Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

http://homepage.cs.latrobe.edu.au/sloke/papers/1-s2.0-S0167739X12001318-main.pdf

Mobile cloud computing

Mathematica--A System for Doing Mathematics by Computer.

Organization for Economic Co-Operation and Development (OECD).

Mobile systems have limited resources, such as battery life, network bandwidth, storage capacity, and processor performance. These restrictions may be alleviated by computation offloading: sending heavy computation to resourceful servers and receiving the results from these servers. Many issues related to offloading have been investigated in the past decade. This survey paper provides an overview of the background, techniques, systems, and research areas for offloading computation. We also describe directions for future research.

/pdf/a-survey-of-computation-offloading-for-mobile-systems-24sqh42qcl.pdf

A Survey of Computation Offloading for Mobile Systems

The key barrier to widespread uptake of cloud computing is the lack of trust in clouds by potential customers. While preventive controls for security and privacy are actively researched, there is still little focus on detective controls related to cloud accountability and audit ability. The complexity resulting from large-scale virtualization and data distribution carried out in current clouds has revealed an urgent research agenda for cloud accountability, as has the shift in focus of customer concerns from servers to data. This paper discusses key issues and challenges in achieving a trusted cloud through the use of detective controls, and presents the Trust Cloud framework, which addresses accountability in cloud computing via technical and policy-based approaches.

http://www.hpl.hp.com/techreports/2011/HPL-2011-38.pdf

TrustCloud: A Framework for Accountability and Trust in Cloud Computing

We describe a privacy manager for cloud computing, which reduces the risk to the cloud computing user of their private data being stolen or misused, and also assists the cloud computing provider to conform to privacy law. We describe different possible architectures for privacy management in cloud computing; give an algebraic description of obfuscation, one of the features of the privacy manager; and describe how the privacy manager might be used to protect private metadata of online photos.

http://www.hpl.hp.com/techreports/2009/HPL-2009-156.pdf

A Privacy Manager for Cloud Computing

eScience is rapidly changing the way we do research. As a result, many research labs now need non-trivial computational power. Grid and voluntary computing are well-established solutions for this need. However, not all labs can effectively benefit from these technologies. In particular, small and medium research labs (which are the majority of the labs in the world) have a hard time using these technologies as they demand high visibility projects and/or high-qualified computer personnel. This paper describes OurGrid, a system designed to fill this gap. OurGrid is an open, free-to-join, cooperative Grid in which labs donate their idle computational resources in exchange for accessing other labs’ idle resources when needed. It relies on an incentive mechanism that makes it in the best interest of participants to collaborate with the system, employs a novel application scheduling technique that demands very little information, and uses virtual machines to isolate applications and thus provide security. The vision is that OurGrid enables labs to combine their resources in a massive worldwide computing platform. OurGrid is in production since December 2004. Any lab can join it by downloading its software from http://www.ourgrid.org
 .

Labs of the World, Unite!!!

A significant barrier to the adoption of cloud services is that users fear data leakage and loss of privacy if their sensitive data is processed in the cloud. In this paper, we describe a client-based privacy manager that helps reduce this risk, and that provides additional privacy-related benefits. We assess its usage within a variety of cloud computing scenarios. We have built a proof-of-concept demo that shows how privacy may be protected via reducing the amount of sensitive information sent to the cloud.

A client-based privacy manager for cloud computing

We collect BitTorrent usage data across multiple file-sharing communities and analyze the factors that affect users' cooperative behavior. We find evidence that the design of the BitTorrent protocol results in increased cooperative behavior over other P2P protocols used to share similar content (e.g. Gnutella). We also investigate two additional community-specific mechanisms that foster even more cooperation.

/pdf/influences-on-cooperation-in-bittorrent-communities-2yyltmqgq5.pdf

Miranda Mowbray

Papers

TrustCloud: A Framework for Accountability and Trust in Cloud Computing

A Privacy Manager for Cloud Computing

Labs of the World, Unite!!!

A client-based privacy manager for cloud computing

Influences on cooperation in BitTorrent communities