Problem 1: Design of a large-scale musical instrument The purpose of this project is to design a large-scale musical instrument using metallic plates installed in an 8m x 16m room, as shown in Figure 1-1. These plates act as parabolic reflectors (Figure 1-2), causing reflected sound waves to converge at the focal point. Small nibbled slots or multiple small drilled holes should be designed to influence the natural frequencies and responses of the plates. The plates should be designed to possess different fundamental frequencies within the audible range of 50 to 8.5kHz (the region of musical sounds). It's important to note that musical sounds can be obtained when the frequencies of the plates are multiples of each other. Figure 1-1: Geometry of the room with metallic plates installed in it. Figure 1-2 Geometry of the metallic plates. The following assumptions can be made: · The material is 70/30 brass. · The thickness is 3mm. · The other dimensions of the plates can be varied to obtain the desired frequencies. Suggested steps: 1) Conduct a brief literature survey on large scale musical instruments and their frequencies of resonance. 2) Construct a model that predicts the frequencies and modes of vibration and their variations with respect to geometry and material properties. 3) Explore how frequencies can be tuned with geometrical cuts into the plates. 4) Explore practical loading scenario to operate the proposed instrument. 5) Justify your choices Remark: make the assumptions that you believe are necessary to solve the problem. References for Problem 1 [1] Lee, H.P., Lim, S.P., Chow, S.T., 1990. Prediction of natural frequencies of rectangular plates with rectangular cutouts. Computers & Structures 36, 861-869, https://doi.org/10.1016/0045-7949(90)90157-W. [2] Prasshanth, C.V., Anish Kumar, U., Badri Narayanan, S., Bhaskara Rao, L., 2024. Vibration analysis of perforated functionally graded circular plates. Materials Today: Proceedings, 1-7. https://doi.org/10.1016/j.matpr.2024.04.100 [3] Leissa, A.W., 1969 Vibration of Plates, Office of Technology Utilization, National Aeronautics and Space Administration, https://ntrs.nasa.gov/citations/19700009156 [4] Zhou, Q., Sariola, V., Latifi, K., Liimatainen, V., 2016. Controlling the motion of multiple objects on a Chladni plate. Nature Communications 7, 12764-1-10, 10.1038/ncomms12764
Hydrosystems Engineering (EACEE 3250 / 4250) Spring 2025 Homework #5 (Due Friday, May 9th, 11:59 pm) Homework Guidelines: Your solutions to homework assignments will be submitted and graded through Gradescope (see the Gradescope tab on your Courseworks dashboard). You will have two options for submitting your work in Gradescope, either: 1) upload individual scanned images of your handwritten pages (e.g., using your phone), one or more per question; or 2) upload a single PDF that you create which contains the whole submission (e.g., merge files on your computer or phone with a software of your choice). Please use the naming convention Lastname_HWxx.pdf when submitting your homework assignment. You may choose to type up your calculations, in which case show all your steps and highlight your solution. Note: During the upload, Gradescope will ask you to mark which page/s each problem is on (see example here). It is important that you follow that step for grading purposes. It is acceptable to discuss problems with your colleagues, and questions are encouraged during office hours, but all work must be done independently. Make sure to clearly show all work on each problem and that your solutions are presented in an orderly fashion. It is your responsibility to make your solutions easy to grade. Topics/Chapters covered: • Chapter 10: Runoff and streamflow • Chapter 11: Watershed Modeling Problem #1 (50 pts) Runoff After many years of flooding the local Museum of Science, the town planners of Lion Town have decided it’s time to construct a unit hydrograph that represents the rate of discharge at the Museum of Science per 1cm of runoff generated over the Lion Town River Basin. The data below shows the flow at the Museum of Science generated by runoff produced by the Lion Town River Basin (A = 50 km2) for a storm last year. Time (hr) Flow (m3/s) 0 14.16 6 33.98 12 63.71 18 82.68 24 75.61 30 58.55 36 40.49 42 31.15 48 25.77 54 22.09 60 19.26 66 16.99 72 15.29 78 14.44 84 14.16 a) Plot the hydrograph of the storm above. Please use a scientific plotting software of your choice such as MATLAB, Python, Excel, etc. and show your work as needed. b) Calculate the total volume of overland runoff produced by the storm above by assuming a baseflow of 14.16 m3/s. c) Over the area of the basin, what is the average depth of runoff for this storm? d) Construct and plot a Unit Hydrograph (hydrograph corresponding to unit depth of runoff over area of basin) for the Lion Town River Basin. Assuming an instantaneous pulse of rainfall falling at 6AM on both Saturday and Sunday: e) Plot the hydrograph for Saturday’s storm which produces 10.9 cm of runoff. f) Plot the hydrograph for Sunday’s storm which produces 12.44 cm of runoff. g) Plot the total hydrograph. Problem #2 (10 pts) Open channel/streamflow Estimate the steady-state uniform flow in a prismatic rectangular channel with a width of 5 m, a water depth of 2 m, a bed slope of 0.001 and a Manning roughness coefficient of 0.03. Problem #3 (40 pts) Watershed Modeling Access the free, web-based platform Model My Watershed, as reviewed in class (the week of 4/28): https://modelmywatershed.org/. Go through the app workflow covered live in class. Further instructions can be supplied, upon request. • Choose a location of interest to you. It can be in the greater NYC area, your hometown, or anywhere covered by the software. Note that for rural watershed-scale areas, turn on HUC-12 Watershed boundaries using "Select by Boundaries". For smaller urban areas, use "Draw Area" to define your area of interest. • Go through the Analyze Tab, learning about the data in your selected area. • Go to the Model Tab and select the Watershed Multi-year (annual) Model. Using your watershed model, add New Scenarios (+) and apply various Conservation Practices to these scenarios. Explore how basin runoff and other variables change as a result. Use the simulations to test your understanding and/or hypotheses about hydrologic response to static basin characteristics. Create at least 3 engineered scenarios, capture/append the scenarios report from the “Compare” button (top left), and give a brief explanation comparing and contrasting these the influence of changing static parameters (on runoff and streamflow in particular).
FINC5001 FOUNDATION IN FINANCE TOPIC 4 - DEBT AND EQUITY VALUATION PRE-WORKSHOP QUESTIONS Pre-workshop self-access questions set is designed for you to self-access your understanding of the lecture content. You are expected to complete these questions prior to the workshop. These questions will not be discussed in the workshop. Pre-Q1 David Jones’ is considering issuing bonds with ten years to maturity to match the expected life of its store transformation plans. The bonds would have a face value of $1,000 and an annual coupon of $80. Similar bonds currently trade at a YTM of 8% p.a. compounded annually. Calculate the expected issue price of a David Jones’ bond. Is this trading at par, at a premium or at a discount? What would be the price if the annual coupon was set at $60 instead? Or $100 instead? Pre-Q2 Estimate the value of the following shares: a) A share in a corporation that has been paying a semi-annual dividend of $0.40 in recent years, has no growth in earnings and that has a level ofrisk requiring a return on equity of 12.50% p.a. compounding annually. b) A share that has just paid an annual dividend of $1.35, with a dividend growth rate of 2% p.a. expected in perpetuity and a required rate of return of 13% p.a. c) A share issued by a company with current earnings of $2 per share. These earnings are expected to grow at 4% p.a. forever but only 30% of earnings are paid out as annual dividends. Their cost of equity is 12% p.a. compounding semi-annually. d) A share in a company whose ROE and payout ratio is expected to remain constant at 8% p.a. and 60% respectively forever. The required return on equity is also 8% p.a. annually compounded. The next annual dividend is expected to be $0.75 per share.
HLTSC575-25A Applied Science for Nursing Practice III Clinical Project: Summative (Final) Due Date: ● Submission of Clinical Case (via Moodle): 11.59pm, Friday April 25th, 2025 ● Peer Evaluation of Health Education Delivery: Friday May 2nd, 2025 Weighting: 30% • Clinical Case Report: 20% • Peer Evaluation of Health Education Delivery: 10% Within your clinical practice environment, select a case through which you can demonstrate your in depth understanding of the pathophysiological changes associated with long-term conditions. The criteria for theory components are outlined below. Components of Clinical Case: 1. Maintaining confidentiality, you should BRIEFLY describe the patient situation at admission, including kaupapa, the condition(s) diagnosed, length of time for which diagnosis has been in place, and related information about the need for this admission (maximum 150 words). 2. Addressing ONE long-term condition, articulate its impact on two body systems at a cellular, tissue and organ system level (Approximately 400 words). 3. Explain the relationship between clinical findings and related pathophysiological change in body systems that support clinical diagnosis and decision making (Approximately 250 words). 4. Consider the four taha (hinengaro, wairua, whānau and tinana) and the potential for altered psychological functioning in relation to the chosen condition (Approximately 200 words) 5. Predict the potential for alterations in the function of other organ systems because of the long-term condition you identified in point 2 (Approximately 200 words). Components of Health Education Resource: 6. Create a 3D pathological model to demonstrate its pathological effects and clinical progression of the chronic condition, as identified in step 2. Health education using the 3D pathological model aim to help patients and whānau understand their medical conditions, treatment options and the importance of preventive measures. Components of Peer Evaluation: 7. Share your chosen patient, their long-term condition, and the way you envisage the 3D health education model being used, with a group of your peers during identified class time. Your peers will provide you with feedback via an online form. at the end of your presentation. You should prepare to complete this peer sharing activity in no more than 10 minutes. Clinical Case Guidelines: • Third Person • For the report, APA 7th assignment formatting must be followed (Size 12 times New Roman font, 1.5 spacing, 1-inch margins) • Headings are permitted, but not mandatory • Reference list and in-text citation in APA7are required - please ensure these are from Academic sources [Minimum 6 sources please] • Cover Sheet - not required • Maximum Word Count: 1200 words +/- 10% [Going above 10% of word count may incur a grade penalty] o Note: Word Count does not include reference list Health Education Delivery Guidelines: • Create a custom-made 3D pathological model that shows the pathophysiological effects and clinical progression of the chosen chronic condition, as identified in the clinical case. • The 3D pathological model focuses on altered functions of the body systems (e.g., 3D model of a cancerous tumor or a clogged artery). Anatomical models that represent healthy body parts, organs or systems will not be accepted. • For the peer evaluation activity, you will be delivering health education, using the 3D pathological model, to help patients and whānau understand their medical conditions, treatment options and the importance of preventive measures. • The health education delivery should be using language understandable to a general lay person audience, (e.g., patient or whānau).
AMATH 483 / 583 (Roche) - Final Exam Due Friday June 12, 12noon PT June 7, 2025 Final Exam - take home (52 points) 1. (+12) Hamilton paths and cycles. A Hamilton path in a graph G(V,E) is a path that visits each vertex exactly once. Formally, a Hamilton path is a path P = (v1, v2,...,vn) such that each vi is a distinct vertex of V and every pair e(vi, vi+1) is an edge in E. A Hamilton cycle (or Hamiltonian cycle) in a graph G is a cycle that visits each vertex exactly once and returns to the starting vertex. Formally, a Hamilton cycle is a cycle C = (v1, v2,...,vn, v1) such that each vi is a distinct vertex of V (except v1, which appears twice, at the beginning and the end) and every pair e(vi, vi+1) is an edge in E, with e(vn, v1) also being an edge. (a) Submit your written work and solutions. Starting at vertex a, find a Hamilton cycle, if it exists, for each of the graphs (a-f) or multi-graphs in the Figure. If the graph has no Hamilton cycle, determine if it has a Hamilton path and report this if possible. If neither exist, just say so. Submit your work on a Figure 1: Hamilton path / cycle 2. (+10) function fun. 8x on the real line, find all real valued continuously di↵erentiable functions f such that: 3. (+20) threaded image filter. I installed the png16 library for portable network graphics image analysis in C++ on Hyak. Use the codes provided in the exam to implement the threaded transformation from rgb format in png images to grayscale. You are provided a compilation script, a code that implements the sequential transformation and already has a placeholder for the threaded version you will write, and a rou-tine that will compare the output of the correct sequential code I provided with the result of your threaded transformation. Read the README.txt file for guidance. This file, the codes, and build script. are in folder final problem3. (+5, a) Implement the following api and submit your code grayscaleThreaded.cpp with your implementation and any needed helper code in the same file. (+5, b) Submit the threaded .png image your code created for the 4 thread execution event. (+10, c) Submit a strong scaling timing analy-sis comparing the sequential grayscale transformation to your threaded implementation for nt = 1, 2, 3, 4 threads. • void grayscaleThreaded( png_bytep* image , int width , int height , int channels , int numThreads); Figure 2: Left: Input data. Right: Output data (sequential and threaded versions). Your output should like mine for the grayscale for the test input png I provided. 4. (+10) Strong scaling study for parallel Ax = b solver. In this problem you will use the C code provided in final problem4 folder to execute a strong scaling study from 1 to 16 processes on Hyak for a linear solve of dimension dim(A) = 4096 or dim(A) = 8192. Please produce a plot of time for the fixed problem size you choose versus the number of processes. Put the ideal strong scaling curve on your plot as a basis for comparison. Submit the plot. You are advised to read the README.txt file in the folder since it explains each step. Done! :)
Question 3 Use MATLAB to implement sampling and reconstruction of the following signal f(t):=cos(2t)e-t², 0≤t≤2π and is defined to be causal for t≥0 and assumed to be zero for t>2π. (a) Use MATLAB to plot this signal for 0 ≤ t ≤ 2π (b) Use MATLAB to plot the amplitude spectrum |f(α)|. (c) Is f(t) time-limited or band-limited, explain your reasoning? (d) Pick a suitable sampling rate such that f(t) can be reconstructed from the samples and plot the sampled signal using MATLAB. (e) Choose a suitable value Mo based on previous part as a threshold for low-pass filter.Use MATLAB to reconstruct the original signal using Shannon's sampling formula. (f) Plot the reconstructed signal and superimpose the original signal f on the same graph using MATLAB.Comment on the results.
