BU.232.720 Fixed Income Empirical Project 1 In this series of (two) projects, you will analyze the building blocks of fixed-income analytics using data of Treasury bond prices and yields. The following is what you are expected to do in the first project • Read the following article (you can download it from our Canvas course site). John Y. Campbell. “Some Lessons from the Yield Curve,"Journal of Economic Perspectives, Vol. 9, No. 3 (Summer, 1995), 129-152 In particular, read the session of “Understanding the Term Structure" from page 130 to 135; you should be able to understand and explain the concepts in this part. • Download the zero coupon yield curves from our Canvas site. These are the log yields yt,τ of ZC bonds for τ = 1y, 2y, ··· , 30y on each date t. • Do the following calculations and analyses, explaining the procedures and results. – Construct monthly series, using the end-of-month values, for the sample period of January 1981 to December 2018 – Plot the monthly series of the yield curve for maturities of 1y, 2y, ··· , 10y. – In each month, compute the one-year log forward rate ft,τ,1y for maturity τ = 0, 1y, 2y, ··· , 9y. Then plot the monthly series of the 1y forward rate curve. – Compute the modified duration and convexity for τ = 1y, 2y, ··· , 10y. Then plot the monthly series of the two measures. – For all these measures, report their time-series mean and standard deviation as in the following table. What You Need to Submit • The project report • The code you use in generating all the empirical results in the report – The TA Regression Demonstration Session will teach you the key Python procedures in conducting the data analyses, but you are expected to write your own code! – Some notes that discuss the main Python procedures are be provided; see Canvas announcements. – I will check the code to make sure no one simply copies the code from other people (the code will look somewhat different if one really writes his/her own code)!
KLAUSUR Grundlagen der VWL 1: Mikroökonomie 14.02.2024 1. Konsumententheorie (12 Punkte) Marie hat zwei Möglichkeiten, um in die Uni zu fahren. Entweder sie nutzt den Zug (x1) oder das Auto (x2). Eine Bahnfahrt kostet p1 . Wenn sie das Auto nimmt, muss sie Benzinkosten in Höhe von p2 pro Fahrt zahlen. Maries Nachfragefunktion nach Autofahrten sei gegeben durch a) Wie lautet die Gleichung für Maries Budgetgerade? (1 Punkt) b) Wie oft fährt Marie bei einem Einkommen von m = €50 mit dem Auto, wenn die Benzinkosten p2 = €2,50 pro Fahrt betragen? (1 Punkt) c) Ist die Nachfrage nach Autofahrten ein normales oder ein inferiores Gut? Begründen Sie mathematisch. (2 Punkte) d) Es wird eine Steuer aufCO2 eingeführt, welche die Benzinkosten pro Fahrt auf p2 = €3,00 erhöht. Die Preisänderung ist im folgenden Diagramm dargestellt. Die Punkte A, B, C, D bezeichnen jeweils Punkte auf der x2 -Achse. Die Differenz welcher Punkte (zum Beispiel (AB)) stellt den Substitutionseffekt für Gut 2 dar? Die Differenz welcher Punkte den Einkommenseffekt? Welches Vorzeichen haben der Substitutions- und der Einkommenseffekt? (2 Punkte) Substitutionseffekt: , Vorzeichen: Einkommenseffekt: , Vorzeichen: e) Die Einnahmen aus der CO2 Steuer werden pauschal als Klimageld an die Bürger zurückgezahlt. Wie hoch muss Maries neues Budget inkl. Klimageld sein, damit Marie bei einem neuen Preis von p2 = €3,00 gleich viele Autofahrten konsumieren kann wie vor Einführung der Steuer? Wie hoch ist das Klimageld? (3 Punkte) f) Wenn das Klimageld genauso hoch ist, dass Marie weiterhin gleich viele Autofahrten konsumieren kann, wird sie das tun? Begründen Sie (mathematisch, graphisch oder verbal). (3 Punkte) 2. Theorie der Produktion (12 Punkte) Ein Unternehmen produziert gebrannte Mandeln aus Inputfaktoren Mandeln (x1) und Zucker (x2). Der Preis pro Einheit Mandeln ist w1 = €4 und der Preis pro Einheit Zucker w2 = €1. Die Produktionstechnologie ist gegeben durch a) Was ist das Grenzprodukt und Durchschnittsprodukt des Inputfaktors x2? (2 Punkte) GPx2 = DPx2 = b) Wie lautet die Optimalitätsbedingung für eine kosteneffiziente Verwendung der Inputfaktoren? (1 Punkt) c) Wie viele Einheiten von x2 werden verwendet, wenn eine Outputmenge von y = 5 produziert werden soll? (2 Punkte) x2 = d) Von welchem der beiden Inputfaktoren wird im Optimum mehr eingesetzt? Begründen Sie kurz, warum. (1 Punkt) e) Das Marktangebot an gebrannten Mandeln sei gegeben durch die Funktion Qs (p) = 20 + 10p (für p ≥ 0, ansonsten Qs (p) = 0), die Marktnachfrage sei gegeben durch die Funktion QD (p) = 80 − 5p Zeichnen Sie die inverse Nachfragefunktion und die inverse Angebotsfunktion in untenstehendes Diagramm ein. Beschriften Sie die Geraden sowie ihre Schnittpunkte mit den beiden Achsen (Interzepte). (2 Punkte) f) Berechnen Sie den Gleichgewichtspreis und die Gleichgewichtsmenge. (1 Punkt) p* = Q* = g) Zeichnen Sie die Konsumentenrente und Produzentenrente in das Diagramm von Teilaufgabe e) ein. (1 Punkt) h) Berechnen Sie die Höhe der Konsumentenrente und die Produzentenrente. (2 Punkte) Konsumentenrente = Produzentenrente = 3. Externe Effekte (12 Punkte) Die Besitzerin einer Wiese veranstaltet dort einmal im Jahr ein kleines Festival. Hierfür verkauft sie die Anzahl von x Tickets je Festival. Sie werden auf einem kompetitiven Markt zum Preis p = 180 verkauft. Je verkauftem Ticket fallen für die Veranstalterin Kosten in Höhe von C(x) = 60/1 x3 an. Durch das Festival wird jedoch viel Lärm verursacht. Die Lärmbelästigung nimmt mit der Anzahl an verkauften Tickets zu und kann durch folgenden Zusammenhang beschrieben werden: L(x) = 20/1 x. a) Bestimmen Sie den gewinnmaximierenden Output x, den daraus resultierenden Gewinn π , sowie die Höhe an Lärm L. (3 Punkte) x = π = L = b) Durch den Lärm werden umliegende Anwohner beeinträchtigt. Ihre aggregierte Nutzenfunktion kann beschrieben werden durch U(L) = 1000 − 2000L. Die Stadt hat ein Interesse daran, die Summe der Nutzen der Nachbarn und der Veranstalterin zu maximieren und agiert als sozialer Planer. Stellen Sie das Nutzenmaximierungsproblem auf, vor dem die Stadt steht. Wie hoch ist L in dem Fall im Optimum? (3 Punkte) L = c) Es wird festgelegt, dass die maximal zulässige Menge an Lautstärke bei L = 1 liegt. Zur Umsetzung entscheidet der Stadtrat, eine Abgabe A auf jedes verkaufte Ticket einzuführen, die die Veranstalterin bezahlt. Wie hoch muss die Stadt die Abgabe setzen, damit die Vorgabe eingehalten wird? (2 Punkte) A = d) Wird dieVeranstalterintrotz der Abgabe A weiterhin das Festival betreiben? Begründen Sie, wieso (nicht). (2 Punkte) e) Der Stadtrat beschließt, anstatt der Abgabe A eine Pigou-Steuer je verkaufter Karte einzuführen. Die Steuer wird ebenfalls von der Veranstalterin abgeführt. Wie hoch muss die Steuer sein (in %), um weiterhin die maximal zulässige Menge an Lautstärke bei L = 1 zu halten? (2 Punkte) t = 4. Komparative Kostenvorteile (12 Punkte) Es gibt 2 Länder, Österreich und Deutschland. In jedem Land gibt es 360 Arbeiter. Die Arbeiter können entweder Wiener Schnitzel (ws) oder Krapfen (K) produzieren, haben jedoch eine unterschiedliche Produktivität. Die Arbeiter konsumieren Mahlzeiten, die immer genau aus einem Wiener Schnitzel und 2 Krapfen bestehen. Ihre Präferenz für Essen ist demnach gegeben durch U = min {ws, 2k}. Es wird produziert, um diese Nachfrage zu bedienen. Die folgende Tabelle stellt die Produktivitäten der Arbeiter dar: a) Welches Land hat den absoluten Wettbewerbsvorteil in der Produktion von Wiener Schnitzeln? Welches Land hat den absoluten Wettbewerbsvorteil in der Produktion von Krapfen? Begründen Sie. (1 Punkt) b) Autarkie. Wenn die Länder nicht miteinander handeln, also sich in der Autarkie befinden, welche Zuteilung der Arbeiter maximiert die weltweite Produktion? Tragen Sie Ihre Lösung in die Tabelle ein und erklären Sie. (3 Punkte) nzahlProduzierte Güter, AnzahlWienerSchnitzel WSKrapfen KWienerSchnitzel WSKrapfen KÖsterreich(Ö)Deutschland(D)WeltproduktionZugeteilte Arbeiter, A 5. Kurzfragen-Block (12 Punkte) Erläutern Sie kurz, ob und warum die folgenden Aussagen richtig oder falsch sind. a) Zwei Profisportler stehen vor der Entscheidung, ob sie für eine erhöhte Leistungsfähigkeit auf Doping zurückgreifen sollen oder nicht. Doping gibtihnen einen Vorteil im Wettkampf, aber führt auch zu langfristigen gesundheitlichen Schäden und kommt mit der Gefahr, erwischt zu werden. Die numerischen Payoffs der Sportler sind gegeben als: Sportler 2 Sportler 1 Es ist eine strikt dominante Strategie, dass beide Sportler kein Doping betreiben werden. (2 Punkte) b) Indifferenzkurven können nicht kreisförmig sein. (2 Punkte) c) Bei gegebenem Einkommen m und gegebenen Preisen aller Güter ist die Nachfragefunktion von Julian gegeben durch Die Eigenpreiselastizität von Julians Nachfrage nach Gut x1 ist eine Funktion seines Einkommens m. (2 Punkte) d) Preisdiskriminierung ersten Grades durch Monopolisten führt zu Ineffizienzen am Markt. (2 Punkte) e) Durchschnittskostenkurven folgen immer einem U-förmigen Lauf, das heißt, sie fallen zunächst und steigen dann an. (2 Punkte) f) Im Optimum nutzt ein Unternehmen folgende Inputkombination: (x1, x2) = (5, 13). Der Preis von Input x1 erhöht sich von 60€ auf 66€ . Der Preis von Input x2 steigt von 40€ auf 44€ . Weil der Anstieg von 4€ je Einheit geringer ist als 6€ je Einheit, wird das Unternehmen nach den Preisanstiegen im Verhältnis zur verwendeten Menge von Input x1 mehr von Input x2 nutzen als zuvor. (2 Punkte)
MASTER OF SCIENCE IN MANAGEMENT AND SYSTEMSApplied Project CapstoneMASY GC- 4100MEMORANDUMTO: Spring 2025 AP Capstone StudentsDATE: January 3, 2025RE: UNICC Phase 2 Capstone Projects for Your ParticipationWe are pleased to inform NYU Capstone students about a set of capstone projects available for their consideration. NYU SPS and The Digital Forge lab have been selected by the UNICC to work on a media analysis tool to detect inappropriate language usage in media communications. This is a continuation of the successful Phase 1 Fall 2024 projects. The Sponsor:The United Nations International Computing Centre (UNICC) has over 50 years of experience as the largest strategic partner for digital solutions and cybersecurity within the United Nations system. They are pleased to sponsor NYU MASY students in a project competition. The Final Phase 2 ProductThe product is an AI-driven media analysis tool designed to enhance the capacity of media outlets to report ethically and accurately on topics related to refugees, migrants, and other forcibly displaced populations. This tool will support the detection and prevention of xenophobic language, misinformation, and harmful content in media environments, ultimately fostering more informed and empathetic public discourse. The final tool will add multi-language and multimodal (written, audio and video) capabilities to the original prototype.The ProjectsWe have divided the functions of the product into four capstone projects to be completed by a team of four students. Each student completes their project, and the team delivers the integrated product as their entry into the competition. We expect multiple teams of students to compete for the first prize: acceptance by the UNICC as the best.How do you choose a project and get involved?Please review the four project definitions and choose one that interests you. Coordinate with three other capstone students to select the remaining projects as part of a team. Once you have your team, present yourselves as a group with each student and their part of the project clearly identified. Give your selves a team name. Send applications to Dr. Fortino and copy Siri Kostanyan. We anticipate forming of three to four teams, but there's no limit to the number of teams that can be formed. Please note that individual applications for this project will not be considered; you must apply as part of a team.Additional detailsThe projects are three-month engagements, and we have provided all the necessary details below, including information about the company and executive supervisor. Your direct client with whom you will be interfacing with directly will be the product manager, Mrs. Siri Kostanyan, who works for The Digital Forge. We understand that your time is valuable, but we assure you that this is a worthwhile experience, and the organizations and client lead contact have committed to supporting the project with the seriousness it deserves. Upon completion of the project, you are welcome to include it on your resume and use the results in your portfolio. If desired, we can also provide a reference for job applications. Additionally, successful completion of the project may lead to be invited to co-author a research paper with the client sponsors.To apply for consideration for any of these projects, send a cover email to Dr. Fortino ([email protected]), AS A TEAM, with the following:1.Which capstone students will be doing which project for the product2.All parts of the project must be covered; in other words, there have to be four members of your team.3.Include the resume and NYU transcript of each team member.MASY Clinical Associate ProfessorCompany and Sponsor InformationCompany NamesThe Digital ForgeNYU School of Professional Studies and the Management and Systems program (MASY), is a New York-based learning institution.The UNICC (International Computing Center)Ms. Anusha Dandapani, Center Director Company Location NYU School of Professional Studies is at 12 West 43rd Street, NY, NY.Project SponsorThe principal project sponsor for all projects will be Dr. Andres Fortino, Clinical Associate Professor, NYU (https://www.linkedin.com/in/afortino), and Mrs. Siri Kostanyan, MSPM, The ClientsCompany and Sponsor's LocationDr. Andres Fortino ([email protected]) can be reached over virtual conference calls as per project requirements.Mrs. Siri Kostanyan ([email protected]) is available for consultations and support via email or virtual meetings as needed to ensure project success.Description of the Business New York University (NYU) is a private research university based in New York City. The MASY degree is based on a unique curriculum that provides students with experiential learning opportunities to develop strong management and leadership skills and gain a comprehensive knowledge of current information technologies.The United Nations International Computing Centre (UNICC) has over 50 years of experience as the largest strategic partner for digital solutions and cybersecurity within the United Nations system.Relationship to the ClientThe Client’s relationship with the Project Manager will be that of an independent contractor, and nothing in this sponsorship is intended to or should be construed to, create a partnership, agency, joint venture, or employment relationship. Note: use this information to create your project proposal.Project Elements and Deliverables1.In consultation with the client, create a set of functional objectives with deliverables and due dates to break down your project. 