Text Representation for Nonconcatenative Morphology

The last six years have seen the immense improvement of the NMT in terms of translation quality. With the help of the neural networks, the NMT has been able to achieve the state-of-the-art results in transla- tion quality. However, the NMT is still not able to achieve translation quality near human levels. In this thesis, we propose new approaches to improve the language representation as input to the NMT. This can be achieved by exploiting language specific knowledge, such as phonetic alterations, the morphology, and the syntax. We propose a new approach to improve the language representation by exploiting mor- phological phenomena in Turkish and Hebrew and show that the proposed segmentation approaches can improve translation quality. We have used several different segmentation approaches and compared them with each other. All of the segmentation approaches are rooted in the language specific morphological analysis of Turkish and Hebrew. We have also looked at the effect of the specific segmentation approach on translation quality. We have trained six different models of the type transformer with different seg- mentation approaches and compared them with each other. For each of the segmentation approaches, we have evaluated the translation quality using two automatic metrics and the human evaluation. We have also observed that the segmentation approaches can improve the translation quality in the case of the human evaluation, but not in the case of the automatic metrics. We have emphasized the importance of the human evaluation for NMT, and have shown that the automatic metrics can often be misleading

What’s in a Face? Psychophysiological applications of neuroscience for diagnostics and therapies

The idea that the utility of research should be secondary to understanding its subject delays the extraction of potential value. A parallel translational approach to research was applied whereby discovering new findings and testing their validity was performed in parallel. Research about the face was selected for translation as it provided the complexity, diversity, and fidelity necessary for multiple data-driven hypothesis exploration while remaining key to social interaction. For example, emotional contagion, the tendency for an individual to catch someone else’s emotion has been linked to facial contagion: an automatic reaction whereby facial muscles adopt the expression of any emotional face. Based on the reported exaggerated emotional reactions of patients with upper involvement in Motor Neuron Disease (MND) compared to lower involvement, an experiment was devised to make the difference through comparisons of facial contagion responses with recorded Electromyography (EMG) responses (chapter 3). As these patients were expected to have generally weak responses, it became necessary to increase the sensitivity of acquired signals to elucidate differences between subtypes. An adaptive filtering technique for signal processing was developed based on modelling methods and tested with support vector machines (chapter 2). The therapeutic intervention (chapter 4) consisted of a series of experiments seeking to induce emotional contagion of happiness by presenting images of smiling faces through smartphones. This was also gamified in an experiment at the Science Museum in London to test whether the effect could be found over the short term. Lastly, I parametrised faces from a large population of Tibetan residents and predicted haematological and electrocardiographic measures with machine learning methods as a way of screening for cardiovascular disease through photographs of the face (chapter 5). The results were analysed in relation to the field of cognitive neuroscience and the implications for a parallel translational and high-dimensional approach were discussed

RFID Technology in Intelligent Tracking Systems in Construction Waste Logistics Using Optimisation Techniques

Construction waste disposal is an urgent issue for protecting our environment. This paper proposes a waste management system and illustrates the work process using plasterboard waste as an example, which creates a hazardous gas when land filled with household waste, and for which the recycling rate is less than 10% in the UK. The proposed system integrates RFID technology, Rule-Based Reasoning, Ant Colony optimization and knowledge technology for auditing and tracking plasterboard waste, guiding the operation staff, arranging vehicles, schedule planning, and also provides evidence to verify its disposal. It h relies on RFID equipment for collecting logistical data and uses digital imaging equipment to give further evidence; the reasoning core in the third layer is responsible for generating schedules and route plans and guidance, and the last layer delivers the result to inform users. The paper firstly introduces the current plasterboard disposal situation and addresses the logistical problem that is now the main barrier to a higher recycling rate, followed by discussion of the proposed system in terms of both system level structure and process structure. And finally, an example scenario will be given to illustrate the system’s utilization

Recent Advances in Social Data and Artificial Intelligence 2019

The importance and usefulness of subjects and topics involving social data and artificial intelligence are becoming widely recognized. This book contains invited review, expository, and original research articles dealing with, and presenting state-of-the-art accounts pf, the recent advances in the subjects of social data and artificial intelligence, and potentially their links to Cyberspace