5,036 research outputs found
Contested environmental futures: rankings, forecasts and indicators as sociotechnical endeavours
In a world where numbers and science are often taken as the voice of truth and reason, Quantitative Devices (QDs) represent the epitome of policy driven by facts rather than hunches. Despite the scholarly interest in understanding the role of quantification in policy, the actual production of rankings, forecasts, indexes and other QDs has, to a great extent, been left unattended. While appendixes and technical notebooks offer an explanation of how these devices are produced, they exclude aspects of their making that are arbitrarily considered "mundane." It is in the everyday performances at research centres that the micropolitics of knowledge production, imaginaries, and frustrations merge. These are vital dimensions to understand the potential, limitations and ethical consequences of QDs.
Using two participant observations as the starting point, this thesis offers a comprehensive critical analysis of the processes through which university-based research centres create QDs that represent the world. It addresses how researchers conceive quantitative data. It pays attention to the discourses of hope and expectation embedded in the devices. Finally, it considers the ethics of creating devices that cannot be replicated independently of their place of production.
Two QDs were analysed: the Violence Early Warning System (ViEWS) and the Environmental Performance Index (EPI). At Uppsala University, researchers created ViEWS to forecast the probability of drought-driven conflicts within the next 100 years. The EPI, produced at the Yale Centre for Environmental Law and Policy, ranks the performance of countries' environmental policies. This thesis challenges existing claims within Science and Technology Studies and the Sociology of Quantification that QDs co-produce knowledge within their realms. I argue that these devices act as vehicles for sociotechnical infrastructures to be consolidated with little debate among policymakers, given their understanding as scientific and objective tools. Moreover, for an indicator to be incorporated within a QD, it needs to be deemed as relevant for those making the devices but also valuable enough to have been previously quantified by data providers. Even more, existing sociotechnical inequalities, power relations and epistemic injustices could impede disadvantaged communities' (e.g., in the Global South) ability to challenge metrics originated in centres in the Global North. This thesis, therefore, demonstrates how the future QDs propose is unilateral and does not acknowledge the myriad possibilities that might arise from a diversity of worldviews. In other words, they cast a future designed to fit under the current status quo.
In sum, through two QDs focused on environmental-related, this thesis launches an inquiry into the elements that make up the imaginaries they propose following the everyday life of their producers. To achieve this, I discuss two core elements. First, the role of tacit knowledge and sociotechnical inequalities in reinforcing power relations between those with the means to quantify and those who might only accommodate proposed futures. Second, the dynamics between research centres and data providers in relation to what is quantified. By scrutinising mundanity, this work is a step forward in understanding the construction of sociotechnical imaginaries and infrastructures
Modelling, Monitoring, Control and Optimization for Complex Industrial Processes
This reprint includes 22 research papers and an editorial, collected from the Special Issue "Modelling, Monitoring, Control and Optimization for Complex Industrial Processes", highlighting recent research advances and emerging research directions in complex industrial processes. This reprint aims to promote the research field and benefit the readers from both academic communities and industrial sectors
Inclusive Intelligent Learning Management System Framework - Application of Data Science in Inclusive Education
Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceBeing a disabled student the author faced higher education with a handicap which as experience
studying during COVID 19 confinement periods matched the findings in recent research about the
importance of digital accessibility through more e-learning intensive academic experiences. Narrative
and systematic literature reviews enabled providing context in World Health Organization’s
International Classification of Functioning, Disability and Health, legal and standards framework and
information technology and communication state-of-the art. Assessing Portuguese higher education
institutions’ web sites alerted to the fact that only outlying institutions implemented near perfect,
accessibility-wise, websites.
Therefore a gap was identified in how accessible the Portuguese higher education websites are, the
needs of all students, including those with disabilities, and even the accessibility minimum legal
requirements for digital products and the services provided by public or publicly funded organizations.
Having identified a problem in society and exploring the scientific base of knowledge for context and
state of the art was a first stage in the Design Science Research methodology, to which followed
development and validation cycles of an Inclusive Intelligent Learning Management System
Framework. The framework blends various Data Science study fields contributions with accessibility
guidelines compliant interface design and content upload accessibility compliance assessment.
Validation was provided by a focus group whose inputs were considered for the version presented in
this dissertation. Not being the purpose of the research to deliver a complete implementation of the
framework and lacking consistent data to put all the modules interacting with each other, the most
relevant modules were tested with open data as proof of concept.