2.A clearly defined modularization of the project.3.At least four meetings with the client during the project: a.Initial meeting to launch projectb.Second meeting no more than two weeks after launch to review objectivesc.Third meeting to review progress no more than two months after launchd.A final meeting focused on presenting results and handing in deliverables.e.These meetings are to be arranged by the project manager (that’s you!).4.The final report for each project must conform to the template provided by the client.5.All final project files and a README user document must be deposited in a public GitHub repository.6.A Team Deliverable of the integrated product ready to present to UNICC7.Presentation of your product to NYU and UNICC by your team during a day of competition at the end of the semester.Additional Requirements1.All steps in the project must be well documented as the project progresses.2.Weekly written and emailed summary progress reports must be provided. They must include a.what was just accomplished in the past week, b.what you are working on in the coming week c.and any problems you are encountering that need resolution and input from the client.AI-Driven Media Analysis Tool (Phase 2)ObjectiveThe product is an AI-driven media analysis tool designed to enhance the capacity of media outlets to report ethically and accurately on topics related to refugees, migrants, and other forcibly displaced populations. This tool will support the detection and prevention of xenophobic language, misinformation, and harmful content in media environments, ultimately fostering more informed and empathetic public discourse. The final delivered product will incorporate multi-language capabilities, analyzing content in all six official UN languages, as well as multimodal functionalities to process text, audio, and video.Scope of the ProductThe AI-driven media analysis tool will consist of four integrated functions, each serving a specific purpose to ensure comprehensive analysis and support for journalists and media professionals. Teams will review previous prototypes (developed by Fall 2024 teams) and either extend or emulate one as their foundation.The Tool’s Original Functions (Fall 2024) Phase 1The tool builds upon the foundational functionalities developed in Fall 2024, which include the following key features:1.Identification of Xenophobic Language and Mis/Disinformation○Functionality: Detect and flag xenophobic language, racist attitudes, incorrect data, and stereotypes related to human mobility. Analyze media content in real-time to identify harmful narratives that perpetuate discrimination or hostility.○Outcome: Assist media professionals in identifying and avoiding harmful language, fostering a respectful and accurate portrayal of refugees and migrants.2.Fact and Language Checking○Functionality: Verify the accuracy of language and data, focusing on terminology related to migrants and displaced populations. Cross-reference media content with a verified database of facts and terminology to prevent misinformation.○Outcome: Provide journalists with a reliable resource for fact-checking and language verification, supporting ethical journalism practices.3.Topic-Based Analysis on Harmful Content○Functionality: Perform topic-based analysis of media content to identify and categorize harmful narratives related to migration and displacement. Highlight topics contributing to negative perceptions or misinformation about displaced communities.○Outcome: Offer insights into harmful content, enabling media professionals to take corrective actions and promote balanced reporting.4.Integration and Testing○Functionality: Combine all components developed into a cohesive system and ensure functionality through rigorous testing.○Outcome: Deliver a functional prototype that integrates xenophobic language detection, fact-checking, and topic-based analysis into a single reliable tool.Expanded Capabilities for 2025 Phase 2Building on these foundations, the Spring 2025 iteration introduces four new and enhanced functionalities to expand the tool’s capabilities:1.Multi-language Capability○Functionality: Analyze content in any of the six official UN languages (Arabic, Chinese, English, French, Russian, and Spanish), expanding the tool's inclusivity and global relevance.○Outcome: Empower media professionals to work with diverse linguistic content, promoting ethical journalism across cultures and languages.2.Audio Analysis○Functionality: Process journalistic pieces in audio format, such as podcasts and radio programming, by transcribing and analyzing spoken content.○Outcome: Enable media professionals to assess audio media with precision, detecting xenophobic language and misinformation in spoken-word formats.3.Video Analysis○Functionality: Analyze journalistic pieces in video format, including videocasts and television news, by processing both visual and auditory elements.○Outcome: Equip media professionals to evaluate video content comprehensively, ensuring balanced and accurate reporting across multimedia platforms.4.Integration, Design, and Testing○Functionality: Integrate the multilingual, audio, and video analysis features into a unified, user-friendly system. Design an intuitive interface and conduct thorough testing to ensure the tool meets performance standards.○Outcome: Deliver a fully operational and reliable tool that combines all new capabilities, providing media professionals with a seamless platform for ethical and accurate reporting.Implementation and Deployment as NYU Capstone ProjectsDevelopment and Implementation PlanThe development of the AI-driven media analysis tool will be managed pro bono by Siranush 'Siri' Kostanyan, who will serve as the Product Manager. The tool will be developed by teams of four Capstone students from New York University, under the leadership and guidance of Dr. Andres Fortino and in collaboration with UN representatives. The student groups will compete to present the most effective solution, with the winning capstone project selected by UN representatives.The project is structured to be completed over a three-month period, divided into the following phases with key deliverables:1.Research and Definition○Activities: Conduct initial research, define the project scope, and set up the development environment.○Deliverable: Functional Requirements Specification (FRS).2.Prototype Development○Activities:■Data Collection and Preprocessing: Gather and prepare data for the AI models.■Model Development: Design, train, and validate machine learning models.■User Interface Development: Design and develop a user interface that integrates with the AI models.○Deliverable: Product Prototype.3.Proof of Concept○Activities: Integrate all components and conduct comprehensive testing to ensure the system functions as expected.○Deliverable: Proof of concept through rigorous testing.4.Final Documentation and Deployment○Activities: Document the entire process, prepare user guides, and deploy the final product.○Deliverable: Complete documentation and successful deployment of the tool.Each phase will include specific deliverables, such as the development of multimodal and multilingual analysis features, the creation of a user-friendly interface, and comprehensive testing to ensure the tool’s reliability and effectiveness. Mrs. Siranush 'Siri' Kostanyan will oversee the entire process to ensure that the product meets its objectives and is delivered on time.Expected ImpactThe AI-driven media analysis tool is expected to empower media organizations and content creators to report more accurately and sensitively on issues concerning refugees and other forcibly displaced people. By leveraging advanced AI technology, the tool facilitates fact-based reporting and fosters mutual understanding between displaced and host populations. The tool aims to bridge the gap between communities, ensuring that media narratives are informed, inclusive, and conducive to building empathy and understanding across diverse audiences.Breakdown of Functions to Capstone Projects for Spring 2025Breakdown of Functions to Capstone ProjectsThe development of the AI-driven media analysis tool is an ongoing, multifaceted initiative designed to address xenophobic language and misinformation in media reporting. This initiative builds upon the foundational prototypes developed by Capstone teams in Fall 2024. These prototypes focused primarily on analyzing written content in English, providing a robust starting point for this semester's enhanced functionality.For Spring 2025, we are expanding the scope of the tool to include multilingual capabilities, audio and video analysis, and comprehensive system integration. These enhancements are divided into four distinct Capstone projects, ensuring that each critical feature is fully developed, tested, and refined. This structure allows students to build on the achievements of Fall 2024 while addressing new challenges and advancing the tool’s capabilities.The Capstone projects for Spring 2025 are as follows:1.Multilingual Analysis○Objective: Enable the tool to analyze content in all six official UN languages (Arabic, Chinese, English, French, Russian, and Spanish).○Focus: Extend linguistic capabilities by implementing advanced natural language processing (NLP) techniques for multilingual input.○Outcome: Equip media professionals with the ability to process diverse linguistic content, fostering inclusivity and broader usability.2.Audio Analysis○Objective: Develop the ability to process and analyze audio files, including radio programming, podcasts, and other spoken-word content.○Focus: Train machine learning models to evaluate audio inputs for detecting xenophobic language and misinformation.○Outcome: Allow media professionals to assess spoken content with the same precision as text analysis.3.Video Analysis○Objective: Extend the tool’s functionality to process and analyze video content, such as news broadcasts, videocasts, and social media posts.○Focus: Integrate multimodal analysis to evaluate both visual and auditory components in video media.○Outcome: Provide comprehensive insights into video-based narratives, ensuring balanced and ethical reporting across all media formats.4.Integration, Design, and Testing○Objective: Integrate all developed components into a seamless, unified system with a user-friendly interface.○Focus: Perform system integration, intuitive user interface (UI) design, and thorough testing to ensure reliability and functionality.○Outcome: Deliver a fully operational AI-driven tool that combines multilingual, audio, and video analysis for ethical media reporting.Strategic Implementation:●Teams will consist of four students: three members will focus on specific deliverables (multilingual, audio, and video analysis), while the fourth will manage integration and testing.●Teams will utilize insights and reports from the Fall 2024 prototypes as a foundation, ensuring continuity and improvement in the tool’s development.