RLG106H1FLEC0101 20259: Happiness Question - Can you be happy? If no, why? If yes, how? Formulate your answer in the form. of an essay. Appy the perspective of at least two of the four philosophers from the first five weeks of this course: Durkheim, Marx, Freud, Douglas. Cite at least two these philosophers with direct quotes. Your answer should not be longer than 500 words.
COMP714 Advanced Network Technologies Semester 2 2025 Weight: 40% (20% Demonstration; 20% Report) ASSIGNMENT Individual Modelling Gigabit Ethernet Backbone Network with Wireless Extension using OMNET++ 1. Developing a Network Model: In this assignment you will develop a Gigabit Ethernet (use 10 GbE switch) backbone network model using OMNET++ . For wireless technologies, use 802.11ax (Wi-Fi 6) technology. You are required to complete the assignment by investigating the following five scenarios. • Scenario 1: 90 wired video clients (no wireless clients ->100% wired network) • Scenario 2: 70 wired and 20 wireless video clients • Scenario 3: 50 wired and 40 wireless video clients • Scenario 4: 30 wired and 60 wireless video clients • Scenario 5: 90 wireless video clients (no wired clients) 2. Subnet: Your Backbone GbE (Choose 10 GbE switch) can be connected to several subnets (e.g. 1 GbE Ethernet switches). Let us assume that each subnet can support up to 20 clients/nodes for an optimum network performance. So, if you are simulating a network with 100 clients, your network model should have a total of 5 subnets (20 clients/subnet) linked to the GbE backbone. 3. Network configuration/parameter setting and simulation results: Configure the above five scenarios in turn and measure the following performance metrics. (a) Video Throughput (b) Video end-to-end delays (c) Video packet losses 4. Simulation validation and comparative analysis: Validate simulation results and summarise your findings. You may use the following table to summarise your results. Scenario Wired clients Wireless clients Video Throughput (bps) Video End- to-end Delay (s) Video Packet loss 1 90 0 2 70 20 3 50 40 4 30 60 5 0 90 5. Simulation parameters • Total number of clients: 90 • Simulation time: 3600 Sec (longer required for Video streaming) • Traffic type: Video streaming • Packet type: UDP packet • Performance metrics: (a) Video Throughput. (b) Video end-to-end delays. (c) Video packet losses. 6. In your report, answer/address the following questions. Video streaming uses UDP transport layer protocol, and hence UdpVideoStreamClient, UdpVideoStreamServer can be used for video client and video Server, respectively. a) UdpVideoStreamClient: Explain the purpose/function of “ UdpVideoStreamClient” (Hint: Explain the technical details and usage of this command). b) UdpVideoStreamServer: Explain technical details and usage of this command. General Report Format Cover page: Assignment title, student’s name, and ID. Introduction: What, why, and how? – Begin your report with a clear objective (What!) of your assignment. Explain why this assignment/research is needed and how you have completed it. Outline the structure of the rest of the report. Modelling the network: Describe the model that you have developed using appropriate diagram/screenshots for models and subnet (hint: avoid unnecessary screenshots). Results and Analysis: Summarise your simulation results using tables and/or graphs (Excel or MATLAB graphs) and provide a comparative analysis. Model Validation: Discuss how you have validated your simulation results. Lesson learned: Write a paragraph or two, reflecting on your own learning. Conclusion: Summarise the main findings and new ideas for future project/Assignment. References: List at least 5 references that you have used in the report. Report Length: 10-14 pages Spacing: 1.5 Font: 11 Times New Roman.
A Conjoint Analysis of Undergraduate Education The following profiles give possible combinations of attributes of an undergraduate education. Express your preference for each profile by using the scale below it. For each profile, you can mark any point on the scale which indicates your degree of preference. The highest degree of preference would get a "10", the lowest degree would get a "0", and so on. Profiles are various combinations of the following attributes: Annual Tuition level = $7,500 or $15,000 Teaching quality = Average or Excellent Placement service = Average or Excellent Program emphasis is on technical skills (computers, data analysis, financial analysis) or on communication skills (presentations, case studies, writing). Emphasis on e-commerce = Average or Very high Profile 1 Tuition: $15,000 Teaching quality: Average Placement service: Average Program emphasis: Technical skills Emphasis on e-commerce: Average Rating of Profile 1 (circle the appropriate number) Least Most Preferred Preferred 0 1 2 3 4 5 6 7 8 9 10 Profile 2 Tuition: $15,000 Teaching quality: Average Placement service: Excellent Program emphasis: Technical skills Emphasis on e-commerce: Very high Rating of Profile 2 (circle the appropriate number) Least Most Preferred Preferred 0 1 2 3 4 5 6 7 8 9 10 Profile 3 Tuition: $15,000 Teaching quality: Excellent Placement service: Average Program emphasis: Communication skills Emphasis on e-commerce: Very high Rating of Profile 3 (circle the appropriate number) Least Most Preferred Preferred 0 1 2 3 4 5 6 7 8 9 10 Profile 4 Tuition: $15,000 Teaching quality: Excellent Placement service: Excellent Program emphasis: Communication skills Emphasis on e-commerce: Average Rating of Profile 4 (circle the appropriate number) Least Most Preferred Preferred 0 1 2 3 4 5 6 7 8 9 10 Profile 5 Tuition: $7,500 Teaching quality: Average Placement service: Average Program emphasis: Communication skills Emphasis on e-commerce: Very high Rating of Profile 5 (circle the appropriate number) Least Most Preferred Preferred 0 1 2 3 4 5 6 7 8 9 10 Profile 6 Tuition: $7,500 Teaching quality: Average Placement service: Excellent Program emphasis: Communication skills Emphasis on e-commerce: Average Rating of Profile 6 (circle the appropriate number) Least Most Preferred Preferred 0 1 2 3 4 5 6 7 8 9 10 Profile 7 Tuition: $7,500 Teaching quality: Excellent Placement service: Average Program emphasis: Technical skills Emphasis on e-commerce: Average Rating of Profile 7 (circle the appropriate number) Least Most Preferred Preferred 0 1 2 3 4 5 6 7 8 9 10 Profile 8 Tuition: $7,500 Teaching quality: Excellent Placement service: Excellent Program emphasis: Technical skills Emphasis on e-commerce: Very high Rating of Profile 8 (circle the appropriate number) Least Most Preferred Preferred 0 1 2 3 4 5 6 7 8 9 10 Profile 9 Tuition: $7,500 Teaching quality: Excellent Placement service: Excellent Program emphasis: Communication skills Emphasis on e-commerce: Very high Rating of Profile 9 (circle the appropriate number) Least Most Preferred Preferred 0 1 2 3 4 5 6 7 8 9 10 Profile 10 Tuition: $7,500 Teaching quality: Excellent Placement service: Average Program emphasis: Communication skills Emphasis on e-commerce: Average Rating of Profile 10 (circle the appropriate number) Least Most Preferred Preferred 0 1 2 3 4 5 6 7 8 9 10 Profile 11 Tuition: $7,500 Teaching quality: Average Placement service: Excellent Program emphasis: Technical skills Emphasis on e-commerce: Average Rating of Profile 11 (circle the appropriate number) Least Most Preferred Preferred 0 1 2 3 4 5 6 7 8 9 10 Profile 12 Tuition: $7,500 Teaching quality: Average Placement service: Average Program emphasis: Technical skills Emphasis on e-commerce: Very high Rating of Profile 12 (circle the appropriate number) Least Most Preferred Preferred 0 1 2 3 4 5 6 7 8 9 10 Profile 13 Tuition: $15,000 Teaching quality: Excellent Placement service: Excellent Program emphasis: Technical skills Emphasis on e-commerce: Average Rating of Profile 13 (circle the appropriate number) Least Most Preferred Preferred 0 1 2 3 4 5 6 7 8 9 10 Profile 14 Tuition: $15,000 Teaching quality: Excellent Placement service: Average Program emphasis: Technical skills Emphasis on e-commerce: Very high Rating of Profile 14 (circle the appropriate number) Least Most Preferred Preferred 0 1 2 3 4 5 6 7 8 9 10 Profile 15 Tuition: $15,000 Teaching quality: Average Placement service: Excellent Program emphasis: Communication skills Emphasis on e-commerce: Very high Rating of Profile 15 (circle the appropriate number) Least Most Preferred Preferred 0 1 2 3 4 5 6 7 8 9 10 Profile 16 Tuition: $15,000 Teaching quality: Average Placement service: Average Program emphasis: Communication skills Emphasis on e-commerce: Average Rating of Profile 16 (circle the appropriate number) Least Most Preferred Preferred 0 1 2 3 4 5 6 7 8 9 10
Cloud Infrastructure Design for SmartV 41891 42891 CLOUD COMPUTING INFRASTRUCTURE ASSIGNMENT 2 1. Introduction In today’s rapidly evolving digital landscape, cloud computing has emerged as a critical technology that allows businesses to scale, innovate, and operate efficiently. The COVID-19 pandemic further accelerated the reliance on cloud services, especially in the entertainment industry, where streaming platforms have become essential for delivering content to millions of users. SmartV, an online video service provider, is experiencing substantial growth in user demand and has decided to migrate its IT infrastructure to the cloud to ensure scalability and performance. This report focuses on designing a comprehensive cloud infrastructure for SmartV that meets their current business requirements while allowing for future growth. We will address key elements such as cloud architecture design, technology selection, and business requirements and analyse potential challenges. Special attention will be given to addressing scalability, security, and availability, ensuring the infrastructure supports millions of concurrent users. The report will also explore solutions to critical challenges, including security risks, system availability, and performance issues. By implementing a cloud-based prototype, we aim to demonstrate the feasibility and efficiency of the proposed design. We aim to build a cloud infrastructure that enhances SmartV's service reliability, optimises operational efficiency, and positions the company for long-term success in the highly competitive streaming industry. 2. Business Requirements 2.1 Target Clients and Use Case Scenario SmartV’s cloud platform's target clients are users globally looking to search, stream, and edit high-quality videos. With the growing demand for video content and user-generated media, SmartV must provide an infrastructure that caters to various user needs. These include uploading videos, editing them, and making them available for public streaming. SmartV’s users are expected to come from various countries and access the platform from different devices and browsers. The platform. must deliver high-quality streaming services, accommodate large file uploads, and allow users to edit and transcode videos efficiently. By transitioning to a cloud-based infrastructure, SmartV can provide a scalable, secure, and globally accessible platform. that supports current and future demands. The business case for this migration centres around the need for scalability, cost efficiency, and enhanced user experience to keep pace with the increasing demand for video services. 