherical ball of diameter Dd, If the foam ball has a density ρb, its mass So the drag force from the air will introduce an acceleration given by: (2) b. Terminal Velocity Terminal velocity refers to the maximum velocity that an object in free-fall can reach when falling in the presence of aerodynamic drag. It occurs when the drag force acting opposite the motion becomes equal to the gravitational force (weight) acting in the direction of the motion, as shown in Figure 1 for the case of a vertically falling ball. Figure 1: Free body diagram of an object in free fall. Because the motion is rectilinear, the downward acceleration of the ball is given by (3) Where is an acceleration-based drag coefficient. Notice that the value of D decreases (i.e., the drag effect decreases) when the density of the ball material and/or the ball diameter increase. We can use (3) to solve for the ball velocity as a function of time. We have learned in class that for rectilinear motion, when the acceleration is given as a function of velocity, we can use the following approach: Assuming the ball starts from rest, we have: (4) It can be shown that the analytical solution of this integral is: (5) Notice that for large t, the value of v approaches When v = vf the acceleration becomes zero (0), and the velocity remains constant. vf is called the terminal velocity of the falling object. A typical plot of v vs. time starting from rest is shown in Figure 2. Figure 2: Plot of velocity vs. time for an object in free-fall in the presence of aerodynamic drag. The vertical position of the ball (measured downwards from the initial resting position), can be obtained by integrating (5). The solution is: (6) c. General trajectory in the presence of aerodynamic drag: When the motion is not purely vertical, that is, when the ball starts with an initial velocity component on the horizontal direction, it is not possible to analytically solve for the ball velocity from equation (2). In this case numerical integration techniques, such as using finite differences, need to be used to approximately compute the horizontal and vertical components of the velocity as well as to generate the ball trajectory. The document “Projectile Motion Including Drag Effect” included in the Canvas/Modules section for Lab 1 explains how the finite-differences method can be used to compute the velocity and the position of the ball as time advances. The modules page also includes a MATLAB script that uses finite differences to do just that. You will be using that script. as you work on the report for this Lab. The main objective of this lab is to extract the drag coefficient, CD from the motion of a foam ball during free vertical fall, and use it to predict the motion expected when balls of the same material are launched with different initial speeds and orientations. Additionally, by comparing with the expected motion in the absence of drag, you will be able to quantify the drag effect.
ME 3456: Lab for Dynamics and Vibrations Lab #1: Trajectories with Aerodynamic Drag Abstract (5 points) List of Symbols (2 point) Introduction (10 points) ● Describe the physical source of the drag force and its effect on trajectory motion (5) ● Overview of the report and the objective of the experiment (5) Theory and Methods (30 points) ● Write the answer to the pre-lab assignment. (10) ● Derive the two equation of motion of trajectories with drag force in x and y directions (3) ● Explain the effect of the mass and cross section area on the acceleration of free-falling objects (2) ● Write the answer to questions 1,2 and 3 in the post-lab assignment. (10) ● Describe the experiment procedure. (5) Experimental Results (30 points) ● Write the necessary code to compute and plot the position of the vertically falling ball vs. time. Use it to determine the drag coefficient that best matches the experiment data. Show the plots of best prediction and experiment data together. (10) ● Using four different videos, give an answer to questions 5 and 6 of the post-lab assignments. You should include the table shown in the appendix A to summarize your answer. (5) ● Answer to question 7 in the post-lab assignment. (5) ● Answer to questions 8 and 9 in the post-lab assignment. Please, include the four plots of the experimental trajectory data and MATLAB theoretical solution. (10) Discussion and Analysis (15 points) ● Answer to the question 10 in the post-lab assignment. (5) ● Answer to questions 11, 12 and 13 in the post-lab assignment. (10) ● Reasonable work on the bonus question 14 resulting in drag coefficients that lead to better matching of experimental data is worth 1 Conclusions (5 points) Appendices (3 points) ● MATLAB programs used for the data analysis. Note: This report must answer all pre-Lab and post-Lab questions. Appendix A: In your report, you should make a table that contains the horizontal travel distance that is calculated analytically without considering the drag force, numerically with the drag force (assuming CD =0.47) and experimentally. You should use the initial velocity, initial vertical height and initial angle from the experiment to be used in the theoretical calculations. You should include four different measurements into your report. The table should look like the one below. Experiment number Initial x velocity (Vx) Initial y velocity (Vy) Initial height (Yo) Horizontal distance without drag Horizontal distance with drag Experimental horizontal Distance 1 2 3 4
Assignment 2: Putting Values into Action COMM 105: Values, Ethics, and Community Purpose: Values lay the foundation for your notions of how you should behave, but they do not necessarily dictate how you will behave. The future you envision for yourself is unlikely to materialize unless you make an effort to put your values into action. This assignment urges you to clarify the steps needed to craft a life and a career that best express your values. Learning Objectives: To create an imaginative and accountable representation of your values, goals, habits, and commitment to ethical behaviour, so that you will be inspired to revisit this plan at any time in order to check up on yourself and honour your commitment to a values-driven life. Guidelines: It is worth 20% of your overall course grade in COMM 105. Submit on Canvas through the assignment dropbox. Convert your documents to PDF and combine all parts into a single PDF fi le for upload. Multiple files will not be accepted. File naming convention: Last5NumbersOfStudent#-Sec#-AssignmentName.pdf (e.g. 12345-Sec101-PuttingValuesIntoAction.pdf) This is an individual assignment. UBC policies regarding academic integrity and plagiarism apply. You may not work on this project with others. AI is not permitted for this assignment. You may not use ChatGPT or other large language models for this assignment. The use of AI tools will be considered cheating; see 3.b(iv) of the Vancouver Academic Calendar. Keep all drafts and notes in case you are asked to demonstrate that you created this assignment without AI. Use a word processor that tracks edits (e.g. turn on Track Changes in Microsoft Word, or use Version History in Google Docs, etc.). Assignment components: ● Page 1: Cover Page ● Page 2-3: Values System Assessment - Schematic and Written Analysis ● Page 4-5: Goal Hierarchy - Schematic and Written Analysis ● Page 6: Habit Change Plan ● Page 7: Personal Ethics Statement ● Page 8+: References List Instructions: Page 1: Cover page Include the assignment title, your COMM 105 section number, and the last five digits of your student number. We will be uploading assignments to Turnitin, so to protect your privacy, please do not include your full name or full student number anywhere in your assignment. View a sample cover page here. Page 2: Values System Assessment - Schematic Map the core values you listed in Assignment 1: Discovering Values onto Schwartz’s circumplex model. Identify where in the model each of your values fits best and overlay them onto the template of Schwartz’s model found in Canvas (see the bottom of this document for a refresher on the definition of each value category). Then, below Schwartz’ model, copy your definition of each of your core values from Assignment 1. Your mapping of values should be self-evident; do not describe why you put each value in each of Schwartz’ categories. Note: You may revise the values that you listed in Assignment 1, and if you do, please include an additional sentence at the bottom of this page explaining why you decided to make this change (this explanation does not count towards your word count). Page 3: Values System Assessment - Written Analysis (300 word maximum) Provide an overall assessment of the consistency of your core value system. How likely do you think you are to experience tension between your values on a day-to-day basis, based on what the Schwartz model is telling you? Consistency refers to when your actions in your day-to-day life align with multiple values. Tension refers to when your actions would make it difficult for you to hold true to multiple values at the same time. In other words, to what extent can you live your life in a way that allows you to put your values into action without them clashing? Then, identify the two values you think have the greatest potential for tension in future situations, and give a specific hypothetical scenario where they might conflict. Consider actions or behaviours that would make it difficult for you to hold true to both of those values. Values do not necessarily need to be opposite in Schwartz’ model to cause tension. Page 4: Goal Hierarchy - Schematic Create a goal hierarchy schematic, representing three of your most important SMART goals at the top of the hierarchy. These goals should be personally meaningful to you, whether they are outcomes that you hope to achieve or things you want to do. There is no minimum or maximum time-limit for your goals - they might take a month, a year, a decade, or more to achieve. The goal hierarchy should have two layers of sub-goals below the top-level goals (i.e. three layers of goals). Include 3 high-level goals, 4-5 mid-level goals, and 5-7 low-level goals. It’s up to you to determine how your goals link across levels. Cross-linkages (connecting multiple goals from one level to a goal of another level) are not necessary, but if natural cross-linkages between your goals exist, please indicate this in your diagram. This page may be formatted in landscape orientation. View a template here. Page 5: Goal Hierarchy - Written Analysis (300 word maximum) Include a written rationale for why you structured your goal hierarchy the way you did (i.e., the way it is organized into different levels and the way the goals are connected between levels). In other words, what are the properties of your hierarchy that make it more likely that you will achieve your higher-level goals? Clearly refer to course concepts from our Self-regulation, Goals, and Habits lesson. Although not required, incorporating external research from academic sources (e.g. research around goal setting, goal hierarchies, or self-regulation) is a plus. Page 6: Habit Change Plan (400 word maximum) List three habits that you could develop to help you achieve some of the goals you listed above, and identify which goal(s) these habits are meant to support. Then, briefly describe a plan for how you will develop those new habits by using the habit cycle that was discussed in class. These habits may be low-level goals from your goal hierarchy, or they may be other habits that are related to the goals you have set. Your answer should clearly demonstrate an understanding of how the habit cycle works, including a description of each component of the cycle (cue, routine, and reward) for each habit. Page 7: Personal Ethics Statement (450 word maximum) Commit to making a difference at UBC Sauder by creating a personal ethics statement. This is a good time to recognize and declare that the only person who controls your behaviour and your experience is you, and that you can have a profound impact on others at our school. First, write an overview describing why acting morally is uniquely important to you. ● Reference at least one of your core values to illustrate why you strive to be a moral person. We are not asking you to define your morals here; instead, we want you to describe why you strive to be moral. ● Moral transgressions can be categorized as forms of lying, cheating, and stealing. Describe which of these three transgressions you strive to avoid most, and why. Then, outline one specific, hypothetical ethical dilemma that you might face during your time at Sauder, Describe why acting ethically in this situation could be difficult. Identify 3-5 specific steps you would take (both internally and behaviourally) to be proud of your response to the dilemma. ● Obviously, you will be making this up. However, imagining the kinds of ethical quandaries you are likely to face in the future, and how you will specifically deal with them, is a crucial step in your ethical development and preparation. ● Make sure this ethical dilemma is significant. Smaller dilemmas (e.g. “Last week I should have composted my banana peel instead of putting it in the trash”) have less value. ● Incorporate course concept(s) from the framework for moral decision-making introduced in our Behavioural Ethics 1 lessons in your framing of the ethical dilemma. Remember that ethics are rules of conduct for a group, and our group is the UBC Sauder School of Business, so you may find it useful to review UBC Sauder’s Statement of Professionalism, which outlines your ethical expectations as a business student. ● Hint: An ethical dilemma is where you have to decide between “right” and “wrong”. While values may guide your morals, an ethical dilemma is not the same as values tension. Page 8+: References List This assignment does not require outside research or materials, but if you choose to do so, here is some guidance: ● Format all in-text citations and references using APA style, 7th edition. Citations are required when you directly quote outside sources, paraphrase ideas from outside sources, or use data or information from outside sources that is not common knowledge. ● Clip-art or stock images, such as those provided in design programs such as Canva, are generally royalty-free and do not need to be cited unless otherwise stated. Any other images or external materials you find on the internet must include an in-text citation and be included in this References List. In-text citations for external images should be placed next to the image. ● Class materials and course concepts (e.g. from prep materials, video lectures, or lecture slides) do not need an APA citation or reference as long as you are not directly quoting from these materials. However, you must bold and italicize each course concept or framework that you reference in your work. For example, “I believe my biggest ethical challenge will be denial of responsibility, because…“ ● Citations do not contribute to word counts. There is no limit to the number of pages of your References List. If you do not reference outside sources, you do not need to include a References List. It is your responsibility to learn how to use the APA format. Assignments that are inadequately referenced (either lacking detail or using improper format) may be returned for correction and will be subject to penalties, including possible academic misconduct investigations.
