CS 338 - Winter 2025 Assignment 2 March 4, 2025 In this assignment, you will continue to write queries for two databases, TPC-H and CHINOOK, but focusing on more advanced features. Both databases should already be set up on your machines. If they are not, please refer to the instructions in Assignment 1 for installation details. Each question is worth 5 points, except for PARTI-Question #3, which is worth 10 points. The assignment is due on March 11, 2025, at 11:59 pm. Please upload your answers as a single PDF file to the Assignment 2 folder on Learn Dropbox. Note that submissions must be in PDF format only; any submission not meeting this requirement will receive 0 marks. PART I: TPC-H Database: Write SQL queries to answer the following: 1. How many European customers have a balance (column name is c acctbal) that is less than $8000. 2. Which region (Aisa, Europe...) supplies the most distinct parts. 3. What is the average time for shipping items by TRUCK. FOLLOW UP. List the quantity and orderkey of all the items shipped by TRUCK which takes less than the above time. Please note that this question requires addressing two queries. 4. List all the items which have NO discount and not sold to customer in EUROPE. 5. List name(s) of the customer(s) who has not place any orders. 6. List the (distinct) items which are ordered by the customers from the country where suppliers have the highest average balance. 7. Find the average price of all the orders which contain no parts with size larger than 40. 8. List the name of the suppliers which supplies more than 5 parts along with the number of parts their supplied. 9. Find the names of all the distinct parts which receive the highest discount. PART II: The CHINOOK database: 1. List the genre of tracks which is contained in the most playlist 2. Find audio tracks which have a length longer than the average length of all the audio tracks 3. Which playlist(s) contain the largest number of pop tracks 4. Find the number of employees live in the same city with each customer, sorted by descending order 5. Which artist(s) has the most tracks which can be classified to Jazz 6. Find the name of the German customer(s) who has paid the most in total without company name 7. List the name and age of the employees who support more than 5 customers. Hint 1: You can use GETDATE() function to get the current date, and use an other function from last assignment to calculate ages. Hint 2: For more details on SQLite date functions, please visit SQLite Date Functions Documentation. 8. Find the manger who manages most employees but also being managed by someone else (Note: there are employees who do not have managers, i.e., there may be NULL values in ReportsTo column) 9. List the name of the artists with more than 5 tracks 10. Find the playlist(s) which contains most tracks by artist ”AC/DC”
CSCI4430 Assignment 2: Adaptive Video Streaming via CDN Due: March 9th, 2025 @11:59 PM Video traffic dominates the Internet. In this project,you will explore how video content distribution networks(CDNs)work. In particular,you will implement(1)adaptive bitrate selection through an HTTP proxy server and (2)load balancing. This project is divided into Part 1 and Part 2.We recommend that you work on them simultaneously(both of them can be independently tested),and finally integrate both parts together.This is a group project;you may work in groups of up to two people. This project has the following goals: ● Understand the HTTP protocol and how it is used in practice to fetch data from the web. ● Understand the DASH MPEG video protocol and how it enables adaptive bitrate video streaming. ● Use polling to implement a server capable of handling multiple simultaneous client connections. ● Understand how Video CDNs work in real life. Table of contents 1. Background 2. Getting Started 3. Part 1:HTTP Proxy 4. Part 2:Load Balancer 5. Autograder Background Video CDNs in the Real World The figure above depicts a high level view of what this system looks like in the real world.Clients trying to stream a video first issue a DNS query to resolve the service's domain name to an IP address for one of the CDN's video servers.The CDN's authoritative DNS server selects the "best"content server for each particular client based on(1)the client's IP address (from which it learns the client's geographic location)and (2)current load on the content servers (which the servers periodically report to the DNS server). Once the client has the IP address for one of the content servers,it begins requesting chunks of the video the user requested.The video is encoded at multiple bitrates.As the cient player receives video data,it calculates the throughput of the transfer and it requests the highest bitrate the connection can support (i.e.play a video smoothly, without buffering if possible).For instance,you have almost certainly used a system like this when using the default "Auto"quality option on YouTube: Video CDN in this Assignment Normally,the video player clients select the bitrate of the video segments they request based on the throughput of the connection.However,in this assignment,you will be implementing this functionality on the server side.The server will estimate the throughput of the connection with each client and select a bitrate it deems appropriate. You'lwrite the components highlighted in yellow in the diagram above(the proxy and the load balancer). Clients: You can use an off-the-shelf web browser(Firefox,Chrome,etc.)to play videos served by your CDN(via your proxy).You can simulate multiple clients by opening multiple tabs of the web browser and accessing the same video,or even using multiple browsers.You will use network throttling options in your browser to simulate different network conditions(available in both Firefox and Chrome). Video Server(s):Video content will be served from our custom video server;instructions for running it are included below.With the included instructions,you can run multiple instances of video servers as well on different ports. Proxy:Rather than modify the video player itself,you will implement adaptive bitrate selection in an HTTP proxy.The player requests chunks with standard HTTP GET requests;your proxy will intercept these and modify them to retrieve whichever bitrate your algorithm deems appropriate,returning them back to the client.Your proxy will be capable of handling multiple clients simultaneously. Load Balancer:You will implement a simple load balancer that can assign clients to video servers either geographically or using a simple round-robin method.This load balancer is a stand-in for a DNS server;as we are not running a DNS protocol,we will refer to it as a load balancer.The load balancer will read in information about the various video servers from a file when it is created;it will not communicate with the video servers themselves. After you implement the basics of the HTTP Proxy and the Load Balancer, you will integrate the two together.The proxy can query the load balancer every time a new client connects to figure out which video server to connect to. Important:IPs and Ports In the real world,IP Addresses disambiguate machines.Typically,a given service runs on a predetermined port on a machine.For instance,HTTP web servers typically use port 80,while HTTPS servers use port 443. For the purposes of this project,as we want you to be able to run everything locally,we will instead distinguish different video servers by their(ip,port)tuple.For instance,you may have two video servers running on (localhost,8000)and (localhost,8001).We want to emphasize that this would not make much sense in the real world;you would probably use a DNS server for load balancing,which would point to several IPs where video servers are hosted,each using the same port for a specific service. Getting Started This project has been adapted so that it can be run and tested on your own device, without any need for a virtual machine.Although this leads to a slightly less realism,we hope it makes development faster and easier. Note:The only configuration that cannot be tested locally is running a geographic load balancer in conjunction with a load-balancing miProxy.This willhave to occur on Mininet.However,you are able to locally test both(1)miProxy with a round- robin load balancer and (2)a geographic load balancer on its own. To get started,clone this Github repository.We are using google drive to store the video files in the Git repo.You can use this link to download the tears-of-steel video files,and this link to download the Soar with CUHK video files.The downloaded video file should be placed under the videoserver/static/videos folder.The structure of the videoserver/static should be: You can then create your own private GitHub repository,and push these files to that repo.Your repository should be shared only with your group members,and should not be publicly accessible.Making your solution code publicly accessible,even by accident,will be considered a violation of the Honor Code.You can create a private repository through the GitHub website,and add it as a remote to the cloned repository with $git init $git remote add origin [email protected]:[Your-User:Your-Repo] $git add -A $git commit -m "Initial commit" $git push --set-upstream origin main The structure of the files is as follows: Your Code As in Part 1,we will be using CMake as our build system.The top-level CMake file is at cpp/CMakeLists.txt.There are also CMakeLists.txt files in every subdirectory.These files have been filled out for you.We encourage you to take a look and see how they work.You may need to modify them if the structure of your code changes.You may not use any external packages other than the ones we provide:spdlog,cxxopts,pugixml, and boost::regex. We have also included a common folder with a few network utility functions as well as the protocol definition for communicating with the load balancer.You can(and should!) add more network utility functions and other code that can be shared between miProxy and loadBalancer into the common folder. The structure of the project is otherwise self-explanatory;your implementation for miProxy should go in the miProxy folder,and your implementation of the loadBalancer should go in the loadBalancer folder.The following commands should allow us to build your code from the base of the project: $mkdir build $cd build $cmake ../cpp $make This should result in executables build/bin/miProxy and build/bin/loadBalancer. We also encourage you to integrate CMake with your editor.For instance,VSCode has a CMakeTools extension.This enables VSCode's intellisense to properly find code dependencies,which can eliminate annoying fake syntax errors.There are many resources online for you to figure out how to do this. The two parts of the project can each be tested on their own before the integrated version is tested.You may wish to parallelize work among your groupmates;feel free to do so,but please remember that all individuals are responsible for understanding the entire project. Running the Video Server We have provided a simple video server for you,implemented in the videoserver/ directory.First,you will need to unzip some of the video files: $cd videoserver/static/videos $tar -xvzf cuhk.tar.gz $tar -xvzf tears-of-steel.tar.gz This should lead to two folders --cuhk and tears-of-steel --being created inside the videos/folder. For Python,we will be using the uv package manager.Please follow the instructions on the linked Github page to install uv on your machine. Once you have installed uv,you can navigate to the videoserver/directory and run uv sync This willdownload all necessary Python dependencies and create a virtual environment. You can then run uv run launch_videoservers.py to launch videoservers.This takes the following command line arguments ● -n|--num-servers:Defaults to 1.Controls how many video servers will be launched. ● -p|--port:Defaults to 8000.Controls which port the video server(s)will serve on.For multiple videoservers,the ports will be sequential;for instance,running the following command will launch three videoservers on ports 8000,8001,and 8002. uv run launch_videoservers.py -n 3-p 8000 Note:It is not necessary to have multiple videoservers running for the early part of this project.You willonly really need this if you want to test how well your miProxy works with load balancing and separating multiple clients. Once you launch a videoserver (e.g.on port 8000),you can navigate to 127.0.0.1:8000 (or localhost:8000)in your browser to see it.It will look something like this: You can click on the linked pages to play the videos.The first one(Tears of Steel)does not have audio,while the second one(CUHK Video)does;this can help vary your testing.The Tears of Steel video also has watermarks to indicate the bitrate of the current segment.The video server will also log helpful output to stdout that can help you debug. Note that you are currently directly accessing the video server;when testing this project,you will instead navigate to the ip:port of your running proxy,which will communicate with the video server for you. Libraries We expect you to use cxxopts for parsing command-line options,and spdlog for variable-level logging.We will require certain logs to be printed using spdlog from both the HTTP proxy and the load balancer in order to faciliateautograding and debugging. We have also included pugixml,a C++XML-parsing library and the boost::regex library in the CMake files.You do not have to use these libraries,but it will make parsing video manifest files and HTTP requests much easier.Documentation for these libraries is available online. Why not use #include ?The C++standard library's regex header is widely known to be slow and inefficient.This means that you will instead see packages like re2 or boost used in production code. We provide a script. download_deps.sh to download these libraries,all the downloaded libraries will be stored under the deps folder. ./download_deps.sh After downloading,the structure of deps folder should be: You may have to install Boost on your system.If you are on a Mac,this is very easy. Simply use Homebrew and run brew install cmake boost and you're done and ready to skip to "Setting Up the Starter Code". On Windows or Linux,installing CMake and Boost are also relatively simple.On Ubuntu /WSL,you can run sudo apt-get install cmake libboost-all-dev Part 1:HTTP Proxy Many video players monitor how quickly they receivedata from the server and use this throughput value to request better or lower quality encodings of the video,aiming to stream the highest quality encoding that the connection can handle.Instead of modifying an existing video client to perform bitrate adaptation,you will implement this functionality in an HTTP proxy through which your browser will direct requests. You are to implement a simple HTTP proxy,miProxy .It accepts connections from web browsers,modifies video chunk requests as described below,opens a connection with the resulting IP address,and forwards the modified request to the server.Any data (the video chunks)returned by the server should be forwarded,unmodified,to the browser miProxy should: 1.Run as a server on a specified port 2.Accept connections from clients,including multiple clients simultaneously 3.Connect to a video server 4.Forward HTTP requests from clients to the appropriate video server 5.Forward HTTP responses from the video server to the appropriate client 6.Measure the throughput of each video segment to each client 7.Capture video manifest file HTTP requests,returning the no-list manifest file to clients while reqeuesting the regular manifest file for itself 8.Capture video segment HTTP requests and modify the request to have the appropriate bitrate You will implement two modes of miProxy: 1.No load balancing occurs,with a single video server for all clients 2.A load balanced version,where miProxy queries your load balancer (implemented in Part 2)to figure out which video server to assign to each incoming TCP connection. Clients vs.Incoming Sockets For optimization,web browsers may open up several TCP connections for a single tab; this can lead to multiple sockets connecting to your miProxy server for a single client We will use the term "client socket"to refer to an individual socket and "client"to refer to a group of sockets of a single tab that form one logical client.
