Computer Science

Applications are now closed

  1. » Demystifying Online Porn: Characterizing a major adult video streaming service
  2. » How do you App? Understanding mobile traffic characteristics
  3. » How much would you pay for online security?
  4. » Peeking into the Online Social Scene: Understanding usage and performance issues on YouTube and Twitter
  5. » Pirates of the Internet: Analyzing online copyright infringement
  6. » Six Degrees of Complexity: How complex are modern Web sites?
  7. » Towards an era of Fog computing
  8. » Interactive tools for learning computer networking fundamentals
  9. » FPGA Implementation of a LFSR based Pseudo-random Pattern Generator
  10. » FPGA Implementation of an algorithm for the Precise detection of Single and Cluster Cells in Microfluidic Applications
  11. » Development of Design Automation Software for the Design of Integrated Circuits
  12. » glGetFeedback - Towards Automatic Feedback and Assessment for OpenGL Computer Graphics Assignments
  13. » Making Remote collaboration work
  14. » Fast and Vast – utilizing modern storage media
  15. » Stop the Power Hunger
  16. » Mining multi-valued dependencies from data
  17. » Discovering independence relationships in data
  18. » Network-Based Multi-agent Collaborative Machine Learning
  19. » Semantic Rule based Program Behavioural Monitoring
  20. » From Digital Repository to Linked Data - Citation and Statistics
  21. » Planning Tool for Web Service Composition
  22. » Mobile Data Collection & Analysis
  23. » CUDA-based N-body Physics Simulation
  24. » Habit-based Lego Robots
  25. » Hand gesture-based 3D navigation
  26. » Recommender System for an Intelligent Campus
  27. » Terrestrial latency distribution models for satellite teleports
  28. » Tools for satellite simulator system
  29. » Happiness Index: Measuring tipping points in sentiments on Twitter data

Demystifying Online Porn: Characterizing a major adult video streaming service


Supervisor

Aniket Mahanti

Discipline

Computer Science

Project code: SCI084

While mainstream content sharing services (e.g., YouTube, Vimeo, DailyMotion, Netflix) have been studied extensively, there has been limited work on understanding adult media streaming services. In this project, you will utilize a large dataset containing information on over 1.5 millions videos between 2007 and 2016.  You will analyze key features of this video corpus such as upload frequency, content duration, number of uploads per user, category analysis, and popularity characteristics.

Requirements: Basic computer networking knowledge, good analytical skills, good programming skills, desire to explore research literature. 

How do you App? Understanding mobile traffic characteristics


Supervisor

Aniket Mahanti

Discipline

Computer Science

Project code: SCI085

Using Web server logs collected from the university Web sites, you will answer several questions regarding how mobile users (users accessing the sites through smartphones, tablets, etc.) access content online. You will contrast their usage behavior against normal desktop users. You will perform several multi-layered analysis on the already available datasets such as user session behavior, file size characteristics, user domains, popularity of hosts and files.

Requirements: Basic computer networking knowledge, good analytical skills, good programming skills, desire to explore research literature.

How much would you pay for online security?


Supervisor

Aniket Mahanti

Discipline

Computer Science

Project code: SCI086

The project will involve surveying current research literature on the cost of securing computer systems. Specially, the project will look into how much people are willing to pay to get the best computer security. The student will also develop questionnaire that can be posed to users. This data will be analyzed to understand the needs of modern day users.

Requirements: Basic computer networking/security knowledge, good analytical skills, desire to explore research literature.

Peeking into the Online Social Scene: Understanding usage and performance issues on YouTube and Twitter


Supervisor

Aniket Mahanti

Discipline

Computer Science

Project code: SCI087

The focus of this project on understand data dissemination via social media such as video sharing platform - YouTube, and messaging service - Twitter. Both these services provide APIs to collect statistics. For example, in case of YouTube they provide an API to collect statistics on views of a video, the geographic location of the views, etc. One can focus on a news event and follow the event on both Twitter and traditional media. You could analyze how do people interact with Twitter compared to traditional media as well as the veracity of the news spread on Twitter.

Requirements: Basic computer networking knowledge, good analytical skills, good programming skills, desire to explore research literature.