2.2 Key Business Case and Requirements The business case for SmartV’s cloud migration centres around addressing the company’s rapid growth and global expansion. The cloud will provide the following critical benefits: a. Scalability to Support Growth: o Current User Demand: SmartV currently supports 50,000 concurrent streams during peak hours and expects this to double to 100,000 within two years. o Elastic Scaling: Cloud infrastructure will allow SmartV to automatically scale up resources during high-traffic periods, eliminating performance issues with the existing setup. o Capacity Projections: SmartV must increase its storage capacity to meet demand, with each user being allocated 100 GB of cloud storage. Given the projected user growth, the platform. must support at least 1 petabyte (PB) of total storage by the end of next year. b. High Availability and Global Access: o Latency Improvements: Currently, users in regions such as Asia and South America experience up to 30% slower load times due to the lack of local servers. With the migration to cloud-based data centres distributed globally, latency will be reduced by 40%, significantly improving streaming performance in these regions. o Expected Uptime: Cloud providers offer 99.99% availability, which reduces potential downtime to less than 53 minutes per year. This will prevent the estimated $10,000 per hour loss that SmartV experiences during outages, saving the company AUD 200,000 annually in lost revenue and customer dissatisfaction. o User Retention: Reducing latency and improving performance is expected to increase user retention rates by 5%, contributing an additional AUD 500,000 in annual subscription revenue. c. Cost Efficiency: o Cost Savings: By transitioning to the pay-as-you-go model, SmartV will reduce capital expenditure on hardware, with projected savings of AUD 500,000 annually. The cloud infrastructure will also reduce operational expenses by 20%, allowing SmartV to avoid the rising costs of maintaining and upgrading on- premises infrastructure. o Operational Cost Projections: Based on industry reports, organisations that migrate to the cloud experience up to 30% savings in operational IT costs. SmartV is expected to achieve similar savings, amounting to AUD 200,000 per year in reduced energy and maintenance costs. d. Data Security and Compliance: o Regulatory Compliance: As SmartV expands globally, it must comply with data protection regulations such as GDPR in Europe and CCPA in California. Cloud providers like AWS or Azure offer built-in compliance tools that ensure data encryption, access control, and audit logging. This reduces the risk of non-compliance fines, reaching up to 4% of annual global turnover under GDPR. o Security Risk Mitigation: Cloud infrastructure will enhance SmartV's security posture, reducing the risk of data breaches by 40%. SmartV will employ multi- factor authentication (MFA), encryption, and intrusion detection systems (IDS) to safeguard user data, which is critical given the large volumes of personal and video content handled. e. Backup and Disaster Recovery: o Improved Disaster Recovery: Cloud solutions provide real-time replication and automated backups across geographically redundant locations, reducing recovery time from hours to minutes. This minimises the risk of data loss and ensures that SmartV can quickly recover from system failures, preventing losses of up to AUD 500,000 due to potential data loss or extended outages. o Downtime Reduction: With cloud-based disaster recovery, SmartV will experience 99.99% uptime, minimising downtime to less than 10 minutes per year in the event ofa significant failure, compared to the 2 hours of downtime it currently risks per year. 2.3 System Requirements & Use Cases To meet these business objectives, SmartV’s cloud infrastructure must support the following system requirements and use cases: • Video Uploading: The system must allow users to upload video files of any size. With expected growth, the platform. must handle at least 10,000 concurrent video uploads during peak periods. • Video Streaming: The platform. must support up to 100,000 concurrent streams, offering adaptive bitrate streaming to ensure smooth playback across various network conditions and devices. • Data Storage: Each user should have access to 100 GB of cloud storage, with automated backups ensuring that content is never lost. • Security Requirements: Multi-factor authentication (MFA), firewalls, and data encryption must comply with GDPR and other regional regulations. • Scalability: The infrastructure should scale on demand, particularly during high-traffic events like new content launches or viral video trends. • Global Accessibility: Data centres should be strategically located worldwide to reduce latency for users in different regions. 2.4 Cloud Infrastructure Requirements Cloud computing provides a robust, flexible, and scalable solution to meet SmartV’s business needs, particularly in the rapidly growing video streaming industry. Migrating to the cloud will allow SmartV to leverage on-demand computing services, including storage, processing power, and network bandwidth, essential for delivering high-quality video services to a global audience. According to Gartner (2021), organisations can reduce operational costs by up to 30% through cloud migration while gaining the scalability necessary to manage fluctuating demand. SmartV's ability to dynamically allocate resources will enable the company to efficiently handle traffic spikes during peak streaming times while minimising costs during off-peak periods. SmartV will benefit from the following key cloud infrastructure components: p to100,000 simultaneousstreams efficiently.These VMs can beeasily deployed,managed, andscaledacross variousgeographic locations,ensuring the platformcan respond to userdemands anywhere inthe world.HypervisorTechnologyA hypervisor enablesthevirtualisationofphysical servers,allowing multipleVMs to run onasingle improving operationalefficiency.Hypervisor technologyalso provides resourceisolation, ensuring eachVM has dedicatedresources while sharingunderlying hardware. .SAN infrastructureensures data redundancythrough replicationacross multiplegeographic locations,providinghighavailability and disasterrecovery capabilities.The system will bescalable toaccommodate thegrowing demandforstorage, with1 PB ofcapacity projected forthe following year.GlobalNetworkConnectivityCloud datacentresdistributed acrossmultiple geographicregions will ensurelow-latency accessforSmartV’s global users.Using local datacentres,SmartV will reducelatency by up to 40%,ensuring asmootherstreaming experience forusers.With100 Gbps networkbandwidth availablethrough cloud providers,SmartV will have theinfrastructure to deliverhigh-definition video millionsofusers ry-leading securityprotocols, includingMFA, firewalls,andIDS,SmartV willreducetheriskofdata 2.5 Expected Outcomes SmartV’s migration to cloud computing will deliver several key business benefits, including: • Global Reach: The platform. will be accessible from any location and supported by distributed data centres that eliminate the need for local physical infrastructure. • Cost Savings: SmartV is projected to save AUD 500,000 annually on infrastructure costs with pay-as-you-go pricing and reduced hardware maintenance. • Improved Customer Satisfaction: Reducing latency and improving performance will result in higher customer satisfaction, leading to a 5% improvement in retention and an additional AUD 500,000 in subscription revenue. • Enhanced Disaster Recovery: The cloud will enable real-time data replication and automated backups, reducing potential data loss and saving up to AUD 500,000 in the event of system failures. • Minimal Downtime: By achieving 99.99% uptime, SmartV will experience minimal service interruptions, significantly reducing downtime costs by AUD 200,000 per year. 3. Cloud Architecture and Design 3.1 Proposal Infrastructure and Components Cloud computing offers a dynamic solution to SmartV’s expanding business needs by providing scalable storage, processing power, and networking resources. The proposed infrastructure includes the following core components to ensure efficient management and delivery of video services worldwide: a. VMware vCenter (VCSA8) o IP Address: 192.168.10.6 o Username: [email protected] o Purpose: Acts as the central management platform. for virtual machines (VMs) across the cloud infrastructure. With vCenter, SmartV can optimise video streaming, transcoding, and editing resources, allowing for smooth scaling as traffic demands increase. b. Synology NAS iSCSI DataStore o IP Address: 192.168.0.3/24 Purpose: Provides high-speed, redundant storage for video content. Ensures data integrity and high availability, using replication across geographic regions to prevent data loss and fast access. c. ESXi Hosts (ESXi-1, ESXi-2, ESXi-3) o IP Addresses: ESXi-1: 192.168.10.11/24 ESXi-2: 192.168.10.12/24 ESXi-3: 192.168.10.13/24 Purpose: Physical servers running multiple VMs, handling compute-heavy tasks such as video transcoding, editing, and streaming. These hosts distribute workloads efficiently across the platform. Figure 1. The diagram illustrates a VMware vSphere infrastructure with vSphere Client managing vCenter Servers, ESXi hosts, and virtual machines, including Enhanced Linked Mode for multiple vCenter Servers (VMWare, n.d.). 3.2 Implementing Platform. Choices For SmartV, three primary cloud models are considered: a. Infrastructure as a Service (IaaS) o IaaS offers on-demand computing resources such as servers, storage, and networking, allowing for maximum control and customisation of the infrastructure. This is ideal for SmartV’s global operations, enabling flexible scaling as user traffic grows. o Chosen Model: IaaS is the best solution due to its scalability and control over resource allocation. The IaaS model supports video transcoding, global streaming, and security demands for SmartV’s platform. b. Platform. as a Service (PaaS) o PaaS provides a managed platform. for developing and deploying applications. However, it limits control over the infrastructure, which is unsuitable for SmartV's complex needs, such as real-time video processing and storage. c. Software as a Service (SaaS) o SaaS offers fully managed applications but lacks the control and flexibility required for large-scale services like SmartV. Why IaaS: The IaaS model allows SmartV to have complete control over its cloud environment, ensuring efficient handling of high traffic, real-time video processing, and the flexibility to adapt to changing demands in the future. 3.3 Network Topology SmartV will use vSphere Distributed Switch (vDS) to ensure high performance, security, and redundancy to segment and manage network traffic. The network topology is designed to provide reliable data delivery and low-latency streaming. • Management Network (192.168.10.x): Handles administrative tasks such as monitoring, updates, and resource allocation. • User Network (10.0.x.x): Manages streaming traffic and user interaction, ensuring low- latency access for millions of concurrent users. • Storage Network (192.168.0.x): Handles video data flow between VMs and storage systems. Figure 2. SmartV Network Architecture Diagram
