Video-text matching is a critical task in multimedia retrieval, but traditional methods often fail to capture the diversity and depth of video content due to inefficient and inaccurate frame sampling. We propose a novel sparse stratified sampling technique that can substantially improve the video-text matching process by segmenting video content into clusters based on relevant features and selectively sampling representative frames. Our method further introduces a threshold for the feature metric used to divide clusters, eliminating video frames with low relevance. We propose two variants of our approach: an offline approach that performs sampling before training, and an online approach that dynamically conducts sampling based on the relevance between video frames and the text query during training. Extensive experiments on datasets like MSRVTT and AVSD for video retrieval and multiple-choice VideoQA datasets, including AVQA and Music-AVQA, demonstrate the superiority of our method over previous state-of-the-art approaches. Our sparse stratified sampling technique achieves improvements of over 1.2% on MSRVTT and 1.7% on AVSD for R@1 in video retrieval tasks. For multiple-choice VideoQA tasks, our approach achieves significant improvements of 1.8% accuracy on AVQA and 3.9% on Music-AVQA, strongly supporting its effectiveness in enhancing video-text matching systems.

It is often a challenging task to build Machine Translation (MT) engines for a specific domain due to the lack of parallel data in that area. In this project, we develop a range of MT systems for 6 European languages (English, German, Italian, French, Polish and Irish) in all directions and in two domains (environment and economics).

Contact tracing apps used in tracing and mitigating the spread of COVID-19 have sparked discussions and controversies worldwide. The major concerns in relation to these apps are around privacy. Ireland was in general praised for the design of its COVID tracker app, and the transparency through which privacy issues were addressed. However, the ”voice” of the Irish public was not really heard or analysed. This study aimed to analyse the Irish public sentiment towards privacy and COVID tracker app. For this purpose we have conducted sentiment analysis on Twitter data collected from public Twitter accounts from Republic of Ireland. We collected COVID-19 related tweets generated in Ireland over a period of time from January 1, 2020 up to December 31, 2020 in order to perform sentiment analysis on this data set. Moreover, the study performed sentiment analysis on the feedback received from a national survey on privacy conducted in Republic of Ireland. The findings of the study reveal a significant criticism towards the app that relate to privacy concerns, but other aspects of the app as well. The findings also reveal some positive attitude towards the fight against COVID-19, but these are not necessarily related to the technological solutions employed for this purpose. The findings of the study contributed to the formulation of useful recommendations communicated to the relevant Irish actors.

Contact tracing apps used in tracing and mitigating the spread of COVID-19 have sparked discussions and controversies worldwide with major concerns around privacy. COVID Tracker app used in the Republic of Ireland was praised in general for the way it addressed privacy and was used as baseline for other contact tracing apps worldwide. The success of the app is dependent on the general public uptake, hence their voice and attitude is the one that really matters. This paper focuses on developing a survey and the methods aiming to examine the attitudes toward privacy during COVID-19 of the general public in the Republic of Ireland and their impact on the uptake of the COVID tracker app. Various privacy models are used and health belief model as well in this purpose. A pilot study with 286 participants show a change in attitude towards privacy during COVID-19 pandemic, with more people willing to share their data in the interest of saving lives. However, privacy attitudes are shown to have impacted the adoption of the app in Ireland.