Analysis of the opinions of individuals on the COVID-19 vaccination on social media

The COVID-19 pandemic continues to threaten public health globally. To develop effective interventions and campaigns to raise vaccination rates, policy makers need to understand people's attitudes towards vaccination. We examine the perspectives of people in India, the United States, Canada, and the United Kingdom on the administration of different COVID-19 vaccines. We analyse how public opinion and emotional tendencies regarding the COVID-19 vaccines relate to popular issues on social media. We employ machine learning algorithms to forecast thoughts based on the social media posts. The prevailing emotional tendency indicates that individuals have faith in immunisation. However, there is a likelihood that significant statements or events on a national, international, or political scale influence public perception of vaccinations. We show how public health officials can track public attitudes and opinions towards vaccine-related information in a geo-aware manner, respond to the sceptics, and increase the level of vaccine trust in a particular region or community

Towards Personalized and Human-in-the-Loop Document Summarization

The ubiquitous availability of computing devices and the widespread use of the internet have generated a large amount of data continuously. Therefore, the amount of available information on any given topic is far beyond humans' processing capacity to properly process, causing what is known as information overload. To efficiently cope with large amounts of information and generate content with significant value to users, we require identifying, merging and summarising information. Data summaries can help gather related information and collect it into a shorter format that enables answering complicated questions, gaining new insight and discovering conceptual boundaries. This thesis focuses on three main challenges to alleviate information overload using novel summarisation techniques. It further intends to facilitate the analysis of documents to support personalised information extraction. This thesis separates the research issues into four areas, covering (i) feature engineering in document summarisation, (ii) traditional static and inflexible summaries, (iii) traditional generic summarisation approaches, and (iv) the need for reference summaries. We propose novel approaches to tackle these challenges, by: i)enabling automatic intelligent feature engineering, ii) enabling flexible and interactive summarisation, iii) utilising intelligent and personalised summarisation approaches. The experimental results prove the efficiency of the proposed approaches compared to other state-of-the-art models. We further propose solutions to the information overload problem in different domains through summarisation, covering network traffic data, health data and business process data.Comment: PhD thesi

A machine learning taxonomic classifier for science publications

Dissertação de mestrado integrado em Engineering and Management of Information SystemsThe evolution in scientific production, associated with the growing interdomain collaboration of knowledge and the increasing co-authorship of scientific works remains supported by processes of manual, highly subjective classification, subject to misinterpretation. The very taxonomy on which this same classification process is based is not consensual, with governmental organizations resorting to taxonomies that do not keep up with changes in scientific areas, and indexers / repositories that seek to keep up with those changes. We find a reality distinct from what is expected and that the domains where scientific work is recorded can easily be misrepresentative of the work itself. The taxonomy applied today by governmental bodies, such as the one that regulates scientific production in Portugal, is not enough, is limiting, and promotes classification in areas close to the desired, therefore with great potential for error. An automatic classification process based on machine learning algorithms presents itself as a possible solution to the subjectivity problem in classification, and while it does not solve the issue of taxonomy mismatch this work shows this possibility with proved results. In this work, we propose a classification taxonomy, as well as we develop a process based on machine learning algorithms to solve the classification problem. We also present a set of directions for future work for an increasingly representative classification of evolution in science, which is not intended as airtight, but flexible and perhaps increasingly based on phenomena and not just disciplines.A evolução na produção de ciência, associada à crescente colaboração interdomínios do conhecimento e à também crescente coautoria de trabalhos permanece suportada por processos de classificação manual, subjetiva e sujeita a interpretações erradas. A própria taxonomia na qual assenta esse mesmo processo de classificação não é consensual, com organismos estatais a recorrerem a taxonomias que não acompanham as alterações nas áreas científicas, e indexadores/repositórios que procuram acompanhar essas mesmas alterações. Verificamos uma realidade distinta do espectável e que os domínios onde são registados os trabalhos científicos podem facilmente estar desenquadrados. A taxonomia hoje aplicada pelos organismos governamentais, como o caso do organismo que regulamenta a produção científica em Portugal, não é suficiente, é limitadora, e promove a classificação em domínios aproximados do desejado, logo com grande potencial para erro. Um processo de classificação automática com base em algoritmos de machine learning apresenta-se como uma possível solução para o problema da subjetividade na classificação, e embora não resolva a questão do desenquadramento da taxonomia utilizada, é apresentada neste trabalho como uma possibilidade comprovada. Neste trabalho propomos uma taxonomia de classificação, bem como nós desenvolvemos um processo baseado em machine learning algoritmos para resolver o problema de classificação. Apresentamos ainda um conjunto de direções para trabalhos futuros para uma classificação cada vez mais representativa da evolução nas ciências, que não pretende ser hermética, mas flexível e talvez cada vez mais baseada em fenómenos e não apenas em disciplinas