STATS 726 STATISTICS Time Series SEMESTER 2, 2019 1 State whether the following statements are TRUE or FALSE. In each case, you MUST provide a short explanation for your selection. a (9 marks) Let for t ∈ N+, where and {εt}t∈N+ ∼ WN(0, σ2). Define Yt = (1 − B6 )Xt for all t ≥ 7. Statement: {Yt}t≥7 is weakly stationary. Hint: For all α, β ∈ R, we have: cos(α + β) = cos(α)cos(β) − sin(α)sin(β) cos(α − β) = cos(α)cos(β) + sin(α)sin(β). b (7 marks) Let {Xt}t∈Z and {Yt}t∈Z be two weakly stationary time series. Define Zt = Xt + Yt for all t ∈ Z. Statement: {Zt}t∈Z is weakly stationary. c (7 marks) For t ≥ 1, Xt = a + bt + εt , where a, b ∈ R{0} are constants and {εt}t∈N+ ∼ WN(0, 1). Define Yt = (1 − B)Xt for all t ≥ 2. Statement: {Yt}t≥2 is a non-invertible MA(1) process. [23 marks] 2 a (3 marks) Explain briefly what is meant by a causal process. Use your own words since simply copying from the notes will earn no marks. b Consider the weakly stationary ARMA(1, 2) process defined as Xt + 0.5Xt−1 = εt − 0.5εt−1 + 0.25εt−2, t = 0, ±1, ±2, . . . , where {εt}t∈Z ∼ WN(0, σ2). Show that: i (4 marks) {Xt}t∈Z is causal. ii (8 marks) For all t ∈ Z, Xt can be expressed as Hint: You may find the homogeneous difference equation and the initial conditions defined for an ARMA(p, q) process to be useful in the deriva-tions. iii (12 marks) The autocovariance function of {Xt}t∈Z is given by Hint: Use the result from part (ii) or the difference equation defined for the autocovariance function of an ARMA(p, q) process. [27 marks] 3 Suppose that {Xt}t∈Z is defined as Xt = Acos(ωt) + Bsin(ωt), where A, B are uncorrelated random variables with zero mean and unit variance, and ω is a fixed frequency in the interval (0, π). It can be shown that the autocovariance function (ACVF) of {Xt}t∈Z is given by γX(h) = cos(ωh) for h ≥ 0. a (18 marks) Show that the best linear predictor of X3 given X2 and X1 is ˜X3 = 2cos(ω)X2 − X1 and the corresponding mean squared error is zero. Hint: For all α, β ∈ R, we have: cos(α + β) = cos(α)cos(β) − sin(α)sin(β) cos(α − β) = cos(α)cos(β) + sin(α)sin(β) cos2(α) + sin2 (α) = 1. For a, b, c, d ∈ R such that ad − bc ≠ 0, the following identity holds: b (5 marks) Let X = (X1, X2, . . . , Xn)T denote a vector of random variables having mean µ ∈ R n×1 and variance-covariance matrix Γ ∈ R n×n . For an arbitrary vector w ∈ R n×1 , show that Var(wT X) = wT Γw. c (5 marks) Using the result from part (b), show that the ACVF of {Xt}t∈Z is non-negative definite. Hint: A real-valued function f(·) defined for the integers is said to be non-negative definite if for all positive integers n and vectors w = (w1, w2, . . . , wn) > with real-valued entries. [28 marks] 4 Let φ(B) = (1 − φ1B − · · · − φpB p ), θ(B) = (1 + θ1B + · · · + θqB q ), Φ(B s ) = (1 − Φ1B s − · · · − ΦPB sP ), Θ(B s ) = (1 + Θ1B + · · · + ΘQB sQ). A seasonal ARIMA(p, d, q)(P, D, Q)s model is defined as φ(B)Φ(B s )(1 − B) d (1 − B s ) DXt = c + θ(B)Θ(B s )εt , Eq.(1) where c = µ(1 − φ1 − · · · − φp)(1 − Φ1 − · · · − ΦP ), µ ∈ R is the mean of Yt = (1 − B) d (1 − Bs ) DXt and {εt}t∈Z ∼ WN(0, σ2 ). Figure 1 shows the time plot of the quarterly visitor nights (in millions) spent by international tourists to Australia for the period 1999–2015. a (2 marks) Describe the time series components that you can observe in Fig-ure 1. b Figure 2 shows the time plot, autocorrelation (ACF) and partial autocorrela-tion (PACF) plots of • the original series • the non-seasonally differenced time series • the seasonally differenced time series • both non-seasonally and seasonally differenced time series. (8 marks) Using Figure 2, suggest an appropriate seasonal ARIMA model for the given data. Provide reasons for your selection. c (3 marks) Do you consider it appropriate to include a constant term, c (refer Eq.(1)), in the model suggested in part (b)? Explain your answer. d (4 marks) Discuss briefly a few residual diagnostics that can be performed to check the adequacy of the model selected. e (5 marks) Assume that the parameter estimates of the model suggested are given. Explain how you could use this model to forecast the number of visitor nights that will be spent by international tourists to Australia for the next quarter (i.e., Quarter 1 of 2016). [22 marks]
Final Virtual Stock Exchange Report – DUE 3 April A 3-page, double-spaced paper about your Virtual Stock Exchange experience will be due at the end of the semester. Please address the following: • Which positions exceeded your expectations? What conditions caused this? • Which positions underperformed for you? What factors created the performance gap? • What are the key things you learned from your Virtual Stock Exchange experience? • How will your Virtual Stock Exchange experience influence your personal investing in the future? • Benchmark Analysis: An alternative to the active portfolio management strategy that you will follow by trading the securities for the project is the strategy of following market indexes (passive portfolio strategy). The easiest way to follow this strategy is to invest into Exchange Traded Funds (ETFs) that track various market indexes and are traded throughout the day, just like a stock. Keep a daily record of closing prices of three ETFs: (1) Nasdaq 100 Index Tracking Stock (NDX) (2) Dow Jones Industrial Average (DJIA) Index (3) Standard & Poor’s Depository Receipts (SPY) that tracks Standard & Poor’s 500 Composite Stock Price Index. You can obtain the historical and actual data for the ETF’s closing prices on https://finance.yahoo.com/lookup . For the final report, you will have to compare your portfolio returns to passive investment strategies of investing in these ETFs. On a graph compare the dynamics of the portfolio with the three ETF’s (mentioned above). For the portfolio and each ETF compute holding period returns over the whole trading period. What strategy seems to perform. better? Is it consistent with the market efficiency? Explain. Guidelines for IN-PERSON group presentation for Project 2 • The group presentation for Project 2 is an IN-PERSON presentation. Attendance for this session is mandatory. The In-person presentation will take place on Thursday, April 3, 2025, from 11.30 am – 2.30 pm. • Each group MUST make a presentation to be eligible to receive a grade. Students / groups may not re-weigh any of the components of Project 2. • A group will be given 7 (+1) = 8 minutes to present your learnings from Project 2. I will inform. you when 7 minutes are over. You will have to stop after the 8th minute. • The presentation will also have a Microsoft PPT which discusses all findings of Project 2. Details of presentations can include the points in the Report but is not limited to the same. If your group has something you would like to add, please do so. • All group members must be present for the presentation. However, it is not necessary for all members to present. • The presentation file (PPT) is due the night before the presentations. Please upload into Assignments tab on Western Brightspace. Only one member of the group needs to upload the PPT onto Western Brightspace. • Please ensure that your presentation is engaging and interesting – remember, it is up to you to capture the interest of the audience.
A3. Product A/B Test – Individual Report [CLO 2, 4, 5] / Weighting: 40% / Length: Max 2,000 words Description This assessment provides you with the opportunity to undertake product A/B test. Before launching new products, companies test their new products many times to optimize their new product features. In the previous group assignment, your group developed the 1st MVP, your chatbot A. You will improve chatbot A by adding a new feature. This will be your Chatbot B. You will then test both Chatbot A and Chatbot B, with your classmates evaluating these two versions, and then decide which version is better. Finally, you will report the result of this product's A/B test. Details 1. Benchmark other groups’ 1st MVP This task requires you to benchmark another group's 1st MVP to acquire new ideas. All slides and presentation recording are available in the OneDrive submission folder. Choose any group that you want to benchmark and answer the following questions in your report. (1) What are the group’s tutorial session and group number? (2) What is their project goal (Hills statement) regarding Who, What, and Wow? (3) What are the TWO most important pain points that they found? Do you think that they are the biggest pain points? And why or why not? (4) What are the TWO most important “need statements” that they found? Do you think they made the “need statements” properly using 5 whys? And why or why not? (5) What are their TWO most important solutions which they came up with? Do you think that their solutions address their customer problems? And why or why not? (6) What are their 3 chatbot scenarios? Do you think that their 3 chatbot scenarios reflect their solutions well? And why or why not? (7) Among the 3 chatbot scenarios, which scenario would you give the highest accuracy score? Why? (8) Among the 5 overall chatbot performance measures (Accuracy, Efficiency, Knowledge, Satisfaction, Willingness to Buy), which performance would you give the highest (or lowest) score? Why? (9) In their chatbot, what would you like to benchmark the most? It could be a particular scenario, chatbot feature (e.g., slot, option), sentence style, or words (e.g., emotional words). 2. Hypotheses (Hs) for product A/B test To complete Task 2, carefully read and follow the below guidelines before answering all questions in Sections 2.1 - 2.4. Guidelines: A. The requirement of your Hypothesis (H) (1) Specific H: you need to make specific H, rather than exploration type of questions. Below is bad and good H. a. Bad: Which chatbot’s communication style. is more effective? (It is a research question rather than H) b. Good: Chatbot with a passionate tone increases user’s conversation satisfaction compared to chatbot without a passionate tone. (2) Testable H: To test your H, you need to be able to measure the outcome. Thus, make sure whether the necessary outcome can be measured. For example, a. Measurable: user’s communication satisfaction (via a survey) b. Unmeasurable: chatbot’s communication satisfaction (3) Important H: The company offers new products to solve customer problems. Come up with H which is relevant to either the pain points, solutions, or chatbot scenarios which your group figured out. Or you could identify other pain points and solutions. (4) New H: “New” products need to be perceived as “new” ones. Try to make an innovative and creative product feature. (5) Different H within a group: Each member within your group needs to come up with a different H. If you launch a similar product with your competitors or your own company, your new product might not attract many consumers. (6) The hypothesis needs to have both (independent variable - new feature), Y (outcome variable). For example, - H: A chatbot with an energetic tone (X) increases the user’s communication satisfaction (Y) compared to a chatbot without an energetic tone. Then, § Chatbot A: your 1st MVP without energetic tone (X) § Chatbot B: your 1st MVP with energetic tone (X) - H: A chatbot that uses intelligent words (X) more easily persuades consumers to buy the product which the chatbot recommend (Y) compared to chatbot that does not use intelligent words. § Chatbot A: your 1st MVP that does not use intelligent words (X) § Chatbot B: your 1st MVP that uses intelligent words (X) B. Potential X variables: Considering your customer’s problems and the requirement of H, you can simply change ● Conversational words, flow, style, tone, sequence, or the number of questions. ● Conversation styles (e.g., chatbots talk more positively or politely. Chatbots use emoticons frequently.) ● The sequence of conversation (e.g., when to mention “sell product”? early or later?) C. Potential Y6 variable ● Considering your X, make a relevant Y6 in addition to the given Y1 to Y5 below. D. A potential source of your H (1) Customer’s pain points: Considering the customer’s pain points, your group has already made a product roadmap. You could make a hypothesis about your 2nd MVP or Future Product. (2) Market players: A company often benchmark other players in a market. You observed your competitors’ chatbots and other groups’ chatbots. You could benchmark them. (3) Industry articles: Newspapers, blogs, industry reports a. You can easily find many potential popular chatbot features by reading industry articles. b. https://sproutsocial.com/insights/chatbot-marketing/ c. https://www.intercom.com/blog/chatbot-marketing/ d. https://www.revechat.com/blog/chatbot-marketing/ e. https://sproutsocial.com/insights/chatbot-marketing-examples/ f. https://www.hubspot.com/stories/chatbot-marketing-future g. https://www.wordstream.com/blog/ws/2017/10/04/chatbots h. https://www.artificial-solutions.com/chatbots i. https://mobilemonkey.com/blog/chatbot-marketing/ j. https://sendpulse.com/support/glossary/chatbot-marketing k. https://towardsdatascience.com/how-conversational-chatbots-marketing-is-the-future-of-ecommerce-6743268caa11 (4) Academic papers a. Many academic papers have also tested chatbot features. You can benchmark those features. b. Chatbot paper summary: Papers on Chatbot.xlsx, paper pdf c. Google Scholar (search with “chatbot” word): https://scholar.google.com.au/ d. https://medium.com/@ODSC/top-10-ai-chatbot-research-papers-from-axxiv-org-in-2019-1982dddabdb4 e. https://www.topbots.com/most-important-conversational-ai-research/ f. http://www.academia.edu/Documents/in/Chatbot g. https://paperswithcode.com/task/chatbot h. https://www.chatbots.org/papers/ i. https://link.springer.com/article/10.1007/s12525-020-00414-7 Answer the following questions. 2.1. Write down your Hypotheses (Hs). You have been using 5 Ys for the market test of your 1st MVP. For this product A/B test, your task is to make one X and Y6 in addition to the 5 Ys, which are already given below. Consider the requirements of H. Note that you use the same X across 6 Hs. Then, test the following 6 Hs. H1: X increases the user’s perception of the chatbot’s accuracy (Y1). H2: X increases the user’s perception of the chatbot’s efficiency (or speed) (Y2). H3: X increases the user’s perception of the chatbot’s knowledge (or helpfulness) (Y3). H4: X increases the user’s communication satisfaction (Y4). H5: X increases the user’s willingness to buy the product which the chatbot recommends (Y5). H6: X increases the user’s … (Y6). 2.2. State the source of your H6. State the source of your H6. In other words, where did you get the idea of your H6? The example sources are competitors’ chatbot websites, another group’ tutorial session and group number, and references of industry or academic articles. If you make your H for yourself without referencing other sources, you don’t have to cite the source. You can just mention that it is your original idea. 2.3. Explain your H6 logically. Explain why you are expecting that your X increases your Y6 in H6. Support your reasoning with some references. 2.4. Explain the importance of your H in solving your customer problem. How does any of your H (H1 to H6) contribute to solving your customer problem? You can take any H among the above 6 Hs. Then, explain the relevance of your H to either the pain points, solutions, or chatbot scenarios that your group figured out. Or you could identify other pain points and solutions. 3. Conversational Flow and Chatbot Carefully read and follow the below guidelines and complete task required. Guidelines: ● The baseline chatbot is your group’s 1st MVP. This is chatbot A. ● Then, export the JSON file of the 1st MVP (your chatbot A). Then, you can import the JSON file of chatbot A to the Watson Assistant and then revise it. This will be your chatbot B. ● Note that a new feature for your chatbot B does not have to be complex. Depending on your H, you could change just some words. For example, you can add more polite sentences if your H is about a polite chatbot. Complete the following tasks. ● Put screenshots of (1) conversation flow and (2) chatbot communication for both chatbot A and B. Please put only the key parts to show the difference between chatbot A and B. ● Then, describe the difference between chatbot A and B. 4. A/B Test Survey Guidelines: 4.1. Online survey form. ● Create a link to the interface of your chatbot and add it to your survey form. ● Chatbot A/B test survey o You can modify the survey form. of your 1st MVP Market Test. o How to make Google Form. https://www.youtube.com/watch?v=BtoOHhA3aPQ o Submit your survey link before Tutorial. o Classmates will answer your survey questions during the tutorial. 