Assignment 1 COMP9021, Trimester 3, 2025 1 General matters 1.1 Aim The purpose of the assignment is to: • develop your problem solving skills; • practice carefully reading specifications and following them; • design and implement solutions to problems as small Python programs; • practice arithmetic computations, conditional tests, loops, exceptions, fundamental Python data types, and Unicode characters; • gain control over print statements. 1.2 Submission Your programs should be stored in files named solitaire_1 .py and solitaire_2.py. After developing and testing your programs, upload them via Ed (unless you worked directly in Ed). Assignments can be submitted multiple times; only the last submission will be graded. Your assignment is due on October 27 at 11:59am. 1.3 Assessment The assignment is worth 13 marks and will be tested against multiple inputs. For each test, the au- tomarking script allows your program to run for 30 seconds. Assignments may be submitted up to 5 days after the deadline. The maximum mark decreases by 5% for each full late day, up to a maximum of five days. For example, if students A and B submit assignments originally worth 12 and 11 marks, respectively, two days late (i.e., more than 24 hours but no more than 48 hours late), the maximum mark obtainable is 11.7. Therefore, A receives min(11.7; 12) = 11.7 and B receives min(11.7; 11) = 11. The outputs of your programs must exactly match the expected outputs. You are required to save your outputs in .txt files and use the diff command to identity any differences between your outputs and the provided expected outputs (also in .txt files). You are responsible for any failed tests caused by formatting errors that diff would have detected. 1.4 Reminder on plagiarism policy You are encouraged to discuss strategies for solving the assignment with others; however, discussions must focus on algorithms, not code. You must implement your solution independently. Submissions are routinely scanned for similarities that arise from copying, modifying others’ work, or collaborating too closely on a single implementation. Severe penalties apply. 2 Decks, shuffling 2.1 Decks The first exercise simulates a solitaire game played with 32 cards: the Seven, Eight, Nine, Ten, Jack, Queen, King, and Ace of each of the four suits, using the following convention. • Numbers 0 to 7 represent the Hearts, from Seven to Ace. • Numbers 8 to 15 represent the Diamonds, from Seven to Ace. • Numbers 16 to 23 represent the Clubs, from Seven to Ace. • Numbers 24 to 31 represent the Spades, from Seven to Ace. For example, 6 represents the King of Hearts, and 26 represents the Nine of Spades. The second exercise simulates a solitaire game played with 52 cards, with the following convention. • Numbers 0 to 12 represent the Hearts, from Ace to King. • Numbers 13 to 25 represent the Diamonds, from Ace to King. • Numbers 26 to 38 represent the Clubs, from Ace to King. • Numbers 39 to 51 represent the Spades, from Ace to King. For example, 16 represents the Four of Diamonds, and 36 represents the Jack of Clubs. 2.2 Shuffling Both exercises require shuffling a deck of 32 or 52 cards, using the following convention. By shuffling a deck of cards, we mean randomising the associated set of numbers by providing the list of numbers, arranged in increasing order, as an argument to the shuffle() function from the random module. For instance, to shuffle the 52-card deck, we could do >>> cards = list(range(52)) >>> shuffle(cards) To ensure predictable results, the seed() function from the random module should be called with a specified argument just before invoking shuffle(). By shuffling the 52- card deck with 678 passed to seed(), we mean performing the following steps: >>> cards = list(range(52)) >>> seed(678) >>> shuffle(cards) It lets cards denote [11, 12, 22, 38, 15, 16, 14, 28, 4, 34, 46, 48, 33, 18, 5, 17, 27, 37, 50, 51, 31, 41, 9, 1, 39, 3, 29, 40, 43, 23, 25, 13, 19, 35, 26, 42, 24, 32, 44, 45, 6, 36, 8, 47, 2, 30, 10, 49, 21, 0, 20, 7] 3 First solitaire game 3.1 Game description It is played with 32 cards. After the deck has been shuffled, the cards in the deck are face down, with the first card at the bottom and the last card on top. The top four cards are revealed and placed at the following locations: As long as there are at least two cards of the same suit (Hearts, Diamonds, Clubs or Spades), such pairs are discarded simultaneously and replaced with cards drawn from the top of the deck. • If exactly two cards of a suit are present, both are discarded. • If exactly three cards of a suit are present, the two in the lowest-numbered locations are discarded. • If there are two cards each of two different suits, or if all four cards share the same suit, then all four cards are discarded. Empty locations are filled with cards drawn from the top of the deck, starting with the lowest-numbered locations. Note that at the very end, all four cards may be discarded even if only two cards remain in the deck; these are then placed at locations 1 and 2. The game is won when the deck is empty. The game is lost if one card of each suit is on the table while cards remain in the deck. 3.2 Playing a single game (3.5 marks) Your program will be stored in a file named solitaire_1 .py. Executing $ python3 solitaire_1 .py at the Unix prompt should produce the following output (ending with a single space): Enter an integer to pass to the seed() function: with the program awaiting your input, which can be assumed to be an integer. Your program will pass that integer to seed() before calling shuffle(), as described in Section 2, to shuffle the 32-card deck. Here is an example interaction for a game that is lost. Here is an example interaction for a game that is won. Here is another example interaction for a game that is won. Note the following: • There are no tabs in the output. • No line ends with trailing spaces. • Each line of “squares” is preceded by an empty line. • Each “square” is indented with 4 spaces. • Consecutive cards in a “square” are separated by a space. • Each line contains at most six “squares” of cards. 3.3 Playing many games and estimating probabilities (3 marks) Executing $ python3 at the Unix prompt, and then >>> from solitaire_1 import simulate at the Python prompt allows you to call the simulate() function, which takes two arguments. • The first argument, n, is a strictly positive integer representing the number of games to play. • The second argument, i, is an integer. The function simulates the playing of the game n times, • the first time shuffling the 32-card deck with i passed to seed(), • if n ≥ 2, the second time shuffling the 32-card deck with i + 1 passed to seed(), • … • the nth and last time, shuffling the 32-card deck with i + n - 1 passed to seed(). Here is an example interaction. Here is another example interaction. The number of rounds corresponds to the number of “squares” shown in the game display. Pay attention to the formatting. There are no tab characters anywhere in the output, and no line ends with trailing spaces. The text is right-aligned in the first and third columns, and left-aligned in the second. Frequencies are computed as floating-point numbers and formatted to two digits after the decimal point. Only strictly positive frequencies and the corresponding number of cards left are output, including cases smaller than 0.005%, which are displayed as 0.00%. The output is ordered in ascending order of cards left, and for equal values, in ascending order of rounds. 4 Second solitaire game 4.1 Game description It is played with 52 cards. They are shuffled once, immediately before the game begins. The aim is to create four columns of Ace, Three, Five, Seven, Nine, Jack, and King in alternating colours, and four rows of Two, Four, Six, Eight, Ten, and Queen in alternating colours. For instance, a Seven of Hearts or Diamonds can be followed by a Nine of Clubs or Spades, and a Six of Clubs or Spades can be followed by an Eight of Hearts or Diamonds. Up to three rounds are allowed to place all the cards. At each round, the remaining cards (the whole deck at the start of the first round) are stacked face down, and cards are drawn one by one from the top until the stack is empty. An Ace or Two drawn from the stack starts a new column or row. Columns are created left to right, and rows top to bottom. A card drawn from the top of the stack extends a column or row if possible. Otherwise, it is placed face up on the discard pile. When a card drawn from the stack can extend a column or row, the top card of the discard pile, if any, is used to extend a column or row whenever possible. This process continues with the new top card, and so on. When a card can extend two columns or two rows (because both end with black cards or both with red cards of the same value), the leftmost column or the topmost row is chosen. When the stack becomes empty, one of three things happens: • If all cards have been placed on the table, the game is won. • If any cards remain and fewer than three rounds have been played, the discard pile is turned over to become the new stack, and play proceeds exactly as in the previous round. • If any cards remain and this was the third round, the game is lost. 4.2 Playing a single game (3.5 marks) Your program will be stored in a file named solitaire_2.py. Executing $ python3 solitaire_2.py at the Unix prompt should produce the following output (ending with a single space) Enter an integer to pass to the seed() function: with the program awaiting your input, which can be assumed to be an integer. Your program will pass that integer to seed() before calling shuffle(), as described in Section 2, to shuffle the 52-card deck. The output starts with an empty line followed by There are _ lines of output; what do you want me to do? Enter: q to quit a last line number (between 1 and _) a first line number (between -1 and -_) a range of line numbers (of the form m--n with 1 from solitaire_2 import simulate at the Python prompt allows you to call the simulate() function, which takes two arguments. • The first argument, n, is a strictly positive integer representing the number of games to play. • The second argument, i, is an integer. The function simulates the playing of the game n times, • the first time shuffling the 52-card deck with i passed to seed(), • if n ≥ 2, the second time shuffling the 52-card deck with i + 1 passed to seed(), • … • the nth and last time, shuffling the 52-card deck with i + n - 1 passed to seed(). Here is an example interaction. Here is another example interaction. Pay attention to the formatting. There are no tab characters anywhere in the output, and no line ends with trailing spaces. The text is right-aligned. Frequencies are computed as floating-point numbers and formatted to two digits after the decimal point. Only strictly positive frequencies and the corresponding number of cards left are output, including cases smaller than 0.005%, which are displayed as 0.00%. The output is ordered in ascending order of cards left.
