ASSESSMENT TASK 3: PRESENTATION OVERVIEW In this task you are to record a presentation reporting on a research topic on database design and development. An induction day is being held for new students looking to enter Project Emerald 2021. These students do not know what is covered in the project nor much about the topics… Continue reading ASSESSMENT TASK 3: PRESENTATION OVERVIEW In this task you are to record a presentation reporting on a research topic on database design and development. An induction day is being held for new students looking to enter Project Emerald 2021. These students do not know what is covered in the project nor much about the topics covered in the subject. As experience

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## COMP4161 T3/2022 Advanced Topics in Software Verification Assignment 3 This assignment starts on Tue, 8 Nov 2022 and is due on Fri, 18 Nov, 20:00h. We will accept Isabelle .thy files only. In addition to this PDF document, please refer to the provided Isabelle template for the definitions and lemma statements. The assignment is take-home. This does NOT mean you can work in groups. Each submission is personal. For more information, see the plagiarism policy: https://student.unsw.edu.au/plagiarism Submit using

COMP4161 T3/2022 Advanced Topics in Software Verification Assignment 3 This assignment starts on Tue, 8 Nov 2022 and is due on Fri, 18 Nov, 20:00h. We will accept Isabelle .thy files only. In addition to this PDF document, please refer to the provided Isabelle template for the definitions and lemma statements. The assignment is take-home.… Continue reading COMP4161 T3/2022 Advanced Topics in Software Verification Assignment 3 This assignment starts on Tue, 8 Nov 2022 and is due on Fri, 18 Nov, 20:00h. We will accept Isabelle .thy files only. In addition to this PDF document, please refer to the provided Isabelle template for the definitions and lemma statements. The assignment is take-home. This does NOT mean you can work in groups. Each submission is personal. For more information, see the plagiarism policy: https://student.unsw.edu.au/plagiarism Submit using

## Big Data Processing COSC 2637/2633 Assignment 4 – HDFS Monitoring via Spark Streaming Assessment Type Individual assignment. Submit online via Canvas → Assignment 4. Marks awarded for meeting requirements as closely as possible. Clarifications/updates may be made via announcements or relevant discussion forums. Due Date At 23:59, 2 Nov, 2022

Big Data Processing COSC 2637/2633 Assignment 4 – HDFS Monitoring via Spark Streaming Assessment Type Individual assignment. Submit online via Canvas → Assignment 4. Marks awarded for meeting requirements as closely as possible. Clarifications/updates may be made via announcements or relevant discussion forums. Due Date At 23:59, 2 Nov, 2022 Marks 25 Overview Write Spark… Continue reading Big Data Processing COSC 2637/2633 Assignment 4 – HDFS Monitoring via Spark Streaming Assessment Type Individual assignment. Submit online via Canvas → Assignment 4. Marks awarded for meeting requirements as closely as possible. Clarifications/updates may be made via announcements or relevant discussion forums. Due Date At 23:59, 2 Nov, 2022

## MATH5945: Categorical Data Analysis Term 3, 2022 Assignment 2 Submission deadline: Friday 28 October, 12:00pm Deliverables: 2 files uploaded to Moodle: (1) PDF file of your worked solutions, and (2) SAS file forALL computations. Files names should be surname firstname z123456789 ASS2. Assignment length: There is a 5 page limit and minimum 12pt font size. Any pages exceeding this limit or submissions with smaller font sizes will not be marked. Handwritten assignments will not be accepted. This does not include a SAS file of your code. Your document shou

MATH5945: Categorical Data Analysis Term 3, 2022 Assignment 2 Submission deadline: Friday 28 October, 12:00pm Deliverables: 2 files uploaded to Moodle: (1) PDF file of your worked solutions, and (2) SAS file forALL computations. Files names should be surname firstname z123456789 ASS2. Assignment length: There is a 5 page limit and minimum 12pt font size.… Continue reading MATH5945: Categorical Data Analysis Term 3, 2022 Assignment 2 Submission deadline: Friday 28 October, 12:00pm Deliverables: 2 files uploaded to Moodle: (1) PDF file of your worked solutions, and (2) SAS file forALL computations. Files names should be surname firstname z123456789 ASS2. Assignment length: There is a 5 page limit and minimum 12pt font size. Any pages exceeding this limit or submissions with smaller font sizes will not be marked. Handwritten assignments will not be accepted. This does not include a SAS file of your code. Your document shou

## Your gas tank, when full, holds enough gas to go p miles, and you have a map that contains the information on the distances between gas stations along the route. Let d1 < d2 < · · · < dn be the locations of all the gas stations along the route, where di is the distance from the Chicago end of the freeway (your starting point in this hypotehtical trip) to the gas station. You may assume that the distance between neighboring gas sta

Problem 1 [20 points]: Problem 16-1 (Coin Changing) in your textbook. Problem 2 [30 points]: Suppose you were planning a scenic cross country road–trip along the “Mother Road” (i.e., U.S. Route 66). Your gas tank, when full, holds enough gas to go p miles, and you have a map that contains the information on the… Continue reading Your gas tank, when full, holds enough gas to go p miles, and you have a map that contains the information on the distances between gas stations along the route. Let d1 < d2 < · · · < dn be the locations of all the gas stations along the route, where di is the distance from the Chicago end of the freeway (your starting point in this hypotehtical trip) to the gas station. You may assume that the distance between neighboring gas sta

## Description In this assignment, you will be evaluating the performance of the Paxos implementation you implemented in assignment 3. Alternatively, you can also use an existing Paxos implementation, either found by yourself or from the list below. You will be designing your own experimental plan and reporting on your findings through a written report. The experimental plan will aim to answer the following questions: What is the performance in terms of runtime (and other metrics) of your system given specific configurations, in a functioning mode without failures? What is the performance in terms of runtime (and other metrics) of your system given specific configurations, in