Assignment 1: Discovering Values COMM 105: Values, Ethics, and Community Purpose: Your values represent what you believe to be important in life. They are your guiding principles; your personal true north compass. This short assignment asks you to identify your core values so you can confidently declare what standards you wish to follow. When your values are clear, challenging decisions are easier to make, because you are able to take a long-term perspective of situations and outcomes. Learning Objectives: To uncover and commit to the way you wish to approach your work, your relationships, your responsibilities, and your life. To choose and present who you have decided to be, and to commit to further reflection and refinement of your values over time. Guidelines: This assignment is due Sunday, September 14 at 10pm Pacific Time. It is worth 5% of your overall course grade in COMM 105. Submit on Canvas through the assignment dropbox. Convert your documents to PDF and combine all parts into a single PDF file for upload. Multiple files will not be accepted. Use single-spaced 11-point Arial font, 1-inch (2.54cm) page margins, and 8.5x11 inch pages. Ensure your assignment is easy to read. Use section headers to organize your assignment. Bold and italicize any course concept or framework that you reference in your work. File naming convention: Last5NumbersOfStudent#-Sec#-AssignmentName.pdf (e.g. 12345-Sec101-DiscoveringValues.pdf). This is an individual assignment. UBC policies regarding academic integrity and plagiarism apply. You may not work on this project with others. AI is not permitted for this assignment. You may not use ChatGPT or other large language models for this assignment. The use of AI tools will be considered cheating; see 3.b(iv) of the Vancouver Academic Calendar. Keep all drafts and notes in case you are asked to demonstrate that you created this assignment without AI. Use a word processor that tracks edits (e.g. turn on Track Changes in Microsoft Word, or use Version History in Google Docs, etc.). Assignment Components: ● Page 1: Cover Page ● Page 2: Your Takeaway from our What Matters Most lesson ● Page 3: Infographic ● Page 4: Self-Discovery Plan ● Page 5+: References List Instructions: Page 1: Cover Page Include the assignment title, your COMM 105 section number, and the last five digits of your student number. We will be uploading assignments to Turnitin, so to protect your privacy, please do not include your full name or full student number anywhere in your assignment. View a sample cover page here. Page 2: Your Takeaway from our What Matters Most Lesson Share what was most useful in uncovering values from the What Matters Most lesson. This is not graded, but it helps to illustrate your thought process to your reader. You have two options: ● A picture of the personal symbol you created in class. You can make minor adjustments to your personal symbol if you wish. ● Answers to at least three of the guiding questions shared in class. The word count of this page does not matter and you may use any font (or a photo of your written notes). Page 3: Infographic Explain your personal core values using an infographic. You may discover that you subscribe to many values, especially when you write them out, but this often feels like coming up with a list. So, recall what we did in class with the symbol activity. You were asked to ask yourself: “what’s most important to me in life?” When you do this, you will realize that you have to prioritize some values over others. Arrive at a combination of 4 to 6 core values. Values should be specific enough that they can be expressed as a behaviour, but broad enough that they can apply in multiple contexts/situations. One way to reduce a larger set of values down to a small group of core values is to notice that some similar ones can be combined to form. a broader value. For example, concepts like prosperity and freedom might be combined to form. a value like achievement or independence. On your infographic, each of your individual values should be accompanied with: ● A short, one-sentence explanation of how you define this value. Each description will be your interpretation of what the core value means to you, but should conform. to the definition of a “value” that was provided in class. ● A short, one-sentence description of how this value is reflected in your behaviour. This behaviour should be observable. For example, if one of your core values is respecting tradition, then a possible standard of behaviour might include maintaining a family tradition by attending family events. For someone else, respecting tradition might involve upholding religious traditions by celebrating religious holidays or attending services regularly at a church, mosque, or temple. Present your values in an infographic. You may be inspired by examples online such as these. Use colour, images, art, and white space to creatively organize your ideas. Note: You can go beyond the page margin limit for your infographic, and you may format your page in landscape or portrait orientation. You may also use a different font than Arial for your infographic (including hand-written text); keep your text legible and large enough to read. Page 4: Self-Discovery Plan (250 word maximum) The values you highlighted in your infographic on Page 3 should represent what you believe is important to you now. We hope that you will continue to check in with these values throughout your life, as well as explore how you came by them, and how they influence your sense of self. In this section, describe one (only one!) concrete action you will take to further your self-awareness. Be specific in describing the steps you would take to complete this action. Also describe why this action will be uniquely helpful to improving your self-awareness. In other words, what is unique about you - or your current understanding of your self-awareness - that will make your proposed action useful? When describing your self-discovery plan, make a clear connection to a specific course concept from our Self, Identity, and Authenticity lesson (either the prep videos or the in-class content). As you continue uncovering, understanding, and revising your values in the future, you might consider what forces inform. them, how you arrive at self-knowledge, and whether the principles you think you have represent the person you are, and wish to become. Page 5+: References List This assignment does not require outside research or materials, but if you choose to do so, here is some guidance: ● Format all in-text citations and references using APA style, 7th edition. Citations are required when you directly quote outside sources, paraphrase ideas from outside sources, or use data or information from outside sources that is not common knowledge. ● Clip-art or stock images, such as those provided in design programs such as Canva, are generally royalty-free and do not need to be cited unless otherwise stated. Any other images or external materials you find on the internet must include an in-text citation and be included in this References List. In-text citations for external images should be placed next to the image. ● Class materials and course concepts (e.g. from prep materials, video lectures, or lecture slides) do not need an APA citation or reference as long as you are not directly quoting from these materials. However, you must bold and italicize each course concept that you reference in your work. For example, “Based on the concept of psychological asymmetry, I have realized that…“ ● Citations do not contribute to word counts. There is no limit to the number of pages of your References List. If you do not reference outside sources, you do not need to include a References List. It is your responsibility to learn how to use the APA format. Assignments that are inadequately referenced (either lacking detail or using improper format) may be returned for correction and will be subject to penalties, including possible academic misconduct investigations.
Department of Human Development HUDM 4122 | Fall 2025 Probability and Statistical Inference ▮ COURSE DESCRIPTION This course provides an introduction to probability and statistics for students in social sciences. The fundamentals of probability theory will be illustrated with examples drawn from behavioral and social sciences. Topics include elementary probability theory, conditional probability and independence, random variables, probability distributions, and statistical inferences using p-value approach, confidence interval approach, and hypothesis testing approach. The focus of this course is on basic statistics and research tools, which are useful in conducting experiments, describing data, and making inferences about the population. It is the first course of a series of statistics courses offered in the Department of Human Development. Therefore, it is not expected that students will leave the class knowing all the basic research tools, but this course will lay the groundwork for future study. By the completion of the course, students will be familiar with ideas of statistical modeling, data analysis and interpretation. ▮ MEETING TIMES This class is designed to deliver instruction in both an asynchronous and synchronous manner, with typically web-based assignments, discussion board, and other web-assisted learning tools. The instructor will recap information during online zoom meetings -- it will be every Monday (11:00AM - 12:00PM EST), starting from September 8th. Note that CAs offer online office hours as well. Assignments and exchanges will be structured around a weekly schedule. The "Weekly Material" section under the "Modules" tab contains a document listing the topics and readings for the whole semester by week, in addition to a set of week-by-week folders (for example, this week's folder is labeled "Week One (9/2 - 9/8)". Each week's folder contains a list of course work activities (readings, lecture notes, etc.), along with additional resources to use during your study. You should use the information in the weekly folders to create a personal schedule so that all of the work listed in the folder is completed by the end of that week. ▮ TEXTBOOK Mendenhall, W., Beaver, R., & Beaver, B. (2012). Introduction to Probability and Statistics (14th ed.). New York: Duxbury Press. *** Either 12th or 13th edition will be ok to use. It may be much cheaper if you get it online. If you use the earlier editions, the page numbers for reading assignments may not correspond to what I assign. ▮ COURSE REQUIREMENTS Grades in this course will be based on a total number of points earned. Grades will be based on completion of the homework assignments (30%), on the scores of the midterm exam and the final exam (35% each). Attendance will not be counted as a part of your grade. Examinations 70 % of Grade Exam Date: Midterm 10/24 -10/25 Final 12/16 - 12/17 There will be two exams. Both exams are online and timed (3 hours). The final exam is cumulative. Examinations will consist almost entirely of problems whose solution requires calculations. It is highly recommended that you bring a calculator to the exam sessions. For the exams, you may use a single-page (8.5" x 11"; no double-sided) “cheat/reference” sheet, handwritten by you. You must create your own cheat sheet. Sharing, photocopying, or otherwise duplicating another student’s cheat sheet is prohibited and will be considered a form. of academic misconduct. Making a good cheat sheet is effectively studying what you need to know for the exam. The exam is also your application of knowledge so not whether you can memorize a formula, but whether you know how to properly apply that formula. You will not be asked to derive formulas, but will be required to perform. numerous calculations. You are expected to demonstrate how to solve the exam problems; you should show that you know what the calculator is doing, as if you performed the calculations by hand. Show your work to obtain full credit (given your answer is correct) or partial credit (given your answer is incorrect). If you know in advance that there are compelling and unusual circumstances that might interfere with your performance on an examination, please inform. the instructor. A time will be scheduled for a make-up examination to accommodate those who inform. the instructor in advance of a need for one. Homework Assignments 30% of Grade Due Date: See the Schedule below Five homework assignments (5-6% each) will be given and graded. Homework assignments will be distributed on the marked dates (*) and due by a week from the day the homework was issued. It is intended to provide you with practice at solving problems like those that you will encounter as a behavioral and social scientist and that, of course, will be on the examinations. All work must be completed and turned in on time (Late assignments will not be accepted). You may consult the instructor, TA, tutors, classmates, or others while working on the homework assignments, but what you turn in should be in your own words. All work should be lucid, orderly, and self-managed. You can complete and submit your homework in one of 2 different ways. Option 1: Use Statistical Notation symbols. This requires the use of the "Math Equation" editor that is embedded in our course site (click the 5th icon on the 2nd row of the toolbar). It is self-explanatory. Option 2: Spell out the Statistical Notation. In this option, for example, instead of using the symbol "Σ", you would type "sigma". A "Glossary of Statistical Notation Terms" is available for your reference, located in the "Modules" section. Please show your work so that we may give you partial credit if applicable. Please round all numbers in your answers to FOUR decimal places. You are only allowed to submit your homework ONCE. Be sure you have completed every question and have typed all of your answers into the homework module answer spaces to your satisfaction before hitting the "submit" button, as the homework module will not allow multiple submissions. Although you can only submit once, the homework assignments are set so that you can enter, logout and re-enter them and therefore, you DO NOT have to remain online while working on your homework. It is suggested that you access and print out the homework, work out the problems on paper, log back onto the Internet and into our course site and then access the homework module again, type your answers in the appropriate answer spaces and then hit submit.