Assignment 4 BS6203 Accompanying this assignment is an excel sheet containing reported statistics for “Waiting Time for Admission to Ward” in urgent care clinic at Alexandra Hospital (AH), and Emergency Medicine Departments (EMD) at Changi General Hospital (CGH), Khoo Teck Puat Hospital (KTPH), National University Hospital (NUH) (Adults), Ng Teng Fong General Hospital (NTFGH),Sengkang General Hospital (SKH), Singapore General Hospital (SGH), and Tan Tock Seng Hospital (TTSH). The waiting time for admission to ward is calculated as the time from "Decision by doctor to admit patient" to the "Time patient exits EMD” (to go to inpatient ward). Using techniques, you have learnt in this class, try representing this data as a graph to better visualize the underlying data.
Assignment 2 Instructions This assignment is designed to test your familiarity with formulas, filtering, formatting, and other essential commands in Excel. You will also create pivot tables to complete this assignment. Although you will not be asked to upload your dataset, it is strongly encouraged that you complete the assignment using the commands and techniques we learn in class (and the workflow requested on this assignment). Introduction to the dataset The data for this assignment come from the Global Trust Survey, a survey that aims to assess trust in institutions and social networks from citizens in countries around the globe. We have taken a subset of this data of respondents from the United States and Canada. Selected variables id: The respondents’ unique identifier country: The country the respondent is from trust_neighbors: Trust in neighbors on a scale of 1-5 trust_government: Trust in federal government on a scale of 1-5 trust_scientists: Trust in scientists from country on a scale of 1-5 trust_journalists: Trust in journalists from country on a scale of 1-5 trust_doctors_nurses: Trust in doctors and nurses from country on a scale of 1-5 Instructions 1.) Download the dataset trust_survey.xlsx from ELMS and open it in Excel. 2.) Create a new column called trust_total equal to the sum of all the existing trust variables. (Hint: Use a formula and click and drag.) 3.) Create a (pivot) table with the country as the rowand the average of each of the trust variables as the values. 4.) Sort the dataset by country first and trust_total second. 5.) Save the file EXACTLY as Assignment2.xlsx. You will not be able to upload it if it’s not a .xlsx. 6.) Complete the Assignment 2 submission on ELMS by the assigned date. You will upload your .xlsx file, but this will not necessarily be checked for accuracy. (That’s what the questions help assess.)
ISIT312 Big Data Management Assignment 3 Spring 2025 Scope This assignment includes the tasks related to querying a data cube, design and implementation of HBase table, querying and manipulating data in HBase table, data processing with Pig, and data processing with Spark. This assignment is due on Saturday, 01 November 2025, 7:00pm (sharp). This assignment is worth 20% of the total evaluation in the subject. The assignment consists of 4 tasks and specification of each task starts from a new page. Only electronic submission through Moodle at: https://moodle.uowplatform.edu.au/login/index.php will be accepted. A submission procedure is explained at the end of Assignment 1 specification. A policy regarding late submissions is included in the subject outline. Only one submission of Assignment 3 is allowed and only one submission per student is accepted. A submission marked by Moodle as "late" is always treated as a late submission no matter how many seconds it is late. A submission that contains an incorrect file attached is treated as a correct submission with all consequences coming from the evaluation of the file attached. All files left on Moodle in a state "Draft(not submitted)" will not be evaluated. A submission of compressed files (zipped, gzipped, rared, tared, 7-zipped, lhzed, … etc) is not allowed. The compressed files will not be evaluated. An implementation that does not compile well due to one or more syntactical and/or run time errors scores no marks. Using any sort of Generative Artificial Intelligence (GenAI) for this assignment is NOT allowed ! It is expected that all tasks included within Assignment 3 will be solved individually without any cooperation with the other students. If you have any doubts, questions, etc. please consult your lecturer or tutor during lab classes or office hours . Plagiarism will result in a FAIL grade being recorded for the assessment task. Task 1 (5 marks) Querying a data cube Use Hive to create an internal table TRIP with records of individual trips, with each record containing the driver's license, the truck's registration, the trip length in kilometers, and the date of the trip. create table TRIP ( registration char(7), license char(7), kilometers decimal(2), tday decimal(2), tmonth decimal(2), tyear decimal(4) ) row format delimited fields terminated by ',' stored as textfile; Remove a header line from a file task1.csv and save the file. Populate the table by loading data from the file task1.csv. (1) 0.5 mark Implement the following query using GROUP BY clause with CUBE operator. Find the total number of trips per driver (license), per truck (registration), per driver and truck (license, registration), and the total number of trips. (2) 0.5 mark Implement the following query using GROUP BY clause with ROLLUP operator. Find the longest trip (kilometers) per driver (license) and per driver and truck (license, registration) and the longest trip at all. (3) 0.5 mark Implement the following query using GROUP BY clause with GROUPING SETS operator. Find the shortest trip (kilometers) per driver (license) and per truck (registration) and per driver and year (license, tyear) . Implement the following SQL queries as SELECT statements using window partitioning technique. (4) 0.5 mark For each truck, list its registration number, the length of its longest and shortest trips (in kilometers), the total number of trips, and the average trip length (in kilometers) . (5) 0.5 mark For each truck list its registration (registration) and all its trips (license, tday, tmonth, tyear, kilometers) sorted in descending order of trip length (kilometers) and a rank (position number in an ascending order) of each trip. Use an analytic function ROW_NUMBER(). (6) 0.5 mark For each driver, list its license number (license), total length of all his/her trips (kilometers), and the average length of all trips (kilometers) . (7) 0.5 mark For each driver (license) and truck (registration) and for each trip length (kilometers) list the longest trip length (kilometers) aggregated per driver (license) . (8) 0.5 mark For each truck (registration) find how the total trip length (kilometers) changed year by year (tyear). Order the results in the ascending order of years (tyear) . (9) 0.5 mark For each truck (registration) list an average length of the current and previous trip (kilometers). Order the results in the ascending order of trip length (kilometers). (10) 0.5 mark For each truck (registration) list an average length of the current, the previous and the next trip (kilometers) . Order the results in the ascending order of trip length (kilometers) . When ready, save your SELECT statements in a file solution1.hql. Then, process a script. file solution1.hql and save the results in a report solution1.txt. Deliverables A file solution1.txt that contains a report from processing of SELECT statements implementing the queries listed above. Task 2 (5 marks) Design and implementation of HBase table (3 marks) (1) Consider the following conceptual schema of a sample data cube designed to analyze vehicle repairs by mechanics for vehicle owners. Design a single HBase table to store the data described by the conceptual schema above. Create HBase script. solution2-1.hb with HBase shell commands that create HBase table and load sample data into the table. Load into the table information about at least two vehicles, two owners, two mechanics and three repairs. When ready use HBase shell to process a script file solution2-1.hb and to save a report from processing in a file solution2-1.txt. Querying HBase table (2 marks) (2) Consider a conceptual schema given below . The schema represents a data cube where students submit assignments and each submission consists of several files and it is related to one subject. Download a file task2-2.hb with HBase shell commands. Process a script task2-2.hb. Processing of the script creates HBase table task2-2 and loads some data into it. Use HBase shell to implement the queries and data manipulations listed below . Implementation of each step is worth 0.4 of a mark. Save the queries and data manipulations in a file solution2-2.hb. Note that implementation of the queries and data manipulations listed below may need more than one command of HBase shell. (1) Find all information included in a column family SUBJECT qualified by code and column family FILES qualified by fnumber1 and fnumber2. (2) Find all information about a subject that has a code 312, list two versions per cell. (3) Find all information about a submission of assignment 1performed by a student 007 in a subject 312, list one version per cell. (4) Replace a submission date of assignment 1 performed by a student 007 in a subject 312 with a date 02-APR-2019 and then list a column family SUBMISSION to verify the results. (5) Add a column family DEGREE that contains information about titles of degrees enrolled by the students. Assume that a student can enrol only one degree . Then add information about a title of degree enrolled by a student with a number 007. A degree title is up to you . List all information