3,336 research outputs found
Text mining and natural language processing for the early stages of space mission design
Final thesis submitted December 2021 - degree awarded in 2022A considerable amount of data related to space mission design has been accumulated
since artificial satellites started to venture into space in the 1950s. This data has today
become an overwhelming volume of information, triggering a significant knowledge
reuse bottleneck at the early stages of space mission design. Meanwhile, virtual assistants,
text mining and Natural Language Processing techniques have become pervasive
to our daily life.
The work presented in this thesis is one of the first attempts to bridge the gap
between the worlds of space systems engineering and text mining. Several novel models
are thus developed and implemented here, targeting the structuring of accumulated
data through an ontology, but also tasks commonly performed by systems engineers
such as requirement management and heritage analysis. A first collection of documents
related to space systems is gathered for the training of these methods. Eventually, this
work aims to pave the way towards the development of a Design Engineering Assistant
(DEA) for the early stages of space mission design. It is also hoped that this work will
actively contribute to the integration of text mining and Natural Language Processing
methods in the field of space mission design, enhancing current design processes.A considerable amount of data related to space mission design has been accumulated
since artificial satellites started to venture into space in the 1950s. This data has today
become an overwhelming volume of information, triggering a significant knowledge
reuse bottleneck at the early stages of space mission design. Meanwhile, virtual assistants,
text mining and Natural Language Processing techniques have become pervasive
to our daily life.
The work presented in this thesis is one of the first attempts to bridge the gap
between the worlds of space systems engineering and text mining. Several novel models
are thus developed and implemented here, targeting the structuring of accumulated
data through an ontology, but also tasks commonly performed by systems engineers
such as requirement management and heritage analysis. A first collection of documents
related to space systems is gathered for the training of these methods. Eventually, this
work aims to pave the way towards the development of a Design Engineering Assistant
(DEA) for the early stages of space mission design. It is also hoped that this work will
actively contribute to the integration of text mining and Natural Language Processing
methods in the field of space mission design, enhancing current design processes
A knowledge-based approach towards human activity recognition in smart environments
For many years it is known that the population of older persons is on the rise. A recent report estimates that globally, the share of the population aged 65 years or over is expected to increase from 9.3 percent in 2020 to around 16.0 percent in 2050 [1]. This point has been one of the main sources of motivation for active research in the domain of human
activity recognition in smart-homes. The ability to perform ADL without assistance from
other people can be considered as a reference for the estimation of the independent living
level of the older person. Conventionally, this has been assessed by health-care domain
experts via a qualitative evaluation of the ADL. Since this evaluation is qualitative, it can
vary based on the person being monitored and the caregiver\u2019s experience. A significant
amount of research work is implicitly or explicitly aimed at augmenting the health-care
domain expert\u2019s qualitative evaluation with quantitative data or knowledge obtained from
HAR. From a medical perspective, there is a lack of evidence about the technology readiness
level of smart home architectures supporting older persons by recognizing ADL [2]. We
hypothesize that this may be due to a lack of effective collaboration between smart-home
researchers/developers and health-care domain experts, especially when considering HAR.
We foresee an increase in HAR systems being developed in close collaboration with caregivers
and geriatricians to support their qualitative evaluation of ADL with explainable quantitative
outcomes of the HAR systems. This has been a motivation for the work in this thesis. The
recognition of human activities \u2013 in particular ADL \u2013 may not only be limited to support
the health and well-being of older people. It can be relevant to home users in general. For
instance, HAR could support digital assistants or companion robots to provide contextually
relevant and proactive support to the home users, whether young adults or old. This has also
been a motivation for the work in this thesis.
Given our motivations, namely, (i) facilitation of iterative development and ease in collaboration between HAR system researchers/developers and health-care domain experts in ADL,
and (ii) robust HAR that can support digital assistants or companion robots. There is a need
for the development of a HAR framework that at its core is modular and flexible to facilitate
an iterative development process [3], which is an integral part of collaborative work that involves develop-test-improve phases. At the same time, the framework should be intelligible
for the sake of enriched collaboration with health-care domain experts. Furthermore, it
should be scalable, online, and accurate for having robust HAR, which can enable many
smart-home applications. The goal of this thesis is to design and evaluate such a framework.