FIT1047 Introduction to computer systems, networks and security S2 2025 Assignment 3 – Networks Purpose In this assignment, students will record data from a real-world wireless network and demonstrate that they can analyse it, identify its properties and potential issues. Also, students will analyse Internet traffic and identify servers, clients and protocols used. The assignment is related to Unit Learning Outcomes 5 and 6. Your task Part 1: Submit your reflections (Week 7, 8 and 9). Part 2: Submit a report with your findings regarding the analysis tasks. Part 3: In-class in-person test. (Week 12 Applied [Australia] / Workshop [Malaysia] session). Value 30% of your total marks for the unit. (10% for Part 2 and 20% for Part 3) The assignment is marked out of 60 marks. Word Limit See individual instructions. Due Date Parts 1 - 2: 11:55 PM Thursday 25 September 2025 (NOT Friday 26 September which is a public and Monash holiday) Part 3: Week 12 (Your Officially Allocated Applied Session [Australian cohort] / Allocated Workshop Session [Malaysian cohort]) Submission ● Via Moodle Assignment Submission. ● DRAFT upload confirmation email from Turnitin is not a submission. You must click the submit button to accept terms and conditions in Moodle. Note that DRAFT submissions will not be assessed. ● Once the submission is confirmed, any requests to revert it back to DRAFT for resubmission will NOT be accepted. Also, any incorrect, corrupted, empty or wrong file type submission will not be assessed. Please check carefully before confirming your submission. ● Turnitin and MOSS will be used for similarity checking of all submissions. ● This is an individual assignment (group work is not permitted). ● In this assessment, you must not use generative artificial intelligence (AI) to generate any materials or content in relation to the assessment task. INSTRUCTIONS This assignment has THREE parts. Make sure you read the instructions carefully. For Part 1, collect your reflections for weeks 7, 8 and 9 from each week’s Ed Lesson and create a single PDF document. You can simply copy/paste your reflections, but please add headings for each week. A template is available on Moodle. Submit your PDF through the Moodle Assignment 3 Part 1 activity. For Part 2, you need to submit the survey results and report in a single PDF file through the Moodle Assignment 3 Part 2 activity. Part 3 is an in-class test during your allocated Applied [Australia] / Workshop [Malaysia] Class in Week 12. How are marks and grades determined? Part 2 and Part 3 are worth 20 and 40 marks, respectively. The overall mark is the sum of the two individual marks. The assignment is worth 30% of the unit’s marks. If no meaningful/insufficient/irrelevant reflections are submitted for Part 1, the overall mark will be the maximum of 30 and the sum of Part 2 and Part 3 (i.e., the marks are then capped at 30). For example, if the overall combined mark is 31/60, it will be scaled to 30/60. If the overall combined mark is 28/60 then it will remain as 28/60. Part 1: Reflection (Not marked, but cap on overall mark applies) Complete your reflection activities for Week 7 to Week 9 in the corresponding Ed Lesson and copy/paste them into a PDF file. Write at least 100 words for each week (relevant and meaningful to the specific week). Failure to submit all relevant reflections (missing all submissions or incomplete submissions) will result in your Assignment 3 having a maximum mark of 50%. For example, if the overall combined mark is 31/60, it will be scaled to 30/60. If the overall combined mark is 28/60 then it will remain as 28/60. You may use this template: https://docs.google.com/document/d/18UIEJQeyarYW1pl8oDEaf--ubCdJ5LDf-9_jSLbGxrE/e dit?usp=sharing to write down your reflections. Part 2: WLAN Network Design and Security (20 marks) For this part of the assignment, you will perform. a real-world WLAN site survey. Your task is to produce a map of part of a building that gives an overview of the wireless networks that are available, as well as an analysis of the network. What you will need: a WiFi-enabled laptop (some smartphones also work, see below), and a place to scan. You have to perform a survey of parts of the Monash Clayton / Kuala Lumpur campus. You have to complete two tasks (a survey and a report). Task 2.1: Survey (6 marks) For Australian campus cohort: Create a map and survey of (a part of) the following building on our Clayton Campus: ● Students with student number ending with “1” or “6”: Woodside Building ● Students with student number ending with “2” or “7”: Hargrave Andrew Library ● Students with student number ending with “3” or “8”: Sir Louis Matheson Library ● Students with student number ending with “4” or “9”: Learning and Teaching Building ● Students with student number ending with “5” or “0”: Menzies Building (You can find the location of the building here - Choosing Clayton campus) For Malaysian campus cohort: Create a map and survey of (a part of) any building of your own choice on our Malaysian Campus. (You can find the location of the building here - Choosing Kuala Lumpur campus) 1. Draw a floor plan with details A simple floor plan will be sufficient. It does not have to be perfectly to scale. See Appendix A for an example. The map should be labelled with all relevant information (e.g., dimension, door, wall and material such as wood or concrete or glass, if used for the discussion). Your survey should cover an area of at least 60 square metres (e.g., 6x10 metres, or 4x15, or two storeys of 6x5 each). Be sure to take the analysis in Task 2.2 into account, by designing your survey to include walls, doors etc. it will be easier to write something interesting in Task 2.2. For drawing the site maps, any drawing tool should work, for example LucidChart, or even presentation tools such as PowerPoint, Keynote or Google Slides. Modification of screen capture map (e.g., from Monash Digital Map) is acceptable provided that the appropriate references are provided or marks will be deducted. Use the APA 7th referencing style. Scans of hand-drawn maps are not acceptable. An example map is given in the Appendix A. You may use any drawing tool to create a map (excluding heatmap generated by survey tools) or reuse existing floor plans with reference. 2. Conduct the survey Your survey must include at least three WiFi access points. If you want, you can create an additional AP with your phone (using “Personal hotspot” or “Tethering” features). For the survey, use a WLAN sniffing tool (see below) in at least eight different locations on your map. For each location, record the technical characteristics of all visible APs. Depending on the scanning tool you use, you record features such as the network name, MAC address, signal strength, 802.11 version(s) supported, band (2.4/5/6 GHz), channel(s) and security used. Take screenshots of survey data at each survey location, and include the screenshots of raw data in the Appendix of your report. Tools: You can use NetSpot (http://www.netspotapp.com) for macOS and Windows, LinSSID or wavemon for Linux. Acrylic WiFi (https://www.acrylicwifi.com/en/) is another possible choice for Windows. If you have an Android smartphone, apps like Wifi Analyzer (https://play.google.com/store/apps/details?id=abdelrahman.wifianalyzerpro) can also be used. On iOS, WiFi scanning apps do not provide enough detail, so iPhones won’t be suitable for this task. 3. Add the data into the map Add the gathered data from the survey into the map of the covered area. On the map, indicate the location of the access points and the locations where you took measurements. For the access points, use the actual location if you know it, or an approximation based on the observed signal strength (e.g. if you don’t know exactly where it is). For each measurement point, you either add the characteristics directly into the map using any annotation feature/tool, or create a separate table with the details. You can submit several maps if you choose to enter data directly into the maps, or a single map if you use additional tables. Create the map yourself, do not use the heat-map mapping features available in some commercial (i.e., paid) WiFi sniffing tools. Task 2.2: Report (14 marks) Write a report (word limit: no more than 1000 words) on your observations analysing the data collected in the previous step (Task 2.1). Your analysis must investigate the following aspects: 1. Channel allocation and overlap: (2 marks) ○ Are the access points using overlapping or adjacent channels? ○ Is there evidence of co-channel interference? ○ Suggest improvements to channel planning. 2. Signal attenuation and obstacles: (2 marks) ○ How do physical barriers such as walls, glass, or furniture affect the signal? ○ Compare at least two different material types in terms of attenuation. 3. Coverage and dead zones: (2 marks) ○ Identify areas with weak or no connectivity. ○ Recommend changes in AP placement, orientation, or transmit power to improve coverage. 4. Roaming and handoff potential: (2 marks) ○ Is there sufficient overlap between APs to allow seamless roaming? ○ How might roaming performance impact user experience (e.g., VoIP calls, video streaming)? 5. Network load and bandwidth usage: (2 marks) ○ Do multiple APs appear to be heavily utilised? ○ Suggest strategies to balance load or optimise performance. 6. Other analysis of your choice (1 or more): (2 marks) ○ For example: ■ Estimate AP locations from observed signal strength. ■ Test body attenuation. ■ Consider interference from non-WiFi devices (e.g., microwaves, Bluetooth). ○ Explain both your methodology and reasoning. Report structure: (2 marks) Task 2.2 requires you to write a structured report, not just a series of answers to the above questions. Ensure that your discussion flows logically, with each section connected and presented clearly (e.g., using sub-header to indicate the beginning of each section), rather than presenting unrelated short answers, or writing a single paragraph for the whole task and putting everything into it. Figures, tables, and references to your survey data may be included to support your analysis. Part 3: Quiz - Internet Traffic Analysis and Basic Cryptography (40 marks) This part of the assignment must be done in-person during your Week 12 allocated Applied Session (Australian Cohort) / Workshop Session (Malaysian Cohort). You will be given one hour to complete this part. You cannot start this part elsewhere or in another time slot. Bring your student ID and own device. There are two sections for Part 3: Section 1: The first section of the quiz requires you to download a PCAP file, open it in Wireshark and answer a few questions about the captured frames. The PCAP files are individualised, so make sure that you download the correct file while you are logged into Moodle. You can access your individual PCAP file through the Assignment 3 Part 3 In-class test link on Moodle at your Week 12 Applied (Australia) / Workshop (Malaysia) session. All of your answers have to be submitted via Moodle. Here are a few tips on how to approach these tasks. Node Selection: Please make sure you select the correct node (within your given scenario) for traffic analysis. MAC addresses: These are the addresses of individual devices at the Data Link Layer. Each frame contains a sender and receiver MAC address. For each frame, think about which device would be the sender and which the receiver. IP addresses: These are the Network Layer addresses. Remember that we use the DNS protocol to map a human-readable address (such as www.monash.edu) to an IP address (such as 202.9.95.188). So in order to find out the IP address for some of the devices, you may have to try to find DNS requests and responses in the PCAP file. TCP connections: Remember that each TCP connection starts with a three-way handshake. This was covered in the lectures, so you may have to go back to the videos if you’re not sure what those frames look like. Section 2: The second section is about some basic questions of cryptography. More details will be given to you in Week 10 and Week 11. Appendix A: Sample map only showing APs and survey locations Remarks: Note that even if you cannot enter a classroom (e.g., G02 and G03 below), you can still take the reading inside (e.g., 1, 2) and outside the building (e.g., 3, 4, 5) to conduct the survey.
Ethics of AI Assignment 2 September 25, 2025 0 Instructions • Total points: 10 out of 100. • Include your full name and student number at the beginning of your submission. • Deadline: 9 Oct 2025, 4:00 pm (as indicated on the Moodle assignment page) • Submit a PDF file via Moodle (within 3 pages), using the Assignment 2 submission link. • Please ensure compliance with the university’s Generative AI (GAI) policy. You may use GAI tools only for brainstorming or language polishing. But do not use them to directly answer the questions. Also, if you use any GAI tools, you must clearly state at the beginning of your submission which tool(s) you used and for what purpose. 1 Questions 1. This case study concerns a report from The Washington Post that Google’s algorithm displayed prestigious job advertisements more often to men than to women (see below). Answer the following questions: • (10 %) What might be the reasons for this bias? (Refer to course content: bias sources, ML pipeline, etc.) • (10 %) What are possible positive or negative consequences? 2. (10 %) How do you assess the consequences of female/male gender stereotypes manifested in current word embedding technologies? (See the page Man is to Computer Programmer as Woman is to Homemaker) 3. (20 %) What do you think: Should justice be blind and impartial, or does justice mean creating an advantage for those who are already disadvantaged (cf. Coeckelbergh, M. (2020). AI Ethics. MIT Press. Chapter 9)? 4. Concerning the veil of ignorance: • (10 %) Which idea—Harsanyi’s or Rawls’s—do you think better captures the notion of justice? Why? • (15 %) Do you think this helps establish the notion of justice, at least for the cases presented this week? Use one example to explain why or why not. 5. (25 %) How do you assess the reasonableness of this idea? One should use a dataset that mirrors the real world. The data may represent prejudices in society, and the algorithm may model existing biases people have, but this is not a problem developers should be worried about.