about a student with a number 007. When ready, start HBase shell and process a script file solution2-2.hb with the Hbase shell commands. Save report from processing of the script. in a file solution2- 2.txt. Deliverables A file solution2-1.txt with a listing from processing of a script file solution2- 1.hb. A file solution2-2.txt with a listing from processing of a script file solution2- 2.hb. Task 3 (5 marks) Data processing with Pig Latin Consider the following logical schema of two-dimensional data cube. Download a file task3.zip published on Moodle together with a specification of Assignment 3 and unzip it. You should obtain a folder task3 with the following files: driver.csv, truck.csv and trip.csv. Use a text editor to examine the contents ofthe files. Upload the files into HDFS. Open Terminal window and start pig command line interface to Pig. Use pig command line interface to implement the following actions. Implementation of each step is worth 1 mark. (1) Use load command to load the files truck.csv, driver.csv and trip.csv from HDFS into a Pig storage. Use Pig Latin and Pig Grunt command line interface to implement and process the following queries. (2) Find the full names (first-name, last name) of drivers who used the trucks manufactured (make) either by DAF or MAN. (3) Find the full names (first-name, last name) of drivers who used the trucks manufactured (make) by DAF and on the other occasion manufactured by MAN. (4) Find the full names (first-name, last name) of drivers who never travelled to Albany. (5) Find the total number of times each truck (registration) was used on a trip to Albany. There is no need to list the trucks never used on any trip to Albany. Once completed, copy the entire contents of the Terminal window, including data loading outputs, processed queries, ALL messages, and ALL results, to the clipboard. Then, paste these contents into a text file named solution3.txt. Deliverables A file solution3.txt that contains a listing of data loadings and queries performed above , ALL messages and the results of operations. A file solution3.txt must be created through Copy/Paste of the entire contents of Terminal window into a file solution3.txt. No screen dumps are allowed and no screen dumps will be evaluated. Solutions that do not include the query processing messages will not receive any marks. Task 4 (5 marks) Data processing with Spark Consider the following logical schema of two-dimensional data cube. Download a file task4.zip published on Moodle together with a specification of Assignment 3 and unzip it. You should obtain a folder task4 with the following files: driver.csv, truck.csv and trip.csv. Use a text editor to examine the contents ofthe files. Upload the files into HDFS. Open Terminal window and start pyspark command line interface to Spark. Use pyspark command line interface to implement the following actions. Implementation of each step is worth 1 mark. (1) Create the schemas for the files truck.csv, driver.csv, and trip.csv. (2) Create the data frames with the contents of the files truck.csv, driver.csv, and trip.csv using the schemas created in the previous step. Count the total number of rows in each frame and then list the contents of each frame. (3) Create and process the following query directly on the trips DataFrame, without creating a temporary view. Find the total number of times each driver (license, first name, last name) travelled to Albany. There is no need to list the drivers who never travelled to Albany. (4) Create a temporary view over a data frame with information about the trips and drivers. (5) Execute the following query on a temporary view containing information about the trips and drivers. Find the total number of times each driver (license, first name, last name) travelled to Albany. There is no need to list the drivers who never travelled to Albany. When ready, copy into a clipboard the contents of Terminal window with the operations processed above and the results listed in the window and paste the results from a clipboard into a text file solution4.txt. Deliverables A file solution4.txt that contains a listing of operations performed above and the results of operations. A file solution4.txt must be created through Copy/Paste of the contents of Terminal window into a file solution4.txt. No screen dumps are allowed and no screen dumps will be evaluated. Submission of Assignment 3 Note, that you have only one submission. So, make it absolutely sure that you submit the correct files with the correct contents. No other submission is possible ! Submit the files solution1.txt, solution2-1.txt, solution2-2.txt, solution3.txt, and solution4.txt through Moodle in the following way: (1) Access Moodle at http://moodle.uowplatform.edu.au/ (2) To login use a Login link located in the right upper corner the Web page or in the middle of the bottom of the Web page (3) When logged select a site ISIT912/312 (S225) Big Data Management (4) Scroll down to a section Assessment items (Assignments) (5) Click at In this place you can submit the outcomes of your work on the tasks included in Assignment 3 for ISIT312 students link. (6) Click at a button Add Submission (7) Move a file solution1.txt into an area File submissions. You can also use a link Add… (8) Repeat a step (7) for the files solution2-1.txt, solution2-2.txt, solution3.txt, and solution4.txt. (9) Click at a button Submit assignment. (10) Click at the checkbox with a text attached: By checking this box, I confirm that this submission is my own work, I accept responsibility for any copyright infringement that may occur as a result of this submission, and I acknowledge that this submission may be forwarded to a text- matching service. (11) Click at a button Continue (12) Check if Submission status is Submitted for grading.
Assignment ● Do a literature search and summarize an article/journal publication on emerging new techniques in analytical chemistry for the last 4 years(2021 to 2025) on the following areas: ● Spectroscopic Techniques (IR, UV, NMR) ● Mass Spectrometry and Hyphenated techniques ● Chromatography such as Liquid Chromatography, Gas Chromatography ● Create a power point presentation of 5 slides focusing on the what the new technology is about, potential applications it have beside those mentioned, pros and cons of the technique. Upload the slides into Canvas together with the article. Assessment criteria will be based on: ● Accurate interpretation ● Impact of technology ● Use of Al tools are not allowed.
GES1003/GESS1001 CHANGING LANDSCAPES OF SINGAPORE AY2025/2026 SEMESTER 1 Instructions for the Second Commentary (10%) Planning for Inclusive Public Housing Landscapes in Singapore Aims of the Assignment 1. To gain an exposure to people’s views about living in public housing and/or ageing in Singapore. 2. To assess your ability to critically comment on the tensions between and/or alignments with individual and state aspirations. What you need to do? 1. Ask either one person over 60 years old (married or single) or one person under 40 years old (either with a young family, single or in an any other type of family arrangement) the following question: What facilities, amenities or activities* would you like to see incorporated into Singapore’s public housing estates in the next 10-20 years? Notes: These could include features within the flat, amenities in the neighbourhood, or facilities within the wider estate. 2. Provide an ad verbatim transcription of the interviewee’s response. Your interviewee’s response should be no more than 150 words (translated into English if necessary). Include the details of your interviewee’s gender, age, ethnicity, citizenship status, and occupation in brackets, e.g. “(female, 76 years old, Chinese, Singaporean, retired manager)”. These identifying details give context to your commentary and is not included in your 150-word count. 3. Write a 350-word commentary on the extent you think the interviewee’s response aligns with the state’s vision for public housing in Singapore (as described in the website and video you have been asked to view for Tutorial 4 (see Notes below)). You are strongly encouraged (if applicable) to highlight the ways in which interviewee’s suggestions for future changes might contradict or subtly contest state ideologies. Notes: 1. Housing & Development Board. (n.d.). Designing for life: HDB's refreshed roadmap. Housing & Development Board. https://www.hdb.gov.sg/about-us/news-and-publications/publications/dwellings/Designing-for-Life 2. Housing & Development Board. (n.d.). HDB's refreshed roadmap: Designing for life. Housing & Development Board. https://www.hdb.gov.sg/about-us/hdbs-refreshed-roadmap-designing-for-life Important, please note: 1. Your commentary must be submitted into the Turnitin folder on Canvas no later than by 5pm on 10 Oct 2025 (Friday, Week 8). Marks will be deducted for submissions later than this date. 2. The commentary should be no more than 500 words in total. There will be a penalty for exceeding the word limit by 10%. The word count includes references. You do not need to give references for the websites mentioned in the ‘Notes’ section above. However, any other additional references must be included in the reference list. Marking Criteria 1. Accurate understanding of how the extent to which the respondent’s response aligns with or deviates from the state’s vision for public housing in Singapore. 2. Ability to integrate power relations into the comment, e.g. clear understanding of exclusion 3. Ability to integrate geographical concepts into the comment, i.e. space, place, scale. This is a guideline for you to understand what your marker will be looking out for. There is no rubric for this assessment.