Malicious Interlocutor Detection Using Forensic Analysis of Historic Data

The on-going problem of child grooming online grows year on year and whilst government legislation looks to combat the issue by levying heavier penalties on perpetrators of online grooming, crime figures still increase. Government guidance directed towards digital platforms and social media providers places emphasis on child safety online. As this research shows, government initiatives have proved somewhat ineffective. Therefore, the aim of this research is to investigate the scale of the of the problem and test a variety of machine learning and deep learning techniques that could be used in a novel intelligent solution to protect children from online predation. The heterogeneity of online platforms means that a one size fits all solution presents a complex problem that needs to be solved. The maturity of intelligent approaches to Natural Language Processing makes it possible to analyse and process text data in a wide variety of ways. Pre-processing data enables the preparation of text data in a format that machines can understand and reason about without the need for human interaction. The on-going development of Machine Learning and Deep Learning architectures enables the construction of intelligent solutions that can classify text data in ways never imagined. This thesis presents research that tests the application of potential intelligent solutions such as Artificial Neural Networks and Machine Learning algorithms applied in Natural Language Processing. The research also tests the performance of pre-processing workflows and the impact of pre-processing of both online grooming and more general chat corpora. The storage and processing of data via a traditional relational database management system has also been tested for suitability when looking to detect grooming conversation in historical data. The on-going development of Machine Learning and Deep Learning architectures enables the construction of intelligent solutions that can classify text data in ways never imagined. This thesis presents research that tests the application of potential intelligent solutions such as Artificial Neural Networks and Machine Learning algorithms applied in Natural Language Processing. The research also tests the performance of pre-processing workflows and the impact of pre-processing of both online grooming and more general chat corpora. The storage and processing of data via a traditional relational database management system has also been tested for suitability when looking to detect grooming conversation in historical data. Document similarity measures such as Cosine Similarity and Support Vector Machines have displayed positive results in identifying grooming conversation, however, a more intelligent solution may prove to have better currency in developing a smart autonomous solution given the ever-evolving lexicon used by participants in online chat conversations

Ascertaining Pain in Mental Health Records:Combining Empirical and Knowledge-Based Methods for Clinical Modelling of Electronic Health Record Text

In recent years, state-of-the-art clinical Natural Language Processing (NLP), as in other domains, has been dominated by neural networks and other statistical models. In contrast to the unstructured nature of Electronic Health Record (EHR) text, biomedical knowledge is increasingly available in structured and codified forms, underpinned by curated databases, machine-readable clinical guidelines, and logically defined terminologies. This thesis examines the incorporation of external medical knowledge into clinical NLP and tests these methods on a use case of ascertaining physical pain in clinical notes of mental health records.Pain is a common reason for accessing healthcare resources and has been a growing area of research, especially its impact on mental health. Pain also presents a unique NLP problem due to its ambiguous nature and the varying circumstances in which it can be used. For these reasons, pain has been chosen as a use case, making it a good case study for the application of the methods explored in this thesis. Models are built by assimilating both structured medical knowledge and clinical NLP and leveraging the inherent relations that exist within medical ontologies. The data source used in this project is a mental health EHR database called CRIS, which contains de-identified patient records from the South London and Maudsley NHS Foundation Trust, one of the largest mental health providers in Western Europe.A lexicon of pain terms was developed to identify documents within CRIS mentioning painrelated terms. Gold standard annotations were created by conducting manual annotations on these documents. These gold standard annotations were used to build models for a binary classification task, with the objective of classifying sentences from the clinical text as “relevant”, which indicates the sentence contains relevant mentions of pain, i.e., physical pain affecting the patient, or “not relevant”, which indicates the sentence does not contain mentions of physical pain, or the mention does not relate to the patient (ex: someone else in physical pain). Two models incorporating structured medical knowledge were built:1. a transformer-based model, SapBERT, that utilises a knowledge graph of the UMLS ontology, and2. a knowledge graph embedding model that utilises embeddings from SNOMED CT, which was then used to build a random forest classifier. This was achieved by modelling the clinical pain terms and their relations from SNOMED CT into knowledge graph embeddings, thus combining the data-driven view of clinical language, with the logical view of medical knowledge.These models have been compared with NLP models (binary classifiers) that do not incorporate such structured medical knowledge:1. a transformer-based model, BERT_base, and2. a random forest classifier model.Amongst the two transformer-based models, SapBERT performed better at the classification task (F1-score: 0.98), and amongst the random forest models, the one incorporating knowledge graph embeddings performed better (F1-score: 0.94). The SapBERT model was run on sentences from a cohort of patients within CRIS, with the objective of conducting a prevalence study to understand the distribution of pain based on sociodemographic and diagnostic factors.The contribution of this research is both methodological and practical, showing the difference between a conventional NLP approach of binary classification and one that incorporates external knowledge, and further utilising the models obtained from both these approaches ina prevalence study which was designed based on inputs from clinicians and a patient and public involvement group. The results emphasise the significance of going beyond the conventional approach to NLP when addressing complex issues such as pain.<br/

