Title: Designing Blockchain-based Applications
Abstract: Blockchain is an emerging technology that enables decentralisation as new forms of distributed systems where participants can transact without trusting each other. Applications built on blockchain can take advantage of properties such as data immutability, integrity, fair access, transparency, and non-repudiation of transactions. However, blockchain has technical limitations, for example, privacy and scalability. This tutorial presents what blockchain is, why it is important, and how to design blockchain-based applications. Design approaches are discussed, including blockchain design patterns, design processes, model-driven development and design trade-offs.
Organizer: Dr Qinghua Lu
Short Bio: Dr Qinghua Lu is a senior research scientist at CSIRO, Australia. Before she joined CSIRO, she was an associate professor at China University of Petroleum. She formerly worked as a researcher at NICTA (National ICT Australia). She received her PhD from University of New South Wales in 2013. Her research interest includes architecture design and model-driven development of blockchain applications, blockchain for IoT, and reliability of cloud computing. She has published more than 70 peer-reviewed academic papers in international journals and conferences. She serves as an editor or reviewer for many journals and as PC member for many conferences and workshops.
Title: Building Secure and Scalable Private Cloud Infrastructures for Big Data Systems
Abstract: Cloud computing has opened new horizons for organisation for dynamically allocating and reassigning computing and storage resources for mission critical Big Data systems. There is an increasing trend to build and manage private cloud infrastructures for several reasons with security, privacy, and data location management being the predominant concerns. There is also an increasing demand of objective benchmarking and selecting Big Data storage solutions that work with private cloud infrastructure. However, there is not much guidance on building, operating, trouble-shooting, and managing secure and scalable private cloud infrastructures that can be leveraged for Big Data centric mission critical systems.
Drawing on our extensive research on architecting and implementing cloud-based systems and experience of building private cloud infrastructures using Open Source Software such as OpenStack technologies and evaluating Big Data Storage Solutions, this tutorial will discuss the architectural and technological challenges and solutions for designing and implementing cloud systems using Open Source Solutions such as OpenStack and Docker viz-a-viz proprietary solutions such Microsoft Azure.
This tutorial will provide the participants with important knowledge and understanding about the theoretical principles and practical techniques for implementing and managing secure and scalable private cloud infrastructures for business- and mission-critical systems. The presenters will also describe the technical strengths and limitations of OpenStack technologies for designing and implementing a dynamically reconfigurable Cloud computing infrastructure for automating the evaluation of cloud-enabled big data solutions (such as Redis, Riak, MongoDB, CouchDB and Cassandra) for mission critical systems.
The participants will have an opportunity to get involved in practical exercises for deploying and experimenting private cloud infrastructure with OpenStack technologies and discuss their differences and similarities with proprietary solutions such as Microsoft Azure.
Organizer: Victor Prokhorenko
Short Bio: Victor Prokhorenko is a researcher with the Centre for Research on Engineering Software Technologies (CREST) at the University of Adelaide working on projects funded by DST and Data61. Victor has more than 14 years of experience in software engineering while evaluating and recommending emerging computing and storage technologies for mission critical systems for Defence industry. Victor main areas of expertise include investigation of technologies related to software resilience, trust management and big data solutions hosted within OpenStack private cloud platform. Victor has deep knowledge and extensive experience with cloud computing tools such as Chef and Terraform to provision machines within the cloud in a predictable and reproducible manner. Taking automation further than low-level virtual machine preparation, Victor has also worked on the application deployment and performance benchmarking phases. Victor has obtained a PhD in Computer Science from the University of South Australia.
Title: Machine Learning: A Roadmap from Beginners to Experts
Abstract: Machine learning has become one of the key enablers for reshaping the paradigms and models the way nations, organisations, and societies carry out their engagements, businesses, life styles, even hostilities. With machine learning, computing enabled devices learn how to not only carryout mundane tasks but make and/or support highly critical and sophisticated decisions that may have profound impact on businesses and humans.
The huge amount of research and development of machine learning have been producing a large number of techniques and algorithms. It can be intimidating for beginners to start their journey with machine learning. This tutorial tries to offer a roadmap to machine learning for anyone who is interested in this area. The basic concepts and techniques, from simple linear classifiers to advanced neural networks, will be explained in an easy-to-understand manner. This tutorial will also introduce current active research directions in machine learning and give participants a general idea of the kind of machine learning technique they should consider for their problems. The application of machine learning will be particularly focused on in this tutorial. The ML concepts and applications will be explained in the context of use cases from computer vision, natural language processing, data mining, and audio information processing.
Besides the introductory talk, there will also be a discussion session in this tutorial. The participants will have the opportunity to present their problems and facilitate a discussion around the potential solutions.
Organizer: Dr. Lingqiao Liu
Short Bio: Lingqiao Liu is a researcher and lecturer with the Australian Institute for Machine Learning at the University of Adelaide. He has obtained a PhD from the Australian national university in 2014 and joined the University of Adelaide after that. He has 10 years of research experience in machine learning and computer vision. He has been actively publishing in the top conferences and journals of machine learning and computer vision. He is a recipient of ARC DECRA (Discovery Early Career Researcher Award) award in 2016 and the University of Adelaide Research Fellowship award in 2016. His current research interest is low-level supervised approaches, including semi-supervised, weakly-supervised and self-supervised learning strategies for deep learning and generative models. For applications, he is working on various domains including visual recognition, natural language recognition, and music signals processing.