Scientific image processing within the NFFA-EUROPE Data Repository

This thesis is embedded within the NFFA-EUROPE project, that aims to setup the first overarching Information and Data management Repository Platform (IDRP) for the nanoscience community. The main goal of the IDRP developement is to semiautomatize the harvesting of scientific data coming from many different instruments among NFFA European facilities, and identify the correct metadata to allow them to be searchable accordingly to the FAIR Guiding Principles [1]. For our analysis, we selected data from a specific instrument, the Scanning Electron Microscope (SEM). The reasons for this choice are many:..

Design and Deployment of an Access Control Module for Data Lakes

Nowadays big data is considered an extremely valued asset for companies, which are discovering new avenues to use it for their business profit. However, an organization’s ability to effectively extract valuable information from data is based on its knowledge management infrastructure. Thus, most organizations are transitioning from data warehouse (DW) storages to data lake (DL) infrastructures, from which further insights are derived. The present work is carried out as part of a cybersecurity project in a financial institution that manages vast volumes and variety of data that is kept in a data lake. Although DL is presented as the answer to the current big data scenario, this infrastructure presents certain flaws on authentication and access control. Preceding work on DL access control points out that the main goal is to avoid fraudulent behaviors derived from user’s access, such as secondary use1, that could result in business data being exposed to third parties. To overcome the risk, traditional mechanisms attempt to identify these behaviors based on rules, however, they cannot reveal all different kinds of fraud because they only look for known patterns of misuse. The present work proposes a novel access control system for data lakes, assisted by Oracle’s database audit trail and based on anomaly detection mechanisms, that automatically looks for events that do not conform the normal or expected behavior. Thus, the overall aim of this project is to develop and deploy an automated system for identifying abnormal accesses to the DL, which can be separated into four subgoals: explore the different technologies that could be applied in the domain of anomaly detection, design the solution, deploy it, and evaluate the results. For the purpose, feature engineering is performed, and four different unsupervised ML models are built and evaluated. According to the quality of the results, the better model is finally productionalized with Docker. To conclude, although anomaly detection has been a lasting yet active research area for several decades, there are still some unique problem complexities and challenges that leave the way open for the proposed solution to be further improved.Doble Grado en Ingeniería Informática y Administración de Empresa

Machine Learning Methods To Identify Hidden Phenotypes In The Electronic Health Record

The widespread adoption of Electronic Health Records (EHRs) means an unprecedented amount of patient treatment and outcome data is available to researchers. Research is a tertiary priority in the EHR, where the priorities are patient care and billing. Because of this, the data is not standardized or formatted in a manner easily adapted to machine learning approaches. Data may be missing for a large variety of reasons ranging from individual input styles to differences in clinical decision making, for example, which lab tests to issue. Few patients are annotated at a research quality, limiting sample size and presenting a moving gold standard. Patient progression over time is key to understanding many diseases but many machine learning algorithms require a snapshot, at a single time point, to create a usable vector form. In this dissertation, we develop new machine learning methods and computational workflows to extract hidden phenotypes from the Electronic Health Record (EHR). In Part 1, we use a semi-supervised deep learning approach to compensate for the low number of research quality labels present in the EHR. In Part 2, we examine and provide recommendations for characterizing and managing the large amount of missing data inherent to EHR data. In Part 3, we present an adversarial approach to generate synthetic data that closely resembles the original data while protecting subject privacy. We also introduce a workflow to enable reproducible research even when data cannot be shared. In Part 4, we introduce a novel strategy to first extract sequential data from the EHR and then demonstrate the ability to model these sequences with deep learning

European Language Grid

This open access book provides an in-depth description of the EU project European Language Grid (ELG). Its motivation lies in the fact that Europe is a multilingual society with 24 official European Union Member State languages and dozens of additional languages including regional and minority languages. The only meaningful way to enable multilingualism and to benefit from this rich linguistic heritage is through Language Technologies (LT) including Natural Language Processing (NLP), Natural Language Understanding (NLU), Speech Technologies and language-centric Artificial Intelligence (AI) applications. The European Language Grid provides a single umbrella platform for the European LT community, including research and industry, effectively functioning as a virtual home, marketplace, showroom, and deployment centre for all services, tools, resources, products and organisations active in the field. Today the ELG cloud platform already offers access to more than 13,000 language processing tools and language resources. It enables all stakeholders to deposit, upload and deploy their technologies and datasets. The platform also supports the long-term objective of establishing digital language equality in Europe by 2030 – to create a situation in which all European languages enjoy equal technological support. This is the very first book dedicated to Language Technology and NLP platforms. Cloud technology has only recently matured enough to make the development of a platform like ELG feasible on a larger scale. The book comprehensively describes the results of the ELG project. Following an introduction, the content is divided into four main parts: (I) ELG Cloud Platform; (II) ELG Inventory of Technologies and Resources; (III) ELG Community and Initiative; and (IV) ELG Open Calls and Pilot Projects