Data File Information for the 5PAHPRM3 Coursework 2024-25 This 1-page document contains details of a data file, the analysis of which forms part of the materials for the 5PAHPRM3 examination. The coursework task requires to complete a series of analysis on an analysis of a simulated replication of Experiment 1 of Wade-Benzoni et al. (2012). Wade-Benzoni K. A., Tost L. P., Hernandez M., Larrick R. P. (2012). It’s just a matter of time: Death, legacies, and intergenerational decisions. Psychological Science, 23, 704–709. You should familiarise yourself with the procedures, and results and interpretation of Experiment 1 of this paper before the attempting the coursework, to allow you to conduct and interpret the data analyses required in the worksheet and answer the questions in the coursework quiz. Details of the data file to be analysed The data are from a hypothetical direct replication of Experimental 1 of the target article, conducted among UK undergraduates. Tasks were modified to use UK English spelling and currency. The list below shows the variables in the data file. Variable name Label (description) for variable Ppt.number Participant number Gender [1 = male; 2 = female] Age Participant age, in years Group A dummy variable describing the prime and recipient grouping [1 = Death/Immediate; 2 = Death /Future; 3 = Control/Immediate, 4 = Control/Future] Prime Priming task [1 = Death; 2 = Control] Words Number of words the participant wrote during the priming task. Recipient Described aim of charity [1=Immediate needs; 2 = Future needs] Amount Amount (£) committed as donation
Assignment 1: Javascript COMP 315: Cloud Computing for E-Commerce February 2025 1 Introduction A common task when backend programming is data cleaning, which is the process of taking an initial data set that may contain erroneous or incomplete data, and removing or fixing those elements. In this assignment, you will be tested on your knowledge of JavaScript by implementing a set of functions that perform data cleaning operations on a dataset. 2 Objectives By the end of this assignment, you will: ? Gain proficiency in using JavaScript for data manipulation. ? Be able to implement various data cleaning procedures, and understand the significance of them. ? Have developed problem-solving skills through practical application. 3 Initial setup On the canvas page for this assignment, there is a template file calledData Processing.js. Upload this template file to Codegrade. Note that the function signatures and module export statements have been prepared for you. Codegrade hosts a series of unit tests that will auto mark your code, and will present you with your final mark. This allows for immediate feedback and removes any potential bias in marking. You have an unlimited number of submissions to Codegrade. For this assignment you cannot import any additional libraries, as only the File System (FS) library is allowed. The data you will be cleaning can be seen in Table 1. Data name Note id This is a unique integer identifier for each item. name Each product has a name, which for this data set will just be the type of product followed by a unique identifier integer. price This is a real value representing the cost of the product. category Each product has a general category such as Clothing or Electronics, and is saved as a string. type Each product’s type is a string that represents what sort of item it is. quantity This is an integer value that shows how many of this item is currently in stock. rating This is a real value that is a multiple of 0.5, starting from 0 and being capped at 5. This value represents the number of stars users rate the item. image link This is the link to the product in the database, and is saved in the format of “[cate- gory]/[type]/[name].jpg”. Name is all in lowercase with the spaces replaced with under- scores. Table 1: The attributes that are stored for each product 1 Data name Note category Each product has a general category such as Clothing or Electronics, and is saved as a string. type Each product’s type is a string that represents what sort of item it is. min price This number represents the minimum price for this type of product. max price This number represents the maximum price for this type of product. Table 2: The attributes that are stored for pricing each type of product 4 Data cleaning Add the following functionality to each of the functions. Do not modify the function signature or return statement. The file name variable is the string that corresponds to a JSON file name without the ‘.json’ extension. For example if the file is called test.json then file name would have test stored. The entries variable is an array of JSON objects corresponding to products. Each JSON object has the attributes listed in Table 1. The products prices variable is an array of JSON objects corresponding to the minimum and maximum price for each type of product. Each JSON object has the attributes listed in Table 2. 4.1 load JSON This function should take the file name variable and load the corresponding JSON file. Note that the ‘.json’ file extension should be added. If the file cannot be found then an exception should be thrown that says ”‘[file name].json’ cannot be found”, where [file name] is replaced with the actual file name. The input text should be converted to an array of JSON objects. If this process cannot be done, then it should throw the error ”‘[file name].json’ is not a valid JSON file” where again [file name] is replaced with the actual file name. 4.2 clean name This function should iterate through each of the products, and replace null name values with the correct name. If the name value is ‘null’ then the image link variable should be checked. If image link is not ‘null’ then the name should be extracted from that. It should replace the underscores with spaces, and each word should be capitalised. If image link is ‘null’ then the name should be taken from the ‘type’ variable. Each name is unique, and is the type value with a unique integer identifier appended to it. This identifier corresponds to the number of that type of product that have occurred up to that point. For example if the product type is ”Belt”, and it is the third ”Belt” in the product list then it’s name should be ”Belt 3”. 4.3 clean price This function takes in the entries variable, as well as the product prices variable. The price values should always be non-negative number, and should first be changed to positive. The corresponding min price and max price for this type of product should be found from product prices. If the price for the product is less than the min price value for that type of product, then it should be set as the min price value. For example if the price of Shoe4 is 1, and the min price value for Shoe is 3, then the price for Shoe4 should be set to 3. If the price for the product is more than the max price value for that type of product, then it should be set as the max price value. 4.4 clean category This function should check if the category is ‘null’, then it should use the type value to fill in the missing data. It should find other entries with the same type value, and then use this to fill in the corresponding category value. 4.5 clean type This function should check if the type is ‘null’, then it should check the name value. If the name is not ‘null’ then it should remove the unique number and use that value. If the name is ‘null’ then it should derive the name from the image link. 2 4.6 clean quantity The quantity of a product must be a non-negative value, so if the value is negative then it should be set to positive. If the value is not an integer, then it should be rounded to the nearest integer using standard ‘rounding half up’ rounding. 4.7 clean rating If the rating for a product is set to ‘null’, then it should be set to 0. The rating of a product is given as a star value between 0 and 5 stars. It is possible to give a product half a star, therefore the possible values are: 0, 0.5, 1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5. If a value does not correspond to one of these, then it should be rounded to the nearest possible value. 4.8 clean image link This function populates ‘null’ image link values using the other variables for the JSON object. This should be in the format of “[category]/[type]/[name].jpg”. The category and type values are used as they are, however the name value should all be in lowercase with spaces replaced with underscores. The ‘.jpg’ text should be appended to the end. 5 Marking The marking will be carried out automatically using the CodeGrade marking platform. A series of unit tests will be ran, and the mark will correspond with how many of those unit tests were successfully executed. Your work will be submitted to an automatic plagiarism/collusion detection system, and those exceeding a threshold will be reported to the Academic Integrity Officer for investigation regarding adhesion to the university’s policy https://www.liverpool.ac.uk/media/livacuk/tqsd/code-of-practice-on-assessment/appendix L cop assess.pdf. 6 Deadline The deadline is 23:59 GMT Thursday the 20th of March 2025. Late submissions will have the typical 5% penalty applied for each day late, up to 5 days. Submissions after this time will not be marke
Computational Modelling and Prediction: Mini Computational Project Overview: You will carry out a computational study on a molecular system of your choice, using Density Functional Theory at the B3LYP/LANL2DZ level of theory as implemented in Gaussian 16W. In this study, you will use GaussView 6W as the graphical user interface to Gaussian, allowing you to build structures, run calculations and look at results. You should investigate the structure and molecular orbitals of your chosen compound(s) and relate these to spectra and properties. The critical assessment of calculation results is key and you should try to find experimental or calculated data in the literature to compare with your results. You can use your calculations to predict different spectra (IR, NMR, UV/Vis) and might wish to explore other options (e.g. electrostatic potential maps) offered by the software. This should draw on the content covered so far in this unit (Quantum Chemistry), as well as content from the second year Core Chemistry and Practical Chemistry units, along with your knowledge from first year units. Assessment: You should present your results in a short, written report, due in week 21 for upload to Blackboard, which will count for 25% of your unit mark. Note that you will also need to complete an online quiz hosted on Blackboard as part of your training on using Gaussian and interpreting outputs, which will count for a further 5% of your unit mark and is due in week 15. Workplan and Logistics: See the separate workplan file on Blackboard. Note there are separate Office Hours/Surgery/Clinic for the Gaussian Individual Project. These are different from the office hours which support the lecture-based material. It is up to you when you do this work, but support will be provided in weekly drop-in sessions where you can discuss your calculations with postgraduate demonstrators and seek their advice about using GaussView/Gaussian, setting up calculations and interpreting the results. We recommend that you use a PC based in the School of Chemistry for this work if at all