Supply Chain Network Design Pre-class assignment: Read the Designing the Production Network at CoolWipes case study at the end of Ch 4 in Chopra (2026) textbook. Prepare answers to the questions below to be submitted (as a hard copy) prior to the start of class. The spreadsheet provided below contains an optimization model that can be used to answer the study questions at the end of the case study. Please review the spreadsheet, including the Solver model, and make sure you understand where the input data comes from and how the optimization model is set up. Then answer the following questions in writing and submit a hard copy at the start of class on Tuesday, Sept 23: 1. The cells highlighted in blue are the decision variables for the problem. Explain in words (clearly and fully) what decisions the following cells represent: B23-G26 H23-H26 B31-G34 H31-H34 2. The objective function is the total cost, as given in cell B37. Please answer the following questions about that calculation: Notice that the calculation of the total does not include the variable costs, as given in cells C4-C7 and F4 F7. Explain why it is not necessary to include those costs when building the optimization model. Explain why it might be useful to include the variable costs, even though it is not necessary in order to find the optimal solution. 3. The input data and optimization model in Solver are set up to answer the first part of Question 3 at the end of the case study (but not the questions about changing the transportation costs). Run the solver model to obtain the optimal solution for the problem described in the first part of Question 3. Describe the optimal solution in words. In other words, if you worked for Matt O'Grady, the VP of Supply Chain, how would you describe the optimal solution to him? Supply chains and distribution networks are often represented graphically using a network, with a set of nodes (circles, ovals or rectangle) representing facilities or markets, and arcs (arrows) that connect the nodes and indicate product flow. See, for example, Figure 4-11 in the Chopra (2026) textbook. Draw one or more network graph(s) to show the optimal solution found in part a of this question. Make sure to add sufficient detail (e.g., the quantity and type of goods being produced and shipped). Also make sure that your graph(s) are well-formatted and easy to read. You may use whatever tool you prefer to create this graphic; graphs drawn by hand are also acceptable as long as they are readable. 4. The optimization model in Solver can also be used to answer Questions 1 and 2 at the end of the case study. However, to answer these questions, changes need to be made to the model and/or data. For example, for Questions 1 and 2, we need to assume that the existing Chicago plant continues to operate, while in Question 3 we assumed the Chicago plant does not yet exist, but could be built. Therefore, to answer Questions 1 and 2, we need to change the fixed costs and capacity for the Chicago plant to reflect the current costs and capacity, not the values for a new plant. In other words, we would need to change cell B4 to 5,000 and cell D4 to 5,000. In addition, starting from the model for Question 3: Explain what constraints you would need to add to the Solver model to answer Question 1. Describe the constraints in words and write them as you would in Solver, referring to the appropriate cells. Explain what constraints you would need to add to the Solver model to answer the first part of Question 2 (ignore the second part of Question 2, about changing the transportation costs). Describe the constraints in words and also write them as you would in Solver, referring to the appropriate cells. For parts a and b, you are not required to implement these changes in Excel. However, you might choose to do so, and then run the optimization model, to make sure your model works as intended. In-class assignment During class, we will conduct additional analysis related to the case study, including considering the impact of changes in transportation costs and economies of scale when constructing new plants. Please come to class with your laptop and the Cool Wipes spreadsheet and be prepared to conduct additional analysis to make additional recommendations.
ELEC5307 Deep Learning Project #1: Parameters in Neural Networks 1 Objectives This laboratory aims to introduce the basic techniques in deep neural networks. In this labo- ratory you will: • Learn to use PyTorch to load images and train a neural network for classification. • Understand the functions of convolutional layers, pooling layers, fully connected layers and softmax layer, etc. • Become familiar with the activation methods, pooling and initialization methods. • Learn to select proper hyperparameters for better performance. • Visualize your results and the objective to learn how diferent parameters contribute to the final performance. 2 Instructions 2.1 Data description: CIFAR-10 You need to use the CIFAR-10 image dataset. The CIFAR-10 dataset consists of 60000 images in 10 classes, with 6000 images per class. There are 50000 training images and 10000 test images, which are split by the publisher. The details and the downloads of the dataset is in https://www.cs.toronto.edu/~kriz/cifar.html The classes include ‘airplane’, ‘automobile’, ‘bird’, ‘cat’, ‘deer’, ‘dog’, ‘frog’, ‘horse’, ‘ship’, and ‘truck’. All the images are manually labelled, and each image only contains one label. The images in CIFAR-10 are of size 3x32x32, i.e. 3-channel colour images of 32x32 pixels in size. In some applications, each image has already been reshaped into a vector with dimension of 3072 (= 3 × 32 × 32). In PyTorch, you can use the function torchvision .datasets .CIFAR10 to automatically download and read the dataset and the function torch .utils .data .DataLoader to load the data (training and test) into your network. 2.2 Hyperparameters Hyperparameters are crucial to your success in training a neural network. The weights of neural networks will be modified during learning, and the hyperparameters will contribute to the modification of input images, the number of weights, and the way to update the weights. Here are some groups of hyperparameters. For more hyperparameters and their usage in PyTorch, please refer to the official documentation website (https://pytorch.org/docs/stable/index. html) and PyTorch Forum (https://discuss.pytorch.org). 2.2.1 Transformation This will influence the input images. The basic idea to conduct transformation is to add data (called data augmentation in deep learning). The neural network contains many weights to modify, which requires many images. However, the provided data are always not enough. By making transformations, one image could be fed into the networks using different patches or sizes, which could increase the size of training set. These operations are included in torchvision .transforms. Some of the options are: • resize: The images can be resized to square image or larger/smaller. Almost all the transformation will need the resize operation. • crop: You can crop the center (CenterCrop), or center plus the four corners (FiveCrop), or crop at a random location((RandomCrop)). • ip: The images may occur from different view, so flip horizontally (RandomHorizontalFlip) or vertically (RandomVerticalFlip) could provide more possibilities of the images. • a ne: This will modify the image by rotation or translation. It is not suitable to prede- fine some affine function because of the diversity of images, so torchvision only contains (RandomAffine). • normalization The pixel values of the images are normalized to [0, 1] using this operation. You need to use the global mean and standard deviation of the whole dataset if you train the model from scratch. If you would want to fine-tune a model from some model pretrained on ImageNet, you need to use the values from ImageNet. Please note that the normalization operation is not for data augmentation but for faster convergence. The rest four operations mentioned above are used for data augmentation. 2.2.2 Network Structure These parameters will influence the structure of the neural networks. The most important indicator is the capacity of the network, which is roughly equal to the number of parameters. In that case, the deeper and wider the network is, the better potential performance it could provide. However, you need to make sure the parameters are constrained carefully. The operations are included in (torch.nn). Some of the options are: • Depth: Roughly speaking, the deeper network will have the potential to provide better results. However, when the network becomes deeper, it will be more difficult to train as the gradients are more possible to vanish or explode. • Activation function: The most commonly used are ReLU, Tanh and Sigmoid. These functions provide non-linearity to the neural network. • Pooling method: The common choices are max pooling (MaxPool2d) and average pool- ing (AvgPool2d). The size and stride of the pooling layers will change the sizes of feature maps (i.e. width and height). • Channel size: The input channel is 3 (for three colour channels R/G/B), and the output channel should be the number of classes (10 for CIFAR-10). From shallow layers to deep layers, the channel number always gradually increase (with the width and height numbers decreasing). The larger the channel number, the more time you will need for both forward and backward process. In PyTorch, fully connected layers can be basically defined as Linear(in channel, out channel), 2-D convolutional layers can be basically defined as Conv2d(in channel, out channel, kernel). • Convolutional parameters: The kernel size, zero padding number and stride will influ- ence your output width and height by Woutput = (Winput-Kernel+2 × Padding)/Stride+ 1. You can try to use kernel size as 3 × 3, 5 × 5 and 7 × 7. By default, we do not want the convolutional layers change the width/height of the feature maps, so you can select stride and zero padding accordingly. For example, if you have kernel size 5 × 5, we always need the zero padding as 2 and stride as 1. • Dropout: This layer (Dropout) is a method of regularization, which will randomly set zeros to some weights in the according layer. 2.2.3 Training Process The training process is affected by how the data are fed into the network and how the weights are initialized and updated. The data are fed into the network using torch .utils .data .DataLoader. Some of the options are: • shuffle: By default, we usually shuffle the input data in the training and validation part and do not shuffle the input data in test part. • batch size: The larger batch size can often help you get better results, but it is limited by your memory size (computer memory or GPU memory). There are also some exceptions that larger batch sizes will make the performance worse, so you need to be careful in selecting this value. The weights can be initialized from pretrained model or by using torch.nn.init, where the options includes Xavier, Nomal, Uniform, and Constant, etc. The weights will be updated in backward process according to the objective function, op- timization function and learning rate. The objective functions are treated as special layers in PyTorch in torch.nn, and the optimization operations can be found in torch.optim, and some options are listed as below: • Epochs: One epoch means a period that the network has been trained by seeing every training image. After several epochs, your training loss and validation accuracy will not change much. You need to set a good number of epoch in order to get the best accuracy and avoid overfitting. • Objective function: The cross-entropy loss (torch.nn.CrossEntropyLoss) is always used in the classification problems. The soft-margin loss (torch.nn.SoftMarginLoss) and least square error (torch.nn.MSELoss) are also included mainly for binary classifi- cation problems. • Update methods: The commonly used methods are Adam(torch.optim.Adam) and SGD(torch.optim.SGD). The parameters that are to be determined include the base learning rate(lr), momentum rate((momentum)) and regularizer weight((weight decay)). You can also set different learning rate on different layers. • Base learning rate: Larger learning rate (around 0.1) will change the weights dramat- ically, but will be useful when you train the model from scratch. Smaller learning rate (0.01 0.0001) will be useful in fine-tuning from the pretrained models. • Learning rate scheduler: The learning rate need to be cut down as the training is proceeding. The commonly used are step, multiple-step and exponential. You can write the update policy by yourself or use the methods in torch .optim.lr scheduler. 2.3 Result analysis In order to train a deep learning model successfully, the results of the network should be carefully analyzed. The best way to analyze results is to visualize the values. • The loss curve for both training and validation. Ideally, both losses should decrease and converge after several epochs. If the training loss is still going down but the validation loss is increasing, then the model is overfitting. If the losses are still going down when you finish, then the model is underfitting. You should try to avoid both situations in a reasonable number of epochs. • The accuracy changes in the training set and validation set. The values should be increas- ing as the epoch is increasing. The pattern should be similar to the changes of losses but in an opposite direction. 3 Experiments In the experiments, your job is to build a neural network for the classification in CIFAR10 and analyze the results generated from diferent hyperparameters. Your task includes three parts. • The first part is about running a baseline model. You need to run a baseline model and provide your visualization results. • The second part is about finding suitable parameters from the given options. You will be given several options of batch size, base learning rate and number of epochs, try to find the optimal combination of all these three hyperparameters to get the best performance. • The third part is about other options. You need to firstly train a new baseline model and then according to Appendix: Tasks for Part3, analyze the efect of each hyperparameters to your result. The detailed descriptions are as follows. 