●The competition format remains the same, with multiple teams working on the same framework to produce the most effective and impactful solution.This semester’s enhancements aim to deliver a sophisticated, multimodal, and multilingual AI-driven tool, addressing modern media’s ethical challenges and providing actionable solutions for journalists and media professionals.Capstone Project 1: Multilingual Analysis (Phase 2 Spring 2025)Project OverviewThis project focuses on expanding the AI-driven media analysis tool's capabilities to support multilingual analysis. The tool will process and analyze content in all six official UN languages: Arabic, Chinese, English, French, Russian, and Spanish. By leveraging advanced natural language processing (NLP) techniques, the project aims to ensure accurate detection of xenophobic language, misinformation, and harmful narratives across diverse linguistic contexts.Project Goals and ObjectivesGoal: Develop a multilingual analysis feature that processes and analyzes media content in six languages, ensuring inclusivity and cultural sensitivity.Objectives:●Fine-tune NLP models to analyze media content in the six official UN languages.●Develop a robust data pipeline to collect and preprocess multilingual datasets.●Create a scalable architecture to accommodate language-specific nuances and complexities.●Test and validate the multilingual functionality to ensure accuracy and reliability.Project RoadmapPhase 1: Initial Research and Setup (Weeks 1-3)●Deliverable 1: Conduct a literature review on multilingual NLP models and techniques.●Deliverable 2: Define the scope of multilingual analysis, including key criteria for language-specific challenges.●Deliverable 3: Set up the development environment and tools for multilingual dataset collection and preprocessing.Phase 2: Data Collection and Preprocessing (Weeks 4-6)●Deliverable 4: Build a data pipeline to collect diverse datasets in the six UN languages.●Deliverable 5: Preprocess the data to account for linguistic variations, such as syntax, grammar, and idiomatic expressions.●Deliverable 6: Create balanced, labeled datasets tailored for model training and fine-tuning.Phase 3: Model Development and Training (Weeks 7-9)●Deliverable 7: Fine-tune NLP models for each language, focusing on detecting xenophobic language and misinformation.●Deliverable 8: Optimize the models for precision, recall, and overall accuracy.●Deliverable 9: Validate the performance of each model using language-specific test datasets.Phase 4: Integration and Testing (Weeks 10-12)●Deliverable 10: Integrate the multilingual functionality into the AI-driven tool’s existing architecture.●Deliverable 11: Conduct end-to-end testing of the tool with multilingual datasets to ensure seamless operation.●Deliverable 12: Refine the multilingual analysis feature based on feedback and test results.Final Phase: Presentation and Documentation (Week 13)●Deliverable 13: Prepare a final report detailing the development process, challenges, and outcomes.●Deliverable 14: Present the multilingual analysis tool to stakeholders, showcasing its capabilities and real-world applications.●Deliverable 15: Submit all code, documentation, and the final report to a public GitHub repository for future reference and potential further development.Expected OutcomesBy the end of this Capstone project, the team will deliver a multilingual analysis feature capable of processing media content in six languages. The feature will be tested, validated, and ready for integration into the broader media analysis tool, enabling ethical, accurate, and inclusive media reporting across diverse linguistic contexts.Capstone Project 2: Audio Analysis (Phase 2 Spring 2025)Project OverviewThis project focuses on developing the AI-driven media analysis tool’s capability to process and analyze audio content, including journalistic pieces such as podcasts, radio programming, and other spoken-word formats. By leveraging state-of-the-art audio processing and natural language processing (NLP) techniques, the project aims to detect xenophobic language, misinformation, and harmful narratives embedded in audio media.Project Goals and ObjectivesGoal: Develop an audio analysis feature that processes journalistic content in spoken formats to identify and address harmful narratives.Objectives:●Implement audio processing pipelines to transcribe and analyze spoken content.●Fine-tune NLP models for detecting xenophobic language and misinformation in transcribed audio.●Ensure the system accounts for variations in accents, dialects, and languages across diverse audio sources.●Validate the tool’s performance with real-world audio datasets.Project RoadmapPhase 1: Initial Research and Setup (Weeks 1-3)●Deliverable 1: Conduct a literature review on audio processing and speech-to-text technologies.●Deliverable 2: Define the scope of audio analysis, including key challenges such as background noise and speaker variability.●Deliverable 3: Set up the development environment and tools for processing audio files.Phase 2: Audio Data Collection and Preprocessing (Weeks 4-6)●Deliverable 4: Build a data pipeline to collect diverse audio datasets, including podcasts and radio content.●Deliverable 5: Preprocess audio files by cleaning and normalizing sound quality for consistent transcription accuracy.●Deliverable 6: Use speech-to-text models to create transcriptions, ensuring high accuracy for downstream analysis.Phase 3: Model Development and Training (Weeks 7-9)●Deliverable 7: Train and fine-tune NLP models to analyze transcribed audio for harmful language and misinformation.●Deliverable 8: Optimize the models to handle speaker variations, accents, and context-specific language.●Deliverable 9: Validate the models using real-world audio datasets and assess their accuracy and performance metrics.Phase 4: Integration and Testing (Weeks 10-12)●Deliverable 10: Integrate the audio analysis feature into the AI-driven media analysis tool’s architecture.●Deliverable 11: Conduct end-to-end testing with audio content to ensure seamless functionality.●Deliverable 12: Refine the audio analysis tool based on user feedback and test results.Final Phase: Presentation and Documentation (Week 13)●Deliverable 13: Prepare a final report documenting the development process, challenges, and outcomes.●Deliverable 14: Present the audio analysis tool to stakeholders, demonstrating its capabilities and potential applications.●Deliverable 15: Submit all code, documentation, and the final report to a public GitHub repository for future reference and potential further development.Expected OutcomesBy the end of this Capstone project, the team will deliver an audio analysis feature capable of processing and analyzing journalistic audio content. The feature will be tested, validated, and ready for integration into the broader media analysis tool, empowering media professionals to assess spoken content with precision and ethical rigor.Capstone Project 3: Video Analysis (Phase 2 Spring 2025)Project OverviewThis project focuses on expanding the AI-driven media analysis tool to process and analyze video content, including journalistic pieces such as videocasts, television news, and other video-based formats. By incorporating advanced computer vision and natural language processing (NLP) techniques, the project aims to detect xenophobic language, misinformation, and harmful narratives in both the visual and auditory components of video media.Project Goals and ObjectivesGoal: Develop a video analysis feature that processes journalistic content in video formats to identify harmful language, misinformation, and other unethical narratives.Objectives:●Implement computer vision models to analyze visual content, such as text overlays and imagery.●Utilize speech-to-text technology to transcribe audio components of videos for further analysis.●Fine-tune NLP models to evaluate transcribed audio and subtitles for harmful narratives.●Validate the system’s performance across diverse video sources and contexts.Project RoadmapPhase 1: Initial Research and Setup (Weeks 1-3)●Deliverable 1: Conduct a literature review on computer vision techniques and video processing technologies.●Deliverable 2: Define the scope of video analysis, including challenges such as varying resolutions, languages, and media formats.●Deliverable 3: Set up the development environment and tools for processing video files.Phase 2: Video Data Collection and Preprocessing (Weeks 4-6)●Deliverable 4: Build a data pipeline to collect a diverse set of video content, including television news and videocasts.●Deliverable 5: Preprocess video files to ensure compatibility with analysis tools, including audio extraction and frame sampling.●Deliverable 6: Use speech-to-text models to transcribe audio components and extract subtitles for downstream analysis.Phase 3: Model Development and Training (Weeks 7-9)●Deliverable 7: Develop and fine-tune computer vision models to analyze visual elements, including on-screen text and imagery.●Deliverable 8: Train NLP models to evaluate transcribed audio and subtitle content for detecting harmful narratives.●Deliverable 9: Validate the integrated video analysis models using diverse datasets to ensure accuracy and reliability.Phase 4: Integration and Testing (Weeks 10-12)●Deliverable 10: Integrate video analysis capabilities into the AI-driven media analysis tool’s existing architecture.●Deliverable 11: Conduct end-to-end testing with real-world video content to assess functionality and performance.●Deliverable 12: Refine the video analysis tool based on user feedback and test results.Final Phase: Presentation and Documentation (Week 13)●Deliverable 13: Prepare a final report documenting the development process, challenges, and outcomes.●Deliverable 14: Present the video analysis tool to stakeholders, demonstrating its capabilities and real-world applications.●Deliverable 15: Submit all code, documentation, and the final report to a public GitHub repository for future reference and potential further development.Expected OutcomesBy the end of this Capstone project, the team will deliver a video analysis feature capable of processing and analyzing journalistic video content. The feature will be tested, validated, and ready for integration into the broader media analysis tool, enabling media professionals to assess video narratives with ethical and analytical precision.Capstone Project 4: Integration, Design, and Testing (Phase 2 for Spring 2025)Project OverviewThis project focuses on integrating the distinct components of the AI-driven media analysis tool—multilingual, audio, and video analysis—into a unified, user-friendly system. The team will design a seamless user interface (UI) that enables media professionals to access and utilize all functionalities efficiently. Rigorous testing will ensure that the tool meets performance standards, is reliable, and delivers accurate results across diverse use cases.Project Goals and ObjectivesGoal: Create a fully integrated and tested media analysis tool that combines multilingual, audio, and video capabilities within a cohesive platform.Objectives:●Integrate multilingual, audio, and video analysis components into a single system.●Design an intuitive and accessible UI that facilitates easy navigation and functionality for media professionals.●Conduct comprehensive testing, including functional, performance, and user acceptance testing (UAT).●Optimize the tool based on user feedback and test results to ensure reliability and usability.Project RoadmapPhase 1: System Integration (Weeks 1-4)●Deliverable 1: Develop a system architecture plan to integrate multilingual, audio, and video analysis features.●Deliverable 2: Implement APIs and backend services to unify the functionalities into a single system.●Deliverable 3: Ensure compatibility and interoperability between all components.Phase 2: UI/UX Design and Development (Weeks 5-8)●Deliverable 4: Design an intuitive UI that incorporates all features, ensuring accessibility and ease of use.●Deliverable 5: Develop the frontend interface and integrate it with the backend architecture.●Deliverable 6: Test the UI for usability and accessibility, gathering feedback for iterative improvements.Phase 3: Comprehensive Testing (Weeks 9-11)●Deliverable 7: Conduct functional testing to verify the accuracy and reliability of each integrated component.●Deliverable 8: Perform performance testing to ensure the tool operates efficiently under various workloads.●Deliverable 9: Complete user acceptance testing (UAT) with media professionals, collecting feedback for refinement.Phase 4: Refinement and Deployment (Weeks 12-13)●Deliverable 10: Refine the tool based on testing results and user feedback, ensuring reliability and usability.●Deliverable 11: Prepare the tool for deployment, including final checks and optimizations.●Deliverable 12: Provide training materials and user guides to facilitate adoption by media professionals.Final Phase: Presentation and Documentation (Week 13)●Deliverable 13: Prepare a comprehensive final report documenting the integration process, challenges, and outcomes.●Deliverable 14: Present the fully integrated tool to stakeholders, showcasing its capabilities and applications.●Deliverable 15: Submit all code, documentation, and the final report to a public GitHub repository for future reference and potential further development.Expected OutcomesBy the end of this Capstone project, the team will deliver a fully operational, integrated media analysis tool. The system will combine multilingual, audio, and video analysis capabilities within a cohesive platform, providing media professionals with a powerful resource for ethical and accurate reporting. The tool will be thoroughly tested, optimized, and ready for deployment in real-world media environments.