This thesis contributes to the domain of HAR in smart-homes. Particularly the contribution can be divided into three parts. The first contribution is Arianna+, a framework to develop
networks of ontologies - for knowledge representation and reasoning - that enables smart
homes to perform human activity recognition online. The second contribution is OWLOOP,
an API that supports the development of HAR system architectures based on Arianna+. It
enables the usage of Ontology Web Language (OWL) by the means of Object-Oriented
Programming (OOP). The third contribution is the evaluation and exploitation of Arianna+
using OWLOOP API. The exploitation of Arianna+ using OWLOOP API has resulted in four
HAR system implementations. The evaluations and results of these HAR systems emphasize
the novelty of Arianna+
Efficient and Effective Event Pattern Management
The goal of this thesis is to reduce the barriers stopping more enterprises from accessing CEP technology by providing additional support in managing relevant business situations. Therefore we outline the role of event pattern management and present a methodology, methods and tools aiming at an efficient and effective event pattern management. We provide a meta model for event patterns, an event pattern life cycle methodology, methods for guidance, refinement and evolution
Semantic Knowledge Graphs for the News: A Review
ICT platforms for news production, distribution, and consumption must exploit the ever-growing availability of digital data. These data originate from different sources and in different formats; they arrive at different velocities and in different volumes. Semantic knowledge graphs (KGs) is an established technique for integrating such heterogeneous information. It is therefore well-aligned with the needs of news producers and distributors, and it is likely to become increasingly important for the news industry. This article reviews the research on using semantic knowledge graphs for production, distribution, and consumption of news. The purpose is to present an overview of the field; to investigate what it means; and to suggest opportunities and needs for further research and development.publishedVersio
Can Population-based Engagement Improve Personalisation? A Novel Dataset and Experiments
This work explores how population-based engagement prediction can address cold-start at scale in large learning resource collections. This paper introduces i) VLE, a novel dataset that consists of content and video based features extracted from publicly available scientific video lectures coupled with implicit and explicit signals related to learner engagement, ii) two standard tasks related to predicting and ranking context-agnostic engagement in video lectures with preliminary baselines and iii) a set of experiments that validate the usefulness of the proposed dataset. Our experimental results indicate that the newly proposed VLE dataset leads to building context-agnostic engagement prediction models that are significantly performant than ones based on previous datasets, mainly attributing to the increase of training examples. VLE dataset’s suitability in building models towards Computer Science/ Artificial Intelligence education focused on e-learning/ MOOC use-cases is also evidenced. Further experiments in combining the built model with a personalising algorithm show promising improvements in addressing the cold-start problem encountered in educational recommenders. This is the largest and most diverse publicly available dataset to our knowledge that deals with learner engagement prediction tasks. The dataset, helper tools, descriptive statistics and example code snippets are available publicly
Language modelling for clinical natural language understanding and generation
One of the long-standing objectives of Artificial Intelligence (AI) is to design and develop algorithms for social good including tackling public health challenges. In the era of digitisation, with an unprecedented amount of healthcare data being captured in digital form, the analysis of the healthcare data at scale can lead to better research of diseases, better monitoring patient conditions and more importantly improving patient outcomes. However, many AI-based analytic algorithms rely solely on structured healthcare data such as bedside measurements and test results which only account for 20% of all healthcare data, whereas the remaining 80% of healthcare data is unstructured including textual data such as clinical notes and discharge summaries which is still underexplored.
Conventional Natural Language Processing (NLP) algorithms that are designed for clinical applications rely on the shallow matching, templates and non-contextualised word embeddings which lead to limited understanding of contextual semantics. Though recent advances in NLP algorithms have demonstrated promising performance on a variety of NLP tasks in the general domain with contextualised language models, most of these generic NLP algorithms struggle at specific clinical NLP tasks which require biomedical knowledge and reasoning. Besides, there is limited research to study generative NLP algorithms to generate clinical reports and summaries automatically by considering salient clinical information.
