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Computing Science Seminars, Spring 2014

Welcome to the Computing Science Seminars. Unless otherwise stated, seminars will take place in Room 4108,  Cottrell Building  from 15.00 to 16.00 on Friday afternoons during semester time. 

If you would like to give a seminar, suggest a speaker, or need more information,  please contact the seminar organiser: 

Spring 2014

Date Presenter Title/Abstract
14 Feb
Prof Leslie S. Smith
Computing Science and Mathematics
University of Stirling 
Putting Intelligence into Computational Intelligence. What has intelligence got to do with computational intelligence? Is there anything that lies between the clever programming and bottom-up pattern recognition that makes up the (relatively undefined) field of Artificial Intelligence, and the layperson's interpretation of intelligence? Is there something between consciousness and awareness, and computational Intelligence? I will argue that context and invariance in sensory systems are critical, and that we need to reconsider the role of time in artificial systems, in order to build systems that can perform usefully in real environments
19 Feb
Dr Thomas Stidsen
Associate Professor 
DTU Management Engineering
Technical University of Denmark 
Timetabling at DTU and the Generalized Meeting Planning Problem. After a brief introduction to the Operations Research at DTU-Management I will present a recent article about meeting planning using Dantzig-Wolfe decomposition and applying a Branch & Price algorithm to the problem. The approach is tested on two separate problems: Parent Consultation Planning and Supervisor Consultation Planning, both problems occuring in the Danish high schools. The approach is tested on 100 real life instances of each problem type and compared to an Adaptive Large Neighbourhood Search (ALNS) search meta-heuristic and to a Mixed Integer Programming formulation. Excellent performance is obtained. Afterwards I will give a short, more general, presentaion of the advantages and dis-advantages of using Dantzig-Wolfe/Branch & Price. If time permits it, I will furthermore present some other research work in Multi-Objective Mixed Integer Programming optimization, using a Branch & Bound algorithm.
28 Feb

No Seminar

7 March
Prof Hugo Terashima-Marin
Director of Graduate Programs
Tecnológico de Monterrey
Campus Monterrey, Mexico
Towards the Generality of Hyper-heuristics for Solving Bin Packing Problems. The talk will focus on describing an evolutionary hyper-heuristic framework that is capable of tackling various types of bin packing problems: one-dimensional, two-dimensional regular (rectangular) and two-dimensional irregular (concave and convex polygons).  The approach is based on a genetic algorithm (GA) with variable-length individuals representing selective hyper-heuristics that gradually build solutions and are able to decide what to do next, at each step. In selective hyper-heuristics, a high-level heuristic controls the application of single heuristics. Several issues are addressed in this investigation such as characterizing the problem instances to define the adequate state and chromosome representations, deciding on the proper set of single heuristics, and selecting and generating problem instances to construct the testbed. Once a hyper-heuristic has been produced by using a training set of instances, it is reused on an unseen instance set. Results are comparable to those obtained by using the best choice of single heuristic for each problem, but without the considerable expense of determining which single heuristic is the best for that problem. This investigation also provides an insightful empirical analysis and discussion on the application of single heuristics within a hyper-heuristic, aimed at understanding their frequency, alternations and interactions during the solution process.         

14 March
Dr Yuhang Chen
Institute of Mechanical
Process and Energy Engineering
Heriot-Watt University, Edinburgh
Tissue Mechanics: from Microstructure to Function. Unlike the classical biomechanics and one of its major sub-fields cell mechanics, instead of focusing on either system or cell/sub-cellular behaviours, tissue mechanics considers interaction between them at meso-scale which bridges our understanding on human physiology at multiple length scales. One of the critical questions that remain to be resolved in tissue mechanics is ‘how cell activities ‘express’ at system level and how changes taking place at organ level ‘regulate’ the cellular behaviours’. In our group we focus on tissue microstructure as an intermediate player and attempt to answer this question using analytical and engineering approaches. In this talk, some examples that are currently being studied in our group will be presented, including enhanced clinical diagnostics for bone by nonlinear mechanics, diagnostics of cancer in soft tissue (e.g. prostate) and coronary artery disease using dynamic mechanical sensing as well as in vivo design optimisation of tissue scaffold microarchitectures.
21 March
Dr Una Benlic
Postdoctoral Research Assistant
Computing Science and Mathematics
University of Stirling 
Breakout Local Search: Application to Gate Allocation Problem. Breakout Local Search (BLS) is a recent variant of Iterated Local Search with a particular emphasis on the importance of perturbation. It explores the search space by a joint use of a local search procedure (usually a simple descent/ascent algorithm) and a diversification mechanism which adaptively determines the number and type of perturbation moves by considering some information related to the search state. In spite of its conceptual simplicity, BLS often shows to be highly competitive with some well-established metaheuristics. Moreover, it is among the current state-of-art algorithms for several classic NP-hard combinatorial problems. This seminar presents an application of BLS to gate allocation, one of the most important and complex airport related problems.
28 March