possible. The Windows Virtual Desktop can be used to view the results of completed calculations, but it will not allow you to run calculations. Note that the postgraduate demonstrators are PhD students with considerable expertise in computational chemistry, so we strongly recommend that you engage with them and join office hours for help and support. It would be sensible to do this early on so you can spend more time finalising your report. Project Design: You have encountered computational chemistry before. In the first year, you used Entos Envision to look at molecular orbitals and GaussView/Gaussian in experiment 9 (Applications of Computational Chemistry). The second-year practical chemistry unit also includes computational studies using Gaussian, in experiments B3, B8, C7 and C9. Even if you have not done these experiments yourself (or not yet), we suggest that you review them to see how computational studies can be used. When selecting your own system for study, please bear in mind the following: - We ask you to use a standard DFT approach, B3LYP/LANL2DZ. The instructions for experiment C9 show you how to set this up - These DFT calculations are computationally demanding and so we strongly suggest that you limit the size of the molecules you study to 20 atoms or fewer. In consultation with us or the demonstrators, you can stretch this to 30 atoms (we can advise you on pre-optimising structures with lower levels of theory), but please avoid looking at larger systems. In most cases, this will mean that optimisations should not take much longer than around an hour. However, some types of calculations (e.g. TD-DFT) are more demanding and again we suggest that you discuss these with your demonstrators and allow ample time for these to run. You can spend the time while calculations are running on researching the background and analysing other calculation outputs. - Interpreting calculation results can be easier with molecular symmetry and the initial training will show you some examples of this. When selecting your system, favour those with some symmetry. - You may not use any of the molecular structures and calculations described on the DLM for this project and the molecules you focus on should have been observed experimentally. As shown in experiment C9, sometimes further calculations on hypothetical structures can help our understanding and you can include these, but please have a known molecule as your central focus. - We want you to explore GaussView/Gaussian and try out different options (but see advice below!), so please be adventurous, look at the software documentation and discuss your plans and ideas with the demonstrators during the workshop and office hours. As a bare minimum, you should include a geometry optimisation and one other calculation in your report, but we would prefer to see more than that, along with a critical assessment of your results, achieved by comparing them to experimental or calculated data from the literature. We suggest you try to avoid calculating NMR spectra, periodic calculations and calculation of UV-VIS spectra using TD-DFT as these are computationally more costly and can often be problematic with the Gaussian software. Report: Your report should follow a standard structure and contain the following sections: Abstract – what was done, what are the key outcomes Introduction – set your project into a broader research context and use this to justify your choice of compound(s) and the data/properties you have selected for calculation. Computational details – these should give enough information for a trained researcher to repeat your work, meaning software, level of theory, basis set, any additional, non-default settings, all with appropriate references (you can find these in the Gaussian manual). You do not need to give a detailed account of how to build molecules and set up the calculations. A list of the keywords used is not sufficient, though, so please consult appropriate published papers to see how this might be done. Results and discussion – these should normally be integrated and presented in a single section. You should state what you expect, discuss how your own data compare to expectations, compare with external data (from experiment or calculations) and explain any differences and observations in terms of the relevant theory. Conclusions – critically assess your study overall and provide suggestions for improvement. References – use RSC format and include publications from the recent literature context Appendix – where necessary, this should include any additional data/analysis which is not used directly in the results and discussion section but could be useful to have. Separately, please also submit your *log files with your report. Please limit your report to 1500-2000 words (shorter reports are fine). Marking will focus on originality, adventure and insight as demonstrated by the chosen system, the quality of your background research and how it is used throughout the report, the choice and interpretation of data for the compounds considered, including your critical assessment, and the clarity of your writing and presentation. We would prefer to see fewer calculations which have been interpreted fully for a wellchosen system, with clear links to material you have studied and researched – try to tell a story - rather than a larger quantity of results which are only poorly understood. Make sure you check your report for clarity, flow and links between sections. Some possible ideas (amongst others, please use your imagination and we are definitely not being prescriptive here): compare calculated molecular orbital diagrams and bonding including charges with those in standard textbooks; use calculations to assess the accuracy of qualitative arguments on structure and or bonding given in standard textbooks; compare ligands, build up a cluster from say a diatomic and see how properties and bonding change with cluster size; choose a molecule with available spectroscopic data and compare with experiment. Some Possible Sources for Ideas (amongst others) Keeler and Wothers “Why Chemical Reaction Happen” and “Chemical Structure and Reactivity” Albright, Burdett and Whangbo, “Orbital Interactions in Chemistry” Fleming “Molecular Orbitals and Organic Chemical Reactions“ The Journal of Chemical Education. For spectroscopic data http:/www.uv-vis-spectral-atlas-mainz.org or a similar database at the University of Bremen: http://www.iup.physik.uni-bremen.de/gruppen/molspec/index.html
CPT206 Computer Programming for Financial Mathematics: Coursework 1 Task Specification This is the specification task sheet for the Coursework 1 assessment component of your CPT206 module. The task covers all Learning Outcomes, and has a weighting of 15% towards the final grade for this module. For this assessment, you will solve one practice exercise of your choice each week in Weeks 3, 6, 9, and 12, and report on your solution. Your task is described precisely in Section 1. Detailed submission instructions are provided in Section 2. 1 Task description In Weeks 3, 6, 9, and 12, a small number of practice exercises will be uploaded to the Learning Mall (section “CW1 materials”, Quiz “Week X exercises”). In each of these weeks, you should select one of these exercises to solve (four exercises in total), and write a report on your solution (four reports in total). The solution should be submitted to the Learning Mall Quiz, with the code and accompanying report submitted to the dedicated Learning Mall assignment page (see Section 2 for details). The report should consist of the following three sections. 1. Code explanation (15 marks). In this section you should provide a brief explanation of the different parts of your code, and detail how they meet the requirements as laid out in the exercise specification on Learning Mall. 2. Code development and testing (40 marks) . There are no limitations on tools used to help you develop the code solution. In particular, the use of generative AI is permitted for code development. In this section of the report, you should explain how you solved the coding exercise, including any use of generative AI tools or others (for example, by including screenshots of your conversation with XipuAI). You must however ensure that you have a full understanding of your code solution, so as to be able to explain it satisfactorily in Section 2 above. In this section, you should also detail the testing you performed on your code. This should include some test cases not provided in the Learning Mall exercise. Any debugging that was needed to fix your code should also be included in this section. For example, if your code initially failed to pass some of the hidden tests on Learning Mall, explain here how you solved the issue(s). 3. Personal reflection (20 marks). Finally, your report should include a personal reflection on your learning experience through completing the coding exercise. Questions that you may wish to consider could include, but are certainly not limited to, the following. What knowledge did you gain? How did solving the exercise improve your programming skills? If desirable, you may refer to specific objectives of that week’s lecture or the wider aims and learning outcomes of the course itself. Your report should be typed into e.g. a Word document, using single line spacing and a size 12pt font. If including small pieces of code to demonstrate specific aspects , you may wish to distinguish these from your report writing by using a Monospaced font such as Courrier or similar. The total length of the report should not exceed two pages. 2 Submission instructions In the dedicated “Coursework 1 submission” Assignment activity on the Learning Mall in Weeks 3, 6, 9, and 12, you should submit the following two files: • Your report, converted to a PDF file, named as “CPT206 CW1 Week{X} {StudentID}”. • The source code of your solution, in a “.java” file (or “.py” for Week 12). Keep the same file name as you used when developing the solution. The formatting and contents of your code should be identiical to the final version submitted to the Learning Mall. Marks will be awarded not just for correctness of the code, but also for code quality (25 marks in total for code). You will also submit the actual code solution to the Learning Mall weekly practice exercise Quiz. For each week, the submission deadline is Sunday, at 23:59 (China-time), of that week of teaching. So for example, for the Week 3 report, the deadline is Sunday, 9 April, at 23:59 (China-time). No late submissions will be accepted. While the use of various tools, including generative AI, is permitted in the development of the code solution, the report must be individual work. Plagiarism (e.g. copying materials from other sources without proper acknowledgement) is a serious academic offence. Plagiarism and collusion will not be tolerated and will be dealt with in accordance with the University Code of Practice on Academic Integrity. Submitting work created by others, whether paid for or not, is a serious offence, and will be prosecuted vigorously. The use of generative AI for content generation is not permitted in the report, other than the code solution presented in Section 1. Such a use would be considered in breach of the University Code of Practice on Academic Integrity, and dealt with accordingly.