Assignment 4 - Emoji Carousel Due Date: April 8th, 2024 at 23:55 Percentage overall grade: 5% Penalties: No late assignments allowed Maximum Marks: 100 Pedagogical Goal: Refresher of Python and hands-on experience with algorithm coding, input validation, exceptions, ANSI escape codes, queues, doubly linked lists and data structures with encapsulation. Read the whole document before starting to solve the Assignment problem. Submit only your work. While you can collaborate and help each other, do not share code. If you collaborate, acknowledge your collaborator in the code comments. Follow the Software Quality Requirements. Assignment Specifications: Assignment 4 FAQ Problem: Emoji Carousel You are an employee at Zoogle Inc., and fortunately, you are part of an R & D team responsible for creating blueprints for various features of their popular operating system. They never had a “Circular Image Carousel” feature built into their OS. Your job is to create a bare-bones interpretation of that idea using a Circular Doubly-Linked List. For this assignment, instead of images, you will use emojis from a JSON file (Remember them from CMPUT 174? Refer to the refresher at the bottom of the assignment description). Each element of this carousel will be a doubly-linked list node, which will have a reference to the next/”right” node, previous/”left” node, and data about what is in that node. The doubly-linked list node class should have methods to: 1. Initialize the attributes of the class 2. Retrieve the data stored in it 3. Change the data stored in it 4. Get the previous/left node 5. Get the next/right node 6. Set the previous/left node 7. Set the next/right node Any other required methods can be created as per your needs. Appropriate Exceptions should be raised if necessary. Your carousel will be a fixed-length modified doubly-linked list — Going right/next on the last element will take you back to the first one, and going left/previous on the first element will take you to the last element. There are multiple ways to create an efficient class for this, but the class should have methods for initializing, adding a node, removing a node, and getting the active/current node. Initializing proper attributes is also based on your understanding of the project. The class should be named CircularDoublyLinkedList or DoublyLinkedList. You have to create a visual depiction with shapes and emojis. For this task, you will use a dataset of emojis in a JSON file and a art.txt file with the shapes you need in order to create a visual representation. The art.txt file has the ASCII art needed for the visual representation. All the figures are essential and should be present in your version of art.py as they handle different cases of visual representation of the carousel. With slight modifications and formatting, you can put emoticons in the place of “().” (Hint: You might need to look up how to print symbols which are part of escape sequences) Remember that you are not required to read this .txt file; copy-paste the content into a new “art.py” file and create appropriate functions for use in the carousel.py file. To use the JSON file, import the JSON module in your carousel.py file, read the file with the appropriate encoding, and extract the data into a suitable data type for further use. User Interface Implementation You must implement the following methods in the Carousel class: · add: Responsible for adding a new node to the circular doubly-linked list. · goLeft and goRight: Functions to navigate the carousel by moving to the previous or next node. · delete: Removes the current node from the carousel. · getInfo: Retrieves and displays information about the current node, including its Name, Symbol, and Class. · quit: Allows the user to exit the program at any point. Remember to Initialize the circular doubly-linked list with a compulsory attribute max_size. This max_size should be a constant of value 5. Additionally, here is the scenario to keep in mind: 1. Initially, when there’s nothing in the carousel, the user will be prompted to enter “ADD” to add a node or “Q” to quit. Type any of the following commands to perform. the action: ADD: Add a emoji frame. Q: Quit the program >> add What do you want to add? >> tiger 1. After the first addition, the carousel will be displayed with only one frame. with the emoji. Again, the user will be prompted to enter an input with two additional options, specifically “DEL” for delete and “INFO” for information. 1. The user can go left or right after adding the second frame. Two more options: L: Move left and R: Move right will now be included in the menu. The carousel will display three frames, the left frame. being the previous node and the right frame. being the next node. Note: Upon entering the emoji for the second node, your output shows the first node on both the leftmost and rightmost sides, with the second one in the center. This is so because our doubly-linked list wraps around. Please note that your double-linked list data structure DOES NOT have duplicate nodes. 1. As more nodes are added, moving left and right should work endlessly as the list wraps around i.e if we were to move left, our display would look like as follows: 1. Deletion of nodes works the same; if more than one node is in the list, the display should have three frames; only one frame. should be visible when only one frame. is left. At most 3 frames should be displayed: the current frame, one on the left and one on the right. 2. The current (↓↓) always points to the middle frame. that is being displayed. If delete is chosen, the current frame. will be removed from the doubly linked list and not displayed. The new current frame. would be the one on the left. If the frame. removed were the only one left in the carousel, no frame. would be displayed, just like when you run your code the first time. Addition of Nodes There are two scenarios for adding nodes to the carousel: Adding a Node when the Doubly Linked List is Empty: · In this case, no specific input is required from the user regarding the insertion position. · The first node is added directly. Adding a Node when there is more than one node in the Doubly Linked List: · Users can navigate the carousel using the goLeft and goRight methods to change the current node. · After positioning to the desired node, the user is prompted to specify whether they want to insert the new node to the left (L) or right (R) of the current node. If the carousel has available capacity, the selected emoji is inserted at the chosen position. · However, if the carousel is at full capacity, an exception should be raised with the message "You cannot add emojis! Carousel is Full." Please follow this scenario to understand the process of inserting a node. 1. The user must add nodes. The user should be able to search the emojis using their names. (In the example below, the user is searching for a tiger emoji) 1. Check if the word entered by the user exists in the emojis' names. If not, then prompt the user again to enter a word. (Optional-will not be marked) For a slightly advanced way to find the emoji that matches the word entered by the user, you can implement the pseudo spell checker based on Levenshtein distance. Your task is to find the emoji's name out of the JSON data closest to the user input and then confirm if that’s the emoji the user is looking for. 1. Once the emoji matches the word, the relevant data (Class, Symbol, Name; not precisely in this order) should be retrieved and added to the new node. 2. This new node should be added to the appropriate location per user inputs. The first node should be added without asking the user for location, as there’s nothing inside the list. If the list is not empty, the user should be prompted to specify left or right so that the node can be added to the left or right side of the current/active node. Example of Node being added the first time: Example of a Node being added the second time onward: “DEL” is supposed to remove the active/current node. After a successful deletion, the current node will be the one to the left of the deleted node. As previously stated, “INFO” stands for information, and that’s what it’s supposed to display. The information of the node should be presented in the following order: Name, Symbol, and Class. After showing the information, the user should be prompted to press any key to move forward. There should be a brief time gap of 1 second between the information display and asking for input. You must use the os and time modules to make the demonstration presentable and less cluttered. You can use a time delay of one second to create an illusion of loading and clear the screen after executing commands of a functionality. Here is a video demonstration: Notice the pauses in the video. Those are the points where you have to implement the time delays. Also, make a note of the screen clearing and mimic the visual representation to the best of your ability. JSON Refresher This section serves as a short introduction and/or refresher to JSON. In Python, you can work with JSON data using the json module. It provides a way to encode and decode JSON data, allowing you to convert between JSON text and Python data structures. In this example, we will read from the following Pokémon JSON: { "name": "Pikachu", "type": "Electric", "abilities": ["Static", "Lightning Rod"], "stats": { "hp": 35, "attack": 55, "defense": 40, "speed": 90 } } To parse this JSON data, you can use the json.load() function to read the data directly from the file and store it in a dictionary (assume the JSON data is stored in a file named pokemon.json): import json with open('pokemon.json', 'r') as file: pokemon = json.load(file) print(pokemon['type']) print(pokemon['stats']['attack']) After executing the above code, “Electric” and “55” are printed to the terminal. This is information directly from Pikachu’s JSON data entry. Additionally, look up json.loads function on google if you want to learn more.
Assignment 1 IDEC 8022 Economic Development Submit your answer by 23 Aug 2024 1. The data “hs1rt.dta” contains information on a sample of households in country X in year t, where: provid = provincial ID distid = district ID hhid = household ID nrt = household size (# of persons) expr = total household monthly expenditure wert = household sample weight Also, note that file “povlpro1.dta” contains information about poverty line for each province in country X in year t, where: povl = provincial poverty line (i.e., individuals with monthly expenditures below this line are considered poor) (Do not use Stata command related to poverty. Use information on household weight for the following exercises.) a. Calculate monthly expenditure per capita in each province. b. Calculate P0 (head count poverty ratio/index), P1 (poverty gap index) and P2 (poverty severity index) at provincial level in country X at time t as well as the national level indexes. c. Please calculate the average monthly expenditure per capita of the poorest one-fifth of the population in each province and define a relatively poor person as someone with a monthly expenditure below this average expenditure per capita. Calculate the proportion of the population at the provincial level that is considered relatively poor (R0) as well as the proportion at the national level. d. At each province, compare P0 and R0. Which one is larger, and why it is so? List the province which P0 is larger than R0. Note: Please attach your do-file along with your answer sheet (as one pdf file) when you submit. 2. The file “mig1_rev.dta” contains information on a sample of individuals in 4 cities in a certain year, where: citid = city ID hhid = household ID indid = individual ID nrt = household size (# of persons) age = individual’sage hgt = individual body’s height wgt = individual body’s weight lung = individual body’slung capacity mig = migrant status (1=migrant & 0=non migrant) The data “hlt.dta” contain a sample of individuals in the entire country, with the following variables: hgt = height of an individual wgt = body weight of an individual age = age of an individual wert = household sample weight a. Using the data from “hlt.dta”, calculate the height for individuals aged 5 and below, specifically at 2 standard deviations (s.d.) below the mean of the height distribution, and present the results for each age. Additionally, calculate the Body Mass Index (BMI) for the same age group at 2 s.d. below the mean of the BMI distribution and show the results for each age b. Using the data from “mig1.dta”, calculate the number of stunted children at the age of 5 and below (i.e., =
MODEL BUILDING IN MATHEMATICAL PROGRAMMING 13.24 Yield management In order to solve this problem, a stochastic program (as mentioned in Section 1.2) will be built. This will be a three-period model. Solving the model for the first time will give recommended price levels and sales three weeks from departure and recommended price levels and sales for subsequent weeks under all possible scenarios. Account will be taken of the probabilities of these scenarios in order to maximize expected yield. A week later the model will be rerun, taking into account the committed sales and revenue in the first week, to redetermine recommended prices and sales for the second week (i.e. with ‘recourse’) and the third week under all possible scenarios. The procedure will be repeated again a week later. 13.24.1 Variables p1ch = 1 if price option h chosen for class c in week 1 = 0 otherwise (c = 1, 2, 3, h = 1, 2, 3) p2ich = 1 if price option h chosen for class c in week 2 as a result of scenario i in week 1 = 0 otherwise (c = 1, 2, 3, h = 1, 2, 3, i = 1, 2, 3) p3ijch = 1 if price option h chosen for class c in week 3 as a result of scenario i in week 1 and scenario j in week 2 = 0 otherwise s1ich = number of tickets sold in week 1 for class c under price option h and scenario i s2ijch = number of tickets sold in week 2 for class c under price option h if scenario i holds in week 1 and scenario j in week 2 s3ijkch = number of tickets sold in week 3 for class c under price option if scenario i holds in week 1, scenario j in week 2 and scenario k in week 3 r 1ich = revenue in week 1 from class c under price option h and scenario i r 2ijch = revenue in week 2 from class c under price option h if scenario i holds in week 1 and scenario j in week 2 r 3ijkch = revenue in week 3 from class c under price option h if scenario i holds in week 1, scenario j in week 2 and scenario k in week 3 uijkc = slack capacity in class c under scenarios i, j, k in successive weeks vijkc = excess capacity in class c under scenarios i, j, k in successive weeks n = number of planes to fly While it is necessary to make the p variables 0–1 integer and n integer, the s variables can be treated as continuous and their values rounded. 13.24.2 Constraints If a particular price option is chosen (under certain scenarios), then the sales cannot exceed the estimated demand and the revenue must be the product of the price and sales. These conditions can be imposed using the device described in Section 9.2 for modelling the product of a continuous and 0–1 integer variable. r1ich − P1chs1ich ≤ 0, P1chs1ich − r1ich + P1chD1ichP1ch ≤ P1chD1ich for all i, c, h, r2ijch − P2chs2ijch ≤ 0, P2chs2ijch − r2ijch + P2chD2jchP2ich ≤ P2chD2jch for all i, j, c, h, r3ijkch − P3chs3ijkch ≤ 0, P3chs3ijkch − r3ijkch + P3chD3kchp3ijch ≤ P3chD3kch for all i, j, k, c, h, where P and D are the given prices and demands for the corresponding periods, scenarios, classes and options. The seat capacities must be abided by for all scenarios: s1ich + s2ijch + s3ijkch + uijkc − vijkc − Ccn ≤ 0 for all i, j, k, c, where Cc is the given capacity per plane for class c. Adjustment is possible between classes: Exactly one price option must be chosen in each class under each set of scenarios: The above set of constraints could be replaced by SOS1 sets without the need to declare the p variables integer. Numbers sold cannot exceed demand: s1ich ≤ D1ichp1ch for all i, c, h, s2ijch ≤ D2jchp2ich for all i, j, c, h, s3ijkch ≤ D3kchp3ijch for all i, j, k, c, h. Up to six planes can be flown: n ≤ 6. 13.24.3 Objective (measuring in £1000) where Qi is the probability of scenario i. This model has 1200 constraints, one bound and 982 variables, of which 117 are 0–1 integer and one general integer. In defining the data, it is desirable that the demands in the scenarios cover the extremes as well as the most likely cases. The recommended sales will not exceed those of the most extreme scenario in the solution to this model. In this example, it can be seen that final demands (known with hindsight) exceed those of all scenarios in a few cases. As a result, the solution will not result in sales to meet all of these demands. Models for subsequent weeks (with recourse) are obtained from this model by fixing prices and sales for weeks that have elapsed. 13.25 Car rental 1 We model this problem as a deterministic linear programme. There would be advantage to be gained from modelling it as a stochastic programme. In order to do this, we would need to obtain data to quantify the uncertain demand. 13.25.1 Indices i, j = Glasgow, Manchester, Birmingham, Plymouth t = Monday, Tuesday, Wednesday, Thursday, Friday, Saturday k = 1, 2, 3 (days hired) Although this is a ‘dynamic’ problem, we seek a steady-state solution and can therefore regard the set of days as ‘circular’, that is, Monday of a week ‘follows’ after the subsequent Saturday; that is, if t = Monday then t − 1 = Saturday. 13.25.2 Given data Dit = estimated rental demand at depot i on day t as given in Table 12.19 Pij = proportion of cars rented at depot i to be returned to depot j as given in Table 12.21 Cij = cost of transfer of a car from depot i to depot j as given in Table 12.22 Qk = proportion of cars hired for k days