A web-based approach to engineering adaptive collaborative applications

Current methods employed to develop collaborative applications have to make decisions and speculate about the environment in which the application will operate within, the network infrastructure that will be used and the device type the application will operate on. These decisions and assumptions about the environment in which collaborative applications were designed to work are not ideal. These methods produce collaborative applications that are characterised as being inflexible, working on homogeneous networks and single platforms, requiring pre-existing knowledge of the data and information types they need to use and having a rigid choice of architecture. On the other hand, future collaborative applications are required to be flexible; to work in highly heterogeneous environments; be adaptable to work on different networks and on a range of device types. This research investigates the role that the Web and its various pervasive technologies along with a component-based Grid middleware can play to address these concerns. The aim is to develop an approach to building adaptive collaborative applications that can operate on heterogeneous and changing environments. This work proposes a four-layer model that developers can use to build adaptive collaborative applications. The four-layer model is populated with Web technologies such as Scalable Vector Graphics (SVG), the Resource Description Framework (RDF), Protocol and RDF Query Language (SPARQL) and Gridkit, a middleware infrastructure, based on the Open Overlays concept. The Middleware layer (the first layer of the four-layer model) addresses network and operating system heterogeneity, the Group Communication layer enables collaboration and data sharing, while the Knowledge Representation layer proposes an interoperable RDF data modelling language and a flexible storage facility with an adaptive architecture for heterogeneous data storage. And finally there is the Presentation and Interaction layer which proposes a framework (Oea) for scalable and adaptive user interfaces. The four layer model has been successfully used to build a collaborative application, called Wildfurt that overcomes challenges facing collaborative applications. This research has demonstrated new applications for cutting-edge Web technologies in the area of building collaborative applications. SVG has been used for developing superior adaptive and scalable user interfaces that can operate on different device types. RDF and RDFS, have also been used to design and model collaborative applications providing a mechanism to define classes and properties and the relationships between them. A flexible and adaptable storage facility that is able to change its architecture based on the surrounding environments and requirements has also been achieved by combining the RDF technology with the Open Overlays middleware, Gridkit