Assessment Proforma 2024-25 Key Information Module Code CMT316 Module Title Applications of Machine Learning: Natural Language Processing and Computer Vision Assessment Title Coursework 2 (Group Project) Assessment Number 2 of 2 Assessment Weighting 50% Assessment Limits Part 1 Group report: Agroup project report of no more than 4500 words, a zip file with all the Python code for the group project and a README file Part 2 Individual reflection essay: no more than 1500 words. The Assessment Calendar can be found under ‘Assessment & Feedback’ in the COMSC-ORG-SCHOOL organisation on Learning Central. This is the single point of truth for (a) the hand out date and time, (b) the hand in date and time, and (c) the feedback return date for all assessments. Learning Outcomes The learning outcomes for this assessment are as follows: 1. Implement and evaluate machine learning methods to solve a given task 2. Explain the fundamental principles underlying common machine learning methods 3. Choose an appropriate machine learning method and data pre-processing strategy to address the needs of a given application setting 4. Reflect on the importance of data representation for the success of machine learning methods 5. Critically appraise the ethical implications and societal risks associated with the deployment of machine learning methods 6. Explain the nature, strengths and limitations of an implemented machine learning technique to an audience of non-specialists 7. Explain the fundamentals and modern principles of natural language processing or computer vision Submission Instructions The coversheet can be found under ‘Assessment & Feedback’ in the COMSC-ORG- SCHOOL organisation on Learning Central. All files should be submitted via Learning Central. The submission page can be found under ‘Assessment & Feedback’ in the CMT316 module on Learning Central. Your submission should consist of multiple files: This coursework consists of a group project divided into two parts with different weights: - Part (1) consists of a group report on a specific machine learning project. The final deliverable consists of a single PDF file and a zip file with the code. The deliverable includes a zip file with the code, and a written summary (up to 4500 words) describing solutions, design choices, evaluation and a reflection on the main challenges faced during development and insights gained throughout the process. - Part (2) consists of an individual reflective essay (up to 1500 words) where students reflect on the main insights gained as part of the group project. Cover sheet should also be submitted in this part. Description Type Name Part Compulsory One PDF (.pdf) file for group groupreport_[group number].pdf 1 report Part 1 Compulsory One ZIP (.zip) file containing the Python code groupcode_[group number].zip Part Compulsory One PDF (.pdf) file for cover coversheet_[student number].pdf 2 sheet Part Compulsory One PDF (.pdf) file for the individualessay_[student 2 individual essay number].pdf Part 1: The group report should be submitted in learning central (group assignment) by a nominated team member as a single PDF document and a zip file. Prior to handing in, make sure all documentation has been collected. Additional supporting material, such as sources or data may also be submitted if appropriate along with the code zip file. Any code submitted will be run in Python 3 and must be submitted as stipulated in the instructions. All team members must have seen and agreed to the final version of the submission. Make sure the report clearly mentions your group number, a list of all members of the group (with full name and student ID as on learning central), the project title, and the name of the supervisor on the title page of your report. Part 2: Individual submission for the cover sheet and individual reflective essay. Any deviation from the submission instructions above (including the number and types of files submitted) may result in a mark of zero for the assessment or question part. If you are unable to submit your work due to technical difficulties, please submit your work viae-mailto [email protected] notify the module leader. Assessment Description In this coursework, students demonstrate their familiarity with the topics covered in the module via a group project. Marks will be awarded to the individual student based on the quality of the group report and their contribution and the individual report. All students should contribute to the group projects - extenuating circumstances submitted for the spring term project period will be considered pro-rata for the contribution and for an extension on the individual essay. Part 1: Group report In Part 1, students will be allocated in groups to design a machine learning project in one specific topic. The list of all topics along with their descriptions is available in the following link: https://docs.google.com/document/d/1P8jc81L_HW3DDdZaIMfMcekrPeDYuBm- 2qTC7knbKV8/edit?usp=sharing Each group will be assigned a specific dataset and a supervisor. The task of each group consists of developing a whole machine learning pipeline that attempts to solve the task. The usage of neural networks as methods/baselines is not mandatory but will be positively assessed; the non-usage of neural methods should be properly justified. Throughout the course the groups should present their progress to their supervisor each session. Finally, the group will write a report summarizing the steps followed and the main insights gained as part of the process. As part of the group decisions, each student will be allocated to one of the following tasks: - Descriptive analysis of the dataset + Error analysis - Preprocessing + Literature review - Implementation + Results Each of these tasks will typically have two students involved (except in exceptional cases when this is not possible), who will work together in the specific task and as part of the group. The structure of the report will be decided by the group members. In the following link, students can find some guidelines to write the report, including some of the common sections that groups may want to include in their report: https://docs.google.com/document/d/1ku-K6mBH8- Wdfy_Dz_gvpReDv6knWCFxq4rsMDCrPHY/edit?usp=sharing Note: These are just guidelines and students are not forced to follow this structure. New sections may be added or adjusted if necessary. Each student will also be involved in all group activities/tasks and will be responsible for the well functioning and coordination of the team members. Deliverables The deliverables for this part include a report of no more than 4500 words and a zip file with the Python code. The code should contain three specific parts: (1) Code to get the statistics used to complement the descriptive analysis of the dataset. (2) Code to train one of the best performing models in the training set and evaluate it in the test set. This code should also include all steps for preprocessing the original dataset, if it were necessary. (3) A README file explaining how to run the code for each of the two parts. The code will not be marked separately and will only be used as a complement to assess specific parts of the report. Part 2: Individual reflection essay In Part 2, students are asked to write a reflective essay about their group projects. The individual essay must discuss your contribution to the group report and to the overall group work. You must show that you contributed to the group work, which will be determined via the individual report and the contribution monitoring, conducted by the supervisor, if it were necessary. Explain what tasks you have performed and provide evidence of your work (you may refer to the group report for the actual work/results). Discuss how you approached these tasks and how you interacted with other members, both in sharing your results and in organising the team's activities. Consider how well your existing skills were utilised and what new skills you have learned. Then reflect on your overall performance and role in the team and suggest what went well and what changes you will be making to improve (1) your performance in particular, and (2) the performance and results of methods and analyses performed as part of the project. You may also reflect on how your perspective and approach changed over time and adapted to improve your work. Note: Please indicate the information about your group (group number, project name) in a visible place at the top of your essay. The individual essay must have no more than 1500 words. It does not have to be exhaustive, but should contain good examples of what you have done and discuss key aspects. This part weighs 25% of the total marks. Assessment Criteria Criteria for each individual part is provided separately. The final mark will be obtained from a weighted sum of the two parts: Part 1 - 75%; Part 2 - 25%. The final mark for Part 1 (75% of the total marks) will result from the following items: - Descriptive analysis of the dataset + Error analysis (15%) - Preprocessing + Literature review (15%) - Implementation + Results (15%) - Student’s own allocated task from the three above (15%) - Group report as a whole, including its coherence and structure (15%) Note: In addition to the specific individual task assigned, in some cases marks might be weighted by the individual contribution in the project. This would be based on collected evidence. All main criteria carry equal weight as indicated above for your total mark and will be evaluated on the following scale: T1-1. Descriptive analysis of the dataset (7.5%) High Distinction 80%+ Thorough and insightful data exploration. Distinction 70-79% Extensive and informative data exploration. Merit 60-69% Good data exploration but misses some insightful analysis. Pass 50-59% Suitable but limited data exploration. Marginal Fail 40-49% Little and arbitrary data exploration. Fail 0-39% No or meaningless data exploration. T1-2. Result analysis (7.5%) High Distinction 80%+ Thorough and critically insightful result analysis and discussion. Distinction 70-79% Comprehensive and insightful result analysis and discussion. Merit 60-69% Good result analysis and discussion but lack of depth. Pass 50-59% General result analysis and discussion. Marginal Fail 40-49% Little result analysis and discussion. Fail 0-39% No or little meaningful result analysis and discussion. T2-1. Preprocessing (7.5%) High Distinction 80%+ Innovation and extensive pre-processing to deal with all aspects of non-ideal characteristics of the data with an aim to achieve the best performance Distinction 70-79% Extensive pre-processing to deal with major aspects of non-ideal characteristics of the data. Merit 60-69% Adequate pre-processing to prepare the data for model development. Pass 50-59% Some necessary pre-processing is conducted. Marginal Fail 40-49% Some basic but omitted necessary pre-processing. Fail 0-39% No or very little data pre-processing. T2-2. Literature review (7.5%) High Distinction 80%+ Exceptionally well articulated and critical literature review. Distinction 70-79% Extensive and critical literature review. Merit 60-69% Adequate literature review. Pass 50-59% Basic and general literature review. Marginal Fail 40-49% Superficial literature review, missed major related work. Fail 0-39% No or very little literature review.