Mid Semester Break

Mid Semester Break

4 April
Room: 2V1
Prof Ken Turner
Computing Science and Mathematics
University of Stirling

50 Years of Computing and Beyond.This talk will cover my experiences of computing during the past 50 years. A lot has changed in this period. It is therefore hard to know how things used to be, or how we got to where we are today. The talk will consist of personal recollections and anecdotes, with the aim of filling in a bit of the history. The content will be light-hearted and pictorial rather than heavyweight and research-oriented. Some thoughts will be offered as to how computing might evolve over the next 50 years.
11 April
Dr Rik Sarkar
Department of Informatics
University of Edinburgh
Distributed Submodular Maximization: Identifying Representative Elements in Massive Data. Selecting a small representative subset from a large dataset is a type of optimization problem called submodular maximization. Practical examples include: which 5 news articles or web sites will give me the most important information of the day? Or, which places in the city are best for putting up advertisements? Many important computational challenges such as selecting influential nodes in a social network, exemplar based clustering, document summarization etc belong to this category. With the emergence of large datasets, the classical centralized algorithms for submodular optimization are no longer adequate, and such problems must be solved distributively, in parallel.

In this talk I will describe a simple randomized protocol that uses map-reduce style distributed computation and can produce provably good results, with further improvements when the data carries some geometric structure.

The talk is based on a paper recently published at NIPS (Neural Information Processing Systems) 2013.

18 April

Good Friday

Good Friday

25 April
Dr Paul Fergus
Research Fellow
Networked Appliances Laboratory
School of Computing and Mathematical Sciences
Liverpool John Moores University


2 May
Prof Emma Hart
Director Centre of Emergent Computing
Institute for Informatics and
Digital Innovation
Edinburgh Napier University
A Immune Inspired lifelong Learning Method for Solving Combinatorial Optimisation Problems. The previous two decades have seen significant advances in meta-heuristic and hyper-heuristic optimisation techniques that are able to quickly find optimal or near-optimal solutions to problem instances in many combinatorial optimisation domains. Despite many successful applications of both these approaches, some common weaknesses exist in that if the nature of the problems to be solved changes over time, then algorithms needs to be periodically re-tuned. Furthermore, many approaches are likely to be inefficient,  starting from a clean slate every time a problem is solved, therefore failing To exploit previously learned knowledge.

In contrast, in the field of machine-learning, a number of recent proposals suggest that learning  algorithms should exhibit life-long learning, retaining knowledge and using it to improve learning in the future. Looking to nature, we observe that the natural immune system exhibits many properties of a life-long learning system that could be computationally exploited. I will give a brief overview of the immune system, focusing on highlighting its relevant computational properties and then show how it can be used to construct a lifelong learning optimisation system. The system is shown to adapt to new problems, exhibit memory, and produce efficient and effective solutions when tested on a large corpus of bin-packing problems.
9 May
Dr Jun Chen
School of Engineering 
University of Lincoln
Towards a More Cost Effective and Environmentally Friendly Airport Surface Movement through Active Routing and Guidance. The Airport Surface Movement problem is one of the challenging real-world optimisation problems found at airports. In this talk, a new concept called Active Routing and Guidance is proposed and set into a wider context of previous research which saw an evolution from simple routing to a complex decision support system for both air traffic controllers and pilots.

In its nature, the airport surface movement problem is a multi-objective multi-component optimisation problem which combines two components: scheduling and routing of aircraft and speed profile optimisation. In this talk, a multi-objective optimisation framework is presented which combines lower-level algorithms for each component. Different speed up techniques are introduced including a heuristic for the speed profile optimisation and an adaptive speed profile database. The performance of the proposed algorithms is demonstrated using real data from major airports. Finally, a metaheuristic approach is introduced to integrate the airport surface movement problem with other airport problems.


Top image: This is an illustrated example of running the Epsilon-constraint algorithm in order to maximise two objectives: find an optimal solution for objective 1; restrict the solution space according to the solution's value for objective 2 and look for an optimum solution of objective 1 in that space; repeat the previous step until there are no more solutions to be found. Any dominated solutions need to be filtered out of the set of solutions.
Courtesy of Dr. Nadarajen Veerapen

Last Updated: 02 May 2014.