Pirates of the Internet: Analyzing online copyright infringement


Supervisor

Aniket Mahanti

Discipline

Computer Science

Project code: SCI088

The goal of the project is to analyze distribution of copyrighted content such as movies and TV shows online, especially, forums and blogs. You will analyse a large dataset of metadata collected from several popular file sharing forums. The analysis could focus on many aspects such the dynamics of content distribution, frequency, popularity, etc.

Requirements: Basic computer networking knowledge, good analytical skills, good programming skills, desire to explore research literature.

Six Degrees of Complexity: How complex are modern Web sites?


Supervisor

Aniket Mahanti

Discipline

Computer Science

Project code: SCI089

The objective of this project is to measure the complexity of the modern Web. You will utilize content-level metrics (e.g., number of images) and service-level metrics (e.g., number of servers, domains) to measure the complexity of several top global and NZ Web sites. This analysis will provide insights on how these metrics are related to performance of the Websites (i.e, the amount of time to load the Websites).

Requirements: Basic computer networking knowledge, good analytical skills, good programming skills, desire to explore research literature.

Towards an era of Fog computing


Supervisor

Aniket Mahanti

Discipline

Computer Science

Project code: SCI090

It is aimed to implement basic software modules of a hardware unit as a part of an IoT device and establish communication with peers, sensor devices and a cloud. The mentioned unit should also be able to perform data analytic on the input data and produce information for users. An IoT board with the capability of processing, storage and networking (e.g. Intel Galileo, Raspberry Pie , etc.) or a smartphone. There might be need for adding necessary networking dongles to the IoT board (e.g. a WiFi dongle).

Requirements: Basic computer networking knowledge, good analytical skills, good programming skills, desire to explore research literature.

Interactive tools for learning computer networking fundamentals


Supervisor

Aniket Mahanti

Discipline

Computer Science

Project code: SCI091

The objective is building learning tools to aid students in grasping fundamental concepts in computer networking. These include concepts such as visualizing delays at nodes between source and destination, transmission delay vs propagation delay, queuing vs loss, HTTP response time, DNS query/reply, etc. Textbooks such as the one by Kurose and Ross have provided applets to better understand these concepts, however, these applets need a refreshed design and improvements to suit current learning outcomes. There are other learning tools available, however, those are outdated too  The objective of this project is to develop (possibly multi-platform) learning tools for students to easily grasp computer networking fundamentals. The tools should be lightweight, easy to use, user-driven, interactive, and achieve the key learning goals.

Requirements: Basic computer networking knowledge, good analytical skills, good programming skills, desire to explore research literature.

FPGA Implementation of a LFSR based Pseudo-random Pattern Generator


Supervisor

Bruce Sham

Discipline

Computer Science

Project code: SCI092

The objective of this project is to implement the linear feedback shift resister (LFSR) on field programmable gate array (FPGA) platform. The characteristics of LFSR should be high speed and low power consumption. It  is  especially  suitable to the  processing  environment  where  uniform  distribution  random  numbers  are  required. The student should investigate different design approaches. Complexity analysis and random-ness testing (Dieharder: A Random Number Test Suite) should be done at the end of the project.

Students who are interested in this project should have some background knowledge in “Logic Design”, “HDL Programming Language” and FPGA.

FPGA Implementation of an algorithm for the Precise detection of Single and Cluster Cells in Microfluidic Applications


Supervisor

Bruce Sham

Discipline

Computer Science

Project code: SCI093

Recent advances in microfluidic devices have provided important advantages to work with small volumes compartmentalised in droplets. In the project, the student is going to implement an algorithm for precise detection of single and cluster cells in microfluidic applications on field programmable gate array (FPGA) platform. This algorithm consists of the following processes:

  • The detection of the edge of a cell with complex morphology using a wavelet representation
  • The reduction of the noise and halo
  • Linking different parts of an edge to form a recognisable cell shape

Students who are interested in this project should have some background knowledge in “Logic Design”, “HDL Programming Language” and FPGA.

Development of Design Automation Software for the Design of Integrated Circuits


Supervisor

Bruce Sham

Discipline

Computer Science

Project code: SCI094

In the project, the student is going to develop the electronic design automation (EDA) software for the design of integrated circuits. Before EDA, integrated circuits were designed by hand, and manually laid out. Recently, most of those design processes are done automatically by the EDA software. In order to perform design automation efficiently, a few complicated data structures are used. Algorithms and heuristics should also be applied appropriately.