3.1 Part1: Baseline structure You need to go through the part one of the 'project1_2025.ipynb' file first and run the baseline code. This file is modified based on the official tutorial from PyTorch: https://pytorch.org/ tutorials/_downloads/cifar10_tutorial.ipynb. However, please note that their settings are far from ideal. For this reference and other references, if you used their codes, please point out in comments and in the end of your submission in the ‘Reference’ part. You will be punished if you use all others’ codes without changing anything by yourself. You are also be punished if you used others’ codes but did not indicate in your submission. You need to split your dataset into three parts: training, validation and test. The test dataset is ready by default, and you need to separate several images (always smaller than the number of test set) as validation set in your training. The validation set will help you avoid overfitting problems. The average time for running one epoch of such network is around 1-5 minutes depending on the type of your cpu/gpu. The speed is low because the dataset is quite large. In that case, you can try to randomly select a bit data from the original dataset to check the performance. This technique is very useful when you face with large datasets so that you can quickly see the results instead of waiting for a long time. 3.2 Part2: Select hyperparamaters To successfully train this network, you need to select the proper batch size, base learning rate and number of epochs. The options are as below: • batch size: 2, 4, 8 • base learning rate: 0.005, 0.001, 0.0005, 0.0001, 0.00005, 0.00001 • number of epochs: 1, 2, 4, 8, 16 Your job is to select the hyperparameters that will help to train the network to get the best performance in the test set. Meanwhile, the training time should be as short as possible, which means you should not leave the network training for a super long time even it has converged judging from the loss curve. You are supposed to run the codes for several times, plot the corresponding loss curves for training and validation, compute the accuracy for validation and test, and finally make a decision. Please do NOT change the other hyperparameters in the given network for this part. In the writing part, please provide your output images and your analysis. Your analysis should include but are not limited to: • What are the choices that could be empirically ignored without doing any experiments? Were you correct after you conduct the experiments? • How many epochs are passed when the network is converged? • Why too large or too small learning rates are not good choices? • What are the specification of the computer you are using? E.g. the cpu/gpu type and the corresponding memory. • How long do you run an epoch? Did you use samples of the original dataset to speed up your progress, and how did it work? • Are there any overfitting problems? 3.3 Part3: Other hyperparameters For now you are supposed to have the ability to train a neural network. The rest of the experiment is to build a brand new neural network as a baseline and play with some other hyperparameters. Although you have learned the structures of diferent predefined neural networks, you are not allowed to use them in Project 1. Instead, you should only build a network that contains: • 3 convolutional layers, with the activation functions and pooling layers after each convo- lutional layers. • 3 fully connected layers right after the last pooling layer. Please remember to make changes of the data shapes (using function view) to make the feature maps flow into the fully connected layer smoothly. • 1 output layer, which is also a fully connected layer, but the output channel should be 10 (the number of classes). For the channel sizes and convolutional parameters, you are free to select your own hyper- parameters. However, since you are supposed to train the network using cpus, please do not make layers with very large number of channels. The channel numbers for convolutional layer should be no larger than 256, and the channel numbers for fully connect layers should be no larger than 1024. After you build your baseline model, you need to do the analysis on THREE kinds of hyperparameters. The hyperparameters you are going to play with are defined by your SID as indicated in Appendix: Tasks for Part3. For each of the three subtasks, you can play with all the parameters in the methods. For example, you can select any number for weight decay and momentum if you are playing with SGD. Please analyze the three tasks and select the best choice for your own network. You can also change the other hyperparameters in 2.2 to better suit each choice, e.g. batch size, learning rate, etc., but please remember to control variables while you make the analysis. Please note that your modifications of the hyperparameters based on your baseline network may or may not improve the accuracy. You need to figure out how the hyperparameters influence the results and explain why. The analysis should be included in the written part in the .ipynb file. You need to write ONE single python file (not a .ipynb file) that includes your baseline network and your modified network with your trained files, and the output of this python file should be the accuracy of the test set based on your own models. Your accuracy on test dataset is also marked based on your baseline or modified network whichever is higher. 4 Submission and Grades You are supposed to finish this project on your own. Your submission should include the Jupyter Notebook (‘project1.ipynb’) with your modification and written analyses, and the Python file (‘project1.py’) with your trained model (named ‘baseline.pth’ and ‘modified.pth’ respectively). The files should be in a .zip file named as ‘project1 firstname lastname yourSID.zip’ with no spaces in the file name and submitted through Canvas. The .zip file can contain some of the images if necessary. Your codes need to be well commented and the written part in the notebook file need to have clear sections. The final grades are given based on the following criteria. For detailed marking scheme, please refer to Appendix: Marking Scheme. • Your submissions should strictly follow the instructions. • Your accuracy need to be no smaller than 70% either using your new baseline network or your modified network. • Your codes are correct and well organized. Your codes are well commented and the references are clear. • The written part are well organized and has few typos. The report should contain the correct formulas when they are necessary. • You have covered all the three hyperparameters you are assigned. If you did the wrong task, the corresponding analysis will not be marked and you will also be punished. • The visualization results are clear and well defined. • You have shown your insights into the parameters and drawn some reasonable conclusions. • No copy from your classmates. If you and some of your classmates copied codes from the same online resource, you will also be penalized if you do not make any modifications or provide the reference. • Give references on all the codes and papers you referred to.
OMSCS6476-Fall2025/PS3 Problem Set 3: Introduction to AR and Image Mosaic Assignment Description Description Problem Set 3 introduces basic concepts behind Augmented Reality, using the contents that you will learn in modules 3A-3D and 4A-4C: Projective geometry, Corner detection, Perspective imaging, and Homographies, respectively. Additionally, you will also learn how to insert images within images and stitch multiple images together. Learning Objectives Find markers using corner detection and / or pattern recognition. Learn how projective geometry can be used to transform. a sample image from one plane to another. Address the marker recognition problem when there is noise in the scene. Implement backwards (reverse) warping. Implement Harris corner detection to identify correspondence points for an image with multiple views. Address the presence of distortion / noise in an image. All tests in the autograder generate random scenes each time you submit your ps3.py script. Your functions should only return the information specified in each method's description located in ps3.py. FAQs can be found at the bottom of this document. Problem Overview Methods to be Used In this assignment you will use methods for Feature Correspondence and Corner Detection. You will also apply methods for Projective Geometry and Image Warping, however you will do these manually using linear algebra. Rules You may use image processing functions to find color channels, load images, and find edges (such as with Canny). Donʼt forget that those have a variety of parameters that you may need to experiment with. There are certain functions that may not be allowed and are specified in the problem descriptions and the FAQ at the bottom. Please do not use absolute paths in your submission code. All paths should be relative to the submission directory. The staff will not award points if you lose points on the autograder for using absolute paths! Instructions Obtaining the Starter Files Obtain the starter code from the PS3 GitHub repo. Programming Instructions Your main programming task is to complete the API described in ps3.py . The driver program experiment.py helps to illustrate the intended use and will output the files needed for the write-up. Write-Up Instructions Create ps3_report.pdf - a PDF file that shows all your output for the problem set, including images labeled appropriately (by filename, e.g. ps3-1-a-1.png ) so it is clear which section they are for, as well as a number of written responses necessary to answer some of the questions (as indicated). Please refer to the Latex template for PS3. How to Submit Two assignments have been created on Gradescope. One for the report - PS3_report , and one for the code - PS3_code . Report: the report (PDF only) must be submitted to the PS3_report assignment. Code: all files must be submitted to the PS3_code assignment. DO NOT upload zipped folders or any sub-folders, please upload each file individually. Drag and drop all files into Gradescope. Note that your Gradescope submission is your last submission, not your best submission. If you need to revert to a previous submission you can access your previous submissions and download them via Gradescope. Notes You can only submit to the autograder 10 times in an hour. You'll receive a message like "You have exceeded the number of submissions in the last hour. Please wait for 36.0 mins before you submit again." when you exceed those 10 submissions. You'll also receive a message "You can submit 8 times in the next 53.0 mins" with each submission so that you may keep track of your submissions. If you wish to modify the autograder functions, create a copy of those functions and DO NOT mess with the original function call. YOU MUST SUBMIT your report and code separately, i.e., two submissions for the code and the report, respectively. Only your last submission before the deadline will be counted for each of the code and the report Write-up Instructions The assignment will be graded out of 100 points. Only the last submission before the time limit will be considered. The code portion (autograder) represents 60% of the grade and the report the remaining 40%. The images included in your report must be generated using experiment.py. This file should be set to run as-is to verify your results. Your report grade will be affected if we cannot reproduce your output images. The report grade breakdown is shown in the question heading. As for the code grade, you will be able to see it in the console message you receive when submitting. The coding portion is out of 166 points (so 166/166 gets you the full 60% credit). Assignment Overview A glass/windshield manufacturer wants to develop an interactive screen that can be used in cars and eyeglasses. They have partnered with a billboard manufacturer to render marketing products onto markers in the real world. Their goal is to detect four points (markers) currently present in the screenʼs field-of-view and insert an image or video in the scene. To help with this task, the advertising company is installing blank billboards with four distinct markers, which determine the areaʼs intended four corners. The advertising company plans to insert a target image/video into this space. They have hired you to produce the necessary software to make this happen! They have set up their sensors so that you will receive an image/video feed and a target image/video. They expect an altered image/video that contains the target content rendered in the scene, visible on the screen. Part 1: Marker Detection in a Simulated Scene [40] The first task is to identify the markers for this Augmented Reality exercise. In real practice, markers can be used (in the form. of unique pictures) that stand out from the background of an image. Below is an image with four markers. Notice that they contain a cross-section bounded by a circle. The cross-section is useful in that it forms a distinguished corner. In this section, you will create a function/set of functions that can detect these markers, as shown above. You will use the images provided to detect the (x, y) center coordinates of each of these markers in the image. The position should be represented by the center of the marker (where the cross-section is). To approach this problem you should consider using techniques like detecting circles in the image, detecting corners and/or detecting a template. Code: Complete find_markers(image) You will use the function mark_location(image, pt) in experiment.py to create a resulting image that highlights the center of each marker and overlays the marker coordinates in the image. You have lots of flexibility for how you approach this, as a starting point we recommend using template matching. Each marker should present its location similar to this: Images like the one above may not be that hard to solve. However, in a real-life scene, it proves to be much more difficult. Make sure your methods are robust enough to also locate the markers in images like the one below, where there could be other objects in the scene: Letʼs step it up. Now that you can detect markers on a blank background, assume there is “noise” in the scene (i.e. rain, fog, etc.). This helps ensure that our advertisements can be placed reliably in the scene. All tests in this part start by creating an image with a white background. Second, four markers are placed in random locations simulating the scenes that are present in the input_images directory. find_markers on empty background (similar to sim_clear_scene.jpg) find_markers with noise: just circles (similar to sim_noisy_scene_1.jpg) find_markers with noise: circles + gaussian (similar to sim_noisy_scene_2.jpg) Report: This part will only be graded by the autograder. Do not include this part in your report. Part 2: Marker detection in a Real Scene [5] Now that you have a working method to detect markers in simulated scenes, you will adapt it to identify these same markers in real scenes like the image shown below. Use the images provided to essentially repeat the task of section 1 above and draw a box (four 1-pixel wide lines, RED color) where the box corners touch the marker centers. Code: Complete draw_box(image, markers) A blank image and four random marker points are generated. Your output should return just the rectangle perimeter with a line thickness of 1. The number of nonzero pixels in this image should be close to the euclidean distances of each rectangle side: dist(top_left, bottom_left) + dist(top_left, top_right) + dist(bottom_right, top_right) + dist(bottom_right, bottom_left) Report: This part will only be graded by the autograder. Do not include this part in your report. Part 3: Projective Geometry [60] Now that you know where the billboard markers are located in the scene, we want to add the marketing image. The advertising company requires that their clientʼs billboard image is visible from all possible angles since you are not just driving straight into the advertisements. Unphased, you know enough about computer vision to introduce projective geometry. The next task will use the information obtained in the previous section to compute a transformation matrix H . This matrix will allow you to project a set of points (x, y) to another plane represented by the points (xʼ, yʼ) in a 2D view. In other words, we are looking at the following operation: In this case, the 3x3 matrix is a homography, also known as a perspective transform. or projective transform. There are eight unknowns, a through h, and i is 1. If we have four pairs of corresponding (u,v) (u',v') points, we can solve for the homography. The objective here is to insert an image in the rectangular area that the markers define. This insertion should be robust enough to support cases where the markers are not in an orthogonal plane from the point of view and present