696 research outputs found
Trusted Provenance with Blockchain - A Blockchain-based Provenance Tracking System for Virtual Aircraft Component Manufacturing
The importance of provenance in the digital age has led to significant interest in utilizing blockchain technology for tamper-proof storage of provenance data. This thesis proposes a blockchain-based provenance tracking system for the certification of aircraft components. The aim is to design and implement a system that can ensure the trustworthy, tamper-resistant storage of provenance documents originating from an aircraft manufacturing process. To achieve this, the thesis presents a systematic literature review, which provides a comprehensive overview of existing works in the field of provenance and blockchain technology. After obtaining strategies to utilize blockchain for the storage of provenance data on the blockchain, a system was designed to meet the requirements of stakeholders in the aviation industry. The thesis utilized a systematic approach to gather requirements by conducting interviews with stakeholders. The system was implemented using a combination of smart contracts and a graphical user interface to provide tamper-resistant, traceable storage of relevant data on a transparent blockchain. An evaluation based on the requirements identified during the requirement engineering process found that the proposed system meets all identified requirements. Overall, this thesis offers insight into a potential application of blockchain technology in the aviation industry and provides a valuable resource for researchers and industry professionals seeking to leverage blockchain technology for provenance tracking and certification purpose
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Trust, Accountability, and Autonomy in Knowledge Graph-Based AI for Self-Determination
Knowledge Graphs (KGs) have emerged as fundamental platforms for powering intelligent decision-making and a wide range of Artificial Intelligence (AI) services across major corporations such as Google, Walmart, and AirBnb. KGs complement Machine Learning (ML) algorithms by providing data context and semantics, thereby enabling further inference and question-answering capabilities. The integration of KGs with neuronal learning (e.g., Large Language Models (LLMs)) is currently a topic of active research, commonly named neuro-symbolic AI. Despite the numerous benefits that can be accomplished with KG-based AI, its growing ubiquity within online services may result in the loss of self-determination for citizens as a fundamental societal issue. The more we rely on these technologies, which are often centralised, the less citizens will be able to determine their own destinies. To counter this threat, AI regulation, such as the European Union (EU) AI Act, is being proposed in certain regions. The regulation sets what technologists need to do, leading to questions concerning How the output of AI systems can be trusted? What is needed to ensure that the data fuelling and the inner workings of these artefacts are transparent? How can AI be made accountable for its decision-making? This paper conceptualises the foundational topics and research pillars to support KG-based AI for self-determination. Drawing upon this conceptual framework, challenges and opportunities for citizen self-determination are illustrated and analysed in a real-world scenario. As a result, we propose a research agenda aimed at accomplishing the recommended objectives
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Sonic heritage: listening to the past
History is so often told through objects, images and photographs, but the potential of sounds to reveal place and space is often neglected. Our research project ‘Sonic Palimpsest’1 explores the potential of sound to evoke impressions and new understandings of the past, to embrace the sonic as a tool to understand what was, in a way that can complement and add to our predominant visual understandings. Our work includes the expansion of the Oral History archives held at Chatham Dockyard to include women’s voices and experiences, and the creation of sonic works to engage the public with their heritage. Our research highlights the social and cultural value of oral history and field recordings in the transmission of knowledge to both researchers and the public. Together these recordings document how buildings and spaces within the dockyard were used and experienced by those who worked there. We can begin to understand the social and cultural roles of these buildings within the community, both past and present
Machine Learning Algorithm for the Scansion of Old Saxon Poetry
Several scholars designed tools to perform the automatic scansion of poetry in many languages, but none of these tools
deal with Old Saxon or Old English. This project aims to be a first attempt to create a tool for these languages. We
implemented a Bidirectional Long Short-Term Memory (BiLSTM) model to perform the automatic scansion of Old Saxon
and Old English poems. Since this model uses supervised learning, we manually annotated the Heliand manuscript, and
we used the resulting corpus as labeled dataset to train the model. The evaluation of the performance of the algorithm
reached a 97% for the accuracy and a 99% of weighted average for precision, recall and F1 Score. In addition, we tested
the model with some verses from the Old Saxon Genesis and some from The Battle of Brunanburh, and we observed that
the model predicted almost all Old Saxon metrical patterns correctly misclassified the majority of the Old English input
verses
Measuring the concept of PID literacy : user perceptions and understanding of persistent identifiers in support of open scholarly infrastructure