Students who are interested in this project should be familiar with C/C++ language and have strong background knowledge in algorithms and data structures (heaps, double linked-list, quick sort, tree and graphs) but NO circuit knowledge is required.

glGetFeedback - Towards Automatic Feedback and Assessment for OpenGL Computer Graphics Assignments


Supervisor

Burkhard Wuensche

Discipline

Computer Science

Project code: SCI095

Programming assignments are a popular assessment tool in Computer Science education. The value of such assignments depends on how fast and detailed formative feedback is given. This is often a problem due to large class sizes and the cost and difficulty of finding qualified markers.

Initial research in automated feedback for 3D modelling tasks showed that the majority of common student errors can be correctly detected as long as students follow assignment specifications. However, problems exist where students follow a non-standard solution approach. Furthermore, the existing tool is unable to differentiate severe errors from minor errors.

In this research we will investigate three key problems:

  1. The current approach of comparing intermediate data and OpenGL states will be extended with an image-based approach in order to identify solutions which are “visual correct” (i.e. which a human marker would have marked as correct based on visual inspection, even though student’s data does not match a given sample solution precisely)
  2. We will investigate feedback mechanisms for a wider range of computer graphics concepts, such as texture mapping and ray tracing.
  3. We will investigate requirements necessary to integrate the automatic feedback tool into Coderunner, an automatic (text-based) assessment tool for programming assignments.

Making Remote collaboration work


Supervisor

Gerald Weber

Discipline

Computer Science

Project code: SCI096

Remote collaboration with means such as video conferencing makes inroads in the workplace, but faces considerable hurdles: Often the work dynamics are not the same as in physical collaboration. In this project we want to investigate a number of new technologies such as eye gaze tracking and virtual worlds to see whether remote collaboration can be improved.

Fast and Vast – utilizing modern storage media


Supervisor

Gerald Weber

Discipline

Computer Science

Project code: SCI097

There is a sea change in the way we store data with faster, cheaper systems – but does our software keep the pace? In this project we analyse how we can best utilize the novel storage media coming online. This project will allow you to measure state-of-the-art hardware and experiment with novel software concepts.

Stop the Power Hunger


Supervisor

Gerald Weber

Discipline

Computer Science

Project code: SCI098

Modern laptops not just burn your lap, they churn valuable energy. In this project we want to study how software can influence and possibly reduce power consumption of consumer devices.  This project combines empirical research with software development.

Mining multi-valued dependencies from data


Supervisor

Sebastian Link

Miika Hannula

Discipline

Computer Science

Project code: SCI099

Multi-valued dependencies are fundamental for database design because they characterize data sets that can be decomposed without loss of information. This project aims at implementing a machine learning approach for the discovery of all multivalued dependencies that hold in a given data set. The implementation will be experimentally tested on real-world data sets in terms of its results, the level of data redundancy that can be eliminated by decomposing the data sets, and in terms of the efficiency of the algorithm. The results are likely to form the basis for a publication.

Good programming skills (any language), interest in data, and attention to detail are required.

Discovering independence relationships in data


Supervisor

Sebastian Link

Miika Hannula

Discipline

Computer Science

Project code: SCI100

This is the continuation of a dissertation project in data science. Finding independence relationship in data can help with important areas in data management, including query optimization, preserving data integrity, and the speed-up of updates. The work involves the optimization of some algorithms, and an experimental analysis on synthetic and real-world data.

Outcomes are likely to result in a publication.

Good programming skills (any language, although R would be a plus but not necessary), interest in data, and attention to detail are required.

Network-Based Multi-agent Collaborative Machine Learning


Supervisor

Jiamou Liu

Discipline

Computer Science

Project code: SCI101

Collaborative learning is a paradigm where more than one agent learn a task together. Unlike individual learning, agents in a collaborative learning environment capitalise on resources and results of other agents.

This project will explore the potential to employ collaborative learning in a multi-agent system. The result would be to develop a novel machine learning paradigm that simulates collaborative learning over a population of agents embedded in a social network. The goal of the project is to design novel ways to facilitate distributed crowd-based machine learning through information exchanges.