MATH36031 Problem solving by computer. Project 1 - deadline 24th October 2025, time 1600hrs. Submission of the project is via Canvas. In the tasks required for this project you are asked to use an algorithm which originates from the Vedic system of mathematics developed in India several hundred years ago. The algorithm is described in the video clip which is available in Canvas in the Projects folder in the sub-folder Project 1. Project Tasks 1. Consider the multiplication of two polynomials: where m, n are integers and am ≠ 0,bn ≠ 0. Obtain an expression for ck the coefficient of xk , 0 ≤ k ≤ n + m. 2. Suppose now that the coefficients ak, bk are single digit non-negative integers and am ≠ 0, bn ≠ 0. Representing the polynomial coefficients as strings a = ‘am am-1 ... a0 ’, b = ‘bn bn-1 . . . b0 ’and using the MATLAB conv function, write a function myconv(a,b) which takes two input arguments a, b and returns the variable c = ‘cn+mcn+m-1 . . . c0 ’. As an example if a = ‘123’, b = ‘678’ then c=myconv(a,b) would return c as a 1x5 string ‘6 19 40 37 24’. 3. Watch the video clip to learn about the vedic maths algorithm used to multiply two numbers. In the video clip, the algorithm is described for multiplication of numbers up to 5 digits long. Your task is to generalise the algorithm to work out the patterns to multiply two numbers with m and n digits. In your report you should describe and explain how this algorithm works. 4. Write a function vedicmultiply to implement this algorithm and such that c=vedicmultiply(a,b) returns a string c which contains all the digits of the multi- plication of a and b (which are input as a string) . Also the integers represented by a and b can have a di↵erent number of digits. In your report you should explain and provide details of any testing and validation that you have done. Example c=vedicmultiply(’12’,’34’) returns c=’408’. function [myans] = vedicmultiply(a,b) %vedicmultiply computes the product a*b % vedcimultiply computes the product of the numbers a*b where % a and b are input as strings (to allow for long integers ) % usage : a= i 12345 i ; b= i 123 i c=vedicmultiply (a ,b) % myans is a list containing all the digits of the answer % 5. Use the vedicmultiply function to write a function myfactorial(n) which give all the digits of n! (n factorial) . Explain what validation checks you have made. 6. Use your routines to compute 159! (159 factorial) and display all the digits in the answer. Your report should show the full output of this computation. . Additional Information All coding must be done in MATLAB and you are required to submit your report and MATLAB functions and m (or mlx)-files via the appropriate Canvas submission box. There are two submission boxes, one for submitting your report in pdf form only labelled Project 1 (project reports) and another for submitting the codes for the project labelled Project 1 (MATLAB codes) which allows only m or mlx file types. Remember the Turnitin software will automatically scan reports for plagiarism. Please ask if you need help on any commands, or whether there are built-in command- s/functions to accomplish certain tasks (especially important if you think you have a good approach to the questions, but do not know the related commands) . The default datatype is double (decimal number), and be aware of possible side efects when using the numbers as integers. Remember that the same question can be solved by diferent approaches, and the same approach can be implemented in diferent ways. All relevant commands should be covered during the lectures or tutorial exercises, but you are free to explore your own. Make critical judgement to choose the best approach/implementation. Aim for efficiency of the code, which is an additional marking criteria, besides the generic rubric. Although you only need to record the answer for the given input, make sure that the computational time or memory does not increase significantly with larger input parameters (these issues will be mentioned constantly during the class demonstrations) . List the complete code of the whole code for solving the tasks at the end of each question, or in an appendix. Make your source code more readable, by keeping the indentation and stylistic features, and can be copied from the electronic file. The results reported in your report must be reproducible from your codes. Remember that markers will be able to run the codes in case of any doubts and any inconsistencies between reported results and actual results from running codes will lead to reports being marked down. Guidelines for the report. 1. Have a look at the generic rubric and frequently asked questions, which is given on Canvas in the Projects folder and about how your report will be marked. The rubric also describes the intended learning outcomes about what you are expected to achieve at the end. 2. Avoid copying (too many) sentences directly from the project description, and try to restate the problem with your own words or examples if possible. 3. You may use your report in the future as evidence of written work, so take it seriously. 4. Your target audience is a fellow student on your course: explain the questions so that the report can be understood without this project description and your approach can be implemented in another computer. The report should indicate to the reader how well you understand the problem and the approach you have taken, the validation and other checks that you made to ensure your results are credible. Reports submitted containing codes only and with no explanations of how the problem was solved, will result in a failing mark, even though the codes may work perfectly well and give the correct answers. 5. Balance the explanation of the approach and the comments in the code. Avoid under- commenting and over-commenting. 6. Aim for precision and clarity of writing (discussed in Week 5) . 7. Keep your page length not exceeding eight A4 pages, with a font size no smaller than 11, and page margins no smaller than 2cm. There is no need for a title page for a relative short report like this. If more than 8 pages are submitted only the first 8 pages will be marked and the rest of the submission will be ignored. 8. Since there is no final exam, you are advised to spend at least 15 hours on each project. 9. There are two submission boxes, one for submitting your report in pdf form labelled Project 1 (project reports) and another for submitting the codes for the project labelled Project 1 (MATLAB codes). Submission is via Canvas and the submis- sion boxes will be open two weeks before the deadline. You are encouraged to submit an early draft to see how the submission process works. Only your last submission will be marked. Anything submitted after the deadline (except for those with ap- proved extensions) will be subject to late penalties. Any late penalty will be applied by the Teaching and Learning Support Office according to the Undergraduate Student Handbook, and any extension has to be approved from the Office too (not from the lecturer) . 10. Whilst this project can be done without the use of any artificial intelligence (AI) software tools, if you use any AI tools or software to help you with your project, you must mention this in the report. Please study the guidelines at https://manchester-uk.libanswers.com/teaching-and-learning/faq/264824 on how to do this correctly. The content and accuracy of the report will be your responsibility alone, and any factually incorrect statements or mathematically incorrect content will be penalised. 11. Codes submitted should match what is written in the report and they will be tested. Please note that falsifying results is a disciplinary offence and any discrep- ancies between output produced by running the codes and what is written in the report will be penalised.
COMP7015 Artificial Intelligence - Group Project Instructions 1. Overall Requirements 1. Groups: Form. a group of 1 to 5 students. Forming groups with students from other sections is allowed. (At least 3 members in a team are recommended) 2. Milestones: o Group Registration & Topic Selection: Due by 11:59 pm, 24th October 2025. Please register your group members and chosen topic via the following link: https://hkbuchtl.qualtrics.com/jfe/form/SV eS9K0CSvz9A9JvU. o Final Submission: Due by 11:59 pm, 21st November 2025. This includes your source code and project report. One submission for each team will be enough. o In-person Presentation: Scheduled for 22nd and 23rd November 2025. A detailed schedule will be announced after the group registration deadline. 3. Final Submission Package: o Project Report: A PDF document of at most five A4 pages (single column). It should describe your project's motivation, methods, results, and a discussion. A mandatory section must detail the contribution of each group member. o Source Codes: A single .zip file. Your code will be evaluated in the FSC 8/F lab environment, so ensure it runs smoothly there. Acknowledge all major third-party libraries (e.g., PyTorch, TensorFlow, Hugging Face Transformers) in your report. 4. Presentation: Each group will have approximately 8 minutes for their presentation, followed by a Q&A session. Every member must present their part of the work. The use of visualizations (figures, graphs, demos) is highly encouraged to clearly convey your project's story. 5. Academic Integrity: o We have a zero-tolerance policy for plagiarism. All submissions will be checked by anti-plagiarism software. Copying code from online sources or generative AI tools without proper citation and understanding is strictly prohibited. o Submission of work from other courses or previous projects is considered self- plagiarism and is not allowed. 2. Topics You may choose one of the following three topics. For Topics 1 and 2, you are expected to build on the suggested tasks. For Topic 3, you have the freedom to define your own project, keeping in mind that the project's scope and difficulty will be evaluated. Topic 1: Human Action Recognition (Computer Vision) This project focuses on building and evaluating deep learning models for human action recognition using the HMDB51 dataset, a standard benchmark in video understanding. HMDB51 contains short video clips spanning 51 categories of everyday actions (e.g., running, walking, clapping). Dataset: 1. This topic is based on the HMDB51 Dataset, available for public download at http://serre-lab.clps.brown.edu/resource/hmdb-a-large-human-motion-database/ 2. You may find the HMDB51 dataset API from torchvision helpful. For more details, see https://docs.pytorch.org/vision/master/generated/torchvision.datasets.HMDB51.html. 3. To keep the project tractable, you may select at least three categories of your choice and frame. the task as a multi-class classification problem. You are encouraged to explore using more classes, but we understand the limit on computing resources, so you will not be penalized for using fewer classes than other groups as long as you use at least three. Minimum Requirements (you secure 50% scores if the following are done correctly): 1. Data split: Split the data into training, validation, and test sets. 2. Frame. extraction & preprocessing: From each video, extract 3-4 frames, combine them into one image, and treat the problem as a static image classification task. 3. Static 2D CNN models: Train at least one CNN from scratch (e.g., a small custom CNN) and one model leveraging transfer learning (e.g., ResNet18/34) for multi-class classification. 4. Basic experimentation: Explore hyperparameters such as learning rate, batch size, and regularization (dropout, data augmentation). 5. Evaluation: Select proper evaluation metrics and analyze the results you obtained. If you aim to score higher than 50%, pick one direction and explore in-depth: 1. Explore advanced modeling techniques for video data, for example, 3D CNNs or combining other temporal models with CNNs. Explore methods (e.g., regularization, normalization, etc.) that could optimize your model performance. 2. Focus on the same human action recognition task, but use a larger dataset (e.g., UCF101), find a suitable pre-trained model, and fine-tune it. This option requires access to more powerful GPU cards. 3. Any other interesting and creative ideas you might have for human action recognition (you are free to explore other datasets or models). You should explain how it connects to the course content. Topic 2: Sentiment Analysis (Natural Language Processing) This project focuses on building and evaluating models for sentiment analysis, a core task in Natural Language Processing. The goal is to classify movie reviews as either positive or negative using the Large Movie Review Dataset (IMDb), a standard benchmark for binary sentiment classification. Dataset: 1. This topic is based on the Large Movie Review Dataset (IMDb). It contains 25,000 movie reviews for training and 25,000 for testing, which are labeled as either positive or negative. The dataset is available for public download at https://ai.stanford.edu/~amaas/data/sentiment/. 2. The primary task is a binary classification problem (positive/negative). You are encouraged to explore more granular classifications (e.g., predicting 1-10 star ratings) for a more challenging project. Minimum Requirements (you secure 50% scores if the following are done correctly): 1. Data Split: Split the provided training data into your own training, validation, and test sets to properly evaluate your models. 2. Text Preprocessing & Vectorization: Clean the raw text data (e.g., remove HTML tags, convert to lowercase). Then, perform. tokenization, build a vocabulary from your training data, and convert your text sequences into integer sequences. Ensure you handle sequences of varying lengths by implementing padding. 3. Deep Learning Model: Implement and train one recurrent neural network (e.g., an LSTM or GRU) for binary classification. Your implementation must include and compare the following two approaches for the embedding layer: A randomly initialized embedding layer that is trained from scratch, along with the rest of your model, and an embedding layer initialized with pre-trained word embeddings (e.g., GloVe or Word2Vec), which can be either frozen or fine-tuned during training. 4. Basic Experimentation: Explore and report on the effect of key hyperparameters for your deep learning model, such as learning rate, batch size, dropout rate, and the number of recurrent units. 5. Evaluation: Select proper evaluation metrics for classification (e.g., accuracy, precision, recall, F1-score). Analyze and compare the results you obtained from using the trainable embedding versus the pre-trained embedding. If you aim to score higher than 50%, pick one direction and explore in-depth: 1. Explore more advanced modeling techniques by fine-tuning a large, pre-trained language model (e.g., a variant of BERT or another suitable model). You should thoroughly analyze the trade-offs in terms of performance improvement, computational cost, and implementation complexity when compared to your recurrent model. 2. Focus on a more complex or nuanced NLP task using the same or a different dataset. This could involve performing fine-grained sentiment analysis by predicting a star rating, tackling aspect-based sentiment analysis, or attempting to detect more subtle linguistic features like sarcasm or irony. 3. Propose and implement any other interesting and creative ideas relevant to sentiment analysis or text classification. You could explore different model architectures, investigate methods for model interpretability to understand why your model makes certain predictions, or apply your models to a completely different domain, such as social media or product reviews. You must clearly explain how your chosen idea connects to the course content. Topic 3: Open Topic If you are interested in another AI problem that aligns with the course content, you may propose your own project. This is an opportunity to explore areas like generative AI, multimodal learning, foundation models, or other advanced deep learning applications. • You are free to choose any relevant dataset and AI/deep learning methods. • Your project should demonstrate a clear understanding of the principles and techniques taught in this course. • Remember that the chosen topic's difficulty, scope, and creativity will be key factors in your evaluation. 3. Evaluation Criteria Your project will be evaluated based on a holistic assessment of your work across several dimensions: • Project Completeness & Model Performance: How thoroughly the project tasks were completed and the effectiveness of your final model(s) on the chosen task. • Creativity & Difficulty: The novelty of your approach, the technical challenge of the problem, and the sophistication of the methods used. • Code Quality: The readability, organization, and documentation of your source code. Clean and well-structured code is expected. • Storytelling (Report & Presentation): The clarity and depth of your project report and presentation. This includes how well you explain your motivation, describe your methodology, analyze your results, and present your conclusions.
