SISCO Conference, 5 & 6 February
Ian Gent, Chris Jefferson and Simon Dobson are all presenting at the SISCO conference this weekend. There will be social and networking events which include free food in the medicine cafeteria. We think that these will be nice opportunities for all students, speakers and staff to get to know one other.
You are all invited to these events, both on Saturday and Sunday.
You can get your free tickets to attend and further information on their Facebook page
Very many congratulations to our Emeritus Professor Ian Sommerville who has recently been awarded the 2022 Nancy Mead award for excellence in software engineering education.
https://mycseet.wordpress.com/nancy-mead-award-terms-of-reference/
The award will be presented at the (virtual) Conference for Software Engineering and Training next month.
This Award goes to individuals who have demonstrated outstanding contributions to software engineering education and training, as well as to the related area of software engineering professionalism. Contributions may include, but are not limited to: service to the community, papers, reports, books, tools, techniques and media for software engineering education, and outstanding practice of teaching that has been witnessed by the community at large. Contributions should have had an influence over an extended period of time at the international level. Contributions to software engineering itself (peer-reviewed research or practice) should be mentioned in a separate section of any nomination, but having made such contributions is not necessary in order to receive this award.
Well Done Ian!
As part of the schools Distinguished Lecture Series we look forward to welcoming Prof. Giovanna Di Marzo Serugendo on Tuesday 9 November.
Prof. Giovanna Di Marzo Serugendo received her Ph.D. in Software Engineering from the Swiss Federal Institute of Technology in Lausanne (EPFL) in 1999. After spending two years at CERN (the European Center for Nuclear Research) and 5 years in the UK as Lecturer, she became full professor at the University of Geneva in 2010. Since 2016, she is the Director of the Computer Science Center of the University of Geneva, Switzerland. She has been nominated in 2018 among the 100 digital shapers in Switzerland. Her research interests relate to the engineering of decentralised software with self-organising and emergent behaviour. This involves studying natural systems, designing and developing artificial collective systems, and verifying reliability and trustworthiness of those systems. Giovanna co-founded the IEEE International Conference on Self-Adaptive and Self-Organising Systems (SASO) and the ACM Transactions on Autonomous Adaptive Systems (TAAS), for which she served as EiC from 2005 to 2011.
This event will be held on Teams with further details to follow.
Can Language Models be Weak Annotators
We are happy to have Phong Le, from Amazon, talk on Teams on Wed 3 March at 12 noon on Teams.
Abstract
Deep language models e.g. BERT and GPT3 are the breakthrough in Natural Language Processing in the last 3 years. Being trained on massive raw text data, they capture useful priors for several tasks such as syntactic parsing, information extraction, and question answering. Moreover, they are capable of answering factual and commonsense cloze questions such as “Dante was born in _____”. In this talk, I will firstly give an overview about what language models “know”. I will then present our work on exploiting their knowledge as weak supervision for a specific task called relation classification.
Relation classification, the identification of a particular relation type between two entities in text, requires annotated data. Data annotation is either a manual process for supervised learning, or automated, using knowledge bases for distant learning. However, both methodologies are costly and time-consuming since they depend on intensive human labour for annotation or for knowledge base creation. Using language models as annotators, on the contrary, is very cheap but the annotation quality is low. We hence propose NoelA, an auto-encoder using a noisy channel, to improve the accuracy by learning from the low quality annotated data. NoelA outperforms BERT and a bootstrapping baseline on TACRED and reWIKI datasets.
Bio: I’m an applied scientist at Amazon Alexa. Before that, I was a tenure-track research fellow at the University of Manchester. I did a postdoc with Ivan Titov at the University of Edinburgh, and got a PhD from the University of Amsterdam under the supervision of (Jelle) Willem Zuidema. I’m interested in neural networks and deep learning. My current work is to employ them to solve natural language processing tasks such as entity linking, coreference resolution, and dependency parsing. I’m also interested in formal semantics, especially learning semantic parsing.
For more details, please visit my homepage https://sites.google.com/site/lephongxyz/
Please note the session will not be recorded, to preserve the like-for-like nature of physical seminars and also avoid any privacy/rights issues.
As a part of the Love Data Week, Olexandr Konovalov will give a talk on Tuesday 11 February, 3pm, Physics Lecture Theatre C.
Abstract: Have you been frustrated by trying to use someone else’s code which is non-trivial to install? Have you tried to make supplementary code for your paper to be easily accessible for the reader? If so, you certainly know that this may require non-trivial efforts. I will demonstrate some tools that may help to create reproducible computational experiments, and will explain which skills will be needed to use these tools. The talk will demonstrate examples in Python and R runnable in Jupyter notebooks. You are welcome to bring your laptop to try these examples online. No prior knowledge of programming is required.
Links:
References:
Details TBA.