The rigor cycle of DSR started with the inclusion of the previous thesis on Atlântica University Institute
Scientific Repository and is to be completed with the publication of this thesis and the already started
PhD’s findings in relevant journals and conferences
Designing similarity functions
The concept of similarity is important in many areas of cognitive science, computer science, and statistics. In machine learning, functions that measure similarity between two instances form the core of instance-based classifiers. Past similarity measures have been primarily based on simple Euclidean distance. As machine learning has matured, it has become obvious that a simple numeric instance representation is insufficient for most domains. Similarity functions for symbolic attributes have been developed, and simple methods for combining these functions with numeric similarity functions were devised. This sequence of events has revealed three important issues, which this thesis addresses.
The first issue is concerned with combining multiple measures of similarity. There is no equivalence between units of numeric similarity and units of symbolic similarity. Existing similarity functions for numeric and symbolic attributes have no common foundation, and so various schemes have been devised to avoid biasing the overall similarity towards one type of attribute. The similarity function design framework proposed by this thesis produces probability distributions that describe the likelihood of transforming between two attribute values. Because common units of probability are employed, similarities may be combined using standard methods. It is empirically shown that the resulting similarity functions treat different attribute types coherently.
The second issue relates to the instance representation itself. The current choice of numeric and symbolic attribute types is insufficient for many domains, in which more complicated representations are required. For example, a domain may require varying numbers of features, or features with structural information. The framework proposed by this thesis is sufficiently general to permit virtually any type of instance representation-all that is required is that a set of basic transformations that operate on the instances be defined. To illustrate the framework’s applicability to different instance representations, several example similarity functions are developed.
The third, and perhaps most important, issue concerns the ability to incorporate domain knowledge within similarity functions. Domain information plays an important part in choosing an instance representation. However, even given an adequate instance representation, domain information is often lost. For example, numeric features that are modulo (such as the time of day) can be perfectly represented as a numeric attribute, but simple linear similarity functions ignore the modulo nature of the attribute. Similarly, symbolic attributes may have inter-symbol relationships that should be captured in the similarity function. The design framework proposed by this thesis allows domain information to be captured in the similarity function, both in the transformation model and in the probability assigned to basic transformations. Empirical results indicate that such domain information improves classifier performance, particularly when training data is limited
On noise, uncertainty and inference for computational diffusion MRI
Diffusion Magnetic Resonance Imaging (dMRI) has revolutionised the way brain microstructure and connectivity can be studied. Despite its unique potential in mapping the whole brain, biophysical properties are inferred from measurements rather than being directly observed. This indirect mapping from noisy data creates challenges and introduces uncertainty in the estimated properties. Hence, dMRI frameworks capable to deal with noise and uncertainty quantification are of great importance and are the topic of this thesis.
First, we look into approaches for reducing uncertainty, by de-noising the dMRI signal. Thermal noise can have detrimental effects for modalities where the information resides in the signal attenuation, such as dMRI, that has inherently low-SNR data. We highlight the dual effect of noise, both in increasing variance, but also introducing bias. We then design a framework for evaluating denoising approaches in a principled manner. By setting objective criteria based on what a well-behaved denoising algorithm should offer, we provide a bespoke dataset and a set of evaluations. We demonstrate that common magnitude-based denoising approaches usually reduce noise-related variance from the signal, but do not address the bias effects introduced by the noise floor. Our framework also allows to better characterise scenarios where denoising can be beneficial (e.g. when done in complex domain) and can open new opportunities, such as pushing spatio-temporal resolution boundaries.
Subsequently, we look into approaches for mapping uncertainty and design two inference frameworks for dMRI models, one using classical Bayesian methods and another using more recent data-driven algorithms. In the first approach, we build upon the univariate random-walk Metropolis-Hastings MCMC, an extensively used sampling method to sample from the posterior distribution of model parameters given the data. We devise an efficient adaptive multivariate MCMC scheme, relying upon the assumption that groups of model parameters can be jointly estimated if a proper covariance matrix is defined. In doing so, our algorithm increases the sampling efficiency, while preserving accuracy and precision of estimates. We show results using both synthetic and in-vivo dMRI data.