Homework 2, due October 9, before class. 24 points total 1) Upon dissolution of A in CD3CN, a sluggish (k = 1.2 x 10-8 s-1) exchange with CH3CN occurs to give complex B. The reaction rate was found to be independent of [CD3CN]. The activation parameters were ΔH‡ = 37.2 kcal/mol and ΔS‡ = +30.2 e.u.. Based on the given data, does this appear to be an associative or dissociative ligand substitution reaction? Explain. (4 points) The reactivity of a similar iridium complex was investigated. It was found that the reaction with a Tp ligand where R = Me occurred with a very high rate (t1/2 < 5 s at 0 °C), while the unsubstituted Tp complex had a much slower ligand substitution rate (t1/2 = 75 min at room temp). Based on this result, does this complex undergo ligand substitution by the same mechanism as the ruthenium complex, or by another mechanism. Explain. (4 points) 2) Treatment of (C2 H4)Ni(P(OAr)3)2 with excess C2 H4 and hydrogen cyanide leads to the formation of an equilibrium mixture of the complexes A, B, and C (of random ratio 0.14:0.8:0.06), based on the appearance of three resonances in the 31 P NMR spectrum, with C as the major isomer. When the equilibrium mixture of complexes A-C was treated with tri(o-tolyl)phosphite, reductive elimination occurred to regenerate the original ethylene complex and produce propionitrile. Study of this reductive elimination showed that: ΔH‡ = 8.9 kcal/mol and the ΔS‡ = -34 eu (at –40 °C). The reaction was found to have a first order dependence on tri(o-tolyl)phosphite concentration. a) Provide a detailed mechanism for this reductive elimination using the major isomer as your example. Briefly explain how the data supports your mechanism. (4 points) b) Can reductive elimination occur effectively from each of the A-C structures? Explain why or why not. (2 points) 3) Which of the following statements concerning the below reaction is not true? (4 points) a) The reaction proceeds through a five coordinate intermediate. b) In the final product, the trans influence of the methyl group makes the M-CO bond weaker. c) The rate of the reaction shows a dependence on [Ni]. d) The rate of the reaction shows a dependence on [CO]. e) The reaction would be slower if the methyls were replaced with mesityls: 4) In the substitution of V(CO)6, the rate of reaction changes with respect to phosphine nucleophile according to the order PMe3 > PBu3 > P(OMe)3 > PPh3. What does this suggest about the mechanism? (2 points). 5) Which of the following two complexes is predicted to be more reactive towards ligand substitution by an associate pathway and why? (4 points)
Assignment 3 BS6203 Task 1: Gestalt is the interplay between the parts and the whole. Kurt Koffka, one of the founding fathers of Gestalt psychology, made a statement about this. He said, “The whole is ‘other’ than the sum of its parts.” This phrase has been translated to the familiar saying, ‘the whole is greater than the sum of its parts’. (e.g. do you see a triangle in the below image) Koffka insisted that the emergent entity is ‘other’ (not greater or lesser) than the sum of the parts. Based on what you understand about Gestalt, what do you think? (Write an one-page reflection) Task 2: Find a graph from a publication (of reasonable complexity). Assess based on gestalt’s principles whether the graph is effective. Propose (and maybe redraw a simplified version to illustrate your idea) how it can be made effective.
COMP 1117-1A Computer Programming [Semester 1, 2025] Assignment 2 Deadline: October 19, 23:59 p.m. About the submission - This assignment involves some console input/output. You are reminded that the VPL system on HKU Moodle evaluates your program with a full score under the condition that your program output is the exact match of the expected output. In other words, any additional or missing space character, tab character, newline character, etc. will be treated as errors during the evaluation of your program. - Write your programs (Assignment 2 Question 1-2) in the corresponding Moodle VPLs under the Assignments section. - We will grade your programs with another set of test cases (i.e., not limited to the sample test cases in the assignment sheet). Therefore, you are advised to make more test cases on your own for testing your program. - Late submission will NOT be accepted. - Plagiarism is prohibited. Please refer to the email sent by the department about plagiarism. Definition of Plagiarism As defined in the University's Regulations Governing Conduct at Examinations, plagiarism is "the unacknowledged use, as one's own, of work of another person, whether or not such work has been published.", or put it simply, plagiarism is copying (including paraphrasing) the work of another person (including an idea or argument) without proper acknowledgement. In case of queries on plagiarism, students are strongly advised to refer to "What is Plagiarism?" at https://tl.hku.hk/plagiarism/. - If a student commits plagiarism, with evidence after investigation, no matter whether the student concerned admits or not, a penalty will be imposed: First Attempt: if the student commits plagiarism (in an assignment/test of a CS course) for the first time in his/her entire course of study, the student shall be warned in writing and receive zero mark for the whole assignment or the whole test; if the student does not agree, s/he can appeal to the BEng(CompSc) Programme Director within a week; Subsequent Attempt: if the student commits plagiarism more than once in higher course of study, the case shall be referred to the Programme Director for consideration. The Programme Director shall investigate the case and consider referring it to the University Disciplinary Committee, which may impose any of the following penalties: a published reprimand, suspension of study for a period of time, fine, or expulsion from the University. - Both the student who copies other's work and the student who offers his/her work for copying shall be penalized. Question 1: Tiered Electricity Bill Calculator (50%) To encourage energy conservation, a city has implemented a tiered electricity pricing system. The rates are as follows: • Tier 1: For monthly consumption up to and including 150 kWh, the rate is $0.50 per kWh. • Tier 2: For the portion of monthly consumption from 151 kWh to 400 kWh, the rate is $0.80 per kWh. • Tier 3: For the portion of monthly consumption exceeding 400 kWh, the rate is $1.20 per kWh. Please write a program that takes the electricity consumption (in kWh) for several consecutive months as input and calculates the total electricity bill for that period. Input Format A single line of integers, representing the electricity consumption in kWh for each month, separated by spaces. Output Format A single floating-point number representing the total bill, formatted to two decimal places. The test cases are as follows. Sample inputs Sample outputs Explanation 100 200 500 400 835.00 Month 1 (100 kWh): 100 * 0.50 = 50.00 Month 2 (200 kWh): 150 * 0.50 + (200 - 150) * 0.80 = 75.00 + 40.00 = 115.00 Month 3 (500 kWh): 150 * 0.50 + (400 - 150) * 0.80 + (500 - 400) * 1.20 = 75.00 + 200.00 + 120.00 = 395.00 Month 4 (400 kWh): 150 * 0.50 + (400 - 150) * 0.80 = 75.00 + 200.00 = 275.00 Total Bill: 50.00 + 115.00 + 395.00 + 275.00 = 835.00 50 150 75 137.50 Month 1 (50 kWh): 50 * 0.50 = 25.00 Month 2 (150 kWh): 150 * 0.50 = 75.00 Month 3 (75 kWh): 75 * 0.50 = 37.50 Total Bill: 25.00 + 75.00 + 37.50 = 137.50 151 300 270.80 Month 1 (151 kWh): 150 * 0.50 + (151 - 150) * 0.80 = 75.00 + 0.80 = 75.80 Month 2 (300 kWh): 150 * 0.50 + (300 - 150) * 0.80 = 75.00 + 120.00 = 195.00 Total Bill: 75.80 + 195.00 = 270.80 401 600 791.20 Month 1 (401 kWh): 150 * 0.50 + 250 * 0.80 + (401 - 400) * 1.20 = 75.00 + 200.00 + 1.20 = 276.20 Month 2 (600 kWh): 150 * 0.50 + 250 * 0.80 + (600 - 400) * 1.20 = 75.00 + 200.00 + 240.00 = 515.00 Total Bill: 276.20 + 515.00 = 791.20 1000 995.00 Month 1 (1000 kWh): 150 * 0.50 + 250 * 0.80 + (1000 - 400) * 1.20 = 75.00 + 200.00 + 720.00 = 995.00 Total Bill: 995.00 Hint: 1. Reading the Input: Read the entire line and split it into a list of strings. You will need to convert each of these strings into a number to perform calculations. 2. Tiered Logic: Think about how to calculate the cost for a usage amount that spans multiple tiers. For example, for 200 kWh, you don't just calculate 200 * 0.80. You need to calculate the cost for the first 150 kWh at the Tier 1 rate, and then the cost for the remaining kWh (from 151 to 200) at the Tier 2 rate. Note: 1. Floating-Point Numbers: The electricity rates are decimal numbers, so your fee calculations will result in floating-point numbers (float). 2. Edge Cases: Consider how your code would handle edge cases. Your loop should handle both cases correctly. 