Enhancing remanufacturing automation using deep learning approach

In recent years, remanufacturing has significant interest from researchers and practitioners to improve efficiency through maximum value recovery of products at end-of-life (EoL). It is a process of returning used products, known as EoL products, to as-new condition with matching or higher warranty than the new products. However, these remanufacturing processes are complex and time-consuming to implement manually, causing reduced productivity and posing dangers to personnel. These challenges require automating the various remanufacturing process stages to achieve higher throughput, reduced lead time, cost and environmental impact while maximising economic gains. Besides, as highlighted by various research groups, there is currently a shortage of adequate remanufacturing-specific technologies to achieve full automation. -- This research explores automating remanufacturing processes to improve competitiveness by analysing and developing deep learning-based models for automating different stages of the remanufacturing processes. Analysing deep learning algorithms represents a viable option to investigate and develop technologies with capabilities to overcome the outlined challenges. Deep learning involves using artificial neural networks to learn high-level abstractions in data. Deep learning (DL) models are inspired by human brains and have produced state-of-the-art results in pattern recognition, object detection and other applications. The research further investigates the empirical data of torque converter components recorded from a remanufacturing facility in Glasgow, UK, using the in-case and cross-case analysis to evaluate the remanufacturing inspection, sorting, and process control applications. -- Nevertheless, the developed algorithm helped capture, pre-process, train, deploy and evaluate the performance of the respective processes. The experimental evaluation of the in-case and cross-case analysis using model prediction accuracy, misclassification rate, and model loss highlights that the developed models achieved a high prediction accuracy of above 99.9% across the sorting, inspection and process control applications. Furthermore, a low model loss between 3x10-3 and 1.3x10-5 was obtained alongside a misclassification rate that lies between 0.01% to 0.08% across the three applications investigated, thereby highlighting the capability of the developed deep learning algorithms to perform the sorting, process control and inspection in remanufacturing. The results demonstrate the viability of adopting deep learning-based algorithms in automating remanufacturing processes, achieving safer and more efficient remanufacturing. -- Finally, this research is unique because it is the first to investigate using deep learning and qualitative torque-converter image data for modelling remanufacturing sorting, inspection and process control applications. It also delivers a custom computational model that has the potential to enhance remanufacturing automation when utilised. The findings and publications also benefit both academics and industrial practitioners. Furthermore, the model is easily adaptable to other remanufacturing applications with minor modifications to enhance process efficiency in today's workplaces.In recent years, remanufacturing has significant interest from researchers and practitioners to improve efficiency through maximum value recovery of products at end-of-life (EoL). It is a process of returning used products, known as EoL products, to as-new condition with matching or higher warranty than the new products. However, these remanufacturing processes are complex and time-consuming to implement manually, causing reduced productivity and posing dangers to personnel. These challenges require automating the various remanufacturing process stages to achieve higher throughput, reduced lead time, cost and environmental impact while maximising economic gains. Besides, as highlighted by various research groups, there is currently a shortage of adequate remanufacturing-specific technologies to achieve full automation. -- This research explores automating remanufacturing processes to improve competitiveness by analysing and developing deep learning-based models for automating different stages of the remanufacturing processes. Analysing deep learning algorithms represents a viable option to investigate and develop technologies with capabilities to overcome the outlined challenges. Deep learning involves using artificial neural networks to learn high-level abstractions in data. Deep learning (DL) models are inspired by human brains and have produced state-of-the-art results in pattern recognition, object detection and other applications. The research further investigates the empirical data of torque converter components recorded from a remanufacturing facility in Glasgow, UK, using the in-case and cross-case analysis to evaluate the remanufacturing inspection, sorting, and process control applications. -- Nevertheless, the developed algorithm helped capture, pre-process, train, deploy and evaluate the performance of the respective processes. The experimental evaluation of the in-case and cross-case analysis using model prediction accuracy, misclassification rate, and model loss highlights that the developed models achieved a high prediction accuracy of above 99.9% across the sorting, inspection and process control applications. Furthermore, a low model loss between 3x10-3 and 1.3x10-5 was obtained alongside a misclassification rate that lies between 0.01% to 0.08% across the three applications investigated, thereby highlighting the capability of the developed deep learning algorithms to perform the sorting, process control and inspection in remanufacturing. The results demonstrate the viability of adopting deep learning-based algorithms in automating remanufacturing processes, achieving safer and more efficient remanufacturing. -- Finally, this research is unique because it is the first to investigate using deep learning and qualitative torque-converter image data for modelling remanufacturing sorting, inspection and process control applications. It also delivers a custom computational model that has the potential to enhance remanufacturing automation when utilised. The findings and publications also benefit both academics and industrial practitioners. Furthermore, the model is easily adaptable to other remanufacturing applications with minor modifications to enhance process efficiency in today's workplaces

Effective retrieval to support learning

To use digital resources to support learning, we need to be able to retrieve them. This thesis introduces a new area of research within information retrieval, the retrieval of educational resources from the Web. Successful retrieval of educational resources requires an understanding of how the resources being searched are managed, how searchers interact with those resources and the systems that manage them, and the needs of the people searching. As such, we began by investigating how resources are managed and reused in a higher education setting. This investigation involved running four focus groups with 23 participants, 26 interviews and a survey. The second part of this work is motivated by one of our initial findings; when people look for educational resources, they prefer to search the World Wide Web using a public search engine. This finding suggests users searching for educational resources may be more satisfied with search engine results if only those resources likely to support learning are presented. To provide satisfactory result sets, resources that are unlikely to support learning should not be present. A filter to detect material that is likely to support learning would therefore be useful. Information retrieval systems are often evaluated using the Cranfield method, which compares system performance with a ground truth provided by human judgments. We propose a method of evaluating systems that filter educational resources based on this method. By demonstrating that judges can agree on which resources are educational, we establish that a single human judge for each resource provides a sufficient ground truth. Machine learning techniques are commonly used to classify resources. We investigate how machine learning can be used to classify resources retrieved from the Web as likely or unlikely to support learning. We found that reasonable classification performance can be achieved using text extracted from resources in conjunction with Naïve Bayes, AdaBoost, and Random Forest classifiers. We also found that attributes developed from the structural elements—hyperlinks and headings found in a resource—did not substantially improve classification to support learning. We found that reasonable classification performance can be achieved using text extracted from resources in conjunction with Naïve Bayes, AdaBoost, and Random Forest classifiers. We also found that attributes developed from the structural elements—hyperlinks and headings found in a resource—did not substantially improve classification over simply using the text