Skills required: The ideal candidate must have a good programming skill and familiarity with machine learning algorithms and tools. The candidate should also has a good understanding of multi-agent systems, and knowledge about complex networks and social network analysis.  Knowledge in algorithms and distributed computing will be a plus.

The candidate must also have good background in mathematics and probability theory.

Prerequisite: The candidate must have done artificial intelligence at the undergraduate level. A course in machine learning is also beneficial to the project. 

Semantic Rule based Program Behavioural Monitoring


Supervisor

Jing Sun

Discipline

Computer Science

Project code: SCI102

Semantic Web provides a context-enriched framework for future internet applications. Semantic Web Rule Language (SWRL) is a logical based notation for describing relationships among the different data entities documented by the Web Ontology Language (OWL). This project investigates the possibility of using the ontological rules and its reasoning engines to monitor the dynamic behaviours of semantic web applications during run-time executions. The project will examine the existing solutions on program behaviour monitoring, and looking at extending the approaches into the semantic web domain. A prototype system will be developed to realise the proposed solution, and proper evaluations will be conducted to measure the effectiveness of the approach. The project will use Apache Jena - an open source Java framework for building Semantic Web and Linked Data applications.

From Digital Repository to Linked Data - Citation and Statistics


Supervisor

Jing Sun

Discipline

Computer Science

Project code: SCI103

The University of Auckland Library is the most extensive university library system in New Zealand and a national leader in the provision and development of digital resources. Its research repository has over one thousand open access journal articles, and thousands of open access theses. Many of these publications contain named entities, such as, titles, authors, venues, publisher, areas specific keywords, references, etc. Identifying, extracting and classifying these entities and elements will allow us to produce and publish open linked data related to each article, and in time, across University research. This project will deliver a platform independent, open source software to extract, discover, classify and visualise the linkage among the digital documents within the repository, i.e., cross-citation information and usage statistics. The library developers will provide the functional requirements and copies of open access PDFs and metadata records. The outcome of the project will be owned by the library for future research purposes, and used to enhance research repository views by embedding and integrating linked data.

Planning Tool for Web Service Composition


Supervisor

Jing Sun

Discipline

Computer Science

Project code: SCI104

Automated planning is commonly used in the Artificial Intelligence (AI) domain for problem solving. A planner takes the domain (known knowledge and abilities) and the problem descriptions and produces a solution (plan) to solve the given problem. There are quite a few existing planners that support the standard Planning Domain Definition Language (PDDL). This project aims at developing a software application for web service composition using the AI planning technique. The tool will be built based on a selected existing planner. Translations between web service descriptions and their corresponding planning descriptions needs to be defined and implemented, which converts a web service composition problem into a planning problem. In addition, a graphical user interface will be developed to visualize the problem setting (e.g., available services and the request) as well as the composition (planning) outcome.

Mobile Data Collection & Analysis


Supervisor

Sathiamoorthy Manoharan

Discipline

Computer Science

Project code: SCI105

Mobile devices not only allow paperless data collection but also make it possible to gather information such as location and time automatically. Such auto-collected data may also allow picking other dependent data such as address or weather. The goal of this project is to design and develop a versatile data collection application for iOS devices. The application is expected to become a useful and extensible tool for collecting research data offline. Pre-requisites: Familiarity with XCode and Swift language.

CUDA-based N-body Physics Simulation


Supervisor

Matthew Egbert

Discipline

Computer Science

Project code: SCI106

The student will assist in the development of an N-Body simulation of a physical system called a Ramified Charge Transportation Network.

Most of the development will centre around CUDA (NVIDIA’s programming language for harnessing their massively parallel graphics processing units) and the skills learned during the project will transfer well into simulation and/or computer game development.

The ideal student would have some experience with Python plus either CUDA or C/C++. Some experience with optimization, parallel programming and/or maths/physics background would be a plus (none is 100% necessary).

Habit-based Lego Robots


Supervisor

Matthew Egbert

Discipline

Computer Science

Project code: SCI107

The student will develop a wifi-based interface between Lego-mindstorms robots and an experimental robotics controller called the Iterant Deformable Sensorimotor Medium (IDSM) -- a controller that is inspired by observations of habitual behaviour in animals.