4.2. Outcome measures Make relevant survey questions to test your Hs. You can modify the survey template for the 1st MVP. ● Example of satisfaction question ▪ (Quantitative) How much are you satisfied with the conversation with a chatbot? ● Not satisfied at all (1 to 7) Highly satisfied ▪ (Qualitative) Tell me about your good or bad experience with the chatbot ● Example of a knowledgeable chatbot question ▪ (Quantitative) How was the agent's knowledge? ● Not knowledgeable (1 to 7) Very knowledgeable ▪ (Qualitative) Tell me about your impression of the chatbot’s knowledge. Complete the following tasks. ● State all your survey questions here. Since the questions for chatbot A and B are the same, put only one version. Also put the links for your chatbots A and B, and your survey. 5. Analysis 5.1. Demographics ● Summarize the demographics (gender, ….) of survey participants briefly. 5.2. Quantitative questions ● First, plot the survey participant’s answers by comparing the answers from two chatbots. You could use two box plots or other types of plots. ● Survey participants will talk with the two chatbots within the survey. Therefore, conduct the paired t-test. ● Interpret properly whether your 6 Hs are supported or not from their p-values. Due to the small sample size, it is not easy to get significant results. Although insignificant, there is no penalty. 5.3. Qualitative questions · Quote some comments. o E.g., “I like the chatbot because it gives me relevant career option” · Visualize the answers using the Word Cloud. · Compare the sentiment of the answers about the two chatbots. You could use a Python library. Or you can manually count the number of “positive”, “neutral”, and “negative” comments written by survey participants for each chatbot. Then, you can compare the sentiment of the two chatbots. You don’t need to do the paired t-test here. 6. Conclusion ● What are the key results? Does the result support your Hs? If not, explain (1) why your Hs are not supported and (2) what you would do differently so that your Hs are supported. ● Based on this result, what do you want to suggest for your company chatbot? ● What other Hs do you want to test in the future? In completing this assignment, apply appropriate data analytics and consider the concepts introduced in class. Your report should not exceed the word limit, excluding the title page, relevant images, tables, charts, or reference. Title page (1 page) includes (1) the title of your report, (2) Word count, (3) An executive summary (One paragraph) of sections 2, 3, 5, and 6, (4) Course name, tutorial session and group, and a tutor’s name, (5) Your first and last name & zID. Reference: Cite academic papers, newspaper articles, blogs, or industry reports properly. Use APA (American Psychological Association) style. in-text citations and a reference list at the end. https://student.unsw.edu.au/apa Format: Use Word file (.doc), 12pt, 1.5 lines spacing, at least 2.5cm margins on all sides. Make sentences rather than bullet points. Appendixes (no page requirement): Do not put irrelevant or unedited raw results. Submission instructions Submit your report to Turnitin via Moodle. - .doc contains your report. File name: Tutorial_Group_First and Last Name_A3.doc” (e.g., W12_1_Junbum_Kwon_zXXXX_A3.doc) Submit other supporting files (conversation flow, chatbot, data, code, and papers) to Moodle submission folder. 1) .pdf has a conversation flow for the two chatbots A and B. 2) .JSON contains chatbots A and B. 3) .csv contains the dataset from your survey. 4) .ipynb contains all relevant Python code to get the results in your report. 5) .xlsx contains all the cited paper lists with a brief note about why you cited them. 6) .zip contains all the cited papers. Submit a zip file of all the cited pdfs. ● For each missing file among the above (1) to (6), a -1 mark ● Late penalty: - 5% marks per day for the survey link (Week 10 tutorial) and your report, respectively.
MASTER OF SCIENCE IN MANAGEMENT AND SYSTEMS Applied Project Capstone MASY GC- 4100 MEMORANDUM TO: Spring 2025 AP Capstone Students DATE: January 3, 2025 RE: UNICC Phase 2 Capstone Projects for Your Participation We are pleased to inform. NYU Capstone students about a set of capstone projects available for their consideration. NYU SPS and The Digital Forge lab have been selected by the UNICC to work on a media analysis tool to detect inappropriate language usage in media communications. This is a continuation of the successful Phase 1 Fall 2024 projects. The Sponsor: The United Nations International Computing Centre (UNICC) has over 50 years of experience as the largest strategic partner for digital solutions and cybersecurity within the United Nations system. They are pleased to sponsor NYU MASY students in a project competition. The Final Phase 2 Product The product is an AI-driven media analysis tool designed to enhance the capacity of media outlets to report ethically and accurately on topics related to refugees, migrants, and other forcibly displaced populations. This tool will support the detection and prevention of xenophobic language, misinformation, and harmful content in media environments, ultimately fostering more informed and empathetic public discourse. The final tool will add multi-language and multimodal (written, audio and video) capabilities to the original prototype. The Projects We have divided the functions of the product into four capstone projects to be completed by a team of four students. Each student completes their project, and the team delivers the integrated product as their entry into the competition. We expect multiple teams of students to compete for the first prize: acceptance by the UNICC as the best. How do you choose a project and get involved? Please review the four project definitions and choose one that interests you. Coordinate with three other capstone students to select the remaining projects as part of a team. Once you have your team, present yourselves as a group with each student and their part of the project clearly identified. Give your selves a team name. Send applications to Dr. Fortino and copy Siri Kostanyan. We anticipate forming of three to four teams, but there's no limit to the number of teams that can be formed. Please note that individual applications for this project will not be considered; you must apply as part of a team. Additional details The projects are three-month engagements, and we have provided all the necessary details below, including information about the company and executive supervisor. Your direct client with whom you will be interfacing with directly will be the product manager, Mrs. Siri Kostanyan, who works for The Digital Forge. We understand that your time is valuable, but we assure you that this is a worthwhile experience, and the organizations and client lead contact have committed to supporting the project with the seriousness it deserves. Upon completion of the project, you are welcome to include it on your resume and use the results in your portfolio. If desired, we can also provide a reference for job applications. Additionally, successful completion of the project may lead to be invited to co-author a research paper with the client sponsors. To apply for consideration for any of these projects, send a cover email to Dr. Fortino ([email protected]), AS A TEAM, with the following: 1. Which capstone students will be doing which project for the product 2. All parts of the project must be covered; in other words, there have to be four members of your team. 3. Include the resume and NYU transcript. of each team member. Company and Sponsor Information Company Names The Digital Forge NYU School of Professional Studies and the Management and Systems program (MASY), is a New York-based learning institution. The UNICC (International Computing Center) Ms. Anusha Dandapani, Center Director Company Location NYU School of Professional Studies is at 12 West 43rd Street, NY, NY. Project Sponsor The principal project sponsor for all projects will be Dr. Andres Fortino, Clinical Associate Professor, NYU (https://www.linkedin.com/in/afortino), and Mrs. Siri Kostanyan, MSPM, The Clients Company and Sponsor's Location Dr. Andres Fortino ([email protected]) can be reached over virtual conference calls as per project requirements. Mrs. Siri Kostanyan ([email protected]) is available for consultations and support via email or virtual meetings as needed to ensure project success. Description of the Business New York University (NYU) is a private research university based in New York City. The MASY degree is based on a unique curriculum that provides students with experiential learning opportunities to develop strong management and leadership skills and gain a comprehensive knowledge of current information technologies. The United Nations International Computing Centre (UNICC) has over 50 years of experience as the largest strategic partner for digital solutions and cybersecurity within the United Nations system. Relationship to the Client The Client’s relationship with the Project Manager will be that of an independent contractor, and nothing in this sponsorship is intended to or should be construed to, create a partnership, agency, joint venture, or employment relationship. Note: use this information to create your project proposal. Project Elements and Deliverables 1. In consultation with the client, create a set of functional objectives with deliverables and due dates to break down your project. 2. A clearly defined modularization of the project. 3. At least four meetings with the client during the project: a. Initial meeting to launch project b. Second meeting no more than two weeks after launch to review objectives c. Third meeting to review progress no more than two months after launch d. A final meeting focused on presenting results and handing in deliverables. e. These meetings are to be arranged by the project manager (that’s you!). 4. The final report for each project must conform. to the template provided by the client. 5. All final project files and a README user document must be deposited in a public GitHub repository. 6. A Team Deliverable of the integrated product ready to present to UNICC 7. Presentation of your product to NYU and UNICC by your team during a day of competition at the end of the semester. Additional Requirements 1. All steps in the project must be well documented as the project progresses. 2. Weekly written and emailed summary progress reports must be provided. They must include a. what was just accomplished in the past week, b. what you are working on in the coming week c. and any problems you are encountering that need resolution and input from the client. AI-Driven Media Analysis Tool (Phase 2) Objective The product is an AI-driven media analysis tool designed to enhance the capacity of media outlets to report ethically and accurately on topics related to refugees, migrants, and other forcibly displaced populations. This tool will support the detection and prevention of xenophobic language, misinformation, and harmful content in media environments, ultimately fostering more informed and empathetic public discourse. The final delivered product will incorporate multi-language capabilities, analyzing content in all six official UN languages, as well as multimodal functionalities to process text, audio, and video. Scope of the Product The AI-driven media analysis tool will consist of four integrated functions, each serving a specific purpose to ensure comprehensive analysis and support for journalists and media professionals. Teams will review previous prototypes (developed by Fall 2024 teams) and either extend or emulate one as their foundation. The Tool’s Original Functions (Fall 2024) Phase 1 The tool builds upon the foundational functionalities developed in Fall 2024, which include the following key features: 1. Identification of Xenophobic Language and Mis/Disinformation ○ Functionality: Detect and flag xenophobic language, racist attitudes, incorrect data, and stereotypes related to human mobility. Analyze media content in real-time to identify harmful narratives that perpetuate discrimination or hostility. ○ Outcome: Assist media professionals in identifying and avoiding harmful language, fostering a respectful and accurate portrayal of refugees and migrants. 2. Fact and Language Checking ○ Functionality: Verify the accuracy of language and data, focusing on terminology related to migrants and displaced populations. Cross-reference media content with a verified database of facts and terminology to prevent misinformation. ○ Outcome: Provide journalists with a reliable resource for fact-checking and language verification, supporting ethical journalism practices. 3. Topic-Based Analysis on Harmful Content ○ Functionality: Perform. topic-based analysis of media content to identify and categorize harmful narratives related to migration and displacement. Highlight topics contributing to negative perceptions or misinformation about displaced communities. ○ Outcome: Offer insights into harmful content, enabling media professionals to take corrective actions and promote balanced reporting. 4. Integration and Testing ○ Functionality: Combine all components developed into a cohesive system and ensure functionality through rigorous testing. ○ Outcome: Deliver a functional prototype that integrates xenophobic language detection, fact-checking, and topic-based analysis into a single reliable tool. Expanded Capabilities for 2025 Phase 2 Building on these foundations, the Spring 2025 iteration introduces four new and enhanced functionalities to expand the tool’s capabilities: 1. Multi-language Capability ○ Functionality: Analyze content in any of the six official UN languages (Arabic, Chinese, English, French, Russian, and Spanish), expanding the tool's inclusivity and global relevance. ○ Outcome: Empower media professionals to work with diverse linguistic content, promoting ethical journalism across cultures and languages. 2. Audio Analysis ○ Functionality: Process journalistic pieces in audio format, such as podcasts and radio programming, by transcribing and analyzing spoken content. ○ Outcome: Enable media professionals to assess audio media with precision, detecting xenophobic language and misinformation in spoken-word formats. 3. Video Analysis ○ Functionality: Analyze journalistic pieces in video format, including videocasts and television news, by processing both visual and auditory elements. ○ Outcome: Equip media professionals to evaluate video content comprehensively, ensuring balanced and accurate reporting across multimedia platforms. 4. Integration, Design, and Testing ○ Functionality: Integrate the multilingual, audio, and video analysis features into a unified, user-friendly system. Design an intuitive interface and conduct thorough testing to ensure the tool meets performance standards. ○ Outcome: Deliver a fully operational and reliable tool that combines all new capabilities, providing media professionals with a seamless platform. for ethical and accurate reporting. Implementation and Deployment as NYU Capstone Projects Development and Implementation Plan The development of the AI-driven media analysis tool will be managed pro bono by Siranush 'Siri' Kostanyan, who will serve as the Product Manager. The tool will be developed by teams of four Capstone students from New York University, under the leadership and guidance of Dr. Andres Fortino and in collaboration with UN representatives. The student groups will compete to present the most effective solution, with the winning capstone project selected by UN representatives. The project is structured to be completed over a three-month period, divided into the following phases with key deliverables: 1. Research and Definition ○ Activities: Conduct initial research, define the project scope, and set up the development environment. ○ Deliverable: Functional Requirements Specification (FRS). 