January 12, 2017
MIPS | Algorithm | Minecraft-Texturen

作业 Minecraft-Texturen
在本次作业中,您将开发一个 MIPS 汇编程序,包含降低分辨率和降低数字图像 的色彩深度。
程序框架见 vorgabe.s 图像见 Ha01 文件夹
作业 1: Load and save images (7 分)
提示
1. An image can be stored in the memory as an array by continuously storing the individual lines with image points one behind the other.
2. At the beginning of the image there is also a header, which contains the width, height and color depth, which is the number of colors.
要求
1.Your program should be able to read and write image files in the PGM format (binary, P5). a) 提示:To read and write files, QtSPIM, similar, or other operating system, provides different syscalls. You must calculate the value of the number from a string of digits.
To do this You need an ASCII table.
2.Implement a routine load_img that loads an image and processes the header

December 13, 2016
Machine Learning | 代写 | CSE 491: Introduction to Machine Learning (Fall 2016)

CSE 491: Introduction to Machine Learning (Fall 2016)
Exam 3 Take Home, Due: 11:30AM on Dec 14, 2016
• The exam should be completed independently and discussions of any type are NOT allowed.
• A PDF version should be electronically submitted to D2L Dropbox with the file name
LastName_FirstName_CSE491EXAM3.pdf
1. (20 points) Support Vector Machines. Given two data points x1 = (1, 0)T , y1 = −1, and x2 = (3, 0)T , y2 = 1.
(a) Compute the optimal w and b in support vector machine by solving the primal formu- lation given as follows:
min 1wTw w,b 2
subject to yi(wT xi + b) ≥ 1, ∀i.
(b) Compute the optimal α in the dual formulation of support vector machine.
(c) Compute the optimal w based on the optimal α obtained from the dual formulation of support vector machine and compare with the results in (a).

June 24, 2016
Machine Learning | Sentiment Analysis on Tweets about the Scotland Independent Vote in 2014

Topic Description
As we know the result of Scotland independent vote is that Scotland still a part of the UK in 2014. In this practicum, we will implement an appropriate machine learning model which could predict the opinion (supportive or not opposed or neutral) of the related tweets to Scotland independence.

Machine Learning
Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI.

Sentiment Analysis
Sentiment analysis, also called opinion mining or sentiment mining, is a process which analysing and reasoning the subjective sentimental text, is the detection of attitudes.

Brief Study Steps
1. Download the tweets (as the dataset) through the twitter’s API, i.e. each tweet should at least has the hashtag like #Scotland #independent #vote etc. before 18 September 2014 (the voting date), or other conditions could show that tweet is related to the Scotland independent vote around that time. Then clean the dataset, divide it into training set (more than 500 tweets), tuning set (more than 500 tweets) and testing set (more than 500 tweets).

2. Annotate each tweet in the training set as “positive”, “negative” or “neutral”.

3. Training a machine translation model to predict the attitude of the tweets whether it was supportive or opposed or neutral to Scotland independence. Different machine learning models/algorithms/functions will be tested with the combination of different feature selection and parameter setting.

May 29, 2016
Python | Finance | Quantitative Trading

Quantitative Trading Project
April 26, 2016
1 The Task
As part of our program of study, you (and 0, 1 or 2 group partners) will invent, run and analyze a quantitative trading strategy. Such a strategy has the following features:
• Investmentinpubliclytraded(butnotnecessarilyelectronicallytraded) assets
• A “recipe” for evaluating attractiveness of potential trades that – depends on pre-specified classes of information
– Could be followed by anyone, given the recipe and data sources
• A “recipe” for investment sizes, and position entry and exit rules, that
– may link position size to attractiveness

May 28, 2016
Processing | Interactive Design

1000 Hands, Universal Everything, www.universaleverything.com/projects/1000-hands
In this subject you will be generating visual communication outcomes through the formats of visual narrative, motion design and interactive design. For each project you are to identify key narrative ele- ments from one of the radio stories supplied in class and adapt this to a simple visual outcome. Through a process of iterative development you will seek to expand your visual elements utilising the form and structure of your medium.
Through research, development and process you are expected to familiarise yourself with the medium and produce a short nished project that successfully communicates your narrative in an original and engaging way. Your intention should be beyond merely visually echoing the audio or the narrative that you are responding to.
INTERACTIVE DESIGN

May 27, 2016
C | FTP Proxy

The Final Project of Internet Applications
2016.05
Project Title: FTP Proxy
Goal of the project
Deeply understand the related knowledge of FTP (File Transfer Protocol).
Complete a FTP proxy program based on Linux command line terminal.

