ComputeTY 2018 - Fidelity Invest in future IT students

ComputeTY Winners with FidelityYet again 2018 delivered another successful  ComputeTY programme for many transition year students who attended.

ComputeTY is a transition year programme that allows students to visit the campus for one week to learn computer programming, web design and android app development.  It runs for three weeks every year with approximately 380 TY students on campus.

With the finanical assistance of Fidelity Investments Ireland this year, students showcased their own fully fledged websites, game apps and python programming skills at the end of the week. BBC Microbits where presented to the best students to help them to continue exploring their technical skills. Recent graduates employed on the Fidelity Leap programme gave insightful talks to the students at prizegiving, charted their own journey through CAO choices to the world of IT. Each of them emphaised the huge opportunities in the IT sector for all types of university graduates and encouraged the students to consider the sector when they came to making their CAO choices.

Computing PhD canditate, Fiona Dermody wins Best Student Paper at HUCAPP 2018 Conference

Fiona Dermody, PhD student at the School of Computing, won Best Student Paper at the Third International Conference on Human Computer Interaction Theory and Applications in Madeira, Jan 2018.

Co-written with her supervisor, Dr. Alistair Sutherland, it was entitled 'A Multimodal Positive Computing System for Public Speaking - Evaluating User Responses to Avatar and Video Speaker Representations'.

The International Conference on Human Computer Interaction Theory and Applications aims at becoming a major point of contact between researchers, engineers and practitioners in Human Computer Interaction. The conference will be structured along seventeen main tracks, covering different aspects related to Human Computer Interaction, from Theories, Models and User Evaluation, Interaction Techniques and Devices, Haptic and Multimodal Interaction, and Agents and Human Interaction.

A link to the paper can be found here.

DCU Computing teams compete on European Stage

NWERC 2017 Contestants
NWERC 2017 University Teams

On Sunday 26th November, two DCU computing undergraduate teams competed in the Northwestern Europe Regional Contest (NWERC) at University of Bath in the UK as part of the World ACM ICPC competition. Together with Irish undergraduate teams from Trinity, Maynooth and Queens they competed against university teams from 60 different institutions from 10 different countries in North West Europe.

The DCU team of ‘-= [B]ichael [B] [B]iggins =-’ won the ‘Irish’ title at the competition and placed an impressive 11th out of 42 UK and Irish teams, including teams from Cambridge and Oxford. The team consisted of Noah Donnelly (CPSSD3), Cian Ruane (CPSSD3) and Ciara Godwin (CASE3) solved 4 of the 12 algorithmic problems in the shortest time compared to other Irish teams and made attempts on all the problems. Overall they finished 47th out of the best 120 university teams across north Europe.

Silicon Republic article on DCU's Adapt centre's MT Evolution

Adapt LogoAdapt's Prof. Andy Way was recently interviewed by Silicon Republic on the MT evolution and it's application at the Adapt Centre, where Way and his team tackle language barriers that are “key challenges in enabling content to flow fluently across the globe”.

“From the late ’80s to about 2015, the dominant approach to MT was statistical (SMT). We needed large amounts of parallel data, ie source sentences and their human-provided translations, to build our statistical translation models, which essentially would suggest target-language words and phrases, which the model believed to be translations of the source sentence.

The last three years in MT have also seen neural MT (NMT) come to the fore. With NMT, all a research team needs is parallel data. The dominant model encodes the source sentence into a numerical vector representation, “which is in turn sent en bloc to the target-language decoder, whose job it is to generate the most likely target text from that vector”.

Way explained that NMT typically outperforms SMT and could be considered the “new state of the art”, citing more fluent translations and better word order as results. NMT does require much bigger training datasets, and models generally also take longer to train.

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