2. Prototype Development ○ Activities: ■ Data Collection and Preprocessing: Gather and prepare data for the AI models. ■ Model Development: Design, train, and validate machine learning models. ■ User Interface Development: Design and develop a user interface that integrates with the AI models. ○ Deliverable: Product Prototype. 3. Proof of Concept ○ Activities: Integrate all components and conduct comprehensive testing to ensure the system functions as expected. ○ Deliverable: Proof of concept through rigorous testing. 4. Final Documentation and Deployment ○ Activities: Document the entire process, prepare user guides, and deploy the final product. ○ Deliverable: Complete documentation and successful deployment of the tool. Each phase will include specific deliverables, such as the development of multimodal and multilingual analysis features, the creation of a user-friendly interface, and comprehensive testing to ensure the tool’s reliability and effectiveness. Mrs. Siranush 'Siri' Kostanyan will oversee the entire process to ensure that the product meets its objectives and is delivered on time. Expected Impact The AI-driven media analysis tool is expected to empower media organizations and content creators to report more accurately and sensitively on issues concerning refugees and other forcibly displaced people. By leveraging advanced AI technology, the tool facilitates fact-based reporting and fosters mutual understanding between displaced and host populations. The tool aims to bridge the gap between communities, ensuring that media narratives are informed, inclusive, and conducive to building empathy and understanding across diverse audiences. Breakdown of Functions to Capstone Projects for Spring 2025 Breakdown of Functions to Capstone Projects The development of the AI-driven media analysis tool is an ongoing, multifaceted initiative designed to address xenophobic language and misinformation in media reporting. This initiative builds upon the foundational prototypes developed by Capstone teams in Fall 2024. These prototypes focused primarily on analyzing written content in English, providing a robust starting point for this semester's enhanced functionality. For Spring 2025, we are expanding the scope of the tool to include multilingual capabilities, audio and video analysis, and comprehensive system integration. These enhancements are divided into four distinct Capstone projects, ensuring that each critical feature is fully developed, tested, and refined. This structure allows students to build on the achievements of Fall 2024 while addressing new challenges and advancing the tool’s capabilities. The Capstone projects for Spring 2025 are as follows: 1. Multilingual Analysis ○ Objective: Enable the tool to analyze content in all six official UN languages (Arabic, Chinese, English, French, Russian, and Spanish). ○ Focus: Extend linguistic capabilities by implementing advanced natural language processing (NLP) techniques for multilingual input. ○ Outcome: Equip media professionals with the ability to process diverse linguistic content, fostering inclusivity and broader usability. 2. Audio Analysis ○ Objective: Develop the ability to process and analyze audio files, including radio programming, podcasts, and other spoken-word content. ○ Focus: Train machine learning models to evaluate audio inputs for detecting xenophobic language and misinformation. ○ Outcome: Allow media professionals to assess spoken content with the same precision as text analysis. 3. Video Analysis ○ Objective: Extend the tool’s functionality to process and analyze video content, such as news broadcasts, videocasts, and social media posts. ○ Focus: Integrate multimodal analysis to evaluate both visual and auditory components in video media. ○ Outcome: Provide comprehensive insights into video-based narratives, ensuring balanced and ethical reporting across all media formats. 4. Integration, Design, and Testing ○ Objective: Integrate all developed components into a seamless, unified system with a user-friendly interface. ○ Focus: Perform. system integration, intuitive user interface (UI) design, and thorough testing to ensure reliability and functionality. ○ Outcome: Deliver a fully operational AI-driven tool that combines multilingual, audio, and video analysis for ethical media reporting. Strategic Implementation: ● Teams will consist of four students: three members will focus on specific deliverables (multilingual, audio, and video analysis), while the fourth will manage integration and testing. ● Teams will utilize insights and reports from the Fall 2024 prototypes as a foundation, ensuring continuity and improvement in the tool’s development. ● The competition format remains the same, with multiple teams working on the same framework to produce the most effective and impactful solution. This semester’s enhancements aim to deliver a sophisticated, multimodal, and multilingual AI-driven tool, addressing modern media’s ethical challenges and providing actionable solutions for journalists and media professionals. Capstone Project 1: Multilingual Analysis (Phase 2 Spring 2025) Project Overview This project focuses on expanding the AI-driven media analysis tool's capabilities to support multilingual analysis. The tool will process and analyze content in all six official UN languages: Arabic, Chinese, English, French, Russian, and Spanish. By leveraging advanced natural language processing (NLP) techniques, the project aims to ensure accurate detection of xenophobic language, misinformation, and harmful narratives across diverse linguistic contexts. Project Goals and Objectives Goal: Develop a multilingual analysis feature that processes and analyzes media content in six languages, ensuring inclusivity and cultural sensitivity. Objectives: ● Fine-tune NLP models to analyze media content in the six official UN languages. ● Develop a robust data pipeline to collect and preprocess multilingual datasets. ● Create a scalable architecture to accommodate language-specific nuances and complexities. ● Test and validate the multilingual functionality to ensure accuracy and reliability. Project Roadmap Phase 1: Initial Research and Setup (Weeks 1-3) ● Deliverable 1: Conduct a literature review on multilingual NLP models and techniques. ● Deliverable 2: Define the scope of multilingual analysis, including key criteria for language-specific challenges. ● Deliverable 3: Set up the development environment and tools for multilingual dataset collection and preprocessing. Phase 2: Data Collection and Preprocessing (Weeks 4-6) ● Deliverable 4: Build a data pipeline to collect diverse datasets in the six UN languages. ● Deliverable 5: Preprocess the data to account for linguistic variations, such as syntax, grammar, and idiomatic expressions. ● Deliverable 6: Create balanced, labeled datasets tailored for model training and fine-tuning. Phase 3: Model Development and Training (Weeks 7-9) ● Deliverable 7: Fine-tune NLP models for each language, focusing on detecting xenophobic language and misinformation. ● Deliverable 8: Optimize the models for precision, recall, and overall accuracy. ● Deliverable 9: Validate the performance of each model using language-specific test datasets. Phase 4: Integration and Testing (Weeks 10-12) ● Deliverable 10: Integrate the multilingual functionality into the AI-driven tool’s existing architecture. ● Deliverable 11: Conduct end-to-end testing of the tool with multilingual datasets to ensure seamless operation. ● Deliverable 12: Refine the multilingual analysis feature based on feedback and test results. Final Phase: Presentation and Documentation (Week 13) ● Deliverable 13: Prepare a final report detailing the development process, challenges, and outcomes. ● Deliverable 14: Present the multilingual analysis tool to stakeholders, showcasing its capabilities and real-world applications. ● Deliverable 15: Submit all code, documentation, and the final report to a public GitHub repository for future reference and potential further development. Expected Outcomes By the end of this Capstone project, the team will deliver a multilingual analysis feature capable of processing media content in six languages. The feature will be tested, validated, and ready for integration into the broader media analysis tool, enabling ethical, accurate, and inclusive media reporting across diverse linguistic contexts. Capstone Project 2: Audio Analysis (Phase 2 Spring 2025) Project Overview This project focuses on developing the AI-driven media analysis tool’s capability to process and analyze audio content, including journalistic pieces such as podcasts, radio programming, and other spoken-word formats. By leveraging state-of-the-art audio processing and natural language processing (NLP) techniques, the project aims to detect xenophobic language, misinformation, and harmful narratives embedded in audio media. Project Goals and Objectives Goal: Develop an audio analysis feature that processes journalistic content in spoken formats to identify and address harmful narratives. Objectives: ● Implement audio processing pipelines to transcribe and analyze spoken content. ● Fine-tune NLP models for detecting xenophobic language and misinformation in transcribed audio. ● Ensure the system accounts for variations in accents, dialects, and languages across diverse audio sources. ● Validate the tool’s performance with real-world audio datasets. Project Roadmap Phase 1: Initial Research and Setup (Weeks 1-3) ● Deliverable 1: Conduct a literature review on audio processing and speech-to-text technologies. ● Deliverable 2: Define the scope of audio analysis, including key challenges such as background noise and speaker variability. ● Deliverable 3: Set up the development environment and tools for processing audio files. Phase 2: Audio Data Collection and Preprocessing (Weeks 4-6) ● Deliverable 4: Build a data pipeline to collect diverse audio datasets, including podcasts and radio content. ● Deliverable 5: Preprocess audio files by cleaning and normalizing sound quality for consistent transcription accuracy. ● Deliverable 6: Use speech-to-text models to create transcriptions, ensuring high accuracy for downstream analysis. Phase 3: Model Development and Training (Weeks 7-9) ● Deliverable 7: Train and fine-tune NLP models to analyze transcribed audio for harmful language and misinformation. ● Deliverable 8: Optimize the models to handle speaker variations, accents, and context-specific language. ● Deliverable 9: Validate the models using real-world audio datasets and assess their accuracy and performance metrics. Phase 4: Integration and Testing (Weeks 10-12) ● Deliverable 10: Integrate the audio analysis feature into the AI-driven media analysis tool’s architecture. ● Deliverable 11: Conduct end-to-end testing with audio content to ensure seamless functionality. ● Deliverable 12: Refine the audio analysis tool based on user feedback and test results. Final Phase: Presentation and Documentation (Week 13) ● Deliverable 13: Prepare a final report documenting the development process, challenges, and outcomes. ● Deliverable 14: Present the audio analysis tool to stakeholders, demonstrating its capabilities and potential applications. ● Deliverable 15: Submit all code, documentation, and the final report to a public GitHub repository for future reference and potential further development. Expected Outcomes By the end of this Capstone project, the team will deliver an audio analysis feature capable of processing and analyzing journalistic audio content. The feature will be tested, validated, and ready for integration into the broader media analysis tool, empowering media professionals to assess spoken content with precision and ethical rigor. Capstone Project 3: Video Analysis (Phase 2 Spring 2025) Project Overview This project focuses on expanding the AI-driven media analysis tool to process and analyze video content, including journalistic pieces such as videocasts, television news, and other video-based formats. By incorporating advanced computer vision and natural language processing (NLP) techniques, the project aims to detect xenophobic language, misinformation, and harmful narratives in both the visual and auditory components of video media. Project Goals and Objectives Goal: Develop a video analysis feature that processes journalistic content in video formats to identify harmful language, misinformation, and other unethical narratives. Objectives: ● Implement computer vision models to analyze visual content, such as text overlays and imagery. ● Utilize speech-to-text technology to transcribe audio components of videos for further analysis. ● Fine-tune NLP models to evaluate transcribed audio and subtitles for harmful narratives. ● Validate the system’s performance across diverse video sources and contexts. Project Roadmap Phase 1: Initial Research and Setup (Weeks 1-3) ● Deliverable 1: Conduct a literature review on computer vision techniques and video processing technologies. ● Deliverable 2: Define the scope of video analysis, including challenges such as varying resolutions, languages, and media formats. ● Deliverable 3: Set up the development environment and tools for processing video files. Phase 2: Video Data Collection and Preprocessing (Weeks 4-6) ● Deliverable 4: Build a data pipeline to collect a diverse set of video content, including television news and videocasts. ● Deliverable 5: Preprocess video files to ensure compatibility with analysis tools, including audio extraction and frame. sampling. ● Deliverable 6: Use speech-to-text models to transcribe audio components and extract subtitles for downstream analysis. Phase 3: Model Development and Training (Weeks 7-9) ● Deliverable 7: Develop and fine-tune computer vision models to analyze visual elements, including on-screen text and imagery. ● Deliverable 8: Train NLP models to evaluate transcribed audio and subtitle content for detecting harmful narratives. ● Deliverable 9: Validate the integrated video analysis models using diverse datasets to ensure accuracy and reliability. Phase 4: Integration and Testing (Weeks 10-12) ● Deliverable 10: Integrate video analysis capabilities into the AI-driven media analysis tool’s existing architecture. ● Deliverable 11: Conduct end-to-end testing with real-world video content to assess functionality and performance. ● Deliverable 12: Refine the video analysis tool based on user feedback and test results. Final Phase: Presentation and Documentation (Week 13) ● Deliverable 13: Prepare a final report documenting the development process, challenges, and outcomes. ● Deliverable 14: Present the video analysis tool to stakeholders, demonstrating its capabilities and real-world applications. ● Deliverable 15: Submit all code, documentation, and the final report to a public GitHub repository for future reference and potential further development. Expected Outcomes By the end of this Capstone project, the team will deliver a video analysis feature capable of processing and analyzing journalistic video content. The feature will be tested, validated, and ready for integration into the broader media analysis tool, enabling media professionals to assess video narratives with ethical and analytical precision. Capstone Project 4: Integration, Design, and Testing (Phase 2 for Spring 2025) Project Overview This project focuses on integrating the distinct components of the AI-driven media analysis tool—multilingual, audio, and video analysis—into a unified, user-friendly system. The team will design a seamless user interface (UI) that enables media professionals to access and utilize all functionalities efficiently. Rigorous testing will ensure that the tool meets performance standards, is reliable, and delivers accurate results across diverse use cases. Project Goals and Objectives Goal: Create a fully integrated and tested media analysis tool that combines multilingual, audio, and video capabilities within a cohesive platform. Objectives: ● Integrate