Requirements of the project
1. The FTP server can be set up using the existing software, for example FileZilla server. For the FTP client, students can use one general FTP client tool software, for example FileZilla client.
2. The FTP proxy performs as both FTP client and FTP server. FTP client will connect FTP proxy first and send the FTP requests. FTP proxy is able to receive the requests, and then forward the requests to FTP server. After that, it can receive the replies from the FTP server, and then forward the replies to the FTP client.

May 25, 2016
Processing | Game | Plane | COMP115: Assignment 2

COMP115: Assignment 2
May 8, 2016
In this assignment you will create a complete implementation of the Paper Plane game. The assignment is broken into parts, separate tasks which can be done in isolation and which combine to make a full solution. Each part has a pass level (worth half the marks allocated for that part) and a distinction level (worth the other half of the marks for that part. For each part we indicate which module is most useful for completing it.
The total marks available in this assignment is 100. Part 0 is worth 10 marks, Part 1 is worth 30 marks, Part 2 is worth 20, Part 3 is worth 20, and Part 4 is worth 20 marks. In each part, half the marks are allocated to pass-level functionality and the other half to distinction level functionality. For example, pass-level functionality for Part 1 is worth 15 marks overall.
Part 0: Plane – 10 marks
Most useful module is pixels and variables
An isocolese triangle, representing a paper plane, descends from the top of a 512 pixel wide and 768 pixel high window. As it exits the bottom it is never seen again. If any key is pressed, the plane re-appears at the top of the window and starts descending again.
Pass Level
The plane is drawn and moves down the screen.

May 20, 2016
C | Algorithm | Bi-tree | Assignment 3

Assignment 3 Due: Fri 27 May, 23:59
Question 1 (5 marks)
Answer the following questions and justify the reasons of your answers.
1) (1 mark) Suppose that a file is sorted against a search key A. An index is also built against the search key A. We also assume that A is a candidate key. Please show that using the index to conduct an equality search regarding A to find a record typically involves less I/O costs than that of conducting a binary search over the sorted file. Assume that the ordered file occupies blocks and the index file occupies C blocks. (You can use a concrete index to justify your answers)
2) (2 marks) Is it possible that deleting an entry reduces global depth by 2 in the Extendible Hashing?
3) (2 marks) Find a scenario in Linear Hashing that splitting whenever an overflow occurs performs worse, in terms of the number of total pages, than splitting only when the number of overflow pages exceeds a given threshold.

May 19, 2016
Prolog | Gridword | Introduction

Introduction
In this assignment, you will implement the basic functions of a simple BDI Agent that operates in a Gridworld, and by doing so, learn about the ideas underlying BDI agents.
Gridworld
The Gridworld consists of a two-dimensional grid of locations, extending to infinity in both directions. Some locations contain “junk” which the agent must “clean up” in order to score points. An agent cleans up a piece of junk by moving to its location and executing a pickup action. Agents can move one square at a time either horizontally or vertically. The world is dynamic in that junk may spontaneously appear at randomly determined locations at any time, though there is never more than one item of junk in the same location.

May 8, 2016
C | MIPS | Algorithm QuickSort algorithm

QuickSort algorithm
In this project, you will be implementing the QuickSort algorithm in MIPS. The QuickSort algorithm can be used to recursively sort an array in-place in O() running time in the average case.
Brief explanation of the terms above:
● Recursive: the main sorting function calls on itself until a trivial case is reached. This approach is also called “divide and conquer”, where a large problem is solved by breaking it down into multiple simple problems
● In-place: the algorithm runs without requiring additional memory to be allocated in during run-time. Thus, you do not need to create a temporary copy of the array in memory to implement this algorithm (unlike merge sort, for example)
● Running time: although the worst case running time for QuickSort is O(), for most array inputs it will run in O() time, which is the same time complexity as provided by algorithms like merge sort, heapsort, and binary tree sort. The main advantage of quicksort over the other average-case O() algorithms is the smaller constants associated with quicksort [1]