European Language Grid

This open access book provides an in-depth description of the EU project European Language Grid (ELG). Its motivation lies in the fact that Europe is a multilingual society with 24 official European Union Member State languages and dozens of additional languages including regional and minority languages. The only meaningful way to enable multilingualism and to benefit from this rich linguistic heritage is through Language Technologies (LT) including Natural Language Processing (NLP), Natural Language Understanding (NLU), Speech Technologies and language-centric Artificial Intelligence (AI) applications. The European Language Grid provides a single umbrella platform for the European LT community, including research and industry, effectively functioning as a virtual home, marketplace, showroom, and deployment centre for all services, tools, resources, products and organisations active in the field. Today the ELG cloud platform already offers access to more than 13,000 language processing tools and language resources. It enables all stakeholders to deposit, upload and deploy their technologies and datasets. The platform also supports the long-term objective of establishing digital language equality in Europe by 2030 – to create a situation in which all European languages enjoy equal technological support. This is the very first book dedicated to Language Technology and NLP platforms. Cloud technology has only recently matured enough to make the development of a platform like ELG feasible on a larger scale. The book comprehensively describes the results of the ELG project. Following an introduction, the content is divided into four main parts: (I) ELG Cloud Platform; (II) ELG Inventory of Technologies and Resources; (III) ELG Community and Initiative; and (IV) ELG Open Calls and Pilot Projects

Cross-Platform Text Mining and Natural Language Processing Interoperability - Proceedings of the LREC2016 conference

No abstract available

Continuous Rationale Management

Continuous Software Engineering (CSE) is a software life cycle model open to frequent changes in requirements or technology. During CSE, software developers continuously make decisions on the requirements and design of the software or the development process. They establish essential decision knowledge, which they need to document and share so that it supports the evolution and changes of the software. The management of decision knowledge is called rationale management. Rationale management provides an opportunity to support the change process during CSE. However, rationale management is not well integrated into CSE. The overall goal of this dissertation is to provide workflows and tool support for continuous rationale management. The dissertation contributes an interview study with practitioners from the industry, which investigates rationale management problems, current practices, and features to support continuous rationale management beneficial for practitioners. Problems of rationale management in practice are threefold: First, documenting decision knowledge is intrusive in the development process and an additional effort. Second, the high amount of distributed decision knowledge documentation is difficult to access and use. Third, the documented knowledge can be of low quality, e.g., outdated, which impedes its use. The dissertation contributes a systematic mapping study on recommendation and classification approaches to treat the rationale management problems. The major contribution of this dissertation is a validated approach for continuous rationale management consisting of the ConRat life cycle model extension and the comprehensive ConDec tool support. To reduce intrusiveness and additional effort, ConRat integrates rationale management activities into existing workflows, such as requirements elicitation, development, and meetings. ConDec integrates into standard development tools instead of providing a separate tool. ConDec enables lightweight capturing and use of decision knowledge from various artifacts and reduces the developers' effort through automatic text classification, recommendation, and nudging mechanisms for rationale management. To enable access and use of distributed decision knowledge documentation, ConRat defines a knowledge model of decision knowledge and other artifacts. ConDec instantiates the model as a knowledge graph and offers interactive knowledge views with useful tailoring, e.g., transitive linking. To operationalize high quality, ConRat introduces the rationale backlog, the definition of done for knowledge documentation, and metrics for intra-rationale completeness and decision coverage of requirements and code. ConDec implements these agile concepts for rationale management and a knowledge dashboard. ConDec also supports consistent changes through change impact analysis. The dissertation shows the feasibility, effectiveness, and user acceptance of ConRat and ConDec in six case study projects in an industrial setting. Besides, it comprehensively analyses the rationale documentation created in the projects. The validation indicates that ConRat and ConDec benefit CSE projects. Based on the dissertation, continuous rationale management should become a standard part of CSE, like automated testing or continuous integration

Cross-Platform Text Mining and Natural Language Processing Interoperability - Proceedings of the LREC2016 conference

No abstract available

Algorithms in future capital markets: A survey on AI, ML and associated algorithms in capital markets

This paper reviews Artificial Intelligence (AI), Machine Learning (ML) and associated algorithms in future Capital Markets. New AI algorithms are constantly emerging, with each 'strain' mimicking a new form of human learning, reasoning, knowledge, and decisionmaking. The current main disrupting forms of learning include Deep Learning, Adversarial Learning, Transfer and Meta Learning. Albeit these modes of learning have been in the AI/ML field more than a decade, they now are more applicable due to the availability of data, computing power and infrastructure. These forms of learning have produced new models (e.g., Long Short-Term Memory, Generative Adversarial Networks) and leverage important applications (e.g., Natural Language Processing, Adversarial Examples, Deep Fakes, etc.). These new models and applications will drive changes in future Capital Markets, so it is important to understand their computational strengths and weaknesses. Since ML algorithms effectively self-program and evolve dynamically, financial institutions and regulators are becoming increasingly concerned with ensuring there remains a modicum of human control, focusing on Algorithmic Interpretability/Explainability, Robustness and Legality. For example, the concern is that, in the future, an ecology of trading algorithms across different institutions may 'conspire' and become unintentionally fraudulent (cf. LIBOR) or subject to subversion through compromised datasets (e.g. Microsoft Tay). New and unique forms of systemic risks can emerge, potentially coming from excessive algorithmic complexity. The contribution of this paper is to review AI, ML and associated algorithms, their computational strengths and weaknesses, and discuss their future impact on the Capital Markets