Seminars will take place in Room 4B96, Cottrell Building, University of Stirling. Normally, from 15.00 to 16.00 on Friday afternoons during semester time, unless otherwise stated. For instructions on how to get to the University, please look at the following routes.
Reader in Computer Science
Fellow of King's College
University of Cambridge
|On the forwarding paths produced by Internet routing algorithms. Most Internet routing protocols have one of two algorithms lurking at their core — either Dijkstra’s algorithm in the case of link-state protocols or a distributed Bellman-Ford algorithm in the case of distance-vector or path-vector protocols. When computing simple shortest paths these protocols can be modified to utilize all best paths with a combination of nexthop sets and Equal Cost Multi-Path (ECMP) forwarding. We show that this picture breaks down even for simple modifications to the shortest path metric. This is illustrated with widest-shortest paths where among all shortest paths only those with greatest bandwidth are considered best. In this case Bellman-Ford and Dijkstra may compute different sets of paths and neither can compute all best paths. In addition, some paths computed by Dijkstra’s algorithm cannot be implemented with next-hop forwarding. We provide a general algebraic model that helps to clarify such anomalies. This is accomplished by computing paths within the route metric rather than with specialized algorithmic extensions. Our results depend on the distinction between global and local optima that has hitherto been applied almost exclusively to more exotic routing protocols such as BGP.
This is joint work with Seweryn Dynerowicz (University of Namur) and appeared as in in ICNP 2013.
Reader in Pervasive Computing
Faculty of Engineering and Computing
|Design patterns for
wireless sensor networks with integrated data
compression. Development of application-specific
wireless monitoring systems can benefit from concept reuse
and design patterns can form the enabling medium for such
reuse. This presentation discusses a set of five
fundamental node-level patterns that resolve common
problems when programming low-power embedded wireless
sensing devices. The pattern set forms a framework that is
aimed at ensuring simple and robust deployed systems.
Furthermore, I discuss a compression approach called G-SIP
that arose from these patterns. Depending on the
application, typical packet reduction can vary from 20
fold (for temperature apps) up to 7000 fold (for a
wearable posture sensing application).
|Dr Marwan Fayed
Computing Science and Mathematics
University of Stirling
|Network-layer fairness for adaptive video streaming. Netflix, iPlayer, YouTube, and the like, are now the dominant sources of traffic on the Internet. Recent studies observe that competing adaptive video streams generate flows that lead to instability, under-utilization, and unfairness behind bottleneck links. Additional measurements suggest there may also be a negative impact on users' perceived quality of experience as a consequence. While it may be intuitive to resolve application-generated issues at the application layer, in this presentation I shall demonstrate the merits of a network layer solution. I will present a new network-layer metric that reflects user experience. A performance evaluation using our open-source implementation in the home environment reveals that the network-layer may just be the right place to attack the general problem.
Computing Science and Mathematics
University of Stirling
|A data-driven, complex networks view of computational search spaces. Computational search is fundamental for solving optimisation problems arising in industry, science and society. A variety of search algorithms have been proposed, but little attention has been devoted to understanding the structure of search spaces. This talk starts by introducing computational search and optimisation using the famous travelling salesman problem as a case study. It then overviews a new model of computational search spaces based on complex networks. Search spaces can be analysed and visualised as complex networks, revealing intriguing structures that shed new light into why some problems are harder to solve than others.