This thesis aims to design and develop novel NLP algorithms especially clinical-driven contextualised language models to understand textual healthcare data and generate clinical narratives which can potentially support clinicians, medical scientists and patients. The first contribution of this thesis focuses on capturing phenotypic information of patients from clinical notes which is important to profile patient situation and improve patient outcomes. The thesis proposes a novel self-supervised language model, named Phenotypic Intelligence Extraction (PIE), to annotate phenotypes from clinical notes with the detection of contextual synonyms and the enhancement to reason with numerical values. The second contribution is to demonstrate the utility and benefits of using phenotypic features of patients in clinical use cases by predicting patient outcomes in Intensive Care Units (ICU) and identifying patients at risk of specific diseases with better accuracy and model interpretability. The third contribution is to propose generative models to generate clinical narratives to automate and accelerate the process of report writing and summarisation by clinicians. This thesis first proposes a novel summarisation language model named PEGASUS which surpasses or is on par with the state-of-the-art performance on 12 downstream datasets including biomedical literature from PubMed. PEGASUS is further extended to generate medical scientific documents from input tabular data.Open Acces
Designing for practice-based context-awareness in ubiquitous e-health environments
Existing approaches for supporting context-aware knowledge sharing in ubiquitous healthcare give little attention to practice-based structures of knowledge representation. They guide knowledge re-use at an abstract level and hardly incorporate details of actionable tasks and processes necessary for accomplishing work in a real-world context. This paper presents a context-aware model for supporting clinical knowledge sharing across organizational and geographical boundaries in ubiquitous e-health. The model draws on activity and situation awareness theories as well as the Belief-Desire Intention and Case-based Reasoning techniques in intelligent systems with the goal of enabling clinicians in disparate locations to gain a common representation of relevant situational information in each other's work contexts based on the notion of practice. We discuss the conceptual design of the model, present a formal approach for representing practice as context in a ubiquitous healthcare environment, and describe an application scenario and a prototype system to evaluate the proposed approach
Maintaining Structured Experiences for Robots via Human Demonstrations: An Architecture To Convey Long-Term Robot\u2019s Beliefs
This PhD thesis presents an architecture for structuring experiences, learned through demonstrations, in a robot memory. To test our architecture, we consider a specific application where a robot learns how
objects are spatially arranged in a tabletop scenario.
We use this application as a mean to present a few software development guidelines for building architecture for similar scenarios, where a robot is able to interact with a user through a qualitative shared knowledge stored in its memory. In particular, the thesis proposes a novel technique for deploying ontologies in a robotic architecture based on semantic interfaces. To better support those interfaces, it also presents general-purpose tools especially designed for an iterative development process, which is suitable for Human-Robot Interaction scenarios.
We considered ourselves at the beginning of the first iteration of the design process, and our objective was to build a flexible architecture through which evaluate different heuristic during further development
iterations.
Our architecture is based on a novel algorithm performing a oneshot structured learning based on logic formalism. We used a fuzzy ontology for dealing with uncertain environments, and we integrated the algorithm in the architecture based on a specific semantic interface.
The algorithm is used for building experience graphs encoded in the robot\u2019s memory that can be used for recognising and associating situations after a knowledge bootstrapping phase. During this phase, a user is supposed to teach and supervise the beliefs of the robot through multimodal, not physical, interactions. We used the algorithm to implement a cognitive like memory involving the encoding, storing, retrieving, consolidating, and forgetting behaviours, and we showed that our flexible design pattern could be used for building architectures where contextualised memories are managed with different purposes, i.e. they contains representation of the same experience encoded with different semantics.
The proposed architecture has the main purposes of generating and maintaining knowledge in memory, but it can be directly interfaced with perceiving and acting components if they provide, or require, symbolical knowledge. With the purposes of showing the type of data considered as inputs and outputs in our tests, this thesis also presents components to evaluate point clouds, engage dialogues, perform late
data fusion and simulate the search of a target position. Nevertheless, our design pattern is not meant to be coupled only with those components, which indeed have a large room of improvement
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