"the easter rising of 1916: A step towards the independence of ireland analysed through the works of irish writers."

The 1916 Easter Rising, an unsuccessful insurrection which resulted in the Irish War of Independence, generated a deep change in the political landscape in Ireland. The purpose of this work is to describe this crucial period in the history of Ireland through the voices of Irish writers who expressed their ideas and feelings about the way Ireland was close to gaining its independence. Thanks to songs, poems and literature, I analysed the events of that period through the eyes of the Irish people. Authors like Roddy Doyle and William Butler Yeats were fundamental in examining this topic very thoroughly. Through their works, they were able to convey their knowledge about the events of those years and, at the same time, to give their own opinion, as Irish people, on the topic.

Production Lot Detection through Synthetic Generated Data

Correctness of information gathered in production environments is an essential part of quality assurance processes in many industries, this task is often performed by human resources who visually take annotations in various steps of the production flow. Depending on the performed task the correlation between where exactly the information is gathered and what it represents is more than often lost in the process. The lack of labeled data places a great boundary on the application of deep neural networks aimed at object detection tasks, moreover supervised training of deep models requires a great amount of data to be available. Reaching an adequate large collection of labeled images through classic techniques of data annotations is an exhausting and costly task to perform, not always suitable for every scenario. A possible solution is to generate synthetic data that replicates the real one and use it to fine-tune a deep neural network trained on one or more source domains to a different target domain. The purpose of this thesis is to show a real case scenario where the provided data were both in great scarcity and missing the required annotations. Sequentially a possible approach is presented where synthetic data has been generated to address those issues while standing as a training base of deep neural networks for object detection, capable of working on images taken in production-like environments. Lastly, it compares performance on different types of synthetic data and convolutional neural networks used as backbones for the model.