rotations. Here are two examples of what you should achieve: When implementing project_imageA_onto_imageB() you will have to make the design choice between forward or backward warping. To make the best choice, you should test both approaches and comment in the report on what helped you choose one method over the other. (Note: to better see differences between the two methods you should pick a marketing image with low resolution). Code: Complete the following functions: get_corners_list() : Your output is checked to see if it returns the right type and complies the ordering specified in the ps3.py documentation. find_four_point_transform(src_points, dst_points) : Random points are generated and, from these, a reference transformation matrix H is calculated. Your output is used to transform. the reference points and verify them with a reference solution using the matrix H. project_imageA_onto_imageB(imageA, imageB, homography) : Two random images are generated one with all zeros and the second one with a random gradient color configuration. The gradient image is then projected to the black image plane using a reference homography. Your output is then compared to a reference solution using the same similarity function provided in ps3_test.py. Report: Report what warping technique you have used and comment on what led you to choose this method. Part 4: Finding Markers in a Video [35] Static images are fine in theory, but the company wants this functional and put into practice. That means, finding markers in a moving scene. In this part, you will work with a short video sequence of a similar scene. When processing videos, you will read the input file and obtain images (frames). Once the image is obtained, you will apply the same concept as explained in the previous sections. Unlike the static image, the input video will change in translation, rotation, and perspective. Additionally, there may be cases where a few markers are partially visible. Finally, you will assemble this collection of modified images into a new video. Your output must render each marker position relative to the current frame. coordinates. Besides making all the necessary modifications to make your code more robust, you will complete a function that outputs a video frame. generator. This function is almost complete and it is placed so that you can learn how videos are read using OpenCV. Follow the instructions placed in ps3.py. First we will start with the following videos. Input: ps3-4-a.mp4 Input: ps3-4-b.mp4 Output: ps3-4-a-1.png, ps3-4-a-2.png, ps3- 4-a-3.png, ps3-4-a-4.png, ps3-4-a-5.png, ps3-4-a-6.png Now work with noisy videos: Input: ps3-4-c.mp4 Input: ps3-4-d.mp4 Output: ps3-4-b-1.png, ps3-4-b-2.png, ps3- 4-b-3.png, ps3-4-b-4.png, ps3-4-b-5.png, ps3-4-b-6.png Code: Complete video_frame_generator(filename) : A video path is passed to this function. The output is then verified for type and shape. After this, the number of frames counted by repeatedly calling the next() function is compared to the original number of frames. Report: Report the 3 keyframes per video in the report. Part 5: Final Augmented Reality [55] Now that you have all the pieces, insert your advertisement into the video provided. Pick an image and insert it in the provided video. First we will start with the following videos. Input: ps3-4-a.mp4 Input: ps3-4-b.mp4 Now work with noisy videos: Input: ps3-4-c.mp4 Input: ps3-4-d.mp4 - Frames to record: 207, 367, and 737 Output: ps3-5-b-4.png, ps3-5-b-5.png, ps3- 5-b-6.png Report: In order to grade your implementation, you should extract a few frames from your last generated video and add them to the corresponding slide in your report. In the next few tasks, you will be reusing the tools that you have built to stitch together 2 images of the same object from different viewpoints to create a combined panorama. Part 6: Finding Correspondence Points in an Image [10] In this part of the project, you have to manually select correspondence points with mouse clicks from two views of the input image. The functions for this task will be provided to you in the class Mouse_Click_Correspondence(object) . The points selected will have to be used to get the homography parameters. The sensitivity of the result would depend heavily on the accuracy of these correspondence points. Make sure to choose distinctive points in the image that are present in both the views. The functions in the class Mouse_Click_Correspondence(object) does not return anything and will create 2 numpy files ( p1.npy and p2.npy ) which will store the coordinates of the selected correspondence points. Report: In order to grade your implementation, attach a screenshot of the images with the manually selected points in the corresponding slide in the report. Image 1: ps3-6-a-1 Image 2: ps3-6-a-2 Part 7: Image Stitching I (Manual Mosaic) [30] In this task, you will be completing the code to perform. the final image stitching and create the output mosaic. So far, you have calculated the homography transform. from one image to the other. Use perspective transformation to stitch the two images together. NOTE: Ensure that you stitch (or attach) the destination image onto the source image and not the other way around. This is purely for the purpose of matching the convention on the autograder while evaluating your code. Code: Complete the following function from the Image_Mosaic() class: image_warp_inv() output_mosaic() Recall concepts from Projective Geometry section. Report: Place the generated panorama ( image_mosaic_a ) into ps3-9-1 (9: Image Stitching) Part 8: Automatic Correspondence Point Detection In this task, instead of manually selecting the correspondence points, you will write code to automate this process. The inputs to this task are the two images and the output needs to be the homography matrix of the required transformation. Use Harris Corner Detection to perform. this task. The implemented solution must be able to work with RGB images. You should refer to module 4A-L2 to learn about Harris corners. Code: Complete the following functions under the Automatic_Corner_Detection() class: gradients() second_moments() harris_response_map() nms_maxpool() harris_corner() Report: There is no Report section for this part. Part 9: Image Stitching II: (RANSAC) [20] In this last section, you will implement the RANSAC algorithm to obtain the best matches among the detected corners using a feature detector (like Harris Corners). You may refer to Lecture 4C-L2 to understand how RANSAC works. Code: There is no template provided for this task and you are free to implement it as creatively as you like. There is also no autograder component for this section. Add your code to the existing ps3.py file. Report: Place the mosaic generated using RANSAC into ps3-9-2. Comment on the quality difference between the two outputs and how it relates to the importance of choosing the correct correspondence points for the image.
Exercise 3 handout: Design a heat exchanger network for crude oil preheat Q1: Started with “EX-3 Template.net”, with the following stream data, design a heat exchanger network with DTmin=10°C. Q2: Design a network with minimum total Cost (Set the NLP optimizer option to DTmin = 1, see Appendix). This question is only for competition. The winner will receive ONE additional bonus mark. Requirements: (1) All exchangers’ DTs ≥ 10°C ± 0.1(Network report) (2) All dTT =0 ± 0.1 and dDH = 0 ± 0.1 (Stream balance report) Submission Deadline: Report should be submitted by the Coming Thursday noon to Canvas. Marking Scheme: No or not meaningful submission (0 points) Meet some Expectations (1 points) Meet most Expectations (2 points) Exceed Expectations (3 points) Score No submission or very little meaningful result is presented Works is not completed or with major error. Result is poorly presented Works is completed with minor mistakes. Result is properly presented Works is well completed and presented Appendix: To allow the NLP optimizer to vary DTs below 10°C, please change the Minimum Approach from 10 [C] to 1 [C] before running the optimizer.
Experimental physics Static Friction, μs and Kinetic Friction μk Introduction In this experiment you will be measuring the angle at which an object starts to move down a slope. You will be using this angle to calculate the coefficient of static friction between the surface of your object and the slope. You will repeat your experiments by finding the angle at which the object The investigation is designed to give you an understanding of: • Separating forces into components • Frictional forces • Newton’s second law Theory The following information may be helpful. What you will need Assemble this equipment before you start the exercise: • A ramp (this may be a plank of wood, a flattened cardboard box or even a very large textbook) • A protractor • A box that fits on your ramp (e.g., an empty tissue box) • Objects that can be used to evenly distribute mass over the box (could be weights, stones, books) • A set of kitchen scales Maintaining the integrity of your work Turnitin Submission Your submission must be typed, not handwritten, so that it can be read by Turnitin. Hand drawn diagrams are accepted but data, tables, graphs and explanations must be typed. Collaboration You may either do this investigation alone or with up to one other person currently enrolled in the course (i.e., you may not work as a group of three or more). ▲l If you work with someone else, you need to acknowledge your collaborator (include their name and student number in the report and include them in your photo). In this case you must conduct the analysis and answer the questions individually, even if your data is the same. If you have identical answers that will be considered plagiarism, so while it is OK to collect the data together you need to answer the questions and plot the graphs etc. as individuals. Photographic evidence You must include two (2) photographs in your report: • A close-up image of your UNSW photo ID card with your name and photo clearly visible. • A photograph of yourself with your student card, the equipment and a piece of paper clearly showing the date and your name and student number. If you worked with a partner this photo must include both of you. ▲l You MUST provide these photos in your assessment. Assessments without the photos will not be marked and you will receive a zero. < Sample photo of equipment, faces of people who completed the experiment, your student cards and paper with names, student numbers and date. You also need to include a close-up of your student card. If the experiment asks you to include additional photos, such as to record and report observations, then you must include a label in the image showing your name and zID (just like in the photo above except you do not need to be in it) Risk Assessment Read over the experimental method and then complete a risk assessment in a table similar to the one below and include it in your report. You must minimize all risks before you start. Add as many rows to the table as you need. If any of the risks rank above medium, you need to adapt the experiment to make it safer before proceeding. Tasks Hazards (Step 3) Associated risks (Step 4) Existing risk controls Risk rating with existing controls * (Step 5) C L R By proceeding with the experiment, you are agreeing to follow these risk control methods and conduct the experiment safely. If you have any doubts about your ability to complete the experiment safely then you should not proceed. ▲l. In your report you need to include your risk assessment and state that you have agreed to follow the risk controls. Without these, your report will not be marked. Procedure Phase 1: Static Friction 1. Set up your ramp. You will need to be able to change the angle of the ramp so that you can accurately measure that angle at which the object just starts to move down the slope. You will need to work out how to do this, you could use a car jack, chair or a stack of books to support one end of the ramp. 2. Record the masses of the object and the box that you place on the slope. 3. Place the object in the box on the slope. Take a photograph of it to submit in your report (include your name, zID and student card in this image, too). Increase the angle of your slope from horizontal until the box just starts to move. Measure this angle using the protractor and record it in the results table. In your report, describe the method you used to make your reading of the protractor as accurate as possible. 4. Repeat the measurement twice (so that you have three measurements in total). 5. Place more mass on the object and repeat steps 3 and 4. 6. Repeat steps 3-5 until you have recorded results for 5 different masses (keep the surfaces in contact the same). Results Record your results in a table like the one below. Mass (kg) Uncertainty in mass (kg) Angle 1 (°) Angle 2 (°) Angle 3 (°) Average angle (°) Uncertainty in angle (°) μs Uncertainty μs Phase 2: Kinetic Friction 7. Set up your ramp. You will need to be able to change the angle of the ramp so that you can accurately measure that angle at which the object just starts to move down the slope. You will need to work out how to do this, you could use a car jack, chair or a stack of books to support one end of the ramp. 8. Place an amount of mass in the box; you should use the same mass(es) as you did in Phase 1. 9. Place your box on the slope, at a specific spot; lightly tap the object so that it moves and then comes to a stop. 10. Repeatedly tap the box (returning it to the original position on the ramp each time) while increasing the angle of the ramp. There will be one angle for which the box keeps sliding; record this angle! 11. Repeat the measurement twice (so that you have three measurements in total). 12. Place more mass on the object and repeat steps 9 - 12. 13. Repeat steps 9-12 until you have recorded results for 5 different masses (keep the surfaces in contact the same). Results Record your results in a table like the one below. Mass (kg) Uncertainty in mass (kg) Angle 1 (°) Angle 2 (°) Angle 3 (°) Average angle (°) Uncertainty in angle (°) μk Uncertainty μk You may find the following equations useful: Analysis 1. Show that μs = tan θs, using a diagram. ▲l. If you find it difficult to type your working or draw diagrams in your document, then you can do these with pen/pencil and paper and insert an image ofit into your report. 2. For each mass calculate the coefficient of static friction between the object and the ramp. Calculate the uncertainty in this coefficient for each mass. 3. Calculate the overall coefficient of static friction between the two surfaces that you have chosen. Include an uncertainty. For the uncertainty in the friction coefficient, use 4. For each mass calculate the coefficient of kinetic friction between the object and the ramp. Calculate the uncertainty in this coefficient for each mass. 5. Calculate the overall coefficient of kinetic friction between the two surfaces that you have chosen. Include an uncertainty. For the uncertainty in the friction coefficient, use Questions ▲l. If you find it difficult to type your working or draw diagrams in your document, then you can do these with pen/pencil and paper and insert an image ofit into your report. 1. For static friction: Draw a labelled diagram showing all the forces acting on the object when the slope is at an angle halfway between 0° and the angle at which the object started to move (for the highest mass case). Label your forces as weight force, normal reaction force and frictional force. 2. What is the net force acting on the object in the case you have just drawn? 3. For static friction: Calculate the component of the gravitational force acting parallel to the slope just before the object starts to move (for the case with the highest mass). 4. For static friction: Calculate the normal reaction force experienced by the object on the slope just before it starts to move (for the case with the highest mass). 5. What is the relationship between μs and μk? Is that what you expect?. 6. Plot a graph of μs versus mass and μk verses mass.. Does this graph agree with your expectation? Why or why not? ▲l. Include a screenshot of this graph in your report. What to submit ▲l. You should type up your report to submit it to Turnitin. The report needs to be in a single file. It is better to submit a .pdf rather than a .docx Word document as Word documents can display different on different computers. Your tutor will mark the report as it is displayed on their machine. The report should include: • A statement of the aim of the investigation • The risk assessment and a statement saying that you will follow the risk controls recommended in the risk assessment • A description of how you measured the angle accurately • Everything in the results and analysis section of the investigation • Details of your analysis of the results and predictions (i.e., show how you did the calculations) • A photograph of yourself and partner (if you had one), your student card, a piece of paper with the date, name and student number and the equipment as well as a close up of your student card. • Answers to the questions • A conclusion stating what you have found out