multilingual, audio, and video analysis components into a single system. ● Design an intuitive and accessible UI that facilitates easy navigation and functionality for media professionals. ● Conduct comprehensive testing, including functional, performance, and user acceptance testing (UAT). ● Optimize the tool based on user feedback and test results to ensure reliability and usability. Project Roadmap Phase 1: System Integration (Weeks 1-4) ● Deliverable 1: Develop a system architecture plan to integrate multilingual, audio, and video analysis features. ● Deliverable 2: Implement APIs and backend services to unify the functionalities into a single system. ● Deliverable 3: Ensure compatibility and interoperability between all components. Phase 2: UI/UX Design and Development (Weeks 5-8) ● Deliverable 4: Design an intuitive UI that incorporates all features, ensuring accessibility and ease of use. ● Deliverable 5: Develop the frontend interface and integrate it with the backend architecture. ● Deliverable 6: Test the UI for usability and accessibility, gathering feedback for iterative improvements. Phase 3: Comprehensive Testing (Weeks 9-11) ● Deliverable 7: Conduct functional testing to verify the accuracy and reliability of each integrated component. ● Deliverable 8: Perform. performance testing to ensure the tool operates efficiently under various workloads. ● Deliverable 9: Complete user acceptance testing (UAT) with media professionals, collecting feedback for refinement. Phase 4: Refinement and Deployment (Weeks 12-13) ● Deliverable 10: Refine the tool based on testing results and user feedback, ensuring reliability and usability. ● Deliverable 11: Prepare the tool for deployment, including final checks and optimizations. ● Deliverable 12: Provide training materials and user guides to facilitate adoption by media professionals. Final Phase: Presentation and Documentation (Week 13) ● Deliverable 13: Prepare a comprehensive final report documenting the integration process, challenges, and outcomes. ● Deliverable 14: Present the fully integrated tool to stakeholders, showcasing its capabilities and applications. ● Deliverable 15: Submit all code, documentation, and the final report to a public GitHub repository for future reference and potential further development. Expected Outcomes By the end of this Capstone project, the team will deliver a fully operational, integrated media analysis tool. The system will combine multilingual, audio, and video analysis capabilities within a cohesive platform, providing media professionals with a powerful resource for ethical and accurate reporting. The tool will be thoroughly tested, optimized, and ready for deployment in real-world media environments.
INFS6004 Digital Business Transformation Assignment 1 2025 News Research & Analysis – Specification This assignment assesses skills in research and critical analysis required for an independent learner able to keep up to date with the latest practices in digital transformation and change. You are required to submit a brief written business report on a company based on no less than THREE news articles, which are relevant to INFS6004, with your original description and original analysis of a company’s digital transformation presented in the article. Marks: 15% of unit assessment. Note: Individual assignment Due date: 21 March 2025, before 11:59 pm, submit the assignment through INFS6004 CANVAS site. Requirements: A maximum of 700 words (+ 10% allowance) in a business report Report: Your report includes a report title page with the company name and your name and SID, followed by the company and news articles summary, analysis, conclusion, and Appendix. The Appendix lists the references you used with sufficient details for a reader to locate each reference directly. A THREE marks penalty will apply for the report with less than THREE news articles. Activities & Deliverables • Search for at least THREE news articles on a company of interest to you that presents details on a digital transformation relevant to INFS6004 that meets Assignment 1’s requirements and Marking Guide. • To reduce the risk of plagiarism, the news articles need to be published after 1st January 2023. • Prepare and submit a brief review in a business report format that addresses Assignment 1’s requirements and Marking Guide. • Marks are awarded for consistency with the business report structure, format and requirements, your own original description and your own original analysis. • Copies of the major news articles used in your report must be included. The Appendix and References are NOT included in your word count. Notes: 1. All reports need to be carefully checked prior to submission to ensure there are no spelling or grammatical errors. 2. All references should adhere to the guidelines laid out in the APA Referencing Guide: https://libguides.library.usyd.edu.au/citation/apa7 3. Penalties for late submission without prior approval will apply – see the Business School’s policy: http://sydney.edu.au/business/currentstudents/policy 4. Submit your file name in this format: INFS6004-25-Assign-1-SID-Student-names e.g., INFS6004-25-Assign-1-SID-123456789-Roberta-Lee INFS6004– Digital Business Transformation Assignment 1 – News Analysis Marking Guide • • • blished after1stJanuary2023)Spelling or Grammatical errors (none / some / unaccept ticles summary:(250 words)Marks:/4Context (country, industry, company, products/services)SummaryofthekeyfindingsofthenewsarticlesThe analysis must be original and truthfully reflect the contentsofnewsarticles3.Analysis ofbusiness significance: (450 words)Marks: /8Is the digital transformation sustainable (i.e., alignment withbusiness goals) in the long run? Why?How applicable is it to other countries,marketsa • •
COMP5425 Week9 Semester 1, 2025 Recommender Systems Background Recommendation algorithms Collaborative filtering ◼ User based ◼ Model based ◼ Matrix factorization Content-based ◼ Product, document, image, video, audio Learning based Context Aware Recommendation Evaluation Recommendation is everywhere eCommerce Amazon, eBay, … Social Facebook, LinkedIn, … ◼ Friends, groups, jobs Media Youtube, Netflix, Spotify, … News Advertisement Others MOOC, tourism, … Benefits of RecSys For customers or users Find relevant things Narrow down the set of choices Help explore the space of options Discover new things … For providers or vendors Additional and probably unique personalized or customized service Increase trust and customer loyalty Increase sales (30% - 70%), click through rates, conversion etc. Opportunities for promotion, persuasion Obtain more knowledge about customers … Problem Statement Input User model and profile (e.g., ratings, preferences, and other meta. data) Items (with or without attributes) Goal Recommend items to potential users ◼ Relevance score in terms of various criteria (e.g., context) Obtain missing values between users and items ◼ Netflix: 100K movies, 10M users, 1B ratings
Dissertation Guide Institute for International Management & Entrepreneurship Updated: January 2025 1. Introduction An MSc dissertation is an extended, written piece of work undertaken in the spring and summer of the programme. Contributing one third of the overall mark, the dissertation is an important component of the MSc. It is also significant for the following reasons: 1. Demonstrates knowledge and skills developed during the modules 2. Provides opportunity for independent work 3. Provides opportunity to synthesize diverse subject areas studied earlier in the year 4. Provides experience of the research process (particularly of formulating problems, gathering and analysing data, and presenting results and conclusions) A successful dissertation requires strong motivation and planning, and a capacity for independent work. The module Handbook distinguishes two routes for dissertations: a “standard route” and an “collaborative dissertation.” The latter refers to dissertations where a student works on a research question that has been developed by an external organisation, either one sourced by the Future Space Team or they have personally sourced. This can happen while the student is placed inside the organisation (internship-based) or remotely (project-based). In all cases – including the standard route and the collaborative route – a dissertation is expected to meet the following aims and objectives: 1. Draw attention to a novel research topic – define a gap through a critical examination of the existing academic literature 2. Develop an analytical framework (theory) based on the academic literature 3. Collect empirical data (normally primary data) for analysis – to test or develop theory 4. Relate your specific findings (based on bringing data to bear on theory) to more general theoretical concerns, drawing conclusions through reasoned argument 2. Dissertation Types The following dissertation types apply to all dissertations whether they are on the standard route or the collaborative route. We use the “interpretive model” of social science presented in Ragin and Amoroso’s Constructing Social Research. According to this model, the sharp separation of social research into either deductive (quantitative hypothesis testing) or inductive (qualitative theory building) approaches is an unhelpful oversimplification. In fact, all forms of social research involve retroduction: the interplay of deduction (theory) and induction (evidence) to produce findings. Research questions are always developed based on a literature review, meaning that both theory and evidence enter into the specification of the question. For example, quantitative hypothesis testing is often said to be a purely deductive approach, but in practice it typically involves using evidence from previous studies as part of the basis for developing hypotheses (induction). Likewise, qualitative research is often said to be purely inductive, but it nearly always involves using existing theory to inform questions and construct an analytical framework (deduction). That being said, hypothesis testing does generally place more emphasis on deduction (empirically testing hypotheses that have been deduced from the literature), while qualitative research generally does have a strong element of induction (using empirical data to develop and revise theory). A Literature Review is a survey of the theoretical claims and empirical findings of other researchers on the topic. Apart from reviewing existing knowledge, it also points to unanswered questions from previous studies. Expected to be critical in nature, it concludes with an indication of what the present study purports to add to extant literature. The literature review is not merely a summary of previous studies, but needs to (a) find a gap, puzzle, contradiction, or disagreement in the extant literature, and (b) develop an analytical framework. The analytical framework is a systematic, detailed, theoretical sketch that aids the examination of the phenomenon under consideration. As explained in Ragin and Amoroso’s Constructing Social Research, there are two general types of analytical frameworks. Fixed analytical frames are generally used for quantitative studies and typically consist of a set of hypotheses to be tested against the data. In this approach, the analytical frame. is normally finalized before data analysis begins. By contrast, flexible or fluid frames are generally used for qualitative (and comparative) research. The frame initially directs researcher to focus on specific factors. It is a sketch of the theory the paper will use, focusing on key concepts, and attempting to motivate these based on existing theory and literature. It is flexible or fluid because the frame. is continually refined through inductive analysis of the data. The flexible or fluid frame is finalized only after the empirical analysis is complete. Even though you inductively develop it during your data analysis, the final frame. is to be written independently of your data, based on existing theory and literature, without discussing your data. That is left for the Findings and the Discussion/Conclusion. Dissertations must engage theory and should generally include a strong empirical element. There are five general types of dissertations. 1) Statistical analysis using secondary data This type of study generally develops a set of hypotheses to be tested with a statistical model, and can be analysed both descriptively and statistically – typically with a regression model. Data sources include official statistics (e.g. UK Office of National Statistics, US Census), other statistical databases collected international organizations (e.g. OECD, World Bank) and proprietary databases (e.g. FAME – Financial Analysis Made Easy; IBISWorld; Mintel; Nexis UK; Statista). The emphasis of this type of study is generally, though not exclusively, on deductive hypothesis testing. 2) Case study using in-depth interviews or participant observation This type of study generates new data to inductively develop theory, although, again, analysis will typically also involve deductive use of theory to help interpret data. It is generally based on a case study of one or a few companies. The most common data collection method for MSc dissertation case studies is to conduct in-depth interviews, i.e. to use a “semi-structured interview instrument” to elicit detailed responses from subjects. The focus is on finding subjects’ views, concerns and ways of understanding their social context. Another possible method for the MSc dissertation is participant observation, i.e. direct involvement in the regular activities of a subject population (e.g. working in a company). In this case the student would be expected to document his/her experiences by collecting data and notes in the form of diaries or other approaches to data recording. As will be explained in the Dissertation module, because the goal of case study research is not to generalize to a population but to undertake holistic analysis of a case, it is fine to study any organisation you can get access to – to use a convenience sample – so long as it is a case of what you want to study. We will explain in the Dissertation module how such research can be the basis for social science research that seeks to develop findings of general interest. 3) A study using secondary data or texts This type of study may be more deductive or inductive, depending on the questions and orientation of the researcher. There are generally two types of data used for this type of dissertation: a) Case study drawing on secondary data (company reports, government reports, newspaper articles, websites, official statistics, previously published data in academic studies, etc.). For the MSc dissertation, a typical case study using secondary data would be a national-level comparison of two or more countries; or two or more companies from different countries. This type of dissertation is riskier than types 1) and 2) because, when a novel statistical model is not developed or no primary data are collected, the burden of making a novel contribution is higher. In order to do so, a case study using secondary data will typically have to compile a set of secondary material in a way that shines new light on a case. b) Content or Text analysis focusing on company documents or samples of social media content (tweets, Facebook posts, Instagram posts/comments, etc). Such texts are considered primary data as they have not previously been collected for research and therefore need to be identified, sampled and assembled by the researcher. The researcher will have to rigorously, systematically and critically analyse these texts in order to produce a novel and insightful contribution to academic debate. Appropriate methods of analysis can be discussed with your supervisor. 4) Experiments We do not have strong expertise with experimental methods in the Institute. However, if a student is interested in doing an experiment and can find a supervisor willing to supervise an experiment, then it can be done. Experiments are typically deductive in orientation. 