Assignment 4 Due 5 Nov by 16:59 Points 100 Submitting a file upload File types pdf Start Assignment This assignment is for students enrolled both in the PG and UG versions of this course. Note that this is a group assignment, with groups of maximum three students. Please write the student ids at the top… Continue reading Description In this assignment, you will be evaluating the performance of the Paxos implementation you implemented in assignment 3. Alternatively, you can also use an existing Paxos implementation, either found by yourself or from the list below. You will be designing your own experimental plan and reporting on your findings through a written report. The experimental plan will aim to answer the following questions: What is the performance in terms of runtime (and other metrics) of your system given specific configurations, in a functioning mode without failures? What is the performance in terms of runtime (and other metrics) of your system given specific configurations, in

## CSE 594: Spatial Data Science & Engineering Overview of Moving Object Data Trajectory of Moving Objects A type of spatiotemporal data generated by moving objects A trajectory is a polyline in three-dimensional space Two dimensions refer to the space and the third dimension refers to the time Represented as a

CSE 594: Spatial Data Science & Engineering Overview of Moving Object Data Trajectory of Moving Objects A type of spatiotemporal data generated by moving objects A trajectory is a polyline in three-dimensional space Two dimensions refer to the space and the third dimension refers to the time Represented as a sequence of position points Tr(P1,… Continue reading CSE 594: Spatial Data Science & Engineering Overview of Moving Object Data Trajectory of Moving Objects A type of spatiotemporal data generated by moving objects A trajectory is a polyline in three-dimensional space Two dimensions refer to the space and the third dimension refers to the time Represented as a

## Purpose This assignment will develop your skills in designing, constructing, testing, and documenting a Python program according to specific programming standards. This assessment is related to the following learning outcome (LO): ● LO2 – Restructure a computational program into manageable units of modules and classes using the object-oriented methodology ● LO3 – Demonstrate Input/Output strategies in a Python application and apply appropriate testing and exception handling techniques ● LO4 – Investigate useful Python packages for scientific computing and data analysis; ● LO5 – Experiment with data manipulation, analysis, and visualisation technique to formulate business insight. Your task This assignmen

FIT9136 Algorithms and Programming Foundations in Python Assignment 3 OCT 2022 1 Table of Contents 1. Key Information 2. The Assignment 2.1. The Dataset: HardwareRecs 2.2. Task 1: Handling with File Contents and Preprocessing 2.3. Task 2: Building a Class for Data Analysis 2.4. Task 3: Analyzing the File for Data Visualization 2.5. User Manual… Continue reading Purpose This assignment will develop your skills in designing, constructing, testing, and documenting a Python program according to specific programming standards. This assessment is related to the following learning outcome (LO): ● LO2 – Restructure a computational program into manageable units of modules and classes using the object-oriented methodology ● LO3 – Demonstrate Input/Output strategies in a Python application and apply appropriate testing and exception handling techniques ● LO4 – Investigate useful Python packages for scientific computing and data analysis; ● LO5 – Experiment with data manipulation, analysis, and visualisation technique to formulate business insight. Your task This assignmen

## To help with your implementation, we have provided a few sample inputs. They can be found in the data/inputs folder. The ground-truth files can be found in data/ground-truth. Your predictions will be stored in data/predictions. For test cases 1 and 2, the inputs will be on the example given in Lecture 9: For case 3, we will use a slightly larger graph. Note that the ground-truth might be slightly different from your answers. We will assume a tolerance up to 1 decimal place. During grading, your code will be e

S5340 Assignment 4 (Semester 1, AY2022/2023) 1 CS5340 ASSIGNMENT 4: MONTE CARLO INFERENCE 1. OVERVIEW In this assignment, you will write code to perform Monte Carlo inference i.e. Importance sampling and Gibbs sampling. Often, marginalization over large/continuous probability distributions can be intractable. In Monte Carlo inference, we circumvent this problem by sampling from simpler proposal… Continue reading To help with your implementation, we have provided a few sample inputs. They can be found in the data/inputs folder. The ground-truth files can be found in data/ground-truth. Your predictions will be stored in data/predictions. For test cases 1 and 2, the inputs will be on the example given in Lecture 9: For case 3, we will use a slightly larger graph. Note that the ground-truth might be slightly different from your answers. We will assume a tolerance up to 1 decimal place. During grading, your code will be e

## Rate-Distortion and Echos In class the VAE we trained used a loss function composed of a data term (the MSE) and a prior term (the KL divergence). A week or so later we derived the β-VAE, which relaxes a constrained optimization problem to the following (unconstrained) loss: L[p, q] = ? log p(x|z)︸ ︷︷ ︸ MSE +β KL[ q(z|x) ∥ p(z) ]︸ ︷︷ ︸ prior a) Describe VAE behavior at the extreme cases of β → 0 and β →∞. b) We also discussed the

CS 8395: Homework 2 Overall directions Any resource available to you may be used to answer the homework questions, and you may collaborate with anyone in the class, with the following caveats: You must record the names of your collaborators at the top of your homework. You are strongly encouraged to avoid copying code/answers directly;… Continue reading Rate-Distortion and Echos In class the VAE we trained used a loss function composed of a data term (the MSE) and a prior term (the KL divergence). A week or so later we derived the β-VAE, which relaxes a constrained optimization problem to the following (unconstrained) loss: L[p, q] = ? log p(x|z)︸ ︷︷ ︸ MSE +β KL[ q(z|x) ∥ p(z) ]︸ ︷︷ ︸ prior a) Describe VAE behavior at the extreme cases of β → 0 and β →∞. b) We also discussed the