EDUC91372 Disciplined Inquiry Capstone For this task, we are asking you to develop a Methodological Note. This is a schematic document that scaffolds your reflective process and guides it towards a more action-based approach. The output will be a collection of ideas and methods to enact some sort of change in the real world of education. The note is comprised of three parts: • A written summary providing an updated description of your inquiry. • A "stakeholder interview": a video recording in which you interact with a stakeholder- someone directly or indirectly relevant to your inquiry- who will provide feedback and advice about scope, strengths and weaknesses of vour proiect. This is consistent with the notion of "partnership model" that we use in the Capstone: educational change is never a purely individual effort but requires collaboration with several individuals and groups. The recording shouldl not be longer than 10 minutes. • A structured evaluation of three "evidence sources", which together exemplify the types of new knowledge that your inquiry will need to succeed. You will use a template to assist you in this task. In addition to the completed template, a video recording of your stakeholder interview must be uploaded as a media file. This means you will need to submit two files: • A word file (completed templatel • A 10-minute video recording (stakeholder interview) If either file is not present, the submission will be deemed incomplete and you will fail the task. Support • Retrace your steps during the second module "extending your inguiry"- ie. week 5 to week 9 - and reflect on the asynchronous learning activities and the discussions during the workshops. Assessment instructions Methodological note (approximately 2000-2300 words) Type of file: Word or PDF For this task, work directly in the template provided. Start from the first section in which you are asked to provide an updated critical description of your inquiry that considers the reflective work you have carried out until now. The definition should include a brief discussion of the sensitizing concepts that are supporting the refinement of your inquiry. It is very important that you show a degree of progression in your thinking compared to AT1. Overall, you are required to write approximately 500 words in this section. Here are some stimulus questions that you can use to structure the section: • What is the problem at the heart of your inquiry? Are you interested in promoting innovation in teaching and learning? Or perhaps in tackling a specific challenge in your school? Are you interested in a broader policy issue? Remember, a "problem" should not be merely an academic research question. You should make an effort to ground that problem in an actual or future professional context. • In response to that problem, what aspects of practice and policy should change? What aspects should remain the same? Once more, focus on a real or future professional context to articulate your change strategy: a school or university faculty, an industry setting, a government department, a specific policy or legislative processes, and so on. • How is reading the literature supporting your understanding of your problem and the development of an inquiry to tackle that problem? We understand this is an ongoing process, and that you have already engaged with three "foundational" readings for AT1. For this task, we are asking you to compare/contrast 2 references. One reference should be one of the foundational readings from ATi, and one reference should demonstrate independent/autonomous reading around a sensitizing concept. The new source must be carefully chosen, and it should be highly relevant to your inquiry. You should read it deeply and multiple times, taking several reflective notes. Once more, we are emphasising deep and meaningful engagement with influential texts rather than coverage. IMPORTANT: While the task requires you to engage with only two readings in the way described above, remember that established citation rules still apply: all sources and authors should be acknowledged and added to a list of references (not included in the wordcount). So, for example, if you happen to mention other authors as you compare/contrast your key texts, these authors should be referenced appropriately using APA style. The second section of the template asks you to summarise your updated stakeholder strategy. Develop a 500 words reflection which must be informed by the stakeholder mapping exercise carried out for AT1 and, to a much larger extent, by the stakeholder interview in week 6. Describe briefly the interview (when and how it happened, and whether you had a real stakeholder or a role-playing peer), then reflect on the main themes that emerged during the discussion to develop an updated definition of your partnership strategy. The key questions at the heart of a partnership strategy are: why is your inquiry problem important for your stakeholders? How can those stakeholders be meaningfully involved in shaping a professional change project? The third section of the methodological note requires you to identify and evaluate "new" forms of knowledge and evidence. Use the evaluation framework provided in the template. Copy and paste the framework in the same document to complete the activity. For each source, you are required to write 300-350 words explaining its relevance to your inquiry. Answer these questions to explain the relevance of a source: • Why did you choose this source? • What keywords did you use to find the source (don't use chatbots to find sources, they are unreliable and give you" readymade" outputs that undermine your critical thinking skills. Use the Library search engine or Google Scholar). • How has this source shaped your thinking around the problem at the heart of your inquiry? • How has the source shaped your thinking around the types of evidence needed to support your inquiry? Remember: All direct quotes from the literature must be in quotation marks and include page numbers. Please include page numbers even when you're not directly quoting. The fourth section of the methodological note asks you to reflect on the ethical considerations of your inquiry. Ethics is fundamental to a disciplined inquiry because it ensures that any activity that involves human participants is conducted with integrity, fairness, and respect, while also safeguarding the credibility and impact of findings. What are some of the ethical considerations for your disciplined inquiry? Consider the concepts listed in the template and write a short description if they apply to your inquiry. Refer to materials and workshop activities in W8 and W9. The only compulsory consideration is informed consent (you must write something about it, and it's highlighted in the template), the others may or may not apply but it is advised to consider at least three of the dimensions below. 300 words.
BADM 2301 Lab Session Lab 5 A – Microsoft Access Background In this lab session, you will learn how to use Microsoft Access to manage order data in a sales organization, where customer, item and order information are stored in three separate tables. We will make connections between the tables by defining the relationships between them, and then create multiple queries and a report. Primary Keys To start with, set the primary keys for each table. 1. Right click on “Items” in the Table pane on the left; choose “Design View” to open it. 2. To set the primary key, highlight “Item ID” as the primary key by selecting the corresponding row heading. In the ribbon, find and click on the “Primary Key” button. If set correctly, you should see a small key logo by the row headings. ➢ Please follow similar procedures to set “Customer ID” as the primary key for the “Customer” table. 3. Now set both “Customer ID” and “Item ID” as primary key(s) for the “Order” table. ➢ This is called a Composite Primary Key. ➢ To do so, select both rows in the table by using CTRL+click. Then click the “Primary Key” button on the menu bar. Hint: when you see an arrow in the first row, drag it down to select both rows. 4. Save all the work and close all open tabs. Understanding and Managing Relationships The three tables of the database contain three different sets of information that is likely input and maintained by different departments at the organization. We can integrate the information from these tables by defining relationships between them. 1. In the menu, select “Database Tools” → “Relationships” to bring out the “Relationships” window. If the “Show Table” dialog box does not show up, select “Relationships” → “Show Table” from the menu bar. Select each table and click “Add” to add them into “Relationship” window. Relocate the tables in proper positions. 2. Drag “Customer ID” of “Customer” table to “Customer ID” in “Order” table, check “Enforce referential integrity”, and click “Create”. ➢ You just created a one-to-many relationship between the two tables using the common field “Customer ID” . ➢ Notice an infinity symbol (i.e. ∞) appearing by the registration tables. It suggests the one-to-many relationship you just created. 3. Do the same thing for “Item ID” in “Items” and “Order” table to create the second relationship. 4. Save the work and close the Relationship tab. Query and Parameter Query We need to use queries to get data from a database. Queries are a convenient tool to extract the information we need by following a set of specified criteria. In this part of the tutorial, we will learn how to create a “Looking for Items” query that will extract records for all items from your database. 1. Choose “Create” in the menu bar and then click on “Query Design” . In the “Show Table” dialog box, select “Items” and click on “Add” . Close the “Show Table” dialog box; double- click the fields you need, to select them into the lower table. These are the fields that will show up in the query results. Close the query design window and save it as “Looking for Items” . 2. Click the “Run” button on the menu bar to test your query. Next, we will create a parameter query which will allow users to search for customers information. 3. Choose “Create” in the menu and then click “Query Design” . In the “Show Table” dialog box, select “Customers” and click on “Add” . Close the “Show table” dialog box; double-click the fields to select them into the lower table. These are the fields that will show up in the results. First, locate the “ Criteria” row of the field “State“ in the lower table. Next, type “ [Input a State]” (do NOT omit the SQUARE brackets!). Close the query design window and save it as “Looking for Customers by State” . 4. Test your query using a state, say “VA” . Click the “Run” button. Next, we will learn how to create a new parameter query which will allow us to list items bought by customers based on Item IDs. 5. Choose “Create” in the menu and then click on “Query Design” . In the “Show Table” dialog box, select all three tables this time. Close the “Show table” dialog box. To select fields into the lower table, double-click on Item ID, Item Name, and Item Description, from the "Items" table. Then, double-click on “Customer Name” from "Customer" table. These are the fields that will show up in the query results. Add “ [Input Item ID]” in the “Criteria” row of “Item ID” field. Close the query design window and save it as “ Looking for Items bought by Customers” . 6. Run and test your query - similar to what you did above. 7. Save your work. As you can see, by setting queries in a database management system, you can retrieve data from multiple tables - as long as they have been “connected” using proper relationships. Please remember if your relationships are not formed correctly, the results of the query will be wrong. Create a Report 1. From the menu bar, choose “Create” → “Reports” → “Blank Report.” A new empty report will appear in the window with a Field List, including all the tables on the right. 2. On the Field List (on the right side), click on "Show all tables". 3. Locate "Customer" table, click on the + sign. Add customer name customer address and customer state fields in the report by double-clicking on them. 4. Similarly, you can choose date of order, quantity and amount fields from the “Order” table and item name field from the “Items” table to add to this report. 5. You can resize a column just like in an Excel window. First click on the column header to select it. Next, move the mouse to the right border of the column header until shape of your cursor changes to a double arrow. Click and drag the border to the left to make it smaller or to the right to make it bigger. 6. You can use the “Format” tab and then “Font” on the menu bar to adjust the font size, color and alignment. 5. Add a title to the report by choosing “Design” → “Title”. A title text box will appear on top of the report. Name the report “Customer Report.” 6. Add page numbers to the report by choosing “Design” → “Page Numbers” . Choose the format and the position of the page number. 7. To add the current date, choose “Design” → “Date and Time.” Select the format you like to present the current date and time. 8. To see a preview of what the printed report will look like, right-click the report title bar and choose “Print Preview” from the drop-down menu. Or you can also click on the View button, then choose “Print Preview.” 9. To go back to the Layout View, right-click anywhere on the report and select “Layout View.” Close the report and when prompted, save the report as “Customer Report.” 10. Your file should have the following on the left side bar: a. Tables: Customer, Items, Order b. Queries: Looking for Items, Looking for Customers by State, Looking for Items Bought by Customers c. Reports: Customer Report Submission Save the Access file as “FirstName_Lab5” and download it from the Virtual Lab to your local computer. Please make sure to submit it in the Assignment Lab 5 link. Submit the downloaded file in the file format .accdb to Blackboard. Please make sure that the extension of your Access file MUST be in .accdb. Files ending with .laccdb will NOT be accepted.