5) Original surveys Original surveys are not allowed in most cases. In general, the goal of surveys is to provide data that allows generalisation from a sample to a population. In order for such generalization to be possible, it is necessary to have a random (probability) sample. However, doing so requires a known population (a sampling frame) and the assigning of equal probabilities of being picked to all members of the population. Since this is in most cases impractical to do in the space of this MSc, surveys are generally not allowed. Similarly, surveying people on social media, for instance, is generally not allowed because it will generate a biased sample. Where the use of non-probability sample surveys is common in the literature reviewed (particularly for marketing dissertations) a supervisor may allow the use of a survey questionnaire. Or where the goal is not to generalize to a larger population, but for example to sample an entire workforce in a single company or to do a marketing study, a survey may be allowed. But even in these cases, it is very difficult to construct a valid and reliable survey instrument (questionnaire). Doing so requires developing questions grounded in the literature, motivated by theoretical hypotheses and designed to be tested with a statistical regression model. A supervisor will allow a student to do a survey only if they think the student is capable of developing a valid and reliable questionnaire. … In general, you must decide early in the process which general type of dissertation you will do. It is vital to define a project that is manageable in the time available. A purely descriptive account, without much theoretical framework or discussion, or a routine analysis with no novelty in either application or conceptualization, is not acceptable. As a social-science based programme, the MSc requires you to produce an academic piece of work. Purely practitioner-orientated, ‘problem-solving’ pieces are not acceptable (e.g. a report answering a specific practical question, such as ‘how to improve company X’s competitive strategy?’). 3. The Academic Literature The dissertation needs to be grounded in the academic literature. This includes scholarly journals and books from academic presses (Oxford, Cambridge, Harvard, Princeton, Routledge, Palgrave, etc.). Academic journals are one of the most important means of publishing and disseminating the results of academic research and scholarship. A list of major journals you might use is below. This list is not exhaustive and you may use journals not listed here. Your supervisor can advise you on additional journals and whether a given journal you have found is appropriate. General Management Academy of Management Review Academy of Management Journal Administrative Science Quarterly Journal of Management Journal of Management Studies Harvard Business Review British Journal of Management Human Resource Management and Employment Studies Human Resource Management Industrial Relations: A Journal of Economy and Society British Journal of Industrial Relations Work, Employment and Society Industrial and Labor Relations Review International Journal of Human Resource Management Work and Occupations Gender, Work & Organization Entrepreneurship Strategic Entrepreneurship Journal Entrepreneurship Theory and Practice Journal of Business Venturing Entrepreneurship and Regional Development Innovation Research Policy Journal of Product Innovation Management R&D Management Technovation International Business and Area Studies Journal of International Business Studies Journal of Common Market Studies Journal of World Business International Business Review Management International Review Asia Pacific Business Review Journal of International Management Global Strategy Journal Organization Studies Organization Science Organization Studies Leadership Quarterly Human Relations Social Sciences American Journal of Sociology American Sociological Review Annual Review of Sociology Socio-Economic Review British Journal of Sociology World Development Economy and Society Industrial and Corporate Change Review of International Political Economy Economics Journal of Political Economy American Economic Review Quarterly Journal of Economics Journal of Economic Literature Cambridge Journal of Economics Journal of Institutional Economics 4. The dissertation supervision process The dissertation you write as part of your Master’s programme is a major piece of independent research. Your dissertation supervisor is there to guide you in this process and help you get it right. However, he or she can only do this effectively if you take your dissertation seriously from the beginning and engage in the research and supervision process fully. Each student is allowed five meetings with his or her supervisor. These should normally consist of the following: • The first meeting happens before the proposal is due, and is to help you shape your ideas and prepare to submit your proposal; • The second meeting happens after the proposal has been submitted and marked, and will help you identify focus areas for development; • The third meeting will be to discuss your literature review and theoretical framework; • The fourth will focus on your methodological approach and research instrument; o You must discuss your specific method with your supervisor (which type of regression model you will use; how you will conduct interviews) before you begin collecting data; o You must get approval for your research instrument (questionnaire or interview guide) from your supervisor before you begin collecting data; failure to get my approval is grounds for failing the dissertation • The final meeting will review progress with data collection, your plan for analysis, and any other outstanding issues. Each meeting can last up to 60 minutes but will normally be around 30 minutes in duration. Outside of the formal meetings, your supervisor will be available for shorter discussions (or email exchanges) at other times if particular problems or questions arise. Supervisors will provide feedback on one complete written chapter of your dissertation (maximum 3,000 words), which should be decided in discussion with them.
Computer science guide Assessment Internal assessment Purpose of internal assessment Internal assessment is an integral part of the course and is compulsory for both SL and HL students. It enables students to demonstrate the application of their skills and knowledge, and to pursue their personal interests, without the time limitations and other constraints that are associated with written examinations. The internal assessment should, as far as possible, be woven into normal classroom teaching and not be a separate activity conducted after a course has been taught. The internal assessment requirements at SL and at HL are the same. However, these requirements contribute to a different percentage of the overall mark. Students are required to produce a solution that consists of a cover page, the product and the documentation. The focus of the solution is on providing either an original product or additional functionality to an existing product for a client. The internal assessment component (solution), as well as being practical and productive, forms an important part of the assessment of the computer science course. It is imperative, therefore, that the teacher provides appropriate guidance to students. Guidance and authenticity The solution submitted for internal assessment must be the student’s own work. However, it is not the intention that students should decide upon a title or topic and be left to work on the internal assessment component without any further support from the teacher. The teacher should play an important role during both the planning stage and the period when the student is working on the internally assessed work. It is the responsibility of the teacher to ensure that students are familiar with: • the requirements of the type of work to be internally assessed • the ethical guidelines mentioned in the “Requirements and recommendations” section of this document • the assessment criteria; students must understand that the work submitted for assessment must address these criteria effectively. Teachers and students must discuss the internally assessed work. Students should be encouraged to initiate discussions with the teacher to obtain advice and information, and students must not be penalized for seeking guidance. However, if a student could not have completed the work without substantial support from the teacher, this should be recorded on the appropriate form. from the Handbook of procedures for the Diploma Programme. It is the responsibility of teachers to ensure that all students understand the basic meaning and significance of concepts that relate to academic honesty, especially authenticity and intellectual property. Teachers must ensure that all student work for assessment is prepared according to the requirements and must explain clearly to students that the internally assessed work must be entirely their own. As part of the learning process, teachers can give advice to students on a first draft of the internally assessed work. This advice should be in terms of the way the work could be improved, but this first draft must not be heavily annotated or edited by the teacher. The next version handed to the teacher after the first draft must be the final one. All work submitted to the IB for moderation or assessment must be authenticated by a teacher, and must not include any known instances of suspected or confirmed malpractice. Each student must sign the coversheet for internal assessment to confirm that the work is his or her authentic work and constitutes the final version of that work. Once a student has officially submitted the final version of the work to a teacher (or the coordinator) for internal assessment, together with the signed coversheet, it cannot be retracted. Authenticity may be checked by discussion with the student on the content of the work, and scrutiny of one or more of the following: • the student’s initial proposal • the first draft of the written work • the references cited • the style. of writing compared with work known to be that of the student. The requirement for teachers and students to sign the coversheet for internal assessment applies to the work of all students, not just the sample work that will be submitted to an examiner for the purpose of moderation. If the teacher and student sign a coversheet, but there is a comment to the effect that the work may not be authentic, the student will not be eligible for a mark in that component and no grade will be awarded. For further details refer to the IB publication Academic honesty and the relevant articles in the General regulations: Diploma Programme. The same piece of work cannot be submitted to meet the requirements of both the internal assessment and the extended essay. Group work The development of the solution must be undertaken by the student on an individual basis. Collaborative or group work may not be undertaken by students. Time allocation It is recommended that a total of approximately 30 teaching hours for both SL and HL should be allocated to the work. This should include: • time for the teacher to explain to students the requirements of the internal assessment, including codes of ethical behaviour and confidentiality • class time for students to work on the internal assessment • time spent by the student making arrangements to collect data as appropriate • time for consultation between the teacher and each student • time to review and monitor progress, and to check authenticity. Additional time may be needed outside normal class time for students to work independently, such as acquiring additional skills required for the project and consulting with other people. Requirements and recommendations Teachers and students will need to discuss issues relating to the design of the product, the collection of data and consultations with others. Students should be encouraged to initiate discussions with the teacher to obtain advice and information, and will not be penalized for seeking advice. Ethical guidelines for internal assessment Given the nature of the project, students must take into account ethical problems and implications for undertaking research and developing the solution, for example, ensuring the confidentiality and security of data. Wherever possible, original data should be used or be collected by the student. The following guidelines must be applied. • Consent must be obtained from people who will be involved in the development of the solution before any investigation is begun. • All data collected must be stored securely in order to maintain confidentiality. • Only the data collected for the solution can be used. It must not be used for any other purpose without explicit permission. Teachers should refer to the Ethical practice in the Diploma Programme poster for further guidance. Health and safety guidelines Schools are advised to follow local best practice in health and safety for research linked to the development of the solution. Each school is ultimately responsible for the health and safety of students. Word count Students must produce a solution that includes supporting documentation up to a maximum of 2,000 words. If the word limit is exceeded, the teacher’s assessment of the documentation must be based on the first 2,000 words. Work that falls significantly beneath the stated word count is unlikely to fully meet the stated requirements of the task and is likely to receive low marks. Using assessment criteria for internal assessment For internal assessment, a number of assessment criteria have been identified. Each assessment criterion has level descriptors describing specific levels of achievement together with an appropriate range of marks. The level descriptors concentrate on positive achievement, although for the lower levels failure to achieve may be included in the description. Teachers must judge the internally assessed work at SL and at HL against the criteria using the level descriptors. • The same assessment criteria are provided for SL and HL. • The aim is to find, for each criterion, the descriptor that conveys most accurately the level attained by the student, using the best-fit model. A best-fit approach means that compensation should be made when a piece of work matches different aspects of a criterion at different levels. The mark awarded should be one that most fairly reflects the balance of achievement against the criterion. It is not necessary for every single aspect of a level descriptor to be met for that mark to be awarded. • When assessing a student’s work, teachers should read the level descriptors for each criterion until they reach a descriptor that most appropriately describes the level of the work being assessed. If a piece of work seems to fall between two descriptors, both descriptors should be read again and the one that more appropriately describes the student’s work should be chosen. • Where there are two or more marks available within a level, teachers should award the upper marks if the student’s work demonstrates the qualities described to a great extent. Teachers should award the lower marks if the student’s work demonstrates the qualities described to a lesser extent. • Only whole numbers should be recorded; partial marks, that is fractions and decimals, are not acceptable. • Teachers should not think in terms of a pass or fail boundary, but should concentrate on identifying the appropriate descriptor for each assessment criterion. • The highest level descriptors do not imply faultless performance but should be achievable by a student. Teachers should not hesitate to use the extremes if they are appropriate descriptions of the work being assessed. • A student who attains a high achievement level in relation to one criterion will not necessarily attain high achievement levels in relation to the other criteria. Similarly, a student who attains a low level of achievement for one criterion will not necessarily attain low achievement levels for the other criteria. Teachers should not assume that the overall assessment of the students will produce any particular distribution of marks. • It is recommended that the assessment criteria be made available to students.