Assessment Proforma 2024-25 Module Code CMT313 Module Title Software Engineering Learning Outcomes The learning outcomes for this assessment are as follows: 1. Determine requirements for a new system to meet business needs, demonstrating an appreciation of the external factors influencing systems requirements (such as ethics, legal issues, and design issues). 2. Analyse and reflect on personal and team performance (formative). 3. Select, follow, and evaluate software development methodologies 4. Analyse and reflect on personal and team performance 5. Evaluate the outcomes of a project taking into consideration appropriate characteristics of software quality 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 CMT313 module on Learning Central. Your submission should consist of multiple files: Description Type Name Coversheet Compulsory One PDF (.pdf) file StudentNumber _Coversheet.pdf Portfolio including Task 1, 2,3 and 4 Compulsory One .pdf file StudentNumber_Assessment_2.pdf Task 5 - Video – Demo of software and quality criteria, Compulsory MP4 video Max 5 minutes StudentNumber_demoVideo.mp4 Task 6- Peer Review Evaluation Compulsory Buddy check form N/a (this is managed by Learning Central automatically) Replace StudentNumber with your student number without ‘c’, e.g. 1234567_Assessment_2.pdf Any deviation from the submission instructions above (including the number and types of files submitted) may result in a reduction in marks for the assessment. Any deviation from the use of the provided template may result in a reduction in marks for the assessment. If you are unable to submit your work due to technical difficulties, please submit your work viae-mailto [email protected] and notify the module leader, Dr. Usashi Chatterjee via email [email protected] . Assessment Description This assessment involves developing the requirements, high-level design and implementation for the Automated Assessment Tool (AAT) project. The cohort has been provided with a scenario in a separate document, available on Learning Central. this assessment involves team working, each team member is expected to contribute to the team component and compile an individual portfolio of work consisting of: 1. User Story & Acceptance Criteria 2. Non-functional requirements (also known as Software product quality) 3. Test Cases 4. Developing the prototype 5. Video demonstration 6. Peer & Self-Assessment Please make sure to cite any references used in the assessment using one of the following referencing styles: Cardiff Harvard or IEEE (one or the other, not both!). The guides and example of how to use these referencing styles are found at: https://intranet.cardiff.ac.uk/students/study/study-skills/academic-writing-communication- and-referencing/citing-and-referencing-support PRODUCT DELIVERABLES Task 1: User Story & Acceptance Criteria These requirements should be stored in GitLab and you can use them to track the progress of your project next term. Your team should create a project in the School’s GitLab. The project name should identify your team name: [TeamName]CMT313_Assessment2 (eg: Team12CMT313_Assessment2) Make sure all your team members and all the members of the teaching team are given Maintainer permission to your project on GitLab. Teaching Team (not limited to) includes Usashi Chatterjee, Annelies Gibson, Rochan Rochan, Sunbul Ahmad, Carl Jones. Please follow the steps to achieve Task 1: 1. As a team, develop a top-level Use Case diagram that highlights the most important features and services the system will provide. Each Use Case should be named appropriately from the client’sor user’s point of view. (This task will not be assessed.). 2. Individual submission: Include the top-level Use Case diagram (from [1]) as a figure in your proforma. Each team member will be responsible for specifying one of the key Use Cases. Every team member should select a different Use Case and develop it as a User Story with clear acceptance criteria, ensuring that each story is written in away that makes it easily validated. Each User Story, along with its acceptance criteria, should be created as a separate issue in GitLab. All issues must be accessible directly from each issue's page, rather than being available as a downloaded document. The ‘Assessment Proforma’ document must also include a table with working links to the relevant issues in GitLab It should use the following format: Use Case Diagram Team X Use Case Name Student ID Link to GitLab Issue Eg:Use Case 1 C1234567 Task 2. Non-functional requirements 1. As a team, you need to work together to create a complete list of non-functional requirements (these are requirements that describe the quality of the software, such as performance, security, usability, etc.) for your system. This part is a group effort, and the list should cover all relevant non-functional aspects of your system [Not assessed]. 2. Individual submission: Each team member must submit the full list of non-functional requirements that the team has created. Additionally, each member needs to select their own top five (5) non-functional requirements from this list. 3. Justification: For each of the five chosen requirements, the team member should provide a justification explaining why they believe these requirements are important. 4. Testable Requirements: The selected requirements should be formulated in a way that allows them to be easily tested. This means that the requirements should be clear, specific, and measurable so that they can be verified through testing. Note: The five non-functional requirements chosen by each team member do not have to be the same as those chosen by other team members. Each person may have a different perspective on which requirements are the most important. Task 3. Test Cases (ONE TEST CASE only) Although this is an individual portfolio the test case handed in by each member of your team MUST be for a different main requirement. You can refer to the AAT Prototype Features and Mark Scheme sheet attached for the main requirements. Each test case should be presented using the test case template supplied in the lecture notes: • Create ONE test case that a user can follow to validate that your prototype meets ONE of the main requirements. Each Test Case should have a clear procedure, and input Data that can be followed by a tester to carryout the essential steps for the basic flow and a clear indication of the outputs that your prototype should give in response to the tester’sactions. (You can also provide wireframe diagrams to help them carry out the tests. These will be used to help determine if you have provided sufficient information in your test case to cover the essential steps for each requirement.) Task 4. Developing the prototype (700 words limit) Explain and evaluate the effectiveness of your team whilst developing the prototype. This section should consider the project management and development methodologies your team used alongside team dynamics covered in the module. (Artifacts such as - charts, and boards appropriate to the methodology used can be included in an appendix of 3 pages max) Task 5. Video & Quality Criteria Walk through demonstration, including software quality criteria of the elements of the prototype you developed. Make a video (Maximum of 5-minutes) with narrative that runs through the working functionality of the prototype that you developed, This video should: • clearly showing how your section integrates with other related sections of the prototype. • as part of the demonstration highlight TWO examples of how and where your part of the final product took into consideration software quality criteria (e.g. usability, reliability, integrity, maintainability, testability, and flexibility), identified in Task 2. Non-functional requirements section. • highlight any extra features or interesting functionality that were successfully implemented. Task 6. Peer and Self-assessment (use form provided, max 1 page per student) • Looking back at your experience having undertaken the team exercise for module CMT313 Software Engineering during the Autumn and Spring semester, complete a peer/self- assessment for each member of your team including yourself. Weightings The following weightings are allocated for the different components of the assessment: Components Weighting 1. User Story & Acceptance Criteria 15% 2. Non-functional requirements (or Software product quality) 10% 3. Test Cases 15% 4. Developing the prototype 20% 5. Video demonstration 30% 6. Peer & Self-Assessment 10% Total 100% Assessment Criteria Credit will be awarded using the marking criteria detailed below, and using the following marking bands: • High Distinction - 80%+ • Distinction - 70-79% • Merit - 60-69% • Pass - 50-59% • Marginal Fail - 40-49% • Fail - 0-39%
CS206B (Winter 2025) Public Communication Mid-term take-home exam This exam is worth 15%of your final grade.It is due February 12,2025 before midnight. Exams that are not submitted by the start of class will be treated as one day late.All submissions should be made to the course drop box on MyLearningSpace(no hard copy is required). The exam takes the form of two reading reflections worth 50 marks each.The exam requires you to carefully engage with two course readings.Each answer ought to be 750 words,or approximately 21/2 pages.So,the total length of your exam should not exceed 1500 words, or approximately 5 double-spaced typed pages Choose two readings from those listed below;you must select one reading from list"A"and one from list"B." Your reading reflections ought to address the following questions: What is/are the author's main argument(s)? What are the most salient features of the analysis? Can you provide examples to illustrate the author's argument,or,conversely,reveal its shortcomings? Your main task is to demonstrate the depth of your understanding of the arguments put forward by the authors. Use MLA style in-text citations.For example:According to Theodor Adorno(99),"the masses...are an object of calculation..."You do not need to include a bibliography. Use quotations sparingly;use your own voice as much as possible Readings :Choose two Group A-select one ● Henry A.Giroux (2011)."The Crisis of Public Values in the Age of the New Media."CriticalStudies in Media Communication.28(1).8-29. Stuart Allan (2004)."Making News:Truth,Ideology and Newswork,"Nems Cmltre, 3rd Edition.Open University Press.46-76. Yasmin Jiwani(2006)."Erasing Race:The Story of Reena Virk,"Discourses of Demial: Mediations of Race,Gender,and Violence.UBC Press.65-89. Group B-select one Michael Warner (2002)."Publics and Counterpublics(abbreviated version)."Qnartery Journal of Speech.88(4).413-425. Donald Gutstein (2009)."The Propaganda Century."Not a Conspirac Theory:How Business Hijacks Democracy.Key Porter Books.55-85. Stuart Allan(2004)."Racial Diversity in the News,"in Allan.Nens Culture,3rd Edition.Open University Press.171-194.
Econ. 136A Section Assignment Part 4 Requirements 1. Add four adjusting journal entries (AJEs). Your AJEs should include one entry from each of the four types of AJEs described in Chapter 3 of the text. · Accrued revenue · Accrued expense · Deferred revenue · Deferred expense 2. Your AJEs are to be entered on the same “journal entry” sheet used for your other journal entries. Be sure and use the lookup function for all journal entries. 3. Update the T-accounts, the trial balance, and the financial statements for the adjusting journal entries. You may need to add accounts to your T-accounts, trial balance, and financials. 4. Prepare a closing journal entry. Like all of your previous journal entries, place the closing entry on the journal entry page and again be sure and use the lookup function for the account names. 5. Post the closing entry to the related T-accounts. The changes to the T-accounts should update the trial balance and the financial statements. Be sure and add accounts to the T-accounts, trial balance, and financials, if necessary. The requirements for this assignment are due in section next week.