In the second approach, we resort to Simulation-Based Inference (SBI), a data-driven approach that avoids the need for iterative model inversions. This is achieved by using neural density estimators to learn the inverse mapping from the forward generative process (simulations) to the parameters of interest that have generated those simulations. By addressing the problem via learning approaches offers the opportunity to achieve inference amortisation, boosting efficiency by avoiding the necessity of repeating the inference process for each new unseen dataset. It also allows inversion of forward processes (i.e. a series of processing steps) rather than only models. We explore different neural network architectures to perform conditional density estimation of the posterior distribution of parameters. Results and comparisons obtained against MCMC suggest speed-ups of 2-3 orders of magnitude in the inference process while keeping the accuracy in the estimates
Development of linguistic linked open data resources for collaborative data-intensive research in the language sciences
Making diverse data in linguistics and the language sciences open, distributed, and accessible: perspectives from language/language acquistiion researchers and technical LOD (linked open data) researchers. This volume examines the challenges inherent in making diverse data in linguistics and the language sciences open, distributed, integrated, and accessible, thus fostering wide data sharing and collaboration. It is unique in integrating the perspectives of language researchers and technical LOD (linked open data) researchers. Reporting on both active research needs in the field of language acquisition and technical advances in the development of data interoperability, the book demonstrates the advantages of an international infrastructure for scholarship in the field of language sciences. With contributions by researchers who produce complex data content and scholars involved in both the technology and the conceptual foundations of LLOD (linguistics linked open data), the book focuses on the area of language acquisition because it involves complex and diverse data sets, cross-linguistic analyses, and urgent collaborative research. The contributors discuss a variety of research methods, resources, and infrastructures. Contributors Isabelle Barrière, Nan Bernstein Ratner, Steven Bird, Maria Blume, Ted Caldwell, Christian Chiarcos, Cristina Dye, Suzanne Flynn, Claire Foley, Nancy Ide, Carissa Kang, D. Terence Langendoen, Barbara Lust, Brian MacWhinney, Jonathan Masci, Steven Moran, Antonio Pareja-Lora, Jim Reidy, Oya Y. Rieger, Gary F. Simons, Thorsten Trippel, Kara Warburton, Sue Ellen Wright, Claus Zin
LegalBench: A Collaboratively Built Benchmark for Measuring Legal Reasoning in Large Language Models
The advent of large language models (LLMs) and their adoption by the legal
community has given rise to the question: what types of legal reasoning can
LLMs perform? To enable greater study of this question, we present LegalBench:
a collaboratively constructed legal reasoning benchmark consisting of 162 tasks
covering six different types of legal reasoning. LegalBench was built through
an interdisciplinary process, in which we collected tasks designed and
hand-crafted by legal professionals. Because these subject matter experts took
a leading role in construction, tasks either measure legal reasoning
capabilities that are practically useful, or measure reasoning skills that
lawyers find interesting. To enable cross-disciplinary conversations about LLMs
in the law, we additionally show how popular legal frameworks for describing
legal reasoning -- which distinguish between its many forms -- correspond to
LegalBench tasks, thus giving lawyers and LLM developers a common vocabulary.
This paper describes LegalBench, presents an empirical evaluation of 20
open-source and commercial LLMs, and illustrates the types of research
explorations LegalBench enables.Comment: 143 pages, 79 tables, 4 figure
Distributed Spatial Data Sharing: a new era in sharing spatial data
The advancements in information and communications technology, including the widespread adoption of GPS-based sensors, improvements in computational data processing, and satellite imagery, have resulted in new data sources, stakeholders, and methods of producing, using, and sharing spatial data. Daily, vast amounts of data are produced by individuals interacting with digital content and through automated and semi-automated sensors deployed across the environment. A growing portion of this information contains geographic information directly or indirectly embedded within it. The widespread use of automated smart sensors and an increased variety of georeferenced media resulted in new individual data collectors. This raises a new set of social concerns around individual geopricacy and data ownership. These changes require new approaches to managing, sharing, and processing geographic data. With the appearance of distributed data-sharing technologies, some of these challenges may be addressed. This can be achieved by moving from centralized control and ownership of the data to a more distributed system. In such a system, the individuals are responsible for gathering and controlling access and storing data. Stepping into the new area of distributed spatial data sharing needs preparations, including developing tools and algorithms to work with spatial data in this new environment efficiently. Peer-to-peer (P2P) networks have become very popular for storing and sharing information in a decentralized approach. However, these networks lack the methods to process spatio-temporal queries. During the first chapter of this research, we propose a new spatio-temporal multi-level tree structure, Distributed Spatio-Temporal Tree (DSTree), which aims to address this problem. DSTree is capable of performing a range of spatio-temporal queries. We also propose a