The age of Ubiquitous Computing is approaching fast: most people in the UK over the age of 8 carry mobile phones, which are becoming increasingly sophisticated interactive computing devices. Location-based services are also increasing in popularity and sophistication. There are many tracking and monitoring devices being developed that have a range of potential applications, from supporting mobile learning to remote health monitoring of the elderly and chronically ill. However, do users actually understand how much of their personal information is being shared with others? In a recently released report from the UK Information Commissioner, we were warned that the UK in particular is 'sleepwalking into a surveillance society', as ordinary members of the public give up vast amounts of personal information with no significant personal or societal advantage gained. In general, there will be a trade off between usefulness of disclosing private information and the risk of it being misused. This project will investigate techniques for protecting the private information typically generated from ubiquitous computing applications from malicious or accidental misuse.The project will investigate privacy requirements across the general population for a specific set of ubiquitous computing technologies. These requirements will be used to produce a Privacy Rights Management (PRM) framework that enables users to specify privacy preferences, to help visualize them, to learn from the user's behaviour what their likely preferences are, and to enforce privacy policies. We will make use of a large cohort of over 1000 OU students with a broad range of ages and backgrounds, both for identifying requirements and for evaluating tools for privacy management. This work will address a number of research issues:* how do people perceive privacy in ubiquitous systems?* what types of privacy controls would people like to have when using ubiquitous systems?* how to develop privacy control tools that are easy to use via simple interfaces (e.g. mobile phones) as well as large screen devices?* how to detect and resolve inconsistencies in users' privacy requirements?* what mechanisms can be used to automate privacy control in ubiquitous systems?The PRM framework we produce to address these issues will integrate users' privacy policies with their personal information to control how information is used. This is analogous to Digital Rights Management (DRM), which often incorporates information such as 'digital watermarks' in the data being protected or encapsulates the data such that it is self protecting. By providing an analysis and learning system within the framework, we believe that we can produce a usable system that does not burden users with complex privacy rule sets. The project relates to the Memories for Life and Ubiquitous Computing Grand Challenges, both of which raise issues relating to PRM in mobile applications.

The age of Ubiquitous Computing is approaching fast: most people in the UK over the age of 8 carry mobile phones, which are becoming increasingly sophisticated interactive computing devices. Location-based services are also increasing in popularity and sophistication. There are many tracking and monitoring devices being developed that have a range of potential applications, from supporting mobile learning to remote health monitoring of the elderly and chronically ill. However, do users actually understand how much of their personal information is being shared with others? In a recently released report from the UK Information Commissioner, we were warned that the UK in particular is 'sleepwalking into a surveillance society', as ordinary members of the public give up vast amounts of personal information with no significant personal or societal advantage gained. In general, there will be a trade off between usefulness of disclosing private information and the risk of it being misused. This project will investigate techniques for protecting the private information typically generated from ubiquitous computing applications from malicious or accidental misuse.The project will investigate privacy requirements across the general population for a specific set of ubiquitous computing technologies. These requirements will be used to produce a Privacy Rights Management (PRM) framework that enables users to specify privacy preferences, to help visualize them, to learn from the user's behaviour what their likely preferences are, and to enforce privacy policies. We will make use of a large cohort of over 1000 OU students with a broad range of ages and backgrounds, both for identifying requirements and for evaluating tools for privacy management. This work will address a number of research issues:* how do people perceive privacy in ubiquitous systems?* what types of privacy controls would people like to have when using ubiquitous systems?* how to develop privacy control tools that are easy to use via simple interfaces (e.g. mobile phones) as well as large screen devices?* how to detect and resolve inconsistencies in users' privacy requirements?* what mechanisms can be used to automate privacy control in ubiquitous systems?The PRM framework we produce to address these issues will integrate users' privacy policies with their personal information to control how information is used. This is analogous to Digital Rights Management (DRM), which often incorporates information such as 'digital watermarks' in the data being protected or encapsulates the data such that it is self protecting. By providing an analysis and learning system within the framework, we believe that we can produce a usable system that does not burden users with complex privacy rule sets. The project relates to the Memories for Life and Ubiquitous Computing Grand Challenges, both of which raise issues relating to PRM in mobile applications.