FIT5147 Data Exploration and Visualisation Semester 1, 2025 FIT5147 Data Visualisation Project (DVP) In this project, you are asked to create an interactive narrative visualisation that communicates some of your findings from the Data Exploration Project. It is an individual assignment and worth 40% of your total mark for FIT5147. There are two submissions: ● DVP Part 1: Design (Presentation): 3% of the 40%, due Week 11 ● DVP Part 2: Visualisation Project Submission (report and source code): 37% of the 40%, due start of exam period. Relevant Learning Outcomes ● Choose appropriate data visualisations ● Critically evaluate and interpret a data visualisation ● Implement interactive data visualisations using R (Shiny) or JavaScript (D3). Overview of the Tasks 1. Identify which findings from your Data Exploration Project you wish to communicate. You can be selective, and you do not need to share everything you have found. The visualisations and accompanying narration should reflect the answers to (one or more of) the questions in your Project Proposal. 2. Clearly define your intended audience. The audience might be the elderly, young children, your classmates, the general public, politicians or whoever you like, although the choice should make sense for the data and topic of choice. Your subsequent interactive narrative visualisation submission should be designed specifically for the intended audience. 3. Design an interactive narrative visualisation using the Five Design Sheet methodology. 4. Prepare a short presentation based on your five design sheets (one sheet per slide). 5. Submit the five design sheet slides for your presentation in Week 11. 6. Present your presentation to your Applied Session in Week 11 or 12. 7. Implement your visualisation using either R (Shiny) or JavaScript (D3). The use of other visualisation libraries and packages is subject to approval by your Teaching Associate (see the section “Notes on Implementation”). Note that you are not allowed to use R Markdown. 8. Write a report and export it to PDF. 9. Submit your report and your source code (see the section “How to Submit”) at the start of the exam period. Presentation Details The presentation is an opportunity to gain feedback on your designs from your teaching associates and peers. Prepare a three minute presentation based on your five design sheets. Your presentation should consist of 6 slides covering: 1. An Introduction: Name, project title, aims and motivation (one slide) 2. Each of your Five Design Sheets (i.e., one sheet per slide). The five design sheet methodology must be completely followed. All components should be legible and readable. For your sheets you may use pen and paper, and then digitise them (at high quality to ensure readability), or use digital pen technology. If you do import any images from external sources to your sheets you must reference the source appropriately in the relevant design sheet. The design slides you present in person in your Applied session must match those submitted on Moodle. If you do not manage your presentation and go overtime, your Teaching Associate may stop your presentation, which may result in loss of marks and restrict the feedback you get on elements of your design. Report Structure Write a report of up to 15 pages (excluding cover page, table of contents, bibliography, and appendix, which together have a limit of 10 pages) that consists of the following sections: 1. Project title Title of your narrative visualisation. This should be included in the cover page. 2. Your identity Your full name, student ID, Applied session number, and Teaching Associate’s name. This should be included in the cover page. 3. Introduction [Approx. ½-1 page] A precise and succinct description of what findings and messages you wanted your narrative visualisation to convey, and who your intended audience is. 4. Design Process [Approx. 3-5 pages] A description and justification of your narrative visualisation design process. This should briefly refer to each of your five design sheets (you must provide each of your 5 design sheets in the Appendix), and justify your design choices based on the theoretical content of the unit (throughout Weeks 1-12), for instance: identifying consistency in your design and interaction; choice of visualisation, choice of visual variables, reasons for a particular colour palette; justifying your layout structure, choice of typography, aspects of Munzner’s what-why-how framework, choice of genre and/or narrative style, and aspects of the human visual system; etc. It is important that this section is not simply a description of which charts you chose in the different sheets, but must justify your ideas and the process that lead you to your final design choices. Be sure to cite suitable references. All visualisations in your design process should be relevant for your data and there should be a clear indication of how your choice of audience has influenced your design process. 5. Implementation 5.1. Technical Implementation [Approx. ½ - 1 page] This section contains a high-level description of your implementation, including libraries used, references to external code sources such as templates, and reasons for any differences between your final design (that was justified and explained in Design Process) and the implemented design (described in the following section), if applicable. You are not required to explain the code in detail. You should briefly explain the reasons why your project was challenging (e.g., extensive wrangling was required, advanced use of D3, etc. - see Marking Criteria 4 for further information). 5.2. Interactive Narrative Visualisation Implementation [Approx. 2-5 pages] Document and describe your final implemented submission, including screenshots of your final implementation where suitable. This section should clearly explain the final implementation and how its narrative portrays the data insights to your audience. 5.3. Using the Implementation [Approx. 1 page] This section contains instructions for how to run, view and use your interactive narrative visualisation. This should emphasise any parts of your visualisation that may be easily missed by a reader (e.g., some interaction you have implemented that might not be immediately visible). 6. Conclusion [Approx. ½ page] Summarise your findings and what you have achieved with your narrative visualisation. Reflect on what you have learnt in this project, including what in hindsight you might have done differently to improve the result and any future work that you would like to do. 7. Bibliography Appropriate references of all resources that have influenced your work in IEEE or APA style (refer to the Monash Uni library's guide page for Monash citing and reference style). This should include any code templates, design influences and references to design theory, as well as any sources that have influenced any data insights. 8. Appendix Include your five design sheets in the Appendix. Make sure you provide clear images and any handwriting is readable. Your report should contain high-quality images of your narrative visualisation and five design sheets, indicating interaction when relevant. Where possible, avoid using a single screenshot of the entire page since the resolution might be low; instead, crop and explain individual sections of the page. It is also recommended that you export your PDF using a local word processor (e.g., Microsoft Word), as exporting your document as a PDF directly from Google Docs will result in low-quality images. Make sure you can read and understand the PDF document and its images at A4 size without requiring further enlargement. Notes on the Design ● The visualisation must be a narrative. Elements of the design must tell the data story, using text and visualisation techniques to narrate how the data and the findings of the exploration process enable the questions about the topic of interest to be answered. ● Your design must follow the Five Design Sheet process and provide all the required information according to the 5dS template. The designs for Sheets 2-4 must be distinct from each other. The final design on Sheet 5 is expected to be a refinement of one of those sheets (or a combination of components from each). ● Each design for Sheets 2-4 is expected to complete the narrative independently of the other designs. No design sheet should just respond to one of the questions or findings narrated by the visualisation. No design sheet should consist of a single visualisation technique, e.g., one graph. Every design should resemble a complete solution with a clear layout that follows a particular narration style. ● In Sheet 5 (and your final implementation) your final design should include a location to provide information about the data used in this work and a link to the original data source. It should also include how the interface will provide guidance to the audience on using the interactive narrative visualisation. ● Any differences between the final design you submitted and presented in Week 11/12 and that submitted in your report (Appendix) in light of feedback to your presentation should be justified and explained in the Implementation section. Notes on the Implementation ● Your implemented narrative visualisation should be based on the result of your Five Design Sheet process. It does not need to follow it exactly, however it should resemble the final design in Sheet 5. Small changes to your final design are allowed (e.g., layout, visualisation choices, navigation method, colour) but any such differences between your design and how it was implemented must be explained and justified in the Implementation section of your report. ● As a rule of thumb, all visualisation packages and libraries that are included in this unit are allowed for your implementation. This includes, but is not limited to: ○ For R Shiny: ggplot2, ggmap, ggraph, Leaflet, Plotly, igraph, wordcloud, etc. ○ For D3: D3 itself, Leaflet, MapBox, etc. Libraries which act as high-level wrappers for D3 are NOT allowed (e.g., C3.js, dimple). If you are unsure if a particular visualisation package or library is allowed, please discuss it with your Teaching Associate, or ask on the Ed discussion board. ● Tools or packages used for data wrangling, data cleaning, Shiny theming, HTML5 templating, CSS styling, etc. are not subject to these rules and can be used freely (i.e., for anything other than the visualisations themselves). However, you should not use server-side code, like Django or node.js, when implementing your design. Any data used for your DVP must be read from the files submitted with your code. ● For performance reasons, it is recommended that you pre-format all of your data files before loading them into R Shiny or D3. In other words, all data wrangling and cleaning steps (if any) should be performed outside of your narrative visualisation code. You are not required to include the code for data wrangling and cleaning as part of your submission. However, if you have done considerable work since your Data Exploration Project, then you should describe these steps in your DVP report (see Marking Criteria 3). Marking Criteria DVP Part 1: Design (Presentation) [3%] ● Quality of oral presentation (confidence, speed, voice) and quality of slides (legibility, design, images) [1%]. ● Logical structure [1%]. ● Choice of content (completeness, appropriate level, discussion of design and implementation alternatives) [1%]. DVP Part 2: Visualisation Project Submission (Report and Source Code) [37%] When grading your submission, all components (i.e., the quality of your narrative visualisation design, technical implementation, and the written report) are taken into account: 1. Project Continuity [2%] The degree to which the narrative visualisation presents data insights from questions proposed in your submitted Project Proposal and discovered during your Data Exploration Project. This explanation should be clearly outlined in your Introduction. Further exploration or improvements can be done post DEP, when necessary, but must be described and justified within the Technical Implementation section. 2. Design Process and Justifications [8%] a. Appropriate use of the Five Design Sheet methodology and evaluation of your alternative designs [5%]. b. Relevance and quality of your justification of the final design based on data visualisation theory taught in the unit (Weeks 1-11). [3%]. 3. Implementation of Final Visualisation Design [12%] a. Quality of implemented narrative: clear and appropriate guidance and narrative to your audience. This should connect the data to the visualisation(s) through a narrative story. Clean and appropriate layout for the implemented narrative including appropriate sightlines and hierarchy of typography. Limited jargon. [2%]. b. Quality and appropriateness of implemented interactive visualisation: provision of appropriate context, attention to detail, appropriate and clean visualisation layout(s) and use of typography, appropriate and consistent use of colour and other visual variables, appropriate information about the data source(s), and appropriateness for the intended audience [5%] c. Correctness and robustness, performance and usability of the implementation [3%]. d. Code comments and code quality [2%]. 4. Project Difficulty [10%] The degree to which the visualisation project demonstrates sophistication and complexity in terms of its technical, theoretical and design implementation. Marks for this section will be allocated for the following: a. Sophisticated use of different data sources, in particular non-tabular data [2%]. b. Dealing with very large datasets [2%]. c. Advanced implementation of D3 / R (Shiny) [3%] d. Sophisticated user interaction (e.g., animation, linked interaction) [3%] Note: Other technical, theoretical and/or design aspects will be considered for marks in this difficulty section. It is therefore crucial to make the marker aware of the complexity of your project by ensuring you mention and justify all elements in your written report. 5. Project Report [5%] a. Quality of writing, logical structure, quality and suitability of images, grammar/spelling, appropriate figure and table use with clear captions and in-text referencing (e.g. Figure 1, Table 1), appropriate academic referencing (APA or IEEE) and citations [1.5%]. Note: Pay particular attention to your visualisations in your report. They must be clear and legible with readable font and font size, clear titles, legends and labelled axes in written English with no special characters such as underscores. b. Completeness, i.e., all the above sections should be submitted and completed. [3.5%]. Note: Pay attention to the criteria expected and page limit expectations for each section. This forms a guide for completeness. Check Your Code! Please be sure to check that your code runs correctly. If possible, check if your code works on other computers and operating systems. If you do not have access to another computer you can try checking via the Monash MoVE platform. If your code requires some steps for it to run, then be sure to make these very clear in readme notes for your marker and describe this in the User Guide section of your report. Your code must run on your marker’s computer on the first attempt for us to be able to mark your submission. If your submission does not run correctly, 5% (from the Implementation mark) will be instantly deducted from your grade. If after some troubleshooting your grader is still unable to get the code to run, further deductions will occur as we will not be able to fully grade your interactive narrative visualisation. Your code must also contain meaningful comments and be formatted and designed in such a way that it is easily readable and understandable. Originality As this is academic work, it must be original and must clearly indicate what elements were your work and what are based on someone-else’s work. If you are including facts, data, opinions or any other written or graphical information from another source, you must cite the source and reference the bibliographic details for the source, using the APA or IEEE style. guide. This includes any third-party programming code or software you use in your data exploration and analysis. If you directly quote or replicate any material from a reference, you must do so in a manner appropriate to the APA or IEEE style. guide. Be sure to acknowledge sources that influence your code through your code comments and references in your bibliography. Do not copy complete designs from any external sources. If you are retaking this unit from a previous semester, please ensure you choose a completely new design and new visualisation code. The text, design and code cannot have been used in any other unit. Likewise, you cannot reuse any code or written content that you have used in any previous assessment tasks for any units. The only self-plagiarism that is allowed is the questions you set in your Project Proposal this semester and reusing some R code from your Data Exploration Project this semester (if you wish). Any other written content from your Proposal or DEP may not be reused. It must be rewritten for your DVP. Generative Artificial Intelligence (Generative AI) software or systems like ChatGPT or Midjourney cannot be used for any part of this assessment task, including (but not limited to) generating written or visual components of your submitted work. If your work is believed to not be original, due to potential instances of plagiarism, collusion with other students or contract cheating, your academic integrity will be reviewed. If any breaches of the academic integrity are confirmed, penalties may be applied to your assignment, the unit and/or even your enrolment in your course. Submission Due Dates ● Part 1: Design: Submit your presentation slides to Moodle, due Week 11 (see Moodle for date and time). Presentations will take place during Week 11 & 12 in your Applied session. Attendance for both weeks is mandatory. ● Part 2: Submit a PDF report and a zip file containing your code and the data to Moodle, due the first week of exam period (see Moodle for date and time). NOTE: All submission times are in Melbourne, Australia local time. How to Submit Presentation 1. Prepare a PDF file containing all five of your design sheets. 2. Name the file StudentName_StudentID_Presentation.pdf 3. Submit it via Moodle under Assessments/DVP Part 1: Design (3%). Report and Source Code 1. Prepare a PDF report (max 15 pages) and a ZIP file containing the source code for your narrative visualisation and any data files that are required to run it. 2. Name the files using the following format: a. StudentName_StudentID_Report.pdf b. StudentName_StudentID_Code.zip 3. Submit both files via Moodle under Assessments/DVP Part 2: Visualisation Project Submission (37%). These must be two separate files. Do not put your PDF inside of the ZIP archive. Note that only .zip is recommended, and you should not use other extensions such as .rar or .7z. Notes on submissions: ● We cannot mark any work submitted via email or stored on file hosts such as Google Drive or Dropbox. Please ensure that you submit correctly via Moodle since it is only in this process that you complete the required student declaration, without which your work cannot be assessed. ● Your assignment MUST show a status of "Submitted for grading" before it will be marked. If your submission shows a status of "Draft (not submitted)”, it will not be assessed and will incur late penalties if submitted after the due date/time. Note that this applies even if your file was uploaded to Moodle as draft prior to the due date. ● It is your responsibility to ENSURE that the files you submit are the correct files. We strongly recommend after uploading a submission, and prior to actually submitting on Moodle, that you download the submission and double-check its contents. ● Turnitin is used to help staff review the academic integrity for all submissions. It may not be shared with students unless a student’s work is under review. ● There is a maximum file size of 500MB. This is rarely hit by students in the unit, but it can cause an issue if your data files are very large. If you believe the limit affects you, check your zipped folder size and look to reduce the size of your data (e.g., by removing columns you are not using). If this is not possible, then only then can you consider storing your data remotely, e.g., via Google Drive, but be sure to test your code and provide access. Be sure to note this restriction in your code comments and any instructions, if needed. If access and instructions are not provided, your mark will be penalised. ● You do not need to publish your app on the web. Late Submissions and Special Consideration Design Presentation ● We encourage everyone to submit their presentation slides on time. All Presentation Slides submitted late will receive zero marks. Report and Source Code ● Assessments received after the submission deadline, or after the extended submission date for those with special consideration, will be penalised 5% of the available total marks per day up to a maximum of seven days. Submissions more than seven days after the due date will not be graded. ● For information on eligibility for Extensions and Special Consideration, please refer to the relevant section on the Assessment page on Moodle.
