Predicting sense of community and participation by applying machine learning to open government data

Community capacity is used to monitor socio-economic development. It is composed of a number of dimensions, which can be measured to understand the possible issues in the implementation of a policy or the outcome of a project targeting a community. Measuring community capacity dimensions is usually expensive and time consuming, requiring locally organised surveys. Therefore, we investigate a technique to estimate them by applying the Random Forests algorithm on secondary open government data. This research focuses on the prediction of measures for two dimensions: sense of community and participation. The most important variables for this prediction were determined. The variables included in the datasets used to train the predictive models complied with two criteria: nationwide availability; sufficiently fine-grained geographic breakdown, i.e. neighbourhood level. The models explained 77% of the sense of community measures and 63% of participation. Due to the low geographic detail of the outcome measures available, further research is required to apply the predictive models to a neighbourhood level. The variables that were found to be more determinant for prediction were only partially in agreement with the factors that, according to the social science literature consulted, are the most influential for sense of community and participation. This finding should be further investigated from a social science perspective, in order to be understood in depth.

ISIS reading group

Reading group on diverse topics of interest for the Information: Signals, Images, Systems (ISIS) Research Group of the School of Electronics and Computer Science, University of Southampton.

INFO2009 - Computer Crime Learning Resource

An E-Learning Gateway for the latest news and information relating to Computer Crime for INFO2009

Hierarchical Prediction Machines and Big Data Analytics

An emerging consensus in cognitive science views the biological brain as a hierarchically-organized predictive processing system. This is a system in which higher-order regions are continuously attempting to predict the activity of lower-order regions at a variety of (increasingly abstract) spatial and temporal scales. The brain is thus revealed as a hierarchical prediction machine that is constantly engaged in the effort to predict the flow of information originating from the sensory surfaces. Such a view seems to afford a great deal of explanatory leverage when it comes to a broad swathe of seemingly disparate psychological phenomena (e.g., learning, memory, perception, action, emotion, planning, reason, imagination, and conscious experience). In the most positive case, the predictive processing story seems to provide our first glimpse at what a unified (computationally-tractable and neurobiological plausible) account of human psychology might look like. This obviously marks out one reason why such models should be the focus of current empirical and theoretical attention. Another reason, however, is rooted in the potential of such models to advance the current state-of-the-art in machine intelligence and machine learning. Interestingly, the vision of the brain as a hierarchical prediction machine is one that establishes contact with work that goes under the heading of 'deep learning'. Deep learning systems thus often attempt to make use of predictive processing schemes and (increasingly abstract) generative models as a means of supporting the analysis of large data sets. But are such computational systems sufficient (by themselves) to provide a route to general human-level analytic capabilities? I will argue that they are not and that closer attention to a broader range of forces and factors (many of which are not confined to the neural realm) may be required to understand what it is that gives human cognition its distinctive (and largely unique) flavour. The vision that emerges is one of 'homomimetic deep learning systems', systems that situate a hierarchically-organized predictive processing core within a larger nexus of developmental, behavioural, symbolic, technological and social influences. Relative to that vision, I suggest that we should see the Web as a form of 'cognitive ecology', one that is as much involved with the transformation of machine intelligence as it is with the progressive reshaping of our own cognitive capabilities.

Data Science, Microsoft and You

In this session we'll explore how Microsoft uses data science and machine learning across it's entire business, from Windows and Office, to Skype and XBox. We'll look at how companies across the world use Microsoft technology for empowering their businesses in many different industries. And we'll look at data science technologies you can use yourselves, such as Azure Machine Learning and Power BI. Finally we'll discuss job opportunities for data scientists and tips on how you can be successful!

Justified assessments of service provider reputation

Abstract Reputation, influenced by ratings from past clients, is crucial for providers competing for custom. For new providers with less track record, a few negative ratings can harm their chances of growing. In the JASPR project, we aim to look at how to ensure automated reputation assessments are justified and informative. Even an honest balanced review of a service provision may still be an unreliable predictor of future performance if the circumstances differ. For example, a service may have previously relied on different sub-providers to now, or been affected by season-specific weather events. A common way to ameliorate the ratings that may not reflect future performance is by weighting by recency. We argue that better results are obtained by querying provenance records on how services are provided for the circumstances of provision, to determine the significance of past interactions. Informed by case studies in global logistics, taxi hire, and courtesy car leasing, we are going on to explore the generation of explanations for reputation assessments, which can be valuable both for clients and for providers wishing to improve their match to the market, and applying machine learning to predict aspects of service provision which may influence decisions on the appropriateness of a provider. In this talk, I will give an overview of the research conducted and planned on JASPR. Speaker Biography Dr Simon Miles Simon Miles is a Reader in Computer Science at King's College London, UK, and head of the Agents and Intelligent Systems group. He conducts research in the areas of normative systems, data provenance, and medical informatics at King's, and has published widely and manages a number of research projects in these areas. He was previously a researcher at the University of Southampton after graduating from his PhD at Warwick. He has twice been an organising committee member for the Autonomous Agents and Multi-Agent Systems conference series, and was a member of the W3C working group which published standards on interoperable provenance data in 2013.

ImageLearn - Decoding Britain's Landscape

Abstract Ordnance Survey, our national mapping organisation, collects vast amounts of high-resolution aerial imagery covering the entirety of the country. Currently, photogrammetrists and surveyors use this to manually capture real-world objects and characteristics for a relatively small number of features. Arguably, the vast archive of imagery that we have obtained portraying the whole of Great Britain is highly underutilised and could be ‘mined’ for much more information. Over the last year the ImageLearn project has investigated the potential of "representation learning" to automatically extract relevant features from aerial imagery. Representation learning is a form of data-mining in which the feature-extractors are learned using machine-learning techniques, rather than being manually defined. At the beginning of the project we conjectured that representations learned could help with processes such as object detection and identification, change detection and social landscape regionalisation of Britain. This seminar will give an overview of the project and highlight some of our research results.

Data Science Seminar: The data science revolution in Physics and Astronomy

Abstract Heading into the 2020s, Physics and Astronomy are undergoing experimental revolutions that will reshape our picture of the fabric of the Universe. The Large Hadron Collider (LHC), the largest particle physics project in the world, produces 30 petabytes of data annually that need to be sifted through, analysed, and modelled. In astrophysics, the Large Synoptic Survey Telescope (LSST) will be taking a high-resolution image of the full sky every 3 days, leading to data rates of 30 terabytes per night over ten years. These experiments endeavour to answer the question why 96% of the content of the universe currently elude our physical understanding. Both the LHC and LSST share the 5-dimensional nature of their data, with position, energy and time being the fundamental axes. This talk will present an overview of the experiments and data that is gathered, and outlines the challenges in extracting information. Common strategies employed are very similar to industrial data! Science problems (e.g., data filtering, machine learning, statistical interpretation) and provide a seed for exchange of knowledge between academia and industry. Speaker Biography Professor Mark Sullivan Mark Sullivan is a Professor of Astrophysics in the Department of Physics and Astronomy. Mark completed his PhD at Cambridge, and following postdoctoral study in Durham, Toronto and Oxford, now leads a research group at Southampton studying dark energy using exploding stars called "type Ia supernovae". Mark has many years' experience of research that involves repeatedly imaging the night sky to track the arrival of transient objects, involving significant challenges in data handling, processing, classification and analysis.