21-259: Calculus in Three Dimensions Lecture #9 Spring 2025 The Chain Rule Recall that when y = f (x) and x = g (t) where f and g are differentiable functions, that y is also a differentiable function of t as y = f (g (t)) and thus the Chain Rule gives The same is true for multivariate functions, however there may be more than one “path” to the inde-pendent variable(s). The Chain Rule (Case I): Suppose that z = f (x, y) is a differen-tiable function of x and y, where x = g (t) and y = h(t) are both differentiable functions of t. Then z is a differentiable function of t and Example 1. Find if z = arctan(y/x), x = e t , and y = 1−e −t . Example 2. Find when t = 1 if w = xe y/z , x = t 2 , y = 1− t, and z = 1+2t. The Chain Rule (Case II): Suppose that z = f (x, y) is a differentiable function of x and y, where x = g (s,t) and y = h(s,t) are both differentiable functions of s and t. Then z is a differentiable function of s and t, and Example 3. Let z = x 2+x y3 where x = w v2+w and y = u +vew . Find and when u = 2, v = 1,w = 0. If z = f (x, y) is an implicitly-defined function by an equation of the form. F(x, y, z) = 0, then we can use the chain rule to compute the derivative with respect to x or y implicitly: Since ∂x/∂x = 1 and ∂y/∂x = 0, we have which can be solved for . Theorem: If F(x, y, z) = 0 defines z implicitly as a function of x and y, and the below partial derivatives all exist, then This can also by used to find when Example 4. Find Example 5. Find Directional Derivatives and the Gradient Vector Definition: The directional derivative of f (x, y) at (x0, y0) in the direction of a unit vector u =〈a,b〉 is If f = f (x, y, z), the directional derivative in the direction of the unit vector u = 〈a,b,c〉, then In general, the directional derivative of f at x0 in the direction of the unit vector u is Theorem: If f is a differentiable function of x and y, then f has directional derivative in the direction of unit vector u = 〈a,b〉 and Du f (x, y) = fx (x, y)a + f y (x, y)b. If f is a differentiable function of x, y and z, then f has directional derivative in the direction of unit vector u = 〈a,b,c〉 and Du f (x, y, z) = fx (x, y, z)a + f y (x, y, z)b + fz (x, y, z)c. Example 6. Find the directional derivative of the function f (x, y) = x 2 y 3 −4y at the point (2,−1) in the direction of the vector v = 2ı +5ȷ. Example 7. Find the directional derivative of the function f (x, y) = x 3 − 3x y + 4y 2 in the direction of the unit vector that makes an angle of π/6 with the positive x-axis. Example 8. Find Du f at the point (1,3,1) if Definition: Let f be a differentiable function of multiple variables x1,...,xn. The gradient of f is the vector function ∇f given by For example, if f = f (x, y), ∇f = 〈fx , f y〉, and if f = f (x, y, z), then ∇f = 〈fx , f y , fz〉. An alternate notation for ∇f is gradf . The operator ∇ (pronounced “grad” or “del”) is a differential operator, i.e., it is something that we can apply to functions to produce other functions. Specifically, in three dimensions, so ∇f is really right scalar multiplication of ∇ by f . Note: we have that Du f = ∇f ·u. Example 9. If f (x, y, z) = x sin yz, find ∇f and the directional derivative of f at (1,3,0) in the direction of v = ı +2ȷ −k. Theorem: Let f be a differentiable function of multiple variables. The maximum value of Du f (x) is |∇f (x)| and it occurs when u has the same direction as ∇f (x). Example 10. Suppose that the temperature at a point (x, y, z) in space is given by T (x, y, z) = 80/(1+ x 2 +2y 2 +3z 2 ) 2 , where T is measured in degrees Celsius and x, y, z are in meters. In which direction does the temper-ature increase the fastest at the point (1,1,−2)? What is the maximum rate of increase? The gradient gives the direction of maximum increase of a function at a point. Evaluated at x0, the vector ∇f (x0) is orthogonal to the level curves/surfaces of f that pass through the point x0. Example 11. Find the equations of the tangent plane and the nor-mal line at the point (−2,1,−3) to the ellipsoid
ApEc 3002 Spring 2025 Cost Function Estimation (CFE) Memo Lab You’re on the job with the online grocery ordering/delivery service. With plans to expand to new areas of the Metro and the number of orders per week growing in the areas you currently serve. Your boss has asked you to analyze how expansion will affect the cost side of the business. Over the past six months, the average revenue per order has remained constant at approximately $105. Only recently, however, have combined average costs for warehouse and delivery operations fallen below $100 on average. The investor group that has funded the start-up of the business has expressed concern about the ability of the business to cover costs and provide them with some return on their investment. You’ve gone to company records for data you can use in your analysis. It’s saved in an Excel worksheet named APEC3002 Memo Lab CFE.xlsx. In addition to data on orders per week (labeled Orders in the worksheet), you’ve asked accounting to provide data on weekly costs. They maintain and have provided cost records on two distinct aspects of your company’s operations. One series of cost figures is for warehouse operations (labeled Warehouse Variable Cost in the worksheet). This includes labor and supply costs for all activities related to ordering product and assembling grocery orders for customers, as well as the cost of goods sold. The second series of cost figures is for delivery of orders to customers’ homes (labeled Delivery Variable Cost in the worksheet). This includes labor for drivers and dispatchers, fuel, and other vehicle expenses. Because these two aspects of the overall business may be affected differently by expansion, you’ve decided to examine the cost-output relationships for them separately. Since the period for the analysis is relatively short and all the data are from the same operation, you have decided to ignore prices in your analysis. You’ll simply estimate parameters for two variable cost functions that are simple relationships between cost and output: VCWH = fWH (Orders) where VCWH stands for Warehouse Variable Cost VCDEL = fDEL (Orders) where VCDEL stands for Delivery Variable Cost You can choose the functional form. Cobb-Douglas and polynomial are good candidates. In your write-up (which should be a memo addressed to your boss), you should describe the basic features of your analysis including the purpose and your data sources, explain your choice of functional form, and present your estimation results. The real issue however is – how expansion from nearly 3,500 to as many as 7,000 orders per week will affect costs in the warehouse and for deliveries. Are there economies or diseconomies of scale for these activities? Will average variable costs increase or decrease for these two activities with expansion? What will happen to overall average variable costs per order – i.e., the sum of average variable costs for the two activities? What do your findings suggest about the ability of the business to cover variable costs as your service expands? Should the business expand? Grading Rubric for Memo Lab mmary ofpurposeofthe analysis.1098760Summary Findings and Reco at supporta specific recommendation.20181614120AnalysisDescription ofdata.1098760Explanation ofanalysis.1098760Performed correctly.1098760Presentation ofprimary results.1098760Economic interpretation andimplications ofprimary results in thecontext ofrecommendatio figuresandtablesareself two(orthree) significant digits,andparagraphs are clearly delineated109876Readabilitye.g., , ,
EENG20005 – Electrical Energy Conversion and Supply Example Sheet 1 - Review of Components and calculations 1. Calculate the total impedance presented to the supply for the following circuits in both cartesian and polar form, assuming a 50 Hz supply: 2. For the circuits above, calculate the current flowing in polar form. in the circuit when connected to a 12 V, 50 Hz supply. 3. Considering circuit 1 (c) above, when the supply frequency increases, what happens to the phase and magnitude of the current?
Principles of Law (BUS2013) Mid-term Test, Semester 2 of 2020-2021Academic Year Part A Multiple Choice Questions (20 marks in total, each multiple choice question carries 1 mark) Answer ALL the questions in this Section A. 1. Contract law is all of the following, except: A. Private law B. Substantive law C. Civil law D. Administrative law 2. Which has the highest authority wherever there is a conflict among the following sources of law? A. Rules of equity B. Statutory law C. Basic Law D. Common law 3. Which of the following country does not have a common law legal system? A. England B. United States C. Japan D. New Zealand 4. The Doctrine of Precedent refers to: A. the jurisdiction of a court to resolve disputes. B. the hierarchical system of common law courts in which a decision of the higher court binds a lower court. C. the principle of judicial independence. D. None of the above 5. What is obiter dictum? A. Said by the way B. Reason for reaching the decision C. Stand by previous decisions D. The court’s finding 6. Which of the following is true? A. The decision made by the magistrate has binding force B. The decision made by the Small Claims Tribunal has binding force C. Common law country’s court decision bound other common law country D. The District Court decision binds the Small Claims Tribunal 7. Which of the following is NOT automatically binding in Hong Kong courts? A. An English Case in 1899 B. An English Case in 1999 C. A Hong Kong Case in 1996 D. A Hong Kong Case in 2020 8. To win in a civil case: A. the plaintiff’s claim must be more probable than the defendant’s claim B. the plaintiff’s claim should be equally probable to the defendant’s claim C. the plaintiff’s claim must be totally certain D. the plaintiff’s claim must be reasonable 9. Which is NOT true about the dispute resolutions as an alternative to sue in court? A. Decisions of the arbitrator are final and binding on the parties. B. In both mediation and arbitration, neutral third parties assist in settling disputes. C. There are no differences between arbitration and mediation. D. The parties must submit their disputes to arbitration, if they have signed a contract with an arbitration clause. 10. Which statement is the correct description of the ejusdem generis rule? A. Words must be given their literal and grammatical meaning. B. A judge considers what mischief the legislation was intended to prevent. C. General words mean the same kind of thing as the specific words. D. To express one thing is by implication to exclude all others. 11. Norm offers to sell his horse to Gary for $1,000. Gary states that he accepts the offer provided Norm includes a month of riding lessons. What’s the legal status of Gray’s statement? A. counter-offer B. acceptance C. offer D. implied acceptance 12. Dexter is considering purchasing from Lisa a vacant lot and opening a fast-food restaurant. However, he needs a month to obtain his financing. How may Dexter secure an enforceable offer to purchase the lot? A. requirement contract B. firm offer contract C. option contract D. security contract 13. Which of the followings is False? Revocation must be: A. Communicated to the offeree. B. The communication need not be made by the offeror personally. C. The revocation must be given before acceptance. D. If there is a deadline in the offer, the offeror could not revoke it before the deadline. 