How to use the e-Reseach Methods material

Instructions for using the web site and the source material.

Test upload material

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University of Southampton Branding Material

Various templates and logos and brand related media.

MATH2022 Course Material

Mathematics for Electrical and Electronics Engineers (Part 2). Course material (course notes, Formula Sheet, Lecture Slides, Problem sheets) for the course as it ran in 2011/12 and 12/13. Course discontinued after 2012/13 as part of the transition from 10 to 15 credits.

Sustaining Success cover sheet plus supporting material

Download and edit this document to prepare your hand in. The portfolio comprises a cover sheet plus five pages of reflective writing, one page addressing each different portfolio topic This shows the cover sheet, the assessment criteria and the portfolio summary IT IS NOT THE PORTFOLIO TEMPLATE The questions shown under each sub-heading are meant to act as thinking prompts to help you in the reflective process.

WAIS seminar: Provenance Analytics and Crowdsourcing

Wednesday 26th March 2014 Speaker(s): Dr Trung Dong Huynh Organiser: Dr Tim Chown Time: 26/03/2014 11:00-11:50 Location: B32/3077 File size: 349Mb Abstract Understanding the dynamics of a crowdsourcing application and controlling the quality of the data it generates is challenging, partly due to the lack of tools to do so. Provenance is a domain-independent means to represent what happened in an application, which can help verify data and infer their quality. It can also reveal the processes that led to a data item and the interactions of contributors with it. Provenance patterns can manifest real-world phenomena such as a significant interest in a piece of content, providing an indication of its quality, or even issues such as undesirable interactions within a group of contributors. In this talk, I will present an application-independent methodology for analysing provenance graphs, constructed from provenance records, to learn about such patterns and to use them for assessing some key properties of crowdsourced data, such as their quality, in an automated manner. I will also talk about CollabMap (www.collabmap.org), an online crowdsourcing mapping application, and show how we applied the approach above to the trust classification of data generated by the crowd, achieving an accuracy over 95%.

Provenance: Beyond Standardisation

Abstract: Provenance is a record that describes the people, institutions, entities, and activities, involved in producing, influencing, or delivering a piece of data or a thing in the world. Some 10 years after beginning research on the topic of Provenance, I co-chaired the provenance working group at the World Wide Web Consortium. The working group published 4 recommendations and several notes about the PROV standard for provenance in 2013. In this talk, I will present some use cases for provenance, the PROV standard and some flagship examples of adoption. I will then move onto our current research area in exploiting provenance, in the context of the SmartSociety and ORCHID projects. Doing so, I will present some methods, algorithms, and tools that we have developed in Southampton.

Data-Driven Text Generation using Neural Networks & Provenance is Complicated and Boring — Is there a solution?

Title: Data-Driven Text Generation using Neural Networks Speaker: Pavlos Vougiouklis, University of Southampton Abstract: Recent work on neural networks shows their great potential at tackling a wide variety of Natural Language Processing (NLP) tasks. This talk will focus on the Natural Language Generation (NLG) problem and, more specifically, on the extend to which neural network language models could be employed for context-sensitive and data-driven text generation. In addition, a neural network architecture for response generation in social media along with the training methods that enable it to capture contextual information and effectively participate in public conversations will be discussed. Speaker Bio: Pavlos Vougiouklis obtained his 5-year Diploma in Electrical and Computer Engineering from the Aristotle University of Thessaloniki in 2013. He was awarded an MSc degree in Software Engineering from the University of Southampton in 2014. In 2015, he joined the Web and Internet Science (WAIS) research group of the University of Southampton and he is currently working towards the acquisition of his PhD degree in the field of Neural Network Approaches for Natural Language Processing. Title: Provenance is Complicated and Boring — Is there a solution? Speaker: Darren Richardson, University of Southampton Abstract: Paper trails, auditing, and accountability — arguably not the sexiest terms in computer science. But then you discover that you've possibly been eating horse-meat, and the importance of provenance becomes almost palpable. Having accepted that we should be creating provenance-enabled systems, the challenge of then communicating that provenance to casual users is not trivial: users should not have to have a detailed working knowledge of your system, and they certainly shouldn't be expected to understand the data model. So how, then, do you give users an insight into the provenance, without having to build a bespoke system for each and every different provenance installation? Speaker Bio: Darren is a final year Computer Science PhD student. He completed his undergraduate degree in Electronic Engineering at Southampton in 2012.