3. Output Formatting: The output bill should be rounded with 2-digit decimal. You can use print(f"{var:.2f}") to format a floating point number var. Question 2: Autokey Cipher (50%) Cryptography is the fundamental discipline of information security. In this question you are expected to implement encoding and decoding of Autokey Cipher. Autokey Cipher is an example of a polyalphabetic substitution cipher. To use the Autokey Cipher to encrypt a message, a coder first chooses a keyword to use. Then the key corresponds to the keyword concatenated with the plaintext. Each letter of the key will tell what cipher(which row) to use for each letter of the message to be coded. The cipher alphabet on the second row uses B for A and C for B, etc. That is cipher alphabet ‘ B,. Each cipher alphabet is named by the first letter in it. For example, if the keyword is LEMON and the message to encode is ATTACKATDAWN, then the encoding is A B C D E F G H I J K L M N O P Q R S T U V W X Y Z B C D E F G H I J K L M N O P Q R S T U V W X Y Z A C D E F G H I J K L M N O P Q R S T U V W X Y Z A B D E F G H I J K L M N O P Q R S T U V W X Y Z A B C E F G H I J K L M N O P Q R S T U V W X Y Z A B C D F G H I J K L M N O P Q R S T U V W X Y Z A B C D E G H I J K L M N O P Q R S T U V W X Y Z A B C D E F H I J K L M N O P Q R S T U V W X Y Z A B C D E F G I J K L M N O P Q R S T U V W X Y Z A B C D E F G H J K L M N O P Q R S T U V W X Y Z A B C D E F G H I K L M N O P Q R S T U V W X Y Z A B C D E F G H I J L M N O P Q R S T U V W X Y Z A B C D E F G H I J K M N O P Q R S T U V W X Y Z A B C D E F G H I J K L N O P Q R S T U V W X Y Z A B C D E F G H I J K L M O P Q R S T U V W X Y Z A B C D E F G H I J K L M N P Q R S T U V W X Y Z A B C D E F G H I J K L M N O Q R S T U V W X Y Z A B C D E F G H I J K L M N O P R S T U V W X Y Z A B C D E F G H I J K L M N O P Q S T U V W X Y Z A B C D E F G H I J K L M N O P Q R T U V W X Y Z A B C D E F G H I J K L M N O P Q R S U V W X Y Z A B C D E F G H I J K L M N O P Q R S T V W X Y Z A B C D E F G H I J K L M N O P Q R S T U W X Y Z A B C D E F G H I J K L M N O P Q R S T U V X Y Z A B C D E F G H I J K L M N O P Q R S T U V W Y Z A B C D E F G H I J K L M N O P Q R S T U V W X Z A B C D E F G H I J K L M N O P Q R S T U V W X Y Vigenère square Plaintext ATTACKATDAWN Key LEMONATTACKA Ciphertext LXFOPKTMDQGN For example, for the first letter ‘A,, its corresponding key is ‘ L,, so the ciphertext should be row L column A in the Vigenère square, that is L. For the second letter in ciphertext, that is X, which corresponds to row E column T in the square. If the key is shorter than the plaintext, then use the plaintext sequentially after the keyword until the plaintext reaches the end. The program first asks for: - A number on the first line(either 1 for encoding, or 2 for decoding). - The plaintext/ciphertext on the second line. - The keyword on the third line. The test cases are as follows. Input Output Explanation 1 ATTACKATDAWN LXFOPKTMDCGN Encryption process LEMON 2 LXFOPKTMDCGN LEMON ATTACKATDAWN Decryption of “ LXFOPKTMDCGN ” should give the original text “ATTACKATDAWN ” 1 PYTHONPROGRAMMING PINEAPPLE EGGLOCECSVPTTAVCX 2 EGGLOCECSVPTTAVCX PINEAPPLE PYTHONPROGRAMMING Hint: - It is guaranteed that the input plaintexts, keywords and ciphers only contain capital letters, so you only need to handle the case for capital letter texts, ciphers and keys. - You can use ord() function to convert a character to its ASCII value. For example, ord(‘A9) gives you the number 65, which is the ASCII value of A. - You can use chr() function to convert an ASCII value to its corresponding character. For example, chr(65) gives you the character A. - It is convenient to use modulo operation to index elements in a list repeatedly. In python we use % to perform. this operation. For example, 15 % 4 returns 3. - You can create the Vigenère square for encoding and decoding. Since the table is regular, you can also use mathematical operation to find the corresponding character in this table without explicitly creating it. Note: Ascii values for English letters are continuous, you can use this property to index the Vigenère square.
COMP1117A Tutorial 4 LOOPS AND ITERATIONS Q1 What is the output of the following program? for i in range(5): print(i, end=',') A. 1,2,3,4,5, B. 0,1,2,3,4, C. 0,1,2,3,4,5, D. 0 1 2 3 4 Q2 What is the output of the following program? x = 'abcd' while j in x: print(j, end=' ') A. abcd B. a b c d C. abcdabcdabcdabcd D. Error Q3 What is the output of the following program? x = 'abcd' j = 'j' while j in x: print(j, end = ' ') A. no output B. j j j j j…(never stop) C. a b c d D. abcd Q4 What is the output of the following program? x = 'abcd' j = 'a' while j in x: print(j, end = ' ') A. no output B. j j j j j…(never stop) C. a a a a…(never stop) D. a Q5 What is the output of the following program? i = 1 while (i < 12): print(i, end = ' ') i += 2 A. 1 B. 2 4 6 8 10 C. 1 2 3 4 5 6 D. 1 3 5 7 9 11 Q6 What is the output of the following program: for i in range(0,5,-1): print(i,end = ' ') A. Error: invalid syntax B. Nothing will be printed C. 5 4 3 2 1 D. 0 -1 -2 -3 …(never stop) Q7 What is the output of the following program? x=2 for i in range(x): x+=1 print(x, end = ' ') A. 0 1 2 3 4 …(never stop) B. 0 1 2 3 C. 0 1 D. 3 4 Q8 What is the output of the following program? for i in range(1,4): print(i,end = ' ') i = i-1 A. 1 2 3 B. 1 0 -1 C. 1 0 -1 …(never stop) D. Nothing is printed Q9 What is the output of the following program? x = 'abcd' for i in range(x): print(i, end=' ') A. a b c d B. abcd C. abcdabcdabcdabcd D. Error
QUIZ 1: BIG DATA Please generate a single PDF file using R Markdown. You may either knit directly to PDF or create an HTML document and convert it to PDF. Once completed, submit the PDF via Turnitin on the course webpage. Caution: Do not set a seed. If you do, no credit will be given for this quiz. The same penalty applies if you do not use R Markdown to generate a single document. When a word limit is specified (e.g., 50 words), do not exceed it; otherwise, no credit will be given. You may count words at https://wordcounter.net/. Total 10 marks (each 1 mark). 1. Import the dataset Carseats from R-package ISLR2. You can view information about the data by typing > ??ISLR2::Carseats This will display a one-line description of the dataset, along with the sample size and number of variables. Reprint the one-line description in your answer (1 mark). 2. In the command window, type > data("Carseats") Explain in 20 words what you see in the Environment tab of RStudio. Specifically, how many observations and variables are in the Carseats dataset? Hint: You may need to click the dataset name to view details. 3. Create a new binary variable, HighSales, to indicating whether Sales is above its median value. Try: > HighSales = median(Carseats$Sales) Explain in 20 words what changed in the Environment tab of RStudio. 4. Remove HighSales, which you created in Q3, using rm(HighSales), and run the following code. > Carseats$HighSales = median(Carseats$Sales) Explain in 20 words what the code right above produces, referring to the Environment tab of RStudio. 5. Additionally, each observation is assigned to the training set with 75% probability and to the test set with 25% probability. The following three lines of R code perform this sample split. > train mean((Carseats.train$Sales - predict(fit,Carseats.train))Λ 2) 9. Compute the training error rate of a logistic regression for the qualitative variable HighSales. Using the training set from Q5, fit a logistic model to predict HighSales using the predictors (X1 , X2 , X3 ) selected above. Let = 1 if Pr(HighSales = 1j(X1 , X2 , X3 ) = (xi1, xi2, xi3)) ≥ 1/2 and = 0, otherwise, for all training observations. Then, make a confusion table and calculate the error rate , i.e., Hint: see the first five pages of the tutorial material for classification analysis. 10. Compute the test error rate for the logistic regression in Q9 using the test sample in Q5.
Week 1 Goals • Set up your environment (we will be working in Python .ipynb files) o Use whichever IDE you prefer (though I use VSCode) o set up your virtual environment in the space (folder, etc.) that you will be doing all your work in (you’ll use the venv command). o Once that is set up, start installing necessary libraries (pip install, or conda install from CLI). Some libraries you will need are: numpy matplotlib scanpy pandas scikit-learn scikit-image celltypist seaborn ipykernel o make sure to also create your jupyter kernel • Add your project folder to GitHub and make sure it’s in your local environment so you can push/pull updates • Load in the data that I will send you and create an anndata object (this is specifically used in the scanpy library and its the object we will use for data manipulation throughout the single cell project) • Explore the dataset and get comfortable with the structure • Perform. some QC on the cells based on gene expression: o Look at how many genes are in each cell o How many cells express each gene o Clean the data up to make sure we are only keeping healthy cells. You can find some documentation on standards, but we want cells that are expressing at least 300 genes and genes that are expressed in at least 3 cells. o Make some plots to show your findings • Deliverables for next week: o .ipynb jupyter notebook file with the start of you work o summary of what you have done, things you,ve learned, and questions for me in PDF format, uploaded to slack prior to our Wednesday meeting
