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Computing Science Seminars, Autumn 2014

The Division of Computing Science and Mathematics presents the following seminars. Unless otherwise stated, seminars will take place in Room 2A73 of the Cottrell Building, University of Stirling from 15.00 to 16.00 on Friday afternoons during semester time. For instructions on how to get to the University, please look at the following routes.

If you would like to give a seminar to the department in future or if you need more information,  
please contact the seminar organiser,  .

Autumn 2014

Date Presenter Title/Abstract
26 Sep
Prof John McCall
Professor of Computer Science
Director of IDEAS
Research Institute
Robert Gordon
University of Aberdeen
Telling the Wood from the Trees: Essential Structure in Model-based Search. Problem structure, or linkage, refers to the interaction between variables in an optimisation problem. Discovering structure is a feature of a range of search algorithms that use structural models at each iteration to determine the trajectory of the search. Examples include Information Geometry Optimisation (IGO), Covariance Matrix Adaptation Evolution Strategy (CMA-ES), Bayesian Evolutionary Learning (BEL) and Estimation of Distribution Algorithms (EDA)

In particular, EDAs use probabilistic graphical models to represent structure learned from evaluated solutions. Various EDA approaches using trees and graphs have been developed and evaluated on a range of benchmarks.
Benchmarks typically have "known problem structure" determined a priori. In practice, the relationship between the problem structure, structures found by by the EDA and algorithm success is a complex one.

This talk  will explore these ideas using a classification of problems based on monotonicity invariance. We completely classify  all functions on 3 bits and show that conventional concepts of the relationship between structure and problem difficulty are too brittle to capture the subtlety even of this low dimensional function space. The talk concludes with a discussion of related work on algorithm classification and some speculative ideas for topological notions of essential problem structure.

3 Oct
Dr Savi Maharaj
Lecturer in Computing Science
Computing Science and Mathematics
University of Stirling 
Using Computers to Model and Study Human Behaviour - First Steps. The choices we make and the way we behave have important consequences both for us as individuals and for society. Computational techniques can help us both to model and to study human behaviour through techniques such as agent-based modeling and virtual experiments. I have been using both of these approaches in my work. As human behaviour is of course a huge topic, this talk will not even attempt to cover it comprehensively, but will focus on two (possibly three) projects. These involve modelling and investigating behaviour in the contexts of epidemic spread and environmental protection policy, and are collaborations with psychologists and/or economists. The talk will cover the kinds of models used, the experimental techniques used to study real human behaviour in order to parameterise the models, some tentative results, and the difficulties encountered in inducing (and recognising) realistic behaviour in virtual experiments. The talk will end with a speculative discussion of the way forward, with a surprise bonus contribution from students Craig Docherty and Daniel Gibbs.
10 Oct
Alasdair P Anderson 
Chief Architect, Big Data for HSBC Group,
Data Monetization: Leveraging Legacy with Big Data. HSBC is a large international company, with about 250,000 staff and lots of data (about 65Pbytes across over 7000 systems).  They need a single globalised platform, rapidly, with advanced analytical capabilities. How can they go about getting this?
17 Oct
Dr Iain J Gallagher
Lecturer in Health & Exercise
School of Sport
University of Stirling
Big Data Analysis in Biology & Sport. Big data techniques have been routinely used in biology for a number of years. These large data sets are gathered using 'omics' technologies and allow biologists to examine almost all of the entities that make cells and tissues function at a particular moment in time. This informs on important differences in the molecular details of diseased and normal tissue, in the processes and pathways active during development or disease and in the small molecular differences that predispose us as individuals to disease. Thus as well as providing mechanistic insight big data in biology is beginning to allow the stratification of individuals by disease risk. In sport big data is also becoming more and more important. Applications include prediction of player performance and propensity for injury, real time object detection as well as the analysis of results and betting patterns (both legal and illegal). In this talk I will give an overview of big data and the analysis of these data in both biology and sport. After introducing the concepts of big data in these domains I will use a 'toy', but real dataset of Olympic heptathlon results to demonstrate how dimensional reduction can help us identify athletic characteristics that could be used for competitive advantage. I will then give a brief overview of the 'omics' technologies currently used in biology to gather large amounts of information – snapshots of cellular or tissue activity. I will briefly discuss my tools of choice for big data analysis and the concept of 'reproducible research'. Finally, time permitting I will present a couple of examples briefly illustrating how a programmatic approach can inform biological questions.
24 Oct
Dr Rachel Lintott
Research Assistant
Computing Science and Mathematics
University of Stirling
Using Process Algebra to Model Radiation Induced Bystander Effects. Radiation induced bystander effects are secondary effects caused by the production of chemical signals by cells in response to radiation. I will present a Bio-PEPA model which builds on previous modelling work in this field to predict: the surviving fraction of cells in response to radiation, the relative proportion of cell death caused by bystander signalling, the risk of non-lethal damage and the probability of observing bystander signalling for a given dose. This work provides the foundation for modelling bystander effects caused by biologically realistic dose distributions, with implications for cancer therapies.
31 Oct
Mid Semester Mid Semester
7 Nov
Dr Nanlin Jin
Computer Science and Digital Technologies
Northumbria University, Newcastle
Smart Meter Data Analytics by Subgroup Discovery. Based on a recent article.  This talk will cover the following three aspects:
  1.  Improvement on a data mining algorithm
  2.  Application on smart meter data analysis
  3.  Development of a big data platform to manage a huge amount of data.
14 Nov
Dr Alasdair Rutherford
Lecturer in Quantitative Methods
School of Applied Social Science
University of Stirling
Working with and analysing administrative data from linked health and social care records in Scotland. Better evidence is needed on how social care and health care interact to enable people to live independently in their own homes for as long as possible. In most countries, individual-level administrative data on social care, housing support and health care are collected separately by different organisations. Linking such datasets could enable service providers, planners and policy makers to gain an improved understanding of how these services can work together more effectively to improve outcomes for patients and service users. In collaboration with the Scottish Government’s Health Analytical Services Division and the Information Services Division of NHS Scotland we have analysed a new dataset produced as a pilot project which brings together detailed information on individual social care packages, prescribing, diagnoses (including dementia and mental health) and hospital episodes. This enables us to explore pathways through health and social care from the individual perspective. This presentation will describe the building of the dataset, challenges in preparing and analysing the data, and provide some initial findings from the study.
21 Nov
Dr Kira Mourao
Research Associate
School of Informatics
University of Edinburgh
Learning Relational Action Models in Robot Worlds. When a robot, dialog manager or other agent operates autonomously in a real-world domain, it uses a model of the dynamics of its domain to plan its actions. Typically, these domain models are prespecified by a human designer and then used by AI planners to generate plans. However, creating domain models is notoriously difficult. Furthermore, to be truly autonomous, agents must be able to learn their own models of world dynamics. An alternative therefore is to learn domain models from observations, either via known successful plans or through exploration of the world. This route is also challenging, as agents often do not operate in a perfect world: both actions and observations may be unreliable.