Practice Exercise 3 LINC12 Fall 2024 Sept 27, 2024 Practice exercise due: Wednesday October 2nd, 23:59 on Quercus The following exercises must be completed by uploading a PDF document onto Quercus. These exercises cover material through the Thursday September 26 lecture. Remember: These problems are designed to make you think critically and to apply what you have learned this last week to new issues. These problems will be discussed in the following lecture, after this practice exercise is due. The exact same problems appear on your assignments at the end of the month, so do your best, and take good note of the solutions presented in lecture. Enjoy! 1 Generalized Implicatures vs. Use-conditional meaning Generalized implicatures and Use-conditional meanings alike often arise due to the semantics of a particular “trigger”. Generalized implicatures, however, have all the features of conversational implicatures, however, while Use-conditional meanings do not. Below you will find sentences with a highlighted “trigger”, followed by an inference. Your task is to determine whether the inference is a Generalized implicature, a product of Use-conditional meaning, or neither. You can do this by going through the characteristics of each type of inference and performing tests to demonstrate the inferences are more like on type of meaning than the other. (1) Sonya prefers vegetarian meals. Inference: Sonya is not a vegetarian. Cancelability: “Sonya prefers vegetarian meals; she is a vegetarian.” Good! Reinforceability: “Sonya prefers vegetarian meals, but she is not a vegetarian.” Good! Calculability: The calculation would involve the maxim of quantity: why didn’t you make the stronger statement that Sonya was a vegetarian? Survive non-veridical environments: “Does Sonya prefer vegetarian meals?” She is a vegetarian. No contradiction, so the proposition in the inference does not project. Conclusion: The inference is a generalized implicature – it shows the properties of conversational implicatures, and the inference is lost under a non-veridical environment and is therefore not a use- conditional meaning. (2) Ginger rode a bike to work today. Inference: The bike was not her bike. Cancelability: “Ginger rode a bike to work today, in fact it was her bike.” Good! Reinforceability: “Ginger rode a bike to work today, and it was not her bike.” Good! Calculability: The calculation would involve the maxim of quantity: why didn’t you make the stronger statement that “Ginger rode her bike to work”? Survive non-veridical environments: “If Ginger rode a bike to work today, she won’t need a ride home.” no longer implicates that some bike is not hers. (note: contradiction tests for the implicatures of indefinite articles give strange results) Conclusion: The inference is a generalized implicature – it shows the properties of conversational implicatures, and the inference is lost under a non-veridical environment and is therefore not a use- conditional meaning. (3) I was barely able to hear what she said. Inference: It was difficult to hear her. Cancelability: “I was barely able to hear what she said, # but it was not difficult to hear her.” Bad! Reinforceability: “I was barely able to hear what she said, # and it was difficult to hear her.” Kinda weird! Calculability: This meaning is not calculable. Survive non-veridical environments: “If you were barely able to hear what she said, why didn’t you say something?” It was not difficult to hear her. Does not result in a contradiction; the proposition in the inference does not project. Conclusion: The inference cannot be an implicature – it does not show any properties of conver- sational implicatures. However, the inference also does not survive a non-veridical environment, so it is not likely to be a use-conditional meaning. The inference is neither a generalized implicature, nor a use-conditional meaning. (4) Tomakealongstoryshort , we’ve decided to include Regina in the competition. Inference: The details of the decision aren’t worth mentioning. Cancelability: ‘To make a long story short, we’ve decided to include Regina in the competition, # but the details of the decision are worth mentioning.” Kinda weird? Reinforceability: ‘To make a long story short, we’ve decided to include Regina in the competition, and the details of the de- cision aren’t worth mentioning.” Not bad, not normal speech however? Calculability: This meaning is not calculable. Survive non-veridical environments: “If, to make a long story short, you decided to include Regina, why didn’t you say something earlier?” # The details are worth mentioning. This is a very strange dialogue. The speaker-oriented inference re- mains. Conclusion: The inference is likely not an implicature – it does not clearly properties of conver- sational implicatures. On the other hand, the inference seems to still be present after embedding in non-veridical environment, so it is most likely a use-conditional meaning. 2 Speech Acts Below you are presented with a number of sentences. Your job is to identify, descriptively, the illocutionary force of these sentences if uttered in the real world, and then to classify this illocutionary force under one of Searle’s classes of illocutionary acts (see lecture 4, slide 31). (5) Do you know what time it is? Illocutionary force: to get the addressee to tell the speaker what the time is Classification: Directive (6) Stay off the subway tracks! Illocutionary force: to get the addressee to not touch the subway tracks Classification: Directive (7) It will be a clear day, with a high of 23 degrees. Illocutionary force: to assert the fact that the weather will be as such Classification: Representative (8) Sorry for the late response. Illocutionary force: to express remorse Classification: Expressive (9) I brought you this gift back from my trip! Illocutionary force: to present a gift to the addressee Classification: Commissive (10) Can you feed my cat while I’m away? Illocutionary force: to get the addressee to feed their cat Classification: Directive
PRC5201 Fundamental of Computing Coursework #1 Learning Outcomes Assessed: CLO1 Evaluate the basic principles ofsystem modelling in systems analysis, design and development according to life cycle methodology. (C4, PLO1) CLO2 Construct appropriate database techniques in conceptual and logical modeling to complete related activities. (P3, PL06) CLO3 Relate the importance of information security and ethical concernstowards computing context. (C4, PLO2) Instructions: This assignment consists of THREE (3) parts as follows; Part 1 Read the University Management System (UMS) Case Study below: The University Management System (UMS) A mid-sized university seeks to develop a comprehensive University Management System (UMS) to streamline its administrative processes and improve student services. The current system is largely manual, leading to inefficiencies and data errors. Background Based on preliminary research and fact-finding, the UMS includes components • Student Management o Student registration and enrolment o Course registration and schedule management o Management of student records (transcripts, attendance, etc.) • Course Management o Creation and management of course offerings by faculty. o Assignment of instructors to courses. You will now proceed with the systems analysis phase by developing an object-oriented model of the UMS. Tasks 1. Design at least four class objects for the University Management System (UMS) including attributes and methods. 2. Create a class diagram for the system classes that you identified in Task 1 (above). 3. Are there any classes in the UMS that can be grouped into subclasses? Why would you want to group data elements into subclasses? 4. Design a use case model for a method in the new system. Part 2 Based on the Case Study from Part 1, you are required to design and develop a database for the University Management System (UMS) using Microsoft Office Access. You are expected to complete the following tasks: - Tasks 1. Identify relevant business rules related to the Case Study 2. Identify relevant entities and attributes. 3. Draw an Entity-Relationship Diagram (ERD) related to the Case Study 4. From the ERD, create database Table with proper primary key, foreign keys and database relationship. Upon completion of the above tasks, you are required to document all the completed task including the screenshot of created Tables and relationships. Part 3 Based on the Case Study from Part 1, as the Chief Network Security Officer, you are required to investigate and produce a report on the network security requirements needed for this university. You are to produce a complete set of computer security policies and procedure for the director. The breakdown of the report is as follow: 1.0 Executive Summary 2.0 Background Introduction 3.0 Business Strategy 4.0 Threat, Risk & Vulnerability Analysis 5.0 Policies and Goal Security 6.0 Security Policy 7.0 Physical Security 8.0 Network Security architectures, designs 9.0 Operating System and Data Security 10.0 Cryptography 11.0 Ethics 12.0 Privacy 13.0 Intellectual Property 14.0 Conclusion 15.0 References
ECN 100B WQ 2025 Problem set 2 1 Price discrimination question Imagine a firm called Bapple that is the monopoly in the market for smartwatches, with cost- function C(Q) = 16Q2 . Imagine the inverse demand function for smartwatches is p(Q) = 1600 - 4Q. 1.1 A. What are equilibrium price and equilibrium quantity with a single price? 1.2 B. Show the equilibrium price and equilibrium quantity graph- ically. Include the inverse demand curve, firm’s marginal rev- enue curve, and firm’s marginal cost curve. 1.3 C. What are consumer surplus, producer surplus, and dead- weight loss at this equilibrium? Now assume that Bapple is able to perfectly price discriminate in the market for smart- watches. 1.4 D. What three conditions must be true for this perfect price discrimination to be possible? 1.5 E. What are the equilibrium prices and equilibrium quantity with perfect price discrimination? 1.6 F. What are consumer surplus, producer surplus, and dead- weight loss at the perfect price discrimination equilibrium? How do these compare to the single price equilibrium? 2 Static game I Suppose two players are playing a game, Even and Odd. Each player has a quarter and must secretly turn the quarter to heads or tails. The players then reveal their choices simultaneously. If the quarter match (both heads or both tails), then Even keeps both quarter, so wins one from Odd (+25 for Even, -25 for Odd). If the quarters do not match (one heads and one tails) Odd keeps both quarter, so receives one from Even (-25 for Even, +25 for Odd). 2.1 Please draw the payof matrix for this game. 2.2 Does Even have a dominant strategy? Why or why not? 2.3 Does Odd have a dominant strategy? Why or why not? 2.4 What is the Nash Equilibrium of this game? 2.5 If there are multiple Nash Equilibria, which one will be se- lected in the end? If there is no Nash Equilibrium, how will the game end? 3 Static game II Imagine a game with a Professor and Students (who all act together as one player). The Pro- fessor is giving a final exam and has to decide whether to make it easy or hard. Students have to decide whether to put lowefort, medium efort, high efort, or max efort into studying for the exam. Both players decide simultaneously. Payofs are as follows (Professor,Students): Students Max efort High efort Medium efort Low efort Professor Easy 32,24 24,32 16,40 8,16 Hard 48,32 40,24 8,16 0,8 3.1 Does Professor have a dominant strategy? Why or why not? 3.2 Do Students have a dominant strategy? Why or why not? 3.3 What is the Nash Equilibrium of this game? 3.4 If there are multiple Nash Equilibria, which one will be se- lected in the end? If there is no Nash Equilibrium, how will the game end? 4 Sequential game Imagine the same Professor and Students are playing a sequential game. Students move first and decide to show up to class or to not show up. Professor moves second and decides whether to threaten a quiz for the next lecture or not. Students then decide whether to attend the next lecture or not. Professor, who has a quiz prepared, observes whether students attend or not and then decides finally whether or not to actually give the quiz. Payouts (Students,Professor) are as follows: Stud. show up to 1st class, Prof. threatens quiz, Stud. show up to 2nd class, Prof. gives quiz 2,3 Stud. show up to 1st class, Prof. threatens quiz, Stud. show up to 2nd class, Prof. does not give quiz 3,4 Stud. show up to 1st class, Prof. threatens quiz, Stud. don’t show up to 2nd class, Prof. gives quiz 2,2 Stud. show up to 1st class, Prof. threatens quiz, Stud. don’t show up to 2nd class, Prof. does not give quiz 4,3 Stud. show up to 1st class, Prof. does not threaten quiz, Stud. show up to 2nd class, Prof. gives quiz 2,3 Stud. show up to 1st class, Prof. does not threaten quiz, Stud. show up to 2nd class, Prof. does not give quiz 3,4 Stud. show up to 1st class, Prof. does not threaten quiz, Stud. don’t show up to 2nd class, Prof. gives quiz 2,2 Stud. show up to 1st class, Prof. does not threaten quiz, Stud. don’t show up to 2nd class, Prof. does not give quiz 4,4 Stud. don’t show up to 1st class, Prof. threatens quiz, Stud. show up to 2nd class, Prof. gives quiz 3,2 Stud. don’t show up to 1st class, Prof. threatens quiz, Stud. show up to 2nd class, Prof. does not give quiz 4,3 Stud. don’t show up to 1st class, Prof. threatens quiz, Stud. don’t show up to 2nd class, Prof. gives quiz 2,1 Stud. don’t show up to 1st class, Prof. threatens quiz, Stud. don’t show up to 2nd class, Prof. does not give quiz 5,2 Stud. don’t show up to 1st class, Prof. does not threaten quiz, Stud. show up to 2nd class, Prof. gives quiz 3,2 Stud. don’t show up to 1st class, Prof. does not threaten quiz, Stud. show up to 2nd class, Prof. does not give quiz 4,3 Stud. don’t show up to 1st class, Prof. does not threaten quiz, Stud. don’t show up to 2nd class, Prof. gives quiz 2,1 Stud. don’t show up to 1st class, Prof. does not threaten quiz, Stud. don’t show up to 2nd class, Prof. does not give quiz 5,3 4.1 Please draw the game tree for this game. 4.2 How many subgames does this game have? 4.3 What is the subgame perfect Nash Equilibrium for this game? 4.4 Do Students believe Professor’s threat of a quiz? 5 Oligopoly 5.1 Markets difer according to what three dimensions in terms of market structure? 5.2 What is a real-life example of an oligopoly and why? 5.3 What are the three models of oligopoly? 5.4 Would a firm prefer to be in a market with an oligopoly, a monopoly, or perfect competition? Why? 5.5 Would a consumer prefer to be in a market with an oligopoly, a monopoly, or perfect competition? Why?
SCIS 315 For academic year 2025 Histories of people,environment and science in the Asia Pacific Ever wondered why Aotearoa New Zealand has so many farms,or why we are constantly dealing with problems related to introduced pests? Do you want to know the origins of our reliance on fossil fuels and the environmental crisis? This course places environmental, scientific and technological changes within wider historical contexts,mainl from the Asia-Pacific.You willexplore a range of topics,such as introduced plants and animals and their environmental impacts;industrial forms of production and technology,and environmental impacts;western medicine and other ways of ensuring health and well-being;museums,environment and science;conservation and development;genetic organisms. Course content There are five content modulesin the course.Approximately 30 hours of work is expected for each of the five modules,with a total of 150 hours over the course.This includes viewing lectures,reading,researching and writing assignments,completing quizzes,and participating in the discussion forum. Grading wil occur through assessment of material submitted in the quizzes and wrtten assignments.No face-to-facetime is expected, although direct interaction with the course coordinator,lecturers,and tutors is continually available,and encouraged,through the online interface,and during office hours. lf you haven't quite got enough 200 level points,please contact the course coordinator for a chat and we may be able to waive the prerequisites on a case-by-case basis. Course learning objectives Students who pass this course should be able to: 1 Critically examine current local and global scientific, environmental and technological issues in a wider historical context. 2 Evaluate the role that different political ideas, cultural perspectives and economic imperatives have had on environmental, scientific and technological change, including where cultural perspectives have implications for mätauranga Mäori. 3 Present complex environmental, scientific and technological ideas and theories through well structured, dlearly argued, writing.