The increasing centrality of persistent identifiers (PIDs) to scholarly ecosystems and the contribution they can make to the burgeoning 'PID graph' has the potential to transform scholarship. Despite their importance as originators of PID data, little is known about researchers' awareness and understanding of PIDs, or their efficacy in using them. In this article we report on the results of an online interactive test designed to elicit exploratory data about researcher awareness and understanding of PIDs. This instrument was designed to explore recognition of PIDs (e.g. DOIs, ORCIDs, etc.) and the extent to which researchers correctly apply PIDs within digital scholarly ecosystems, as well as measure researchers' perceptions of PIDs. Our results reveal irregular patterns of PID understanding and certainty across all participants, though statistically significant disciplinary and academic job role differences were observed in some instances. Uncertainty and confusion were found to exist in relation to dominant schemes such as ORCID and DOIs, even when contextualized within real-world examples. We also show researchers' perceptions of PIDs to be generally positive but that disciplinary differences can be noted, as well as higher levels of aversion to PIDs in specific use cases and negative perceptions where PIDs are measured on an 'activity' semantic dimension. This work therefore contributes to our understanding of scholars' 'PID literacy' and should inform those designing PID-centric scholarly infrastructures, that a significant need for training and outreach to active researchers remains necessary
Trustworthy journalism through AI
Quality journalism has become more important than ever due to the need for quality and trustworthy media outlets that can provide accurate information to the public and help to address and counterbalance the wide and rapid spread of disinformation. At the same time, quality journalism is under pressure due to loss of revenue and competition from alternative information providers. This vision paper discusses how recent advances in Artificial Intelligence (AI), and in Machine Learning (ML) in particular, can be harnessed to support efficient production of high-quality journalism. From a news consumer perspective, the key parameter here concerns the degree of trust that is engendered by quality news production. For this reason, the paper will discuss how AI techniques can be applied to all aspects of news, at all stages of its production cycle, to increase trust
Integration of heterogeneous data sources and automated reasoning in healthcare and domotic IoT systems
In recent years, IoT technology has radically transformed many crucial industrial and service sectors such as healthcare. The multi-facets heterogeneity of the devices and the collected information provides important opportunities to develop innovative systems and services. However, the ubiquitous presence of data silos and the poor semantic interoperability in the IoT landscape constitute a significant obstacle in the pursuit of this goal. Moreover, achieving actionable knowledge from the collected data requires IoT information sources to be analysed using appropriate artificial intelligence techniques such as automated reasoning. In this thesis work, Semantic Web technologies have been investigated as an approach to address both the data integration and reasoning aspect in modern IoT systems. In particular, the contributions presented in this thesis are the following: (1) the IoT Fitness Ontology, an OWL ontology that has been developed in order to overcome the issue of data silos and enable semantic interoperability in the IoT fitness domain; (2) a Linked Open Data web portal for collecting and sharing IoT health datasets with the research community; (3) a novel methodology for embedding knowledge in rule-defined IoT smart home scenarios; and (4) a knowledge-based IoT home automation system that supports a seamless integration of heterogeneous devices and data sources
Evaluating FAIR Digital Object and Linked Data as distributed object systems
FAIR Digital Object (FDO) is an emerging concept that is highlighted by
European Open Science Cloud (EOSC) as a potential candidate for building a
ecosystem of machine-actionable research outputs. In this work we
systematically evaluate FDO and its implementations as a global distributed
object system, by using five different conceptual frameworks that cover
interoperability, middleware, FAIR principles, EOSC requirements and FDO
guidelines themself.
We compare the FDO approach with established Linked Data practices and the
existing Web architecture, and provide a brief history of the Semantic Web
while discussing why these technologies may have been difficult to adopt for
FDO purposes. We conclude with recommendations for both Linked Data and FDO
communities to further their adaptation and alignment.Comment: 40 pages, submitted to PeerJ C
Enriching information extraction pipelines in clinical decision support systems
Programa Oficial de Doutoramento en TecnoloxÃas da Información e as Comunicacións. 5032V01[Resumo] Os estudos sanitarios de múltiples centros son importantes para aumentar a repercusión dos resultados da investigación médica debido ao número de suxeitos que poden participar neles. Para simplificar a execución destes estudos, o proceso de intercambio de datos deberÃa ser sinxelo, por exemplo, mediante o uso de bases de datos interoperables. Con todo, a consecución desta interoperabilidade segue sendo
un tema de investigación en curso, sobre todo debido aos problemas de gobernanza e privacidade dos datos. Na primeira fase deste traballo, propoñemos varias metodoloxÃas para optimizar os procesos de estandarización das bases de datos sanitarias. Este
traballo centrouse na estandarización de fontes de datos heteroxéneas nun esquema de datos estándar, concretamente o OMOP CDM, que foi desenvolvido e promovido
pola comunidade OHDSI. Validamos a nosa proposta utilizando conxuntos de datos de pacientes con enfermidade de Alzheimer procedentes de distintas institucións.