14. Tisha promises to pay the apartment rent due under her current lease if the landlord constructs covered parking for her. The landlord agrees, Tisha’s promise to pay rent____. A. provides consideration for the landlord’s agreement. B. is not consideration for the landlord’s agreement. C. provides consideration only to the extent that the landlord has begun construction. D. is not consideration but rather a firm offer. 15. Historically, a contract under seal ____. A. requires no consideration. B. requires monetary consideration only. C. requires equal value consideration. D. requires all parties to swear to perform. the contact. 16. Promissory estoppel involves ____. A. principles of equity B. an analysis of the terms of contract between the parties C. an analysis application of what is customary D. an analysis application of federal legislation 17. For a commercial agreement, there is a strong legal presumption about the intention of the contracting parties. Which of the following statement best describe the legal presumption? A. Only the party initiating the offer of the contract has the intention to create a legally binding relationship. B. The intentions of the contracting parties have to be proven by the evidence adduced by the individual parties. C. All the contracting parties have the intention to create a legally binding relationship. D. All the contracting parties have no authority to create a legally binding relationship. 18. The meaning of ‘necessaries’ in the Sale of Goods Ordinance ____. A. is the same to all minors B. varies according to local standards C. varies according to the age of the minor D. varies according to the minor’s living standard 19. Leo, age 16, has bought a pair of sports shoes from Jane, the boss of a sports shop, and taken them home. Jane sold the shoes to Leo for $2,900 while most shops are selling the same shoes for $900. After a complaint made by Leo’s father, Leo must pay ____. A. $2,900 B. $1,050 C. $900 D. nothing if he does not affirm the contact 20. In relation to mental incapacity, which ONE of the following statements is TRUE? A. A contract entered into by a mentally disturbed person is void if the other party knew of the incapacity. B. A mentally disturbed person can enter into a binding contract for the purchase of necessaries. C. A mentally disturbed person never has capacity to enter into a legally binding contract. D. Mentally disturbed persons are always bound by the contracts they enter into. Part 2 Case Questions ANSWER TWO questions out of three, each question carries 40 marks, making a total of 80 marks. Do not answer with just a “yes” or “no”. For each question, state the facts, and then apply the law to the facts in order to arrive at a conclusion to each question. Question 1 Cathy wanted to sell her Cartier watch, which she bought two years ago, and, subsequently on 1 April 2021 advertised to sell in a local fashion magazine for $18,000. The advertisement also contained Cathy’s mobile phone number. In response to the advertisement, Mary called Cathy and made an appointment to see the watch on 3 April. Mary offered to buy the watch for $14,000. Cathy insisted on receiving $17,000 for the watch. On 4 April, Cathy sent a letter to Mary offering the watch for $16,800, indicating that the offer would be kept open until 5:00 pm on 10 April. Mary received the letter on 5 April. In 6 April afternoon, Mary met Loretta in a shopping mall. Loretta told Mary that, Betty, a distant relative of Cathy, had bought the watch earlier on the day from Cathy for $16,900, and that the offer previously made to Mary had been withdrawn. Loretta was a common friend of Cathy and Mary. Mary then wrote a letter to Cathy accepting her offer of $16,800. Mary posted the said acceptance letter in the evening of 6 April. Cathy received the letter on 8 April, and replied immediately by mail, indicating that, “I have already sold my Cartier watch to a distant relative of mine”. Requirements: a) Discuss whether or not Mary could successfully sue Cathy for breach of contract on the above facts. (32 marks) b) Suppose that in Cathy’s letter to Mary dated 4 April offering the watch for $16,800, Cathy provided her email address and indicated that Mary could reply by email. After hearing from Loretta earlier on the day that the Cartier watch had already been sold to Betty, Mary accepted the said offer of $16,800 by sending an email on 6 April, which reached Cathy’s email address as specified, in the evening of 6 April. Would you change your answer to a) above? (8 marks) Please explain your answers to Parts a) and b) above with reference to relevant case law and legislation. Question 2 The London School of Easy (LSE) School of Law is a leading law school in London. In academic year 2020/2021, the LSE suffers a serious budget deficit because many international students admitted refused to attend because of the COVID-19. Kristen is a student attending the course on campus in LSE. As LSE wanted to promote in China, Kristen acted as a school representative voluntarily to hold promotion activities in top schools of China in January 2021, includes the school she got her bachelor degree, Hui Tong University. For Kristen’s effort, lots of outstanding students from Hui Tong University applied and accepted the offers from LSE for the coming academic year. On 1 April, 2021, Simon, an admission director of LSE, said, “Kristen, the school will give you GBP10,000 for appreciating your help!” However, Simon broke his promise on 2 April, 2021, and said that it was just a joke for “April Fool”. a) Simon claims that Kristen’s work done does not deserve GBP10,000, but GBP100, the market price based on the average salary of student helpers. Advise Kristen. Explain your answer by what contractual element Kristen shall prove in order to claim the money under contract law (10 marks). b) Why Simon’s argument is not reasonable? (5 marks) c) As Simon’s argument in section (a) is not sufficient, is there any better argument he may rely on? Explain your answer. (10 marks) d) In 2020, Kristen received offers from LSE and “School of A child on the Street” (SOAS), another top law school in London. Kristen chose LSE instead of SOAS because the student handbook of the LSE stated, “Students of LSE may be paid student helpers”（ ， offer accept， estoppel）. She wanted to earn some money during her study. Could Kristen rely of promissory estoppel based on the LSE student handbook? Explain promissory estoppel（ ， ） and provide 2 reasons to support your conclusion. (15 marks) Question 3 Part A Robert Chan was interested to buy an apartment from Big Name Limited (BNL). Two weeks ago, Robert emailed to William Sung, the sales manager of BNL, asking for the selling price of the apartment. William replied by email that, subject to contract（ Acceptance， binding effect， 「 」）, the price of the apartment was 3.5 million dollars. After receiving the email, Robert replied that the price was acceptable and decided to buy the apartment at 3.5 million dollars. However, two days later, William emailed Robert again and stated that the actual price of the apartment should be 4 million dollars. Could Robert have formed a contract to buy the apartment at 3.5 million dollars? (20 marks) Note: Students should not discuss the problem in terms of offer and acceptance. [legal intention] Part B The price of the apartment had been agreed at 4 million dollars. William then invited Robert to have a business dinner to discuss the details of the sale contract of the apartment. During the dinner, William offered a bottle of brandy and a bottle of whisky to Robert. After drinking a lot of brandy and whisky, Robert was obviously a drunken man. At that time, William took out a prepared sale contract of the apartment and the sale price was 5 million dollars. William told Robert that the price of the apartment was cheap. Robert signed the contract without reading the details. On the next day, Robert read the contract and found out that the price was not 4 million dollars. He has decided not to buy the apartment. （sell of goods ，goods ） Could Robert rescind the sale contract? (20 marks) Note: Students should not discuss the problem in terms of misrepresentation. [Intoxicated person]
MESF5450 Spring 2024/2025 Homework #1 1. The probability W(n) that an event characterized by a probability p occurs n times in N trials was shown to be given by the binomial distribution Consider a situation where the probability p is small (p ≪ 1) and where one is interested in the case n ≪ N. (Note that if N is large, W(n) becomes very small if n → N because of the smallness of the factor pn when p ≪ 1. Hence W(n) is indeed only appreciable when n ≪ N). Several approximations can then be made to reduce the above equation to a simple form. (a) Using the result ln(1 - p) ≈ -p, show that (1-p)N-n ≈ e-Np (b) Show that N!/(N - n)! ≈ Nn (c) Hence shows that the above equation reduces to 2. A number is chosen at random between 0 and 1. What is the probability that exactly 5 of its first decimal places consist of digits less than 5? 3. Consider the random walk problem with p = q and let m = n1 – n2 denote the net displacement to the right. After a total of N steps, calculate the following mean values: m , m2 , m3 and m4 . 4. Derive the binomial distribution in the following algebraic way, which does not involve any explicit combinational analysis. One is again interested in finding the probability W(n) of n successes out of a total of N independent trials. Let w1 = p denote the probability of a success, w2 = 1 –p = q the corresponding probability of a failure. ThenW(n) can be obtained by writing Here, each term contains N factors and is the probability of a particular combination of successes and failures. The sum over all combinations is then to betaken only over those terms involving w1 exactly n times, i.e., only over those terms involving w1n . By rearranging the sum of Eq. (1), show that the unrestricted sum can be written in the form. Expanding this by the binomial theorem to show that the sum of all terms in Eq. (1) involving w1n, i.e., the desired probability W(n), is then simply given by the one binomial expansion term that involves w1n . 5. Two drunks start out together at the origin, each having an equal probability of making a step to the left or right along the x-axis. Find the probability that they meet again after N steps. It is to be understood that the men make their steps simultaneously.
Summary Assignment name: Living the Future of Work Course code: OENG1235 Weighting: 25% Word count: 900 maximum Due date: Week 4, Sunday 30 March @23:59 (Melbourne time) In this task you will identify (a) the trends driving change in future of work over the span of your career, such as the impact of Al; and (b) personal strengths, skills and strategies for a resilient, vibrant and fulfilling future.