CHEM 191 MODULE 7 ORGANIC CHEMISTRY 1 Learning Objectives. By the end of this module you should be able to: • Write names and structural formulae for simple alkanes, alkenes and alkynes including constitutional isomers • Identify and name stereoisomers of alkenes (cis and trans) • Identify combustion, oxidation, substitution, elimination and addition reactions of organic molecules by comparing the structural formulae of reactants and products. • Write equations using structural formulae for reactions of alkanes, alkenes and alkynes • Recognise the monomers and polymers in addition polymerisation reactions Reference: ESA Chapters 12 and 13 INTRODUCTION The term ‘organic’, in common usage refers to the products of agriculture and farming that are produced according to particular methods of fertilisation and pest control. In chemistry, the term organic refers to the study of compounds of carbon (excluding CO, CO2 and the carbonates). Organic chemistry was once referred to as the chemistry of ‘living things’ and it was thought that organic compounds could not be synthesised in a laboratory because they all contained a ‘vital force’. The synthesis of urea by Fredrick Wholer in 1828 proved that organic compounds could be manufactured and there are now millions of different organic compounds known, with new ones discovered each year. Organic compounds are found in living organisms, fossil fuels such as coal and petroleum, and many common products such as soap, plastics, paper, cosmetics and medicines. Carbon is unique among the elements in its ability to form chains, branched chains and rings of carbon atoms of an almost unlimited size and variety. It is able to form 4 stable bonds with itself and other atoms, predominately hydrogen but also oxygen, sulfur, nitrogen, phosphorus and the halogens. HYDROCARBONS The simplest organic compounds contain only carbon and hydrogen and are called hydrocarbons. There are 3 groups of hydrocarbons: • Alkanes –molecules that have only single bonds between the carbon atoms. They are said to be saturated because they have the maximum number of hydrogen atoms possible for the given number of carbon atoms. • Alkenes –molecules with one or more double bond between the carbon atoms. They are said to be unsaturated because it is possible to add hydrogen atoms to them • Alkynes –molecules with one or more triple bonds between the carbon atoms. This class of compounds is also unsaturated. ALKANES Alkanes are the simplest organic compounds and can be regarded as the “parents” of all other organic compounds. Since a carbon atom must form. four covalent bonds to either itself or other atoms, the simplest hydrocarbon will therefore have the formula CH4. CH4 is known as methane and methane molecules have the following Lewis diagram and structure: Methane is a colourless, odourless gas that is the main component of “Natural Gas” which we burn for fuel. It is a “greenhouse gas’ which, along with carbon dioxide, makes a significant contribution to global warming. Methane is the first of the alkane family of hydrocarbons. When a second carbon is introduced to the molecule we need 6 hydrogen atoms to satisfy the bonding requirements of the carbon atoms. The compound has the molecular formula C2H6 and is called ethane. In organic chemistry it is usual to use a structural formula rather than the molecular formula. However, it is convenient to condense the structural formula so that all the bonds to hydrogen are not shown, while still retaining the carbon to carbon framework. This gives the condensed structural formula for ethane as CH3CH3. When a third carbon atom is introduced, the compound is known as propane. Molecular formula: C3H8 Condensed structural formula: CH3CH2CH3 Structural formula: The four carbon alkane, C4H10, is known as butane. The five carbon alkane, C5H12, is pentane. If we keep adding carbon atoms to the chain we can develop a series of molecules that differ by a –CH2- group. Butane: CH3CH2CH2CH3, Pentane: CH3CH2CH2CH2CH3. The names and formulae of the first ten alkanes are given below Name Formula Condensed Structural Formula Boiling Pt Methane CH4 CH4 -161.5oC Ethane C2H6 CH3CH3 -88.5oC Propane C3H8 CH3CH2CH3 -42.1oC Butane C4H10 CH3CH2CH2CH3 -0.5oC Pentane C5H12 CH3CH2CH2CH2CH3 36.1oC Hexane C6H14 CH3CH2CH2CH2CH2CH3 68.7oC Heptane C7H16 CH3CH2CH2CH2CH2CH2CH3 98.4oC Octane C8H18 CH3CH2CH2CH2CH2CH2CH2CH3 125.7oC Nonane C9H20 CH3CH2CH2CH2CH2CH2CH2CH2CH3 150.8oC Decane C10H22 CH3CH2CH2CH2CH2CH2CH2CH2CH2CH3 174.1oC Table 7.1 The First Ten Unbranched Hydrocarbons All the alkanes can be represented using the general formula CnH2n+2 where n represents some integer. As the length of the hydrocarbon chain increases it is sometimes more convenient to summarise the number of CH2 groups; pentane could be written as CH3(CH2)3CH3 and decane as CH3(CH2)8CH3. The naming of these straight chain alkanes is relatively simple. Apart from the first four members of the series, the name is either a Greek or Latin prefix indicating the number of carbon atoms in these molecules and this is followed by the ending -ane. (Hint: To remember the order of the names for the first four hydrocarbons you could use the mnemonic: Must Eat Peanut Butter) Focussing Questions 1 1. What is a hydrocarbon? 2. What is the difference between a saturated and an unsaturated molecule? 3. What are the similarities and differences between alkanes, alkenes and alkynes? 4. What are the names of the first 4 alkanes? 5. What is the general formula for an alkane molecule (ratio of C to H atoms)? 6. What is the shape of the bonding around each C atom in an alkane? Give a reason for your answer. Straight Chains, Branched Chains, and Rings in Alkanes The molecules in Table 7.1 are all examples of so-called straight chain alkanes – each carbon atom (except for those at the two ends of the chain) is connected to two other carbon atoms. In branched- chain alkane molecules some of the carbon atoms are attached to more than two other carbon atoms. For example, A count of the atoms will show that branched-chain alkanes still have the general formula CnH2n+2 . A branched chain molecule is considered to have a ‘long’ chain to which ‘side’ chains are attached. Side chains are are known as alkyl groups. An alkyl group is formed by removing one hydrogen atom from an alkane and is named by replacing the –ane ending with -yl. Thus CH3- is methyl- from methane, CH3CH2- is ethyl- from ethane, CH3CH2CH2- is propyl, etc. The significance of “group” is that it refers to a cluster of atoms that are a part of a molecule, where the cluster cannot exist on its own. Table 7.2 Names of some side chains In cycloalkanes, three or more carbon atoms are linked together in a ring. Ring sizes can be up to 30 or more carbon atoms. For example: Structurally, the difference between a straight chain alkane and a cycloalkane is the absence of one hydrogen atom from each end of the straight chain. Consequently, the general formula for any cycloalkane is CnH2n. When drawing structural diagrams for complex structures, including cycloalkanes, the C and H atoms are not explicitly written. Instead, a line diagram is used, in which the C-C bonds are shown as lines with the end of each line being a CH, CH2 or CH3 as required for the structure to be neutral. For example: ISOMERISM In inorganic chemistry, with very few exceptions, the molecular formula of a compound uniquely identifies that compound. For organic compounds, the molecular formula is rarely unique. Different compounds with the same molecular formula but a different arrangement of atoms are said to be isomers of each other. Structural or constitutional isomers arise when the same atoms can be connected together in different ways. For all hydrocarbons larger than propane, there is more than one way in which the carbon atoms can be interconnected. Butane (C4H10) has two isomers: The second of these two isomers is an example of a branched chain isomer while the first is known as a straight chain isomer. Pentane (C5H12) has three isomers: Isomers have different boiling points, freezing points, and densities. The existence of isomers is one of the reasons for the vast number of organic compounds. As the number of carbon atoms is increased, the number of possible isomers increases rapidly – C8H18 has 18 possible isomers, C20H42 has 366319 isomers and C40H82 has an estimated 6.25 × 1013 possible isomers. Exercise 7.1 (a) Draw full structural formulae for the following alkane molecules: (i) Propane (ii) A straight chain alkane with 5 carbons (iii) The alkane C7H16 (b) Draw condensed structural formulae for each of the molecules in (a) (c) Write the molecular formula for alkane molecules with the following number of carbon atoms: (i) 12 (ii) 27 (iii) 8 (d) Draw the condensed structural formulae for 5 different arrangements (isomers) of the formula C6H14 Naming Branched-Chain Alkanes An international set of rules has been developed by the International Union of Pure and Applied Chemistry (IUPAC) for naming organic compounds. The name will indicate the number of carbon atoms in the molecule, the way these are arranged and, when atoms other than hydrogen and carbon are involved,where these are attached and in what order. The IUPAC name for a straight chain alkane consists of: • a prefix which shows the number of carbons • the suffix –ane which shows the hydrocarbon belongs to the alkane family (see Table 7.1). For branched-chain isomers we need to show: • The parent name which is the longest chain of carbon atoms • The substituents or branches (sometimes known as side chains) and their position on the parent chain. To name a molecule: 1. Identify the longest continuous chain of carbon atoms (the parent chain). 2. Number each carbon of the parent chain, starting from whichever end of the chain gives the lowest numbers to carbon atoms substituted with the branches or side chains. 3. Identify the alkyl group(s)which forms the side chain(s) 4. Prefix the name of the side chain(s) to the name of the parent chain and identify the number(s) of the carbon atom(s) to which it is attached Example 1: To name the molecule: 1. Identify the longest continuous chain of carbon atoms in the structure and let this be the parent chain. • There are 6 molecules in the longest chain so the parent chain is hexane. (Note that for simplicity in this and in the following examples, the molecules have been represented with the longest chain in a straight line but this is not a requirement, nor will it always be the case.) 2. Number each carbon of the parent chain, starting from whichever end of the chain gives the lower number to carbon atom with the branches or side chains. • Had we numbered from right to left, the side chain carbon atom would have been number 4. 3. Identify the alkyl group which forms the branch • In this case it is a methyl group (as it has only 1 carbon atom) 4. Prefix the name of the side chain to the name of the parent chain and identify the number of the carbon atom to which it is attached. • The correct name is 3-methylhexane. Note: There are no spaces between the two parts of the name and the location number is separated from the name by a hyphen. Example 2. What if there is more than one side chain? To name the molecule: 1. Parent chain = heptane 2. Number the parent carbon chain from the end which gives the lowest sum for the carbons on which the side chains are found. • In the example above, there are two options – numbering from the left, the side chains would be on carbons 3 and 6 (3 + 6 = 9). Numbering from the right the side chains would be on carbons 2 and 5 (2 + 5 =7). Numbering from the right gives the lowest sum. 3. Alkyl groups are: methyl on carbon 2 and ethyl (2 carbon chain) on carbon 5. 4. The alkyl group prefixes areplaced in alphabetic order with hyphens used to separate numbers from letters. The correct name is 5-ethyl-2-methylheptane. NOTE – The structure in Example 2 was drawn with the parent chain in a straight line. However, you should always check whether there is a longer chain that includes a group that has been drawn as a side chain. For example – the way the following molecule is drawn the longest chain goes through the side chain. Example 3. What if there are two or more identical side chains? We use di- for two tri- for three, tetra- for four, penta- for 5 etc.to indicate identical side chains. To name the molecule: Name is: 2,5-dimethylheptane. Note: These scaling prefixes do not affect the alphabetic order triethyl still comes before methyl. Example 4. What if identical groups are on the same carbon? Where identical groups are on the same carbon atom, repeat the number of this carbon in the name. To name the molecule: Naming Cyclic Alkanes If there is a ring present, it becomes the parent no matter how long the side-chains might be. The parent name is prefixed by cyclo, and numbering starts at the lowest alphabetic group and proceeds in the direction which gives the lowest overall set of numbers. Example: To name the compound: Name is: 1,2-dimethylcyclohexane (not 1,6-dimethylcyclohexane or 5,6-dimethylcyclohexane or any other combination). Summary • Alkyl groups are listed in alphabetic order, ignoring any scaling prefixes • If there is any ambiguity, numbers are used to identify the carbon atoms to which alkyl side chains are attached. • Numbers are separated from numbers by commas, numbers are separated from letters by hyphens. • There are no spaces between letters
EE6221 - ROBOTICS AND INTELLIGENT SENSORS SEMESTER 1 EXAMINATION 2024-2025 1. A robotic manipulator with six joints is shown in Figure 1. Figure 1 (a) Obtain the link coordinate diagram by using the Denavit-Hartenberg (D-H) algorithm. (12 Marks) Note: Question No. 1 continues on page 2. (b) ।Derive the kinematic parameters of the robot based on the coordinate diagram obtained in part (a). (8 Marks) 2. The dynamic equations of a robot when it is in contact with a workpiece are given as follows: where u, u2, u3 are the control inputs, q1,92.93 are the joint variables, d1,d2, d3 are the unknown disturbances and f=22(q3-0.2) is the contact force. The system possesses unmodelled resonances at 5 rad/s, 10 rad/s and 15 rad/s. (a). If di, d2, d3 are zero, design a hybrid position and force controller for the robot to track the desired trajectories q92 and the desired force fa. The motion control subspace should be overdamped with a damping ratio of 1.05, and the force control subspace should be critically damped. The control system should not excite all the unmodelled resonances. (14 Marks) (b). If d,dz, d3 are not zero, derive the error equations of the system based on the hybrid position and force controller designed in part (a). (6 Marks) 3. (a) A mobile platform. is shown in Figure 2 The platform. has one steered standard wheel and two standard wheels. A local reference frame. (xr, yr) is assigned as shown in the figure. The radius of each standard wheel is 10 cm. If the rotational velocities of the steered standard wheel and the two standard wheels are denoted by ss, s1, s2 respectively, derive the rolling and sliding constraints of the mobile platform. Figure 2 (b). A robot manipulator with four ioint variables is mounted on a mobile platform. The transformation matrix from tool tip to base coordinate of the robot is given as: where q1,q2,q3 are the joint variables for the major axes, q4 is the tool roll angle, Ck = cos qk and S = sin qk. (i) Derive the tool configuration Jacobian matrix of the manipulator. (ii) Given that x= 0.2, y=0.2, z=0, solve the inverse kinematic problem to obtain q1, 92, 93. (Note: orientation is not required). (iii) Determine the approach vector of the robot at this joint configuration. (12 Marks)