Na seguinte etapa, co obxectivo de enriquecer a información almacenada nas bases de datos de OMOP CDM, investigamos solucións para extraer conceptos clÃnicos de narrativas non estruturadas, utilizando técnicas de recuperación de información e
de procesamento da linguaxe natural. A validación realizouse a través de conxuntos de datos proporcionados en desafÃos cientÃficos, concretamente no National NLP Clinical Challenges(n2c2). Na etapa final, propuxémonos simplificar a execución de
protocolos de estudos provenientes de múltiples centros, propoñendo solucións novas para perfilar, publicar e facilitar o descubrimento de bases de datos. Algunhas das solucións desenvolvidas están a utilizarse actualmente en tres proxectos europeos
destinados a crear redes federadas de bases de datos de saúde en toda Europa.[Resumen] Los estudios sanitarios de múltiples centros son importantes para aumentar la repercusión de los resultados de la investigación médica debido al número de sujetos que pueden participar en ellos. Para simplificar la ejecución de estos estudios, el proceso de intercambio de datos deberÃa ser sencillo, por ejemplo, mediante el uso de bases de datos interoperables. Sin embargo, la consecución de esta interoperabilidad
sigue siendo un tema de investigación en curso, sobre todo debido a los problemas de gobernanza y privacidad de los datos. En la primera fase de este trabajo, proponemos varias metodologÃas para optimizar los procesos de estandarización de las
bases de datos sanitarias. Este trabajo se centró en la estandarización de fuentes de datos heterogéneas en un esquema de datos estándar, concretamente el OMOP CDM, que ha sido desarrollado y promovido por la comunidad OHDSI. Validamos nuestra propuesta utilizando conjuntos de datos de pacientes con enfermedad de Alzheimer procedentes de distintas instituciones. En la siguiente etapa, con el objetivo de enriquecer la información almacenada en las bases de datos de OMOP CDM, hemos investigado soluciones para extraer conceptos clÃnicos de narrativas no estructuradas, utilizando técnicas de recuperación de información y de procesamiento del lenguaje natural. La validación se realizó a través de conjuntos de datos proporcionados en desafÃos cientÃficos, concretamente en el National NLP Clinical Challenges (n2c2). En la etapa final, nos propusimos simplificar la ejecución de protocolos de estudios provenientes de múltiples centros, proponiendo soluciones novedosas para perfilar, publicar y facilitar el descubrimiento de bases de datos. Algunas de las soluciones desarrolladas se están utilizando actualmente en tres proyectos europeos destinados a crear redes federadas de bases de datos de salud en toda Europa.[Abstract] Multicentre health studies are important to increase the impact of medical research
findings due to the number of subjects that they are able to engage. To simplify the execution of these studies, the data-sharing process should be effortless, for instance, through the use of interoperable databases. However, achieving this interoperability is still an ongoing research topic, namely due to data governance and privacy issues. In the first stage of this work, we propose several methodologies to optimise the harmonisation pipelines of health databases. This work was focused on harmonising heterogeneous data sources into a standard data schema, namely the OMOP CDM which has been developed and promoted by the OHDSI community. We validated our proposal using data sets of Alzheimer’s disease patients from distinct institutions. In the following stage, aiming to enrich the information stored in OMOP CDM databases, we have investigated solutions to extract clinical concepts from unstructured narratives, using information retrieval and natural language processing
techniques. The validation was performed through datasets provided in scientific challenges, namely in the National NLP Clinical Challenges (n2c2). In the final stage, we aimed to simplify the protocol execution of multicentre studies, by proposing novel solutions for profiling, publishing and facilitating the discovery of databases. Some of the developed solutions are currently being used in three European projects
aiming to create federated networks of health databases across Europe
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