COMM1100 Business Decision Making - 2025 General Course Information Course Code : COMM1100 Year : 2025 Term : Term 1 Teaching Period : T1 Course Details & Outcomes Course Description This is the frst course in the Integrated First Year of the Bachelor of Commerce, which introduces you to business decision making. You will learn about economic, corporate responsibility and legal principles to understand what organisational actors need to consider and what actions they might take. Fundamental economic principles inform decision makers to ask and answer questions about how the economy works, and how these principles infuence the decisions that individuals and organisations make. Core legal principles guide decision makers to protect value for owners and other stakeholders and protect both managers and organisations from public and private legal actions arising from their decisions. Corporate responsibility principles direct decision makers to meet the organisation’s responsibilities to a range of stakeholders Course Aims COMM1100 gives students an understanding of the interplay of economic, legal, and corporate responsibility principles in business decisions. Students learn to identify relevant economic models and analyse how they are used in business decisions; identify legal issues that arise in commercial situations and analyse how they infuence business decisions; and identify the key features of corporate (social, environmental) responsibility, and how the business world can contribute to the greater good. Relationship to Other Courses COMM1100 is a prerequisite for COMM1150 Global Business Environments. Course Learning Outcomes Course Learning Outcomes Program learning outcomes CLO1 : Explain the interplay of economic, legal, and corporate responsibility principles in business decisions • PLO1 : Business Knowledge CLO2 : Identify appropriate economic models and analyse how they are used in business decisions • PLO1 : Business Knowledge • PLO2 : Problem Solving CLO3 : Identify legal issues that arise in commercial situations and analyse how they infuence business decisions • PLO1 : Business Knowledge • PLO2 : Problem Solving CLO4 : Identify the key features of corporate (social, environmental) responsibility, and how the business world can contribute to the greater good • PLO1 : Business Knowledge • PLO5 : Responsible Business Practice CLO5 : Apply appropriate search strategies to research and summarise relevant and valid information from a range of suitable sources • PLO3 : Business Communication Course Learning Outcomes Assessment Item CLO1 : Explain the interplay of economic, legal, and corporate responsibility principles in business decisions • Tutorial participation • Problem Set • Case study analysis • Final exam CLO2 : Identify appropriate economic models and analyse how they are used in business decisions • Tutorial participation • Problem Set • Case study analysis • Final exam CLO3 : Identify legal issues that arise in commercial situations and analyse how they infuence business decisions • Tutorial participation • Problem Set • Case study analysis • Final exam CLO4 : Identify the key features of corporate (social, environmental) responsibility, and how the business world can contribute to the greater good • Tutorial participation • Problem Set • Case study analysis • Final exam CLO5 : Apply appropriate search strategies to research and summarise relevant and valid information from a range of suitable sources • Tutorial participation • Case study analysis • Final exam
ACCT5907 Quiz Answer Template: Skill1: Filter and identify relevant information that is likely to move the price of the company’s stock. (1) What one (1) information in the Earnings Announcement could be considered good news to investors? Give your reason. (1 mark) (2) What one (1) information in the Earnings Announcement could be considered bad news to investors? Give your reason. (1 mark) (3) Prediction: what is the most important item of information from the earnings announcement that will help investors predict the future performance/result of the company, and why? (1 mark) Skill2: Compare performance with respect to relevant benchmarks, e.g. year-on-year, sequential and expectations. (4) What is the year-on-year change in net income for the quarter? (1 mark) (5) What is the quarter-on-quarter change in net income for the quarter? (1 mark) Note: 1 mark for 1 point is the standard for this course. Skill3: Conduct analysis using the Financial Reporting and Earnings Quality (FREQ) framework: Using the Financial Reporting and Earnings Quality (FREQ) framework, analyse and identify ONE issue (only). [Note: giving more than ONE gives me the impression that you do not know the answer and are just throwing darts at the target, and will get zero marks because the explanation, inference and adjustment flows from your ONE answer and may not logically follow if you give more than ONE.] (6) FREQ label: principles of financial reporting/ earnings quality addressed (1 mark) (7) FREQ explanation: (1 mark). Note that both answers to (1) and (2) must be correct and consistent to earn these marks. (8) INFERENCE: how to detect & understand the problem. (1 mark) (9) ADJUST: if the information was available, adjust the information and calculate the figure; if the information is not available describe the steps and the information you would need to adjust the relevant figure, or to obtain the relevant information. (1 mark) (10) RESPONSE: how to respond to the problem. (1 mark) [Do not worry if in typing in your answers, you change the length of this document. This was original designed for in-class hardcopy handwriting and the lines and space was to guide students on how much to write. I do not expect your answers to be very long and only expect at most 3 lines of writing for each mark to be earned. You may write in bullet-points.]
EENG20005 – Electrical Energy Conversion and Supply Example Sheet 2 - AC Theory 1. Sketch two cycles of a 50 Hz sinusoidal voltage waveform. with an RMS of 240 V. Mark on the sketch the relative positions of the half cycle average, rms and peak values of the waveform, and indicate the period of the waveform, with their values. 2. Calculate the average and RMS values for the following waveforms: (a) Sawtooth waveform. (b) Square waveform. (c) Discontinuous triangular waveform. (Hint: Move the start of the time period to beginning of the waveform.
CIT 593 – Module 07 Assignment Operating System, IO Assembly Instructions Assignment Overview In this assignment, you will continue programming in LC4 Assembly. We will be working in the Operating System portion of memory, so you will learn about TRAPs and memory-mapped devices. Learning Objectives This assignment will cover the following topics: ● Work with TRAPS and the Operating System ● Implement an Operating System ● Work with the Keyboard, Display, and Video devices ● (optional) Work with the Timer device Advice ● Start early ● Ask for help early ● Do not try to do it all in one day Getting Started Codio Setup Be sure to open Codio from the Codio Assignment page in Canvas. This is necessary to link the two systems. Refer to the Module 06 Instructions for details about how Codio works. Run user_echo.asm in PennSim 1. From the File Tree, click on os.asm. This will open a new Codio tab named os.asm containing the contents of the file. a. Review the contents of the file. This is the basic framework for the operating system, which you will be writing for this assignment. b. Look in the TRAP vector table for the line JMP TRAP_PUTC Notice how this is the second instructions is after the .ADDR x8000 directive. ● When loaded into PennSim, this instruction will be placed at address x8001. ● We can call TRAP_PUTC by using the TRAP instruction with offset x01. This will be done later in user_echo.asm. c. Scroll down (approximately 115 lines or so) until you reach the lines that read: .CODE TRAP_PUTC This label marks the start of the PUTC TRAP (an OS subroutine) d. When Penn Sim executes JMP TRAP_PUTC instruction from the vector table, the program counter will be advanced to the address labeled by TRAP_PUTC e. Further examine the code that follows; this is the operating system subroutine (a TRAP) called TRAP_PUTC. f. Notice that this is the program we created in lecture to write one character to the ASCII display device on the LC4. 2. From the File Tree, click on user_echo.asm. a. This file is a program to test some of the operating systems TRAPs in os.asm b. Scroll down to about the 24th line, look for the lines that read: CONST R0, x54 TRAP x01 c. The CONST instruction places the number 0x54 (which represents the letter 'T' in ASCII code) into R0. This serves as the argument to the TRAP_PUTC trap. You can look up the ASCII code mappings in the Resources section. d. The TRAP x01 instruction sets PC to x8001, and also sets PSR[15]=1 (OS mode). TRAP x01 is TRAP_PUTC, so this TRAP call will have the effect of outputting a 'T' to the LC4’s ASCII display. e. Examine the rest of this program, you’ll see that its purpose is to output Type Here> on the LC4 ASCII display. 3. From the File Tree, click on user_echo_script.txt. This opens a new Codio tab displaying the script. contents. a. This file is the PennSim script. that assembles and loads os.asm and user_echo.asm b. Look carefully at the contents and compare how it differs from your last HW. Notice how it assembles and loads both os.asm and user_echo.asm 4. Open a PennSim Window and launch PennSim from the Codio command line. Refer to the Module 6 instructions if you need a refresher on how to do this. 5. Go to the command line in the Controls section and enter script. user_echo_script.txt 6. Press the Step button and carefully go line by line until you see the letter 'T' output to the screen. a. Carefully watch how you start in user_echo.asm’s code (in user program memory) and then with the call to TRAP, you enter into OS program memory. b. Understanding this process is crucial to understanding, writing, and debugging this assignment. 7. Finally, press the Continue button to see PennSim run the program until it encounters the END label. 8. Make certain you understand how these files work together before beginning the assignment. Starter Code We have provided some starter files. You will need to modify some files and generate completely new files to succeed in this assignment. os.asm - contains the operating system framework, and the TRAP_PUTC example from lecture. Your will write the remaining TRAPs. user_echo.asm - demo program from lecture user_echo_script.txt - demo script. file for user_echo example PennSim.jar - Runs your programs, debugging, etc. Requirements General Requirements ● Your script. files MUST contain all the necessary commands to assemble and load your programs, as shown in lecture and the multiply example from Module 6. ● Your programs MUST complete the requirements of the problem when loaded into PennSim and clicking the Continue button. ● Your programs MUST NOT throw any Exception unless otherwise noted. ● Your programs MUST be written in LC4 Assembly. ● You MUST comment critical sections your code so we can grade it. This will also help with partial credit. ● You MUST use END as the label that indicates the end of the program. ● You MUST submit to Codio and Gradescope as outlined in the Submission section. ● You SHOULD do all the work in Codio. Do not attempt to run these programs locally. TAs will not assist you if you are trying to do the projects outside of Codio. o Codio provides a standard environment to ensure consistent functionality. o Codio backs up your code. You can restore deleted/modified files by going to Tools->Code Playback. o TAs can login and view your code for asynchronous debugging. o Different operating systems handle endianness differently. Your submission MUST work in the Codio environment, which is where we will be performing all tests. Part 1: Echo with TRAP_GETC/TRAP_PUTC ● You MUST use the traps TRAP_GETC and TRAP_PUTC. ● You MUST implement the traps in os.asm, write the test program in user_echo.asm, and provide a working script. user_echo_script.txt. ● TRAP_GETC MUST take no arguments as input and return the read-in character in R0. ● TRAP_PUTC MUST take a single argument in R0 (the character to display) as input and not return any value. ● Your test program MUST call these two traps in a loop, to echo the user keystrokes to the display. ○ It MUST break out of the loop when the user presses the enter key. Part 2: Print Strings with TRAP_PUTS ● You MUST implement and use the trap TRAP_PUTS. ● You MUST implement the traps in os.asm, write the test program in user_string.asm, and provide a working script. user_string_script.txt. ● TRAP_PUTS MUST: ○ take a single argument in R0 (the address of the start of the string to display) as input and not return any value. ○ check that the provided argument is a valid User Data Memory address. ○ If the address is not valid, immediately return without attempting to print the string. ○ print the entire