СОMР5425 Week10 Semester 1, 2025 Information Summarization Text summarization Video Summarization Applications LifeLogging Scene summarization StoryImaging Information Deluge Approximately 3.5 trillion photos have been taken since Daguerre captured Boulevard du Temple 174 years ago The box Quantum TV DVR that records up to 12 channels at once Summarization Distill the essence Provide a compact yet informative representation of a video Crucial for effective and efficient access of video content Fun Incentive Summly http://summly.com/index.html Founded by 17-year-old Nick D’Aloisio Acquired by Yahoo in 26/03/2013 30 Million!!! Text Summarization Human summarization and abstracting What professional abstractors do “To take an original article, understand it and pack it neatly into a nutshell without loss of substance or clarity presents a challenge which many have felt worth taking up for the joys of achievement alone. These are the characteristics of an art form” - Ashworth. Text Summarization Purpose Indicative, informative, and critical summaries Form. Extracts (representative paragraphs/sentences/phrases) Abstracts: “a concise summary of the central subject matter of a document” Dimensions Single-document vs. multi-document Context Query-specific vs. query-independent TextRank Identify important words or sentences Formulate the problem with graph-based solution Keyword extraction & sentence extraction
UG Assessment Brief – Academic Year 2024/25 Module Code ARTD1109 Assessment Type Illustrated Report (30 CATS) Module Title Consumer Intelligence Weighting 100% Assessment Brief Details: For your referral assignment you must refer to the feedback on your previous submission before you begin work on this referral. Based on the summative feedback provided, you must decide which sections of your [input assessment method e.g. Illustrated report] to re-do or to develop according to the feedback provided. You should make it clear which sections have been improved via changing the colour of the written text OR showing track changes. For portfolio submission you should include a written summary of the improvements you have made to both the development work and the outcomes. The assignment brief is therefore the same as for your original [e.g. illustrated report] and this can be found below: Insert original brief description here with any supporting information for each section AIM OF CONSUMER INTELLIGENCE MODULE The study of consumer behavior. covers a lot of ground: It is the study of the processes involved when individuals or groups select, purchase, use or dispose of products, services, ideas or experiences to satisfy needs and desires (Michael Solomon, 2015) Understanding consumers is at the core of creative marketing and management strategy. The consumer intelligence module will introduce you to the theoretical frameworks, research methods, professional tools and key concepts that will enable you to develop your knowledge of consumer-centric marketing. You will be taught to critically analyse and then apply information from a range of sources. You will be introduced to primary and secondary research sources and methods that will help you to understand consumer groups, and patterns of consumption. You will use a range of consumer profiling resources available through the University blackboard and library including Mintel, Euromonitor and LS:N Global. THE BRIEF (2500 Illustrated Report + Reference list + Image Reference List) The assignment is made up of 2 parts that form. 100% of the brief. The assignment involves analyzing a certain consumer segment, initially based on a generation of consumers, within a specific global location and then narrowed down to a specific subculture within that group. Within the brief you will conduct segmentation and analysis of these consumers and then reflect on the benefits and pitfalls of consumer segmentation and related approaches. HOW TO START After initial research, choose one of the following Generations* from a culture of your choice (you must be able to access resources for your chosen culture from the recommended sources). · Gen Alpha (born between 2010 and 2024) · Gen Z (born between 1997 and 2009) · Gen Y (born between 1981 and 1996) · Gen X (born between 1965 and 1980) · Baby Boomers - Woodstock Generation (born between 1946 and 1964) * You will find different date ranges for these generations from different authors so for the benefit of this project use these above dates as a guide and then define the precise date range you are referring to in your assignment. Please also provide a citation for your selected date range. INTRODUCTION (50 words) What is the focus of your assignment? What sources will you refer to? What frameworks/theories will you engage with? What do you hope to establish? PART A DEMOGRAPHIC ANALYSIS (1000 words) What defines your chosen generation in a specific country or capital city of choice? What events and experiences in their lives have shaped their viewpoints in current society and how might they compare to other generations? Look at social and cultural factors, as well as political and economic factors. Define and analyze the current demographic characteristics of this generation, using a variety of primary and secondary sources. Discuss income, spending, life stage and geography. Use ONS Data, Statista, Acorn Reports (if UK based), Mintel and Euromonitor, etc., as well as further reports to support your analysis considering trends and shifts in current data. Using a combination of visual images, graphs/data and written text to critically analyse this information. PART B PSYCHOGRAPHIC ANALYSIS (1000 words) This section asks you to focus on three subcultures within your generation of choice. Remembering why it is essential for marketers to understand the consumer, you will apply psychographic theories and methods to examine your chosen demographic group in depth. You will use these techniques to deepen your understand of their behaviours as customers. Use pen portraits, empathy mapping and visuals to provide a detailed depiction of an individual at the center of each consumer group. With a clear understanding of the demographics and psychographics, analyze patterns in consumption behavior. both on- and offline. Employ examples to illustrate these. CONCLUSION (450 words) Conclude with a summary of the advantages and disadvantages of consumer segmentation analysis. Employ the core reading (and beyond) to assess the effectiveness of the different approaches currently employed in fashion to examine consumer behaviour. Identify new approaches and assess their potential.
MATH 235 Online Practice Test 2 June 2024 1. (12 points) For the following multiple choice questions, choose the correct answer. There is precisely one correct choice per question. Each correct answer is worth +1, each incorrect answer is worth →1/4, and there is no penalty for questions left blank. (No work needs to be shown for this problem.) (1) The limit as (x, y) → (0, 0) does not exist for a function f(x, y). If f(r, ω) is the same function written in polar coordinates, the following may be possible: (A) f(r, θ) = r2 (B) f(r, θ) = r cos θ (C) f(r, θ) = (1/r) cos θ (D) All of (A)–(C) may be possible (E) None of (A)–(D) (2) Suppose lim(x,y)→(0,0) 2f(x, y) = L for a function f(x, y), where L is a constant. The following must be true: (A) lim(x,y)→(0,0) f(x, y)=1 (B) lim(x,y)→(0,0) f(x, y) = L/2 (C) lim(x,y)→(0,0) f(x, y) does not exist (D) lim(x,y)→(0,0) f(x, y)=2L (E) Not enough information given to decide (3) Suppose h1(x, y) < f(x, y) < h2(x, y) for all (x, y) in the domain of f. The following must be true at a point (a, b): (A) f(a, b) = h1(a, b) = h2(a, b) (B) lim(x,y)→(a,b) f(x, y) exists (C) lim(x,y)→(a,b) f(x, y) = lim(x,y)→(a,b) h1(x, y) (D) All of (A)–(C) (E) None of (A)–(D). (4) γ1(t) and γ2(t) are two parametrizations of the same curve C in R3, each with nonzero speed. If γ1(t1) = γ2(t2) = p, the following must be true: (A) γ'1 (t1) = γ'2 (t2) (B) γ'1 (t1) = -γ'2 (t2) (C) γ'1 (t1) = cγ'2 (t2) for some nonzero constant c (D) Either (A) or (B) (c = ±1 in (C)) (E) None of (A)–(D) (5) γ1(t) and γ2(t) are two parametrizations of the same curve C in R3, each with nonzero speed and traveling in the same direction. Suppose γ1(t1) = γ2(t2) = p and L is the tangent line to C at p. For -∞ < t < ∞, L can be parametrized: (A) p + tγ1(t1) (B) p + tγ2(t2) (C) p + t(γ'1(t1) + γ'2(t2)) (D) Both (A) and (B) (E) None of (A)–(D) (6) γ1(t) and γ2(t) are parametrizations of the curves C1, C2 respectively. If C1 and C2 intersect at a point p, then it must be true that: (A) γ1(t1) = γ2(t2) for a pair of times t1 ≠ t2 (B) γ1(t1) = γ2(t1) at a time t1 (C) There are no times t1, t2 such that γ1(t1) = γ1(t2) (D) Either (A) or (B) must be true (E) There’s not enough information to determine which of (A)–(D) holds. (7) Let f(r, θ) denote a function f(x, y) written in polar coordinates. If limr→0 f(r, θ) = sin θ, then lim(x,y)→(0,0) f(x, y) is: (A) 1 (B) -1 (C) 0 (D) One of (A), (B), or (C) (E) Does not exist (8) A function f(x, y) satisfies lim(x,y)→(1,0) f(x, y) = f(1, 0). It must be true that: (A) f is continuous at (1, 0) (B) ϑxf exists at (1, 0) (C) ϑyf exists at (1, 0) (D) (A) and (B) (E) (A) – (C) are all true. (9) A function f(t, x, y) solves ϑtf + ϑxf - Δf = 0 at a point p = (t, x, y). The following must be true at p for g(t, x, y) = tf(t, x, y): (A) ϑtg + ϑxg - Δg = 0 (B) ϑtg + ϑxf - Δf = 0 (C) ϑtf + ϑxg - Δg = 0 (D) ϑtg + ϑxg - Δg = +f (E) None of (A)–(D) (10) Consider the curve γ(t) = (cos(t), -te3t-5, 14 log t), 0 < t < ∞. Then γ is confined to the following region: (A) {x > 0} (B) {y < 0} (C) {z > 0} (D) Two of (A)–(C) (E) None of (A)–(D) (11) Consider the curve γ(t)=(x(t), e-t - t, z(t)), -∞ < t < ∞. It must be true that: (A) γ is not a line (B) γ cannot lie on a sphere (C) γ can lie on {y = x2 + z2} (D) All of (A)–(C) (E) Not enough information given to decide (12) Consider the curve γ(t)=(x(t), y(t), z(t)), 0 < t < ∞ parametrizing all or a part of the intersection of {z = x2 + y2} with a plane. Then it is impossible that: (A) x(t) = sin(t) and y(t) = cos(t) (B) x(t) = c for a constant c (C) x(t) = y(t) (D) x(t) = t and z(t) = ln t (E) (B) and (D) 2. (9 points) For each of the following functions f(x, y), determine whether the limit as (x, y) → (0, 0) exists. If it does, calculate the limit. If it does not, clearly show why. (a) (b) (c) 3. (7 points) Consider the surface S given by {x2 = y2 + z2}. (a) Give a parametrization of the intersection of S with the plane {2x → y +1=0}. (b) For y defined implicitly by the equation for S, use implicit di”erentiation to find ϑzy and ϑxy at (2, √2, √2). 4. (6 points) Find all points, if any, on the graph of f(x, y)=5→x2 →2y2 at which the tangent plane at that point is parallel to {x + 4y + z = 1}. 5. (12 points) Consider the function (a) Find ϑxf and ϑyf at (0, 0) (b) Find ϑxf and ϑyf at (x, y) ≠ (0, 0) (c) Is f di”erentiable at (0, 0)? Justify. (d) Find ϑxϑyf at (0, 0) if it exists.