In this talk I will present a method which, unlike other approaches, can learn from both observed successful plans and from action traces generated by exploration. Importantly, the method is robust in a variety of settings, able to learn useful domain models when observations are noisy and incomplete, or when action effects are noisy or non-deterministic. The approach first builds a classification model to predict the effects of actions, and then derives explicit planning operators from the classifiers. Through a range of experiments using International Planning Competition domains and a real robot domain, I will show that this approach learns accurate domain models suitable for use by standard planners. I also demonstrate that where settings are comparable, the results equal or surpass the performance of state-of-the-art methods.
28 Nov
John Page
Senior Solutions Architect
MongoDB, a Big data solution for the real world. This talk will introduce the concepts behind MongoDB - the world's most prevalent and popular big data solution and leading NoSQL Database, explaining what matters when you have a 'Big Data' problem in a real world application and how many of the world's leading companies address this.
5 Dic
Patricia Ryser-Welch
Department of Electronics
The University of York
Can Plug-and-Play hyper-heuristic help create state-of-the-art human-readable algorithms? Hyper-heuristics are defined as heuristics optimising heuristics. Previous approaches focus on the quality of the numerical solutions obtained; very little discussion concentrates on the generated algorithms themselves. Some hyper-heuristic frameworks tend to be highly constrained and their architecture prevents the search from finding  state-of-the-art algorithms. Often the generated algorithms are also not human-readable. We discuss a new interpretation of hyper-heuristic techniques, that explicitly and openly offers a flexible and modular architecture. This together with analysis of  evolved algorithms, could lead to human-competitive results.

Top image: A simplified view of the neural circuit of the CA1 region of the mammalian hippocampus (Fig. 2 pg 284 "Hippocampal Microcircuits" book,  coeditored by Prof. Bruce Graham, Springer 2010). Encoding and retrieval of patterns of information in this circuit have been studied by computer simulation, as detailed in Cutsuridis, Cobb & Graham, Hippocampus 20:423-446, 2010. 
Courtesy of Prof. Bruce Graham

Last Updated: 25 November 2014.