Homework 2 Part 1 PCA 1. PCA can be explained from two different perspectives. What are the two perspectives explained in class? 2. The first principal direction is the direction in which the projections of the data points have the largest variance in the input space. We use λ1 to represent the first/largest eigenvalue of the covariance matrix, w1 to denote the corresponding principal vector/direction (w1 has unit length i.e., its L2 norm is 1), μ to represent the sample mean, and x to represent a data point. The deviation of x from the mean μ is x − μ . The forward transform, y = PCA(x), is implemented in sk-learn with "whiten=True". (1) write down the scalar-projection of the deviation x − μ in the direction of w1 ? (2) what is the first component of y ? note: compute it using w1 , x, μ , and λ1 (3) assuming y only has one component, then we do inverse transform. to recover the input ̃(x) = PCA−1(y) computẽ(x) using μ, y, λ1 and w1 (4) assuming x andy have the same number of elements, and we do inverse transform to recover the input ̃(x) = PCA−1(y) what is the value of x −̃(x) ? Note: the question asks for a value/number, not equations (5) For face image generation applications shown in class, what is the major difference between the two methods: eigenface vs. statistical shape model ? Maximum Likelihood Estimation and NLL loss (This is a general method to estimate parameters of a PDF using data samples) 3. Maximum Likelihood Estimation when the PDF is an exponential distribution. We have Ni.i.d. (independently and identically distributed) data samples {x1, x2, x3, … , xN } generated from a PDF that is assumed to be an exponential distribution. xn ∈ ℛ+ for n = 1 to N, which means they are positive scalars. This is the PDF: Your task is to build an NLL (negative log likelihood) loss function to estimate the parameter λ of the PDF from the data samples. (1) write the NLL loss function: it is afunction of the parameter λ (2) take the derivative of the loss with respect to λ , and set the result to 0. After some calculations, you will obtain an equation about λ = ∗∗∗∗∗∗ Hint: read NLL in the lecture of GMM 4. Maximum Likelihood Estimation when the PDF is histogram-like. A histogram-like PDF f(x) is defined on a 1-dimensional (1D) space that is divided into fixed regions/intervals. So, f(x) takes constant value ℎ i in the i-th region. There are K regions. Thus, {ℎ1, ℎ2, … , ℎk } is the set of (unknown) parameters of the PDF. Also, ℎ iΔ i = 1, where Δ i is the width of the i-th region. Now, we have a dataset of N samples {x1, x2, x3, … , xN }, and Ni is the number of samples in the i-th region. The task is to find the best parameters of the PDF using the samples. (1) write the loss function: it is a function of the parameters Note: it is a constrained optimization problem, so we need to use the Lagrange multiplier method to convert constrained optimization to unconstrained optimization. Thus, we add λ( ℎ iΔ i − 1) and the NLL together to get the complete loss function, where λ is the Lagrange multiplier. (2) take the derivative of the loss with respect to ℎ i , set it to 0, and obtain the best parameters along with the value of λ . Is Bayes optimal ? 5. Bayes classifier has the minimum classification error assuming we know the true p(x|y) and p(y) . However, for many applications, reaching the minimum classification error may not be the best objective. Now, let’s consider the application explained in the lecture: there are two classes, class-0 and class-1. In class-0, patients have aneurysms, but the aneurysms will not rupture In class-1, patients have aneurysms, and the aneurysms will rupture almost immediately if left untreated, and therefore surgeries will be performed to prolong the life of the patients. Assume these: (a) The patients in class-0 will live until the age of 100. (b) The patients in class-1 will live until the age of 100 after receiving surgeries but will die immediately if left untreated. (c) Ther risk of the surgery is ε between 0 and 1,e.g., ε=0.01 means there is a 1% chance that a patient may die during surgery. Consider a patient at the age of 60, if the true class label of a patient is class-0, but this patient is misclassified to class-1, thus, this patient will get an unnecessary surgery and may die with the chance of ε . The average cost for this patient is 40×ε Consider another patient at the age of 60, if the true class label of a patient is class-1, but this patient is misclassified to class-0, thus, this patient will not get surgery and die almost immediately. The cost of this misclassification is 40 years for this patient. Now, we have data points {x1, x2, x3, … , xN } with true labels {y1, y2, y3, … , yN }, and xn is the aneurysm feature of the patient-n. The current age of the patient-nistn. We have this cost table for each patient: True label yn Predicted Label ̂(y)n Cost for the patient-n 0 0 0 1 1 0 0 1 (100-tn)×ε 1 0 100-tn ̂(y)n = f(xn; w) is a classification model with internal parameter w The value of ̂(y)n isa real number between 0 and 1. Your task: design a differentiable loss Ln(w) that is the cost of making a wrong classification on xn. “differentiable” means exists, so that exists. Part 2 Complete the task in H2P2T1.ipynb and H2P2T2.ipynb Note: It is very time consuming to fit a GMM to high dimensional data, and therefore PCA + GMM is the "standard" approach. Grading: the number of points Undergraduate Student Graduate Student 1 (PCA) 1 1 2 (PCA) 5 5 3 (NLL) 4 4 4 (NLL) N.A. 5 bonus points 5 (loss) 10 10 H1P2T1 15 15 H2P2T2 15 15 Total number of points 50 +5 50 + 5 Extra Reading PCA is widely used in many applications. Do a google scholar search with PCA + some field, e.g., PCA +bioinformatics or PCA + finance, you will find relevant papers. https://www.nature.com/articles/s41467-018-04608-8 There are many variants of PCA, such as sparse PCA and kernel PCA that are implemented in sk-learn. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.72.7798&rep=rep1&type=pdf https://www.di.ens.fr/sierra/pdfs/icml09.pdf https://www.di.ens.fr/~fbach/sspca_AISTATS2010.pdf Which one is good for your application? Test different algorithms and find the best. Remember that machine learning is more like an experimental science: you need to run lots of experiments.
(2024-25) BHMH2157 Hospitality and Tourism Marketing Individual Assignment Design of a “Tailor-made” Executive Business Package for April 2025* (*The whole April from 1st – 30th April with peak and non-peak periods’ consideration) Objectives: Understand the roles of service marketing in the hospitality and tourism industry. Examine the customer and organizational buyer’s behaviour and their influences to market the hospitality and tourism products. Apply marketing concepts and principles to the hospitality and tourism industry. Deadline: Submit in Hardcopy Tutorial Class (Week 6) – registered class As a reminder: Please staple your assignment and do not fold Before preparing your assignment, please check your assigned hotel on page 3 and 4. Due to fairness, NO marks will be granted for using the wrong hotel Assigned Hotel: W Hong Kong https://w-hotels.marriott.com/hotel/w-hong-kong/ Assigned Hotel: JW Marriott Hotel Hong Kong https://www.marriott.com/en-us/hotels/hkgdt-jw-marriott-hotel-hong-kong/overview/ Assigned Hotel: The Ritz-Carlton, Hong Kong https://www.ritzcarlton.com/en/hotels/hkgkw-the-ritz-carlton-hong-kong/overview/ Assigned Hotel: Island Shangri-La https://www.shangri-la.com/hongkong/islandshangrila/ Grading Aspects: Application of theories and concepts 35% Analysis and interpretation 35% Presentation & layout 15% Structure & organization 10% Referencing 5% Total 100% Instruction: As the director of sales and marketing, you are going to propose an Executive Business Package for travelers. With the aim of attracting your target market, you have to design an attractive and comprehensive promotional leaflet that covers the “WHOLE period” from 1 to 30 April which includes any peak and non- peak seasons/ periods within the month. Students should have a deep understanding about: • the types of accommodation (room type(s)) to be offered to the target markets • facilities and services of the assigned hotel that are suitable for the package • peak and non-peak periods in April, are there any room rates difference? • promotional room rates (rather than the highest rack/ published rate) and/ or packages of the primary competitors nearby. • The privileges/ benefits that are suitable for the target markets (business travellers Students are recommended to go through your spelling and grammar. Marks maybe deducted for poor English writing. l Students should read the “Guidance Notes on Avoiding Plagiarism” and “Bibliographic Referencing”, which is set out in the Student Handbook. l Marks will be penalized for late submission. Down one grade for late submission per day. l Plagiarism in any form. is highly prohibited. First page Cover Page (Student full name, Englishname, Student number, Assigned Hotel Name) Second Page Promotion al leaflet Be professional, comprehensive but creative Promotional Leaflet (Maximum One “A4 SINGLE SIDE” page, other size is not allowed, NO double-side) DARK BACKGROUND IS NOT RECOMMENDED, (use the hotel’s corporate colour is recommended) Based upon your investigation and research on your hotel and your primary competitors from websites, observations, newspapers, magazines, interviews, etc. design the layout of the promotional leaflet identify the suitable room types for the package the suitable room rates for the room types any privileges and benefits any terms and conditions, etc. directly(plagiarism) Third page Short Essay Short Essay (Maximum One SINGLE-side page) • Times New Roman, font size 12, 1.5 line spacing, margin: 1.5-2 cm (if single line spacing or font size is too small, only ½ will be marked due to fairness and marks may be deducted) Explaining your methodology. Your write-up should be based on: the investigation (room rate, facilities and services, privileges and so on) about the assigned hotel – as a reminder, usually not use the “published/ rack hotel” the room rates/ package summary of the primary competitor hotels nearby the high and low season of April how to set/ determine the room rates, package rate, privileges Forth page References/ Appendix (in APA format) - Photos (if any)
QUESTION 1: Excess Burden and Incidence of Taxation a. Suppose you want to redistribute from rich to poor by taking the labor of high earners. Suppose the market for such workers is described by one of the 4 diagrams above. Which one (1, 2, 3, or 4) would provide a best-case scenario for your ability to redistribute? Which one would be the worst-case scenario? Explain your reasoning. For simplicity, you can assume you put no “(marginal) weight” on the well-being of high earners or their employers. Your answer should include the word “elasticity.” b. Suppose you want to help low earners by giving them a labor subsidy, sort of like the Earned Income Tax Credit. Suppose the market for such workers is described by one of the 4 diagrams above. Which one would provide a best-case scenario for your ability to aid them? Which one would be the worst-case scenario? Explain your reasoning. For simplicity, you can assume you put no “(marginal) weight” on the well-being of anyone other than these workers. Your answer should include the words “elasticity” and “incidence.” c. Provide a critique of the Earned Income Tax Credit based on your argument above. What might be a more effective way of transferring money to low earners rather than a labor subsidy? QUESTION 2: Optimal Taxation Consider a simple economy with 2 individuals: 1. Person A is endowed with 99 (dollars) 2. Person B is endowed with 0 The government possess a redistributive mechanism that allows it to a) take T + T2 away from Person A; and b) deliver T to Person B. In the background, you can think of this as an income tax on Person A that affects their willingness to work, but we will abstract from those details for simplicity. Assume that T cannot be negative. a. As a function of T, what is the deadweight loss created by this mechanism? For instance, if T = 2, how much deadweight loss would be created? How do you know? b. Imagine that you have “Utilitarian social preferences.” What is the optimal choice of T? Explain your answer. c. Imagine that you have “Rawlsian social preferences.” What is the optimal choice of T? Explain your answer. (To get a numerical answer, you need to use of the “quadratic formula”) d. Now suppose you are an economist, and you observe that the T = 1.5. Assume that you has marginal social welfare weights of MSWWA and MSWWB, which of course you do not observe. However, you are willing to assume that it has “convex preferences” and set T in order to maximize their Social Welfare Function. Find MSWWA/MSWWB and explain your answer. (Hint: When T = 1.5, the slope of the Possibilities Frontier is 4.) Drawing a diagram is not necessary but can help. e. Finally, suppose you are in the exact same situation as part d, but then you find out that in order to transfer T to Person B, Person A will lose T + 0.1T2 (rather than T + T2). If you were to recalculate the ratio of MSWWs like you did in part d, would you find a higher number or a lower number. Note, you do not need to actually do the calculation; an intuitive argument will suffice (and is in fact preferred). QUESTION 3: Universal Basic Income Consider the following tax scheme: · We will tax the s% highest earners at rate t. · Those earners will earn on average o I is there earnings if untaxed; o e captures that their behavior. is elastic, so they will earn less when the tax is higher. · We will then transfer to all households, guaranteeing them this “Universal Basic Income.” a. Suppose we plan to tax the top 1% of earners and their untaxed income would be I = $2,000,000. Assuming e = 1, what is the highest UBI that can be achieved, and what is the tax rate that achieves it? How does that change if e = 2? Provide economic intuition for this change. You should perform. this analysis in Excel, by simultaneously checking 0.01, 0.02, ..., 0.99 to see what they raise. For the remainder of the problem, assume e = 1. b. Suppose now that we will tax the top 20%, and their average untaxed income is I = 400,000. What is the highest UBI that can be achieved? c. Repeat the analysis assuming we will tax the top 60%, and their average untaxed income is I = 200,000. d. The larger the share of the population that we tax, the larger the UBI is that we can achieve. Explain why that is. QUESTION 4 : Open-ended: UBI and Flat Tax A Flat Tax (“FT”) is typically advocated by analysts who stress the importance of economic efficiency. Universal Basic Income (“UBI”) is typically advocated by analysts who stress the importance of equity. The two ideas, however, are not necessarily in opposition to each other. What is a basic structure of a tax code that both sides could probably endorse? What would the two sides agree on? What would they continue to disagree about fiercely?