string to the display; that is each character from the starting address through the entire array up to but not including the NULL terminator. ● Your test program MUST: ○ use .FILL to pre-load the NULL-terminated string I love CIT 593 into User Data Memory, starting at address x4000. ○ print this string to the display by populating R0 with the starting address and then calling TRAP_PUTS with the appropriate Trap Number from the Trap Vector Table Part 3: Get Strings with TRAP_GETS ● You MUST implement the trap TRAP_GETS and use the traps TRAP_GETS and TRAP_PUTC. ● You MUST implement the traps in os.asm, write the test program in user_string2.asm, and provide a working script. user_string2_script.txt. ● TRAP_GETS MUST: ○ take a single argument in R0 (the address to start storing the string) as input and return the length of the string (not including the NULL terminator) in R1. ○ check that the provided argument is a valid User Data Memory address. ○ If the address is not valid, immediately return without attempting to get a string. ○ continue to read characters entered by the user until the user presses the enter key. ○ store each character typed by the user up to but not including the enter key and add the NULL terminator at the end of the string. ● Your test program MUST: ○ call TRAP_GETS with address x2020. ○ read the arbitrary user string and store it into Data Memory. ○ Print the text: Length = X using TRAP_PUTS for the Length = portion and TRAP_PUTC for X, where X is the length of the string. ○ You MAY assume that the strings will be less than 10. ○ Call TRAP_PUTS with address x2020 to print the string previously entered by the user. Part 4: Draw Rectangles with TRAP_DRAW_RECT ● You MUST implement and use the trap TRAP_DRAW_RECT. ● You MUST implement the traps in os.asm, write the test program in user_draw.asm, and provide a working script. user_draw_script.txt. ● TRAP_DRAW_RECT MUST: ○ take five arguments: ○ R0 - x-coordinate of the upper-left corner of the rectangle, the horizontal distance from (0,0) ○ R1 - y-coordinate of the upper-left corner of the rectangle, the vertical distance from (0,0) ○ R2 - the horizontal length of the rectangle ○ R3 - the vertical width of the rectangle ○ R4 - the color of the rectangle ○ check the bounds of the rectangle compared to the video display ○ if the starting coordinates are outside the video display, immediately return without attempting to draw any part of the rectangle. ○ if the starting coordinates are inside the video display, but the values for length or width would cause the rectangle to be drawn outside the video display, immediately return without attempting to draw any part of the rectangle. ○ draw the rectangle with the appropriate color, filling all interior pixels ● Your test program MUST call TRAP_DRAW_RECT for the following rectangles: ○ a red rectangle with starting coordinates (50,5), length 10, and width 5 ○ a yellow rectangle with starting coordinates (10, 10), length 50, and width 40 ○ a blue rectangle with starting coordinates (120, 100), length 27, and width 10 Extra Credit: Get a Character Within a Time Limit with TRAP_GETC_TIMER ● You MUST implement and use the traps TRAP_GETC_TIMER and TRAP_PUTC. ● You MUST implement the trap in os.asm, write the test program in user_string_ec.asm, and provide a working script. user_string_ec_script.txt. ● TRAP_GETC_TIMER MUST ○ take a single argument in R0 (the desired time to wait for a character) as input. ○ if a key is pressed within the time limit, return the entered character in R0. ○ if a key is not pressed within the time limit, return NULL in R0. ● Your test program MUST: ○ use a time limit of two seconds. ○ print the key that was entered within the time limit using TRAP_PUTC, or print nothing if a key was not entered. Extra Credit: Reset Starting Coordinates When Out of Bounds ● You SHOULD make a copy of your TRAP_DRAW_RECT trap before working on this extra credit, in case you don't get it working before the submission deadline. ● You MUST modify TRAP_DRAW_RECT. It MUST follow the original requirements, except: ○ If the starting coordinates are outside the video display, it MUST reset the starting coordinates to (0,0) and draw the rectangle starting here instead, using the original length/width values. ○ It MUST follow the Wrap the Rectangle Horizontally extra credit exception if attempting both extra credit options. Extra Credit: Wrap the Rectangle Horizontally ● You SHOULD make a copy of your TRAP_DRAW_RECT trap before working on this extra credit, in case you don't get it working before the submission deadline. ● You MUST modify TRAP_DRAW_RECT. It MUST follow the original requirements, except: ○ If the rectangle's horizontal length would take it outside the video display, it MUST wrap around the display and continue drawing the rectangle from the left side of the display at the same vertical width. Do not wrap rectangles if they go outside video memory vertically. ○ It MUST follow the Reset Starting Coordinates When Out of Bounds extra credit exception if attempting both extra credit options. Suggested Approach This is a suggested approach. You are not required to follow this approach as long as you follow all of the other requirements. High Level Overview Work on one problem at a time. 1. Review the starter code to see how the different files work together. This is critical for succeeding in this assignment. TRAP_PUTC is already written for you and you need to understand how it works. 2. Implement user_echo.asm, using TRAP_GETC and TRAP_PUTC. 3. Complete the TRAP_PUTS implementation in os.asm. 4. Implement user_string.asm using TRAP_PUTS. 5. Complete the TRAP_GETS implementation in os.asm. 6. Implement user_string2.asm using TRAP_GETS, TRAP_PUTS, and TRAP_PUTC. 7. Review the implementation of TRAP_DRAW_PIXEL. 8. Complete the TRAP_DRAW_RECT implementation in os.asm. 9. Implement user_draw.asm using TRAP_DRAW_RECT. 10. (optional) Attempt the extra credits.
BUS 1013 Individual Assignment 1 Business, Entrepreneurship, and Innovation (BEI) Semester 2,Academic Year 2024/2025 Task: Write a 5-page report on The economic potential of generative AI: The next productivity frontier. Conduct research and find pieces of news reports about the economic, geopolitical, and other effects of Gen AI on ANY industries and propose a plan for the policy makers and industry leaders to shape that industry in the future Background: Artificial intelligence (AI) has permeated our lives incrementally, through everything from the tech powering our smartphones to autonomous-driving features on cars to the tools retailers use to surprise and delight consumers. As a result, its progress has been almost imperceptible. Clear milestones, such as when AlphaGo, an AI-based program developed by DeepMind, defeated a world champion Go player in 2016, were celebrated but then quickly faded from the public’s consciousness. Generative AI applications such as ChatGPT, GitHub Copilot, Stable Diffusion, and others have captured the imagination of people around the world in away AlphaGo did not, thanks to their broad utility—almost anyone can use them to communicate and create—and preternatural ability to have a conversation with a user. The latest generative AI applications can perform. a range of routine tasks, such as the reorganization and classification of data. But it is their ability to write text, compose music, and create digital art that has garnered headlines and persuaded consumers and households to experiment on their own. As a result, a broader set of stakeholders are grappling with generative AI’s impact on business and society but without much context to help them make sense of it. The speed at which generative AI technology is developing isn’t making this task any easier. ChatGPT was released in November 2022. Four months later, OpenAI released a new large language model, or LLM, called GPT-4 with markedly improved capabilities. Similarly, by May 2023, Anthropic’s generative AI, Claude, was able to process 100,000 tokens of text, equal to about 75,000 words in a minute—the length of the average novel—compared with roughly 9,000 tokens when it was introduced in March 2023. And in May 2023, Google announced several new features powered by generative AI, including Search Generative Experience and a new LLM called PaLM 2 that willpower its Bard chatbot, among other Google products. To grasp what lies ahead requires an understanding of the breakthroughs that have enabled the rise of generative AI, which were decades in the making. For the purposes of this report,we define generative AI as applications typically built using foundation models. These models contain expansive artificial neural networks inspired by the billions of neurons connected in the human brain. Foundation models are part of what is called deep learning, a term that alludes to the many deep layers within neural networks. Deep learning has powered many of the recent advances in AI, but the foundation models powering generative AI applications are a step-change evolution within deep learning. Unlike previous deep learning models, they can process extremely large and varied sets of unstructured data and perform. more than one task. Foundation models have enabled new capabilities and vastly improved existing ones across abroad range of modalities, including images, video, audio, and computer code. AI trained on these models can perform several functions; it can classify, edit, summarize, answer questions, and draft new content, among other tasks. All of us are at the beginning of a journey to understand generative AI’s power, reach, and capabilities. This research is the latest in our efforts to assess the impact of this new era of AI. It suggests that generative AI is poised to transform. roles and boost performance across functions such as sales and marketing, customer operations, and software development. In the process, it could unlock trillions of dollars in value across sectors from banking to life sciences. *(source from: McKinsey & Company, 14, June 2023) Article provided by Dr. Suchan Luo Please Note: • Your analysis should be supported with enough data and references to prove that you have conducted sufficient research in this area. • You should use the concepts from the textbook and class lectures of this course and use additional materials from other books and classes to demonstrate your mastery in the subject area. • Use the references from recent news and industry analysis and provide your logical arguments to explain how this industry can get rid of this adverse effect. • You will be unable to get relevant evidence if you fail to conduct detailed and deep research about this industry. Your solution to overcome the problem should be rational, feasible, and organized. Therefore, spend some time searching for and studying relevant materials. This Individual Assignment 1 is due in Week 4 on Saturday, 8th March 2025, before 5 pm. Instructions: 1. Follow APA guidelines for all citations in the text, and the references. 2. Please DO NOT use Wikipedia as a reference. Use more business reports, company sources, and academic journal/conference articles. 3. Provide a complete and accurate list of “References.” A minimum of 5 references will be required for this assignment. 4. Line spacing for the text (body): 1.5 line spacing; Font size: 12; Font type: Times New Roman; 2.54cm margin on four sides of the page; Use headings and sub-headings to separate your sub-topics. 5. The assignment must be written in English Language only. Ensure that the assignment is written carefully to avoid obvious typographical and grammatical mistakes. 6. You should put all the tables, charts, graphs and pictures in the Appendix. 5- page is for the body of the report and should NOT include the cover page, table of contents, appendix, etc. in the 45-page limit. 7. Your report should be formatted properly. Any unprofessional looking report will be penalized. 8. Submit both the hard copy and the soft copy of the report to the Assistant Instructor. The Assistant Instructor will provide you a space in ispace for you to submit your report. NOTE: The assignment report should be in a Word document and not PDF format. 9. Your total similarity report should be less than 20% and less than 10% from a single source. Failure to comply will be considered as a failure and receive a Zero in the assignment. 10. Limit AI-generated content to no more than 30% of your work. Use AI- powered tools for efficient research and information extraction, such as summarizing reports and analyzing transcripts. Always verify AI-generated data by cross-checking with reliable sources. Use AI to support, not replace, your critical thinking and analysis. 11. Late submission: Reports submitted after the due date will be considered late. Late submissions will attract a penalty of 20% of the total assignment points. No submission will be accepted ONE WEEK beyond the submission deadline. Failure to comply will be considered as a failure and receive a Zero in the assignment.