RESCHEDULED: please note the changed date and a non-standard time!
Abstract: Prest is a recently published piece of open-source software for computational revealed preference analysis that provides novel ways to estimate decision makers’ preferences over choice alternatives by analysing their observable choice behaviour. This software is informed by classic as well as recent developments in economic revealed preference theory. Some of the recent developments take the form of models that are computationally complex. This complexity currently hinders the inclusion of these models in the Prest toolkit. The presentation will first aim to describe the primary ideas underpinning Prest and illustrate them with examples from its existing toolkit. It will then proceed with a discussion of some of the challenges pertaining to the expansion of that toolkit with more models and operations. The presentation will be self-contained and no prior background in economics will be necessary.
Speaker Bio: Georgios is a Reader in Economics at the University of St Andrews, working mainly on decision theory and revealed preference analysis. In the latter research programme, Georgios’ work aims to improve our understanding of people’s decision processes and preferences through theoretical, experimental/empirical as well as computational methods. Georgios co-developed the Prest software program for computational revealed preference analysis (https://prestsoftware.com/).
Abstract: Innovation and creativity are the research drivers of the Human-Computer Interaction (HCI) community which is currently investing a vast amount of resources in the design and evaluation of “new” user interfaces and interaction techniques, leaving the correct functioning of these interfaces at the discretion of the helpless developers. In the area of formal methods and dependable systems the emphasis is usually put on the correct functioning of the system leaving its usability to secondary-level concerns (if at all addressed). However, designing interactive systems requires blending knowledge from these domains in order to provide operators with enjoyable, usable and dependable systems. The talk will present possible research directions and their benefits for combining several complementary approaches to engineer interactive critical systems. Due to their specificities, addressing this problem requires the definition of methods, notations, processes and tools to go from early informal requirements to deployed and maintained operational interactive systems. The presentation will highlight the benefits of (and the need for) an integrated framework for the iterative design of operators’ procedures and tasks, training material and the interactive system itself. The emphasis will be on interaction techniques specification and validation as their design is usually the main concern of HCI conferences. A specific focus will be on automation that is widely integrated in interactive systems both at interaction techniques level and at application level. Examples will be taken from interactive cockpits on large civil commercial aircrafts (such as the A380), satellite ground segment application and Air Traffic Control workstations.
Speaker Bio: Dr. Philippe Palanque is Professor in Computer Science at the University Toulouse 3 “Paul Sabatier” and is head of the Interactive Critical Systems group at the Institut de Recherche en Informatique de Toulouse (IRIT) in France. Since the late 80s he has been working on the development and application of formal description techniques for interactive system. He has worked for more than 10 years on research projects to improve interactive Ground Segment Systems at the Centre National d’Etudes Spatiales (CNES) and is also involved in the development of software architectures and user interface modeling for interactive cockpits in large civil aircraft (funded by Airbus). He was involved in the research network HALA! (Higher Automation Levels in Aviation) funded by SESAR programme which targets at building the future European air traffic management system. The main driver of Philippe’s research over the last 20 years has been to address in an even way Usability, Safety and Dependability in order to build trustable safety critical interactive systems. He is the secretary of the IFIP Working group 13.5 on Resilience, Reliability, Safety and Human Error in System Development, was steering committee chair of the CHI conference series at ACM SIGCHI and chair of the IFIP Technical Committee 13 on Human-Computer Interaction.
The medical school is holding a Seminar on Wednesday 8th January @1400-1530
New tools and methods to prevent blindness.
Seminar room 1, Medical and Biological Sciences Building
Abstract: In this talk, I will present a case study to illustrate how automated algorithm configuration can be used to gain insights into theoretical results on an evolutionary algorithm, namely the (1+(λ,λ)) Genetic Algorithm. This work is a collaboration with Carola Doerr.
The (1+(λ,λ)) Genetic Algorithm is an evolutionary algorithm that has interesting theoretical properties. It is the first algorithm where the benefit of crossover operator is rigorously proved. It is also the first example where self-adjusting parameter choice is proved to outperform any static parameter choice. However, it is not very well understood how the hyper-parameter settings influences the overall performance of the algorithm. Analyzing such multi-dimensional dependencies precisely is at the edge of what running time analysis can offer. In this work, we make a step forward on this question by presenting an in-depth study of the algorithm’s hyper-parameters using techniques in automated algorithm configuration.
Speaker bio: Dr Nguyen Dang is a post-doc in the Constraint Programming group at the University of St Andrews. Her main research focus is on automated algorithm configuration, algorithm selection and their applications in various contexts. These techniques make use of statistical methods and machine learning for fine-tuning of algorithm parameters, assessing parameters’ importance and building algorithm portfolios. Another line of her research is about solving combinatorial optimisation problems using metaheuristic algorithms.