The goal of this proposal is to advance a research program of developing sublinear-time algorithms for estimating a wide range of natural and important classes of probability distributions. We live in an era of "big data," where the amount of data that can be brought to bear on questions of biology, climate, economics, etc, is vast and expanding rapidly. Much of this raw data frequently consists of example points without corresponding labels. The challenge of how to make sense of this unlabeled data has immediate relevance and has rapidly become a bottleneck in scientific understanding across many disciplines. An important class of big data is most naturally modeled as samples from a probability distribution over a very large domain. The challenge of big data is that the sizes of the domains of the distributions are immense, typically resulting in unacceptably slow algorithms. Scaling up a computational framework to comfortably deal with ever-larger data presents a series of challenges in algorithms. This prompts the basic question: Given samples from some unknown distribution, what can we infer? While this question has been studied for several decades by various different communities of researchers, both the number of samples and running time required for such estimation tasks are not yet well understood, even for some surprisingly simple types of discrete distributions. The proposed research focuses on sublinear-time algorithms, that is, algorithms that run in time that is significantly less than the domain of the underlying distributions. In this project we will develop sublinear-time algorithms for estimating various classes of discrete distributions over very large domains. Specific problems we will address include: (1) Developing sublinear algorithms to estimate probability distributions that satisfy various natural types of "shape restrictions" on the underlying probability density function. (2) Developing sublinear algorithms for estimating complex distributions that result from the aggregation of many independent simple sources of randomness. We believe that highly efficient algorithms for these estimation tasks may play an important role for the next generation of large-scale machine learning applications.

This 8 month project brings together internationally leading expertise in High Dynamic Range (HDR) imaging from the University of Warwick with the innovation and in-depth market knowledge of the IBM Systems and Technology Group, Austin, USA. Together the partners will demonstrate the technical and commercial viability of an embedded HDR decoder-viewer which could be included in all future TVs and even retrofitted in existing TVs.HDR is a set of techniques that allow a greater dynamic range of luminances between light and dark areas of a scene than normal digital imaging techniques or photographic prints do. This wider dynamic range allows HDR images to more accurately represent the wide range of light intensity levels found in real scenes ranging from direct sunlight to faint starlight. Tone mapping techniques, which reduce overall contrast to facilitate display of HDR images on devices with lower dynamic range, can be applied to produce images with preserved or exaggerated local contrast for artistic effect. Although the process is complex, the end product seems very natural. This is because the eye and the visual cortex of the brain, unlike a camera, can deal with light variances of 10,000-fold within a single scene and adapt automatically without any conscious effort. Cameras capable of capturing dynamic HDR content are now appearing. The problem is: capturing the wide range of natural lighting results in a substantial increase in data. A highly efficient compression algorithm (of at least 100:1) for HDR video content has been developed at the University of Warwick as part of our research undertaken in EPSRC grant EP/D032148/2, for which a patent has been filed. Associated with this encoder is the need for a decoder and viewer which can deliver HDR content in real-time directly to HDR displays or tone mapped to existing Low Dynamic Range (LDR) displays, including computer monitors and televisions. A prototype down-loadable version of this decoder-viewer exists for PCs. A solution for televisions is not so straightforward. TV manufacturers need to embed the decoder-viewer into their display devices, so the decision to be HDR enabled would be made by the manufacturer, not the user. This embedded software may be adopted by TV manufacturers quite rapidly if it is well designed and easy to incorporate, as it adds another product distinguishing sales feature to their product. The television market is huge with about 170 million displays are sold annually in a market worth over $30 billion. Within this market, digital LCD to 1080 High Definition specification has become almost standard. The market has now stabilised, with further cost reduction being the major market driver, or they may move onto an even higher-specification standard. We believe that the latter is the more likely, and that HDR will be that standard. There is, however, one restraint on the rapid adoption of HDR television. Dolby tightly control all the IP related to HDR displays after their acquisition of BrightSide in February 2007 (for $29 million). It could thus take a few years while licensing agreements are resolved or other innovations for HDR displays start to appear. The embedded system we are developing in this project will enable existing television designs to be HDR enabled . The embedded decoder-viewer will allow HDR content to be tone mapped in real-time for display on these televisions. While a tone mapped image will never be as rich as a true HDR one, as we, and others have shown, modern tone mappers can give a significantly enhanced viewing experience on an LDR display which are perceptually close to the HDR experience. This proposal thus bridges the gap from the research results from a previous EPSRC grant to develop a robust demonstrator of an embedded HDR decoder-viewer and a commercial exploitation plan. On completion of the project, we will be in a strong position to secure commercial support from venture capital or seed funds.

Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.