218.320 Civil & Services Measurement Semester 2, 2025 PORTFOLIO TWO (A) GENERAL INSTRUCTIONS: 1. This is a group assignment. Your group should contain a maximum of 4 students. Should you choose, you do have the option to work individually. If you work individually, it should be presented to the same quality required expected from a group. 2. This portfolio contains TWO questions. 3. Total marks for PORTFOLIO TWO: 100 marks 4. Total course weightage and contribution of the assessment component = 40% and covers learning outcomes 1 and 2 5. Submission date (Stream deadline): Friday, 17th October 2025, no later than 11:50 pm 6. NO HARD COPY SUBMISSION IS REQUIRED 7. Online submission on Stream: COMPULSORY. 8. Late assignments will be penalised as per the course guide and the school’s policy. 9. You must include your names and IDs, the course number, the course title, and the names of the course teaching team on the cover page. 10. You are only allowed to submit a PDF on Stream. 11. Please read and consider all documents in the assessment folder. 12. Include all stream communications on clarifications about the project / assessment that was used for preparing your assessment in the query sheets. All clarification communications regarding the assessment and project will stop on 5th October 2025. 13. Complete your group selection on the stream by 14th September. Students who are not in groups by the given time will be randomly allocated to groups. 14. Covered learning outcomes a. Use industry-standard software to measure building quantities according to the Standard Method of Measurement. b. Prepare Bills of Quantities using industry-standard software (A) QUESTION 1 Measure and prepare bills of quantities only for the following sections of Project A (Refer to the attached “Portfolio 2 - 218320 - set 1”) according to CESMM4 revised. 1. Excavation a. Topsoil (100mm thick from the existing ground level and 300mm beyond the plan building edge) b. Cutting and filling • BOQ should contain the following sections (a) a List of principal quantities, (b) Preambles, (c) Daywork Schedule, (d) Work items (grouped into parts) (e) Summary pages and (f) Grand Summary and be built according to CESMM4 revised. • Show all the workings, including, o Grid breakdown (Use a 10m x 10m grid) o Cutting calculations o Taking off sheets o Query sheets if necessary • For day work sections, add some of the material, labour and equipment that you think might be needed for the project, but rates are not required. [20 marks] QUESTION 2 The client for Project Vortex has decided to use a Lump Sum pricing method for the contract. Your company has been selected as the contractor for this project. Tasks to be Measured by Your Team Your manager has instructed your team to measure the following items for: 1. Roads and paving, including excavation for road work 2. Stormwater Pipework (This would relate to Pipes, Fittings and valves, Manholes and pipework ancillaries, Supports and protection, and ancillaries to laying and excavation sections). Include only new construction work with connections to the existing. 3. Wastewater Pipework (This would relate to Pipes, Fittings and valves, Manholes and pipework ancillaries, Supports and protection, and ancillaries to laying and excavation sections). Include only new construction work with connections to the existing. • BOQ should contain the following sections (a) a List of principal quantities, (b) Preambles, (c) Daywork Schedule, (d) Work items (grouped into parts) (e) Summary pages and (f) Grand Summary and be built according to CESMM4 revised. • Show all the workings including, o Calculation sheets o Taking off sheets o Query sheets if necessary • For day work sections, add some of the material, labour and equipment that you think might be needed for the project, but rates are not required. [80 marks] Assessment instructions for questions • Some drawings are not to scale. You can use drawings and dimensions from detail drawings to measure. If there are no dimensions shown on the drawings, then develop an appropriate scale for measurement. • The accuracy of descriptions and quantities accounts for a considerable part of the final mark. • You can choose to use Costx or other software to prepare the document. However, your submission should be according to examples 1, 2, and 3 (refer to the last three pages) and the CESMM revised 4 example book and handbook. You are required to show clearly how you came up with the quantities where necessary. • Consider how the Bill of Quantities (BOQ) should be structured and presented in professional industry practice, and prepare your submission accordingly. You are expected to go beyond the basic classroom examples and demonstrate a higher level of application. Your work should reflect what would be considered excellent by current construction industry standards. • You should include the following files in your submission (refer to the file generation steps mentioned below) o One PDF document for each question, which includes (t Front page (example 1) General and specific preamble notes for each section BOQ summary Detailed calculations of each item Drawings with dimensions take off details RFI sheet / Query sheet - (refer to example 3) Peer review report (refer to example 2) (you can choose to submit this individually) Generative AI uses statements from each student (attached at the end) (B)MARKING RUBRIC Assignment & Marking criteria Marks Portfolio 2 • Question 1 1. Preparation of BOQ with supportive documentation suitable for industry practice, with clear documentation. i. Coverage of all documentation, including required content, including grid maps, cut and fill calculations, and topsoil calculations ii. Presentation of each section, including Bills of Quantities according to CESMM 4 revised directions (including preambles). • Question 2 a. Preparation of BOQ with supportive documentation suitable for industry practice, with clear documentation. Coverage of all documentation, including required content. Presentation of each section, including Bills of Quantities according to CESMM 4 revised directions (including preambles). b. Correct descriptions and appropriate headings / sub-headings for the measured items, including generic & specific preamble notes i. Roads and paving, including excavations ii. Stormwater iii. Wastewater c. Correct quantities for the measured items, including side- casting, entry of measures, and quantity referencing. Schedule of quantities for the trades, presented in the correct SoQ or BOQ format. Include query sheets (if necessary). i. Roads and paving, including excavations ii. Stormwater iii. Wastewater Peer review report Include peer review with the Student's name, ID, and Work section completed by each student d. Question 1 e. Question 2 15 4 15 14 8 5 16 12 6 1 4 GRAND TOTAL 100 (C) LATE SUBMISSION & ACADEMIC INTEGRITY a. Plagiarism: Plagiarism is a very serious offence. DO NOT PLAGIARISE. Read the rules here:https://owll.massey.ac.nz/referencing/plagiarism.php. b. Late submission: A penalty of 3 marks will be deducted for each calendar day (including weekends and public holidays) or a part of a day an assignment is submitted late after the Stream deadline. Assignments submitted more than 7 calendar days after the Stream deadline will not be marked and will receive a mark of 0. c. Extension of time: An extension may only be granted by the course coordinator based on the circumstances.
STAT 2510SEF, STAT S251F, and STAT S251W Statistical Data Analysis Autumn Term, 2025 Assignment 1 Question 1 (10 marks) For each scenario below, decide if the sampling method used is random or non-random. Provide a clear reason for your choice. (a) HKMU wants to survey its students about campus life. The administration obtains an alphabetical list of all 20,000 enrolled students. They select every 100th student on the list after the subjectively determined starting point, 54 until they have a sample (54th, 154th, 254th student, etc.) of 200 students. [5] (b) An Education Bureau official wants to understand public opinion on increasing the percentage of non-local undergraduate students to government funded universities. He posts a link to an online survey on the bureau’s official Facebook page and encourage all followers to participate. [5] Question 2 (13 marks) (a) For each scenario below: • Identify the scale of measurement being used: nominal, ordinal, interval, or ratio • State whether the data produced would be discrete or continuous (i) A chef asks diners to categorize the spiciness level of a new dish as mild, medium, or hot. [2] (ii) A meteorologist records the temperature (in degrees Fahrenheit) at a weather station at noon each day. [2] (iii) A librarian counts the number of books checked out by a student during the semester. [2] (iv) A survey asks individuals to report their primary mode of transportation: car, bus, bicycle, or walking. [2] (b) For each scenario below, state whether the data collected is primary or secondary. Provide a brief reason for your answer. (i) An economist is studying long-term trends in flat prices in Hong Kong. She downloads a comprehensive dataset on Hong Kong median flat prices from 1980 to 2020 from the Census & Statistics Department website to analyze in her research. [3] (ii) A tech company wants to understand how users interact with a new feature in its mobile app. It develops a program that anonymously records the number of times each user clicks the new button over a one-week period. [2]
ESL 112 Problem Analysis Essay (20% of final grade) Purpose The purpose of this assignment is to help you refine/review the essay writing skills covered in ESL 111 as well as to give you an opportunity to explore the topic (i.e., a problem in a society) that you are interested in learning more about. Assignment Description Choose a problem in a society (e.g., a community, a state, a region, a country) affecting a specific population (e.g., an ethnic or cultural group, an age or gender group, an animal species) and analyze the causes and/or effects of the problem. This means your essay can focus on both causes and effects or just on causes or just on effects. Your analysis should be based on evidence from reliable sources. Note: Based on what you find during the process of writing this essay, you may decide to write about this topic for the Solution Critique Paper (where you will identify and evaluate potential solutions for a problem in a society.) Deadlines are available in the course calendar Problem Analysis Essay Outline Points: 10 Problem Analysis Essay Draft 1 Write a complete first draft of this essay, meeting all assignment requirements, including expectations for formatting and source requirements. Points: 20 Problem Analysis Essay Draft 2 Revise the first draft of this essay based on peer feedback and self-revision, improving writing to not only meet all assignment requirements, but also to address specific criteria indicated in the rubric. Points: 20 Problem Analysis Essay Final Draft Revise the second draft of this essay based on instructor feedback and self-revision, improving writing to not only meet all assignment requirements, but also to address criteria indicated in the rubric. Points: 100 Source Requirements Use at least four (4) reliable sources in your essay. All sources should be written in English (or, if written in another language, there should be an easy way to access an official English translation.) The final draft should meet APA-formatting requirements including a reference page. Fabrication of sources is a violation of academic integrity (see below). Formatting Requirements Essays should be 3-4 double-spaced pages (about 800 - 1200 words) in length. The title page and reference page are not counted as part of this length requirement. It should be written using an APA recommended font like 11-point Arial, Calibri, or Georgia, or 12-point Times New Roman. Paragraphs should be indented and the document should be formatted with 1-inch margins on all sides. The first page should be formatted as a title page (see APA 7th edition title page formatting guidelines here) and the “References” list should begin on a new page after the final line of the conclusion. Final drafts should include your UIN (9-digit number).