After developing the interface, and time permitting, the student will conduct pilot-study experiments investigating how people can train IDSM-based robots to accomplish various tasks.

Hand gesture-based 3D navigation


Supervisor

Prof Robert Amor

Discipline

Computer Science

Project code: SCI108

Gesture-based interactions are becoming popular with technologies such as LeapMotion and Kinect in the consumer market. However, identifying good gestures for particular use cases is still a research topic with few accepted norms associating gestures to particular functions.

This project will investigate the development of a hand gesture set (utilising LeapMotion) which is suitable for navigation of a 3D environment (which could be inside a VR environment with an Oculus Rift). 3D models of buildings are provided as a use case and development within the Unity3D environment is expected for the project.

Outcome:

  • A tested set of gestures which map to required 3D navigation functions
  • An 'accurate' gesture recogniser for the developed gestures
  • A Unity3D environment which allows navigation of a 3D building model through the gesture set 

Recommender System for an Intelligent Campus


Supervisor

Prof Robert Amor

Discipline

Computer Science

Project code: SCI109

Smart Buildings and Smart Cities are an exciting confluence of big-data, open systems, and intelligent systems. These concepts can be applied to different clusters of infrastructure, and in this case we will investigate the Smart Campus concept. Looking to tie together information streams from various parts of the university, along with models of campus buildings, to deliver greater information through to students, staff and visitors on the campus.

Outcome:

  • A mobile/tablet-based application to support campus users 

Terrestrial latency distribution models for satellite teleports


Supervisor

Ulrich Speidel

Discipline

Computer Science

Project code: SCI110

Our Internet satellite simulator facility tries to understand how Internet protocols behave on satellite links and how they can be supported in order to make more efficient use of such links. We are currently extending our basic simulator setup, which already consists of over 100 computers, in order to be able to simulate a larger range of scenarios in a more realistic fashion. Part of this challenge is that we need to model the delay (latency) that occurs between machines on the “world” side of the satellite link and machines that sit on the side for which the link supplies the Internet connectivity. This delay is made up of the satellite link itself and the delay between the world side satellite gateway (teleport) and the world side machines. Depending on where the world side teleport is located (Hawaii, US mainland, Australia/NZ, Asia…), these delays can vary considerably. Exactly which machines in which countries users of the link access also plays a big role. For this project, we want to use data collected on real links in order to identify the target machines, and then measure the delay seen to and from these machines. You will use a variety of data sources (own measurement, RIPE Atlas, etc.) to model distributions which we can then use to program our software defined networking (SDN) switches for particular scenarios. The project will expose you to Internet measurement, shell scripting, experimental techniques, numerical modelling and SDN basics. Prerequisites: Candidates who have completed COMPSCI215 and COMPSCI314 (or will be taking COMPSCI314 in S2) will be given priority.

Tools for satellite simulator system


Supervisor

Ulrich Speidel

Discipline

Computer Science

Project code: SCI111

Our Internet satellite simulator facility tries to understand how Internet protocols behave on satellite links and how they can be supported in order to make more efficient use of such links. We are currently extending our basic simulator setup, which already consists of over 100 computers, in order to be able to simulate a larger range of scenarios in a more realistic fashion. As the number of machines grows, it is becoming important to ensure that the entire fleet of machines is operational and correctly configured before we start experiments - otherwise, we can lose valuable experiment time. This project aims to build a monitoring package which lets us query each machine to establish its current configuration state before and during experiments, and which will alert us to any machines that have gone down or are incorrectly configured. The project will expose you to shell scripting, distributed Linux/Unix administration, client-server technologies, network programming and network measurement.

Prerequisites: Candidates who have completed COMPSCI215 and COMPSCI314 (or will be taking COMPSCI314 in S2) will be given priority.

Happiness Index: Measuring tipping points in sentiments on Twitter data


Supervisor

Yun Sing Koh

Discipline

Computer Science

Project code: SCI112

In this project, we will develop a tool to analyse the sentiment on a social networking site, Twitter, to find the tipping point of when the sentiments change based on temporal and geographical locations. This is a machine learning project concentrating in the area of data stream mining, concept drift detection, and information retrieval. You need to be able to confidently program in Java and confident in using Twitter API.