|Mid Semester Break
||Mid Semester Break|
Engineering & Applied Science
Aston University, Birmingham
|Evolutionary art. A journey from abstract to figurative from a machine intelligence perspective. In this talk I shall first discuss evolutionary art in the broader context of computer art and generative art. Exciting questions in generative art from the perspective of machine intelligence will follow. Two personal projects will be presented in more detail. The first project considers the question of understanding human aesthetic preference: can a computer system learn what a person likes and produce images to the person’s liking? To answer this question, an experiment with a simple state-of-the-art interactive evolutionary art system was conducted. I shall show some encouraging results. The second project considers the creation of ambiguous figurative images. The question here is, can the computer system “understand” the notion of ambiguity and produce ambiguous images? Again, encouraging results will follow.|
Senior Lecturer, Chancellors Fellow
Computer and Information Science
University of Strathclyde
|Wellness in the city:
Design and evaluation of mobile apps for health and
wellness. There are literally hundreds of thousand of smartphone apps for health and wellness available on the app stores. But do they work? How do we know which ones are right for us? Which ones have been validated? And would you use one your doctor prescribed or would you rather listen to your friends and social network when it comes to managing your own personal health and wellbeing? This talk will describe some of the design and evaluation of mobile apps that we have done in computer and information sciences as part of the newly formed Digital Health and Wellness Group and highlight some of the ongoing challenges and opportunities for the future development of apps for health and wellness.
|Friday 04 Dec||Dr
School of Computing Science
|Network visualisation - how to tame the data complexity? Drawing networks used to be simple. Draw nodes as circles, connect them with edges, job done. If you lazy, use one of the specialised layout algorithms to position the nodes. The problem is that over time our networks got bigger. Manual methods became extremely time consuming and the algorithms were no longer able to generate readable layouts. Our networks turn into complex messy hair balls, impenetrable by human eye.|
But a network is really just a data structure. We do not have to rely on the concept of a graph to visualise it. In last 10 years, a number of alternative visualisation techniques have been developed. Many of them go beyond a static image generation and produce interactive explorable dynamic structures. In this talk I'm going to present an overview of the non-standard visualisation methods designed for complex data, and show a few examples from my own work on biological networks.
Computational Intelligence Group
Robert Gordon University, Aberdeen
|Truck Scheduling Optimisation In Practice. In this talk, I will show an example of optimisation problems seen in the transportation industry and how it is solved in practice. Recent projects with a large Scottish haulier company have focused on optimising their fleet of vehicles as the increasing number of jobs they have to handle is growing. The optimisation problem is a rich, constrained and dynamic vehicle routing problem, requiring real-time recommendations. The talk will cover three main points. First, it will focus on the current practice and the ways in which the problem differs from common benchmark problems. Second, ways to solve the problem and evaluate performance will be explained. Finally, it will concentrate on the integration of the solutions in practice and its challenges.|
Top image: Visualisations of
aircraft taxi movements at Manchester Airport (Map imagery
©2013-2015 Google, Infoterra Ltd & Bluesky), generated as
part of the project “SANDPIT: Integrating and Automating Airport
Operations”. This part of the project focused on automatically
generating routes for taxiing aircraft in real time. Clockwise
from top-left: cleaned and processed aircraft movements taken
from ADS-B (GPS) data; traffic levels per taxiway over a period
of 3 hours; a comparison of a raw ADS-B aircraft movement with
the cleaned and processed one; part of the graph of all taxiways
at Manchester Airport; a single aircraft movement automatically
divided into “straight” and “turn” sections. The visualisations
were generated using tools described in Brownlee, A.E.I., Atkin,
J.A.D., Woodward, J.R., Benlic, U. and Burke, E.K. (2014).
Airport Ground Movement: Real World Data Sets and Approaches to
Handling Uncertainty, Proc. of the Practice and Theory of
Automated Timetabling (PATAT) Conference, York, UK, pp. 462-464.
Courtesy of Sandy
|Previous Seminar Series|
|2018: Spring Autumn
||2017: Spring Autumn
||2016: Spring Autumn
|2015: Spring Autumn
||2014: Spring Autumn||2013: Spring Autumn|
|2012: Spring Autumn||2011: Spring Autumn||2010: Spring Autumn|
|2009: Spring Autumn||2008: Spring Autumn||2007: Spring Autumn|
|2006: Spring Autumn||2005: Spring Autumn||2004: Spring Autumn|
|2003: Spring Autumn||2002: Spring Autumn||2001: Spring Autumn|
|2000: Spring Autumn||1999: Spring Autumn||1998: Spring Autumn|
|1997: Spring Autumn||1996: Autumn|
Last Updated: 08 December 2015.