framework that uses blockchain to share a DSTree on the distributed network, and each user can replicate, query, or update it. Next, we proposed a dynamic k-anonymity algorithm to address geoprivacy concerns in distributed platforms. Individual dynamic control of geoprivacy is one of the primary purposes of the proposed framework introduced in this research. Sharing data within and between organizations can be enhanced by greater trust and transparency offered by distributed or decentralized technologies. Rather than depending on a central authority to manage geographic data, a decentralized framework would provide a fine-grained and transparent sharing capability. Users can also control the precision of shared spatial data with others. They are not limited to third-party algorithms to decide their privacy level and are also not limited to the binary levels of location sharing. As mentioned earlier, individuals and communities can benefit from distributed spatial data sharing. During the last chapter of this work, we develop an image-sharing platform, aka harvester safety application, for the Kakisa indigenous community in northern Canada. During this project, we investigate the potential of using a Distributed Spatial Data sharing (DSDS) infrastructure for small-scale data-sharing needs in indigenous communities. We explored the potential use case and challenges and proposed a DSDS architecture to allow users in small communities to share and query their data using DSDS. Looking at the current availability of distributed tools, the sustainable development of such applications needs accessible technology. We need easy-to-use tools to use distributed technologies on community-scale SDS. In conclusion, distributed technology is in its early stages and requires easy-to-use tools/methods and algorithms to handle, share and query geographic information. Once developed, it will be possible to contrast DSDS against other data systems and thereby evaluate the practical benefit of such systems. A distributed data-sharing platform needs a standard framework to share data between different entities. Just like the first decades of the appearance of the web, these tools need regulations and standards. Such can benefit individuals and small communities in the current chaotic spatial data-sharing environment controlled by the central bodies
New Pathways to support social-ecological Systems in Change
Klimawandel und Biodiversitätsverlust sowie Verstädterung und demografischer Wandel haben tiefgreifende Auswirkungen auf Städte und ihre Ökosysteme und damit auf die Lebensbedingungen der Mehrheit der Menschheit. Die Geschwindigkeit des Wandels und die Dringlichkeit der Folgen macht Umweltmonitoring zu einem potentiell interessanten Tool für nachhaltige und resiliente Stadtentwicklung. Der erste Artikel gibt einen Überblick über den aktuellen Stand der Fernerkundung in Bezug auf Stadtökologie und zeigt, dass Fernerkundung relevant für nachhaltige Stadtplanung ist. Es bestehen jedoch bestehen Mängel, da viele Studien nicht direkt umsetzbar sind. Der zweite Artikel zeigt, dass eine wachsende Stadt Möglichkeiten für den Ausbau der grünen Infrastruktur bieten kann. Im dritten Artikel wird untersucht, wie sich die städtische Dichte auf die Bereitstellung von Ökosystemdienstleistungen der grünen Infrastruktur auswirkt. Es wird gezeigt, dass eine hohe Siedlungsdichte nicht zwangsläufig zu einem geringeren Biodiversitätspotenzial oder einer geringeren Kühlkapazität führt. Allerdings sind dicht bebaute Gebiete mit geringer Vegetationsbedeckung besonders auf grüne Infrastruktur angewiesen. Der vierte Artikel befasst sich mit der Frage, wie naturbasierte Lösungen durch eine bessere Vernetzung der Beteiligten gestärkt werden können. Auf der Grundlage einer gezielten Literaturrecherche über Informationstechnologie zur Unterstützung sozial-ökologischer Systeme wird ein Instrument zur Entscheidungshilfe entwickelt. Dieses kombiniert ökologische und soziale Indikatoren, um Klimawandeladaption in Übereinstimmung mit den sozio-ökologischen Bedingungen entwickeln zu können. Der fünfte Artikel bietet eine grundsätzliche Perspektive zur Unterstützung der städtischen Nachhaltigkeit, die auf dem ökologischen-Trait Konzept basiert. Zusammen bieten die fünf Artikel Wege für die Fernerkundungswissenschaft und die angewandte Raumplanung für nachhaltige und resiliente Entwicklungen in Städten.Climate change and biodiversity loss, as well as urbanisation and demographic change, are major global challenges of the 21st century. These trends have profound impacts on cities and their ecosystems and thus on the living conditions of the majority of humanity. This raises the need for timely environmental monitoring supporting sustainable and resilient urban developments. The first article is an overview of the state of the art of remote sensing science in relation to urban ecology. The review found that remote sensing can contribute to sustainable urban policy, still insufficiencies remain as many studies are not directly actionable. The second article shows that a growing city can provide opportunities for an increase in green infrastructure. Here, remote sensing is used for long-term analysis of land-use in relation to urban forms in Berlin. The third article examines how urban density affects ecosystem service provision of urban green infrastructure. It is shown that residential density does not necessarily lead to poor biodiversity potential or cooling capacity. However, dense areas with low vegetation cover are particularly dependent on major green infrastructure. The fourth article explores ways to reinforce nature-based solutions by better connecting and informing stakeholders. Based on a focussed literature review on information technology supporting urban social-ecological systems, a decision support tool is developed. The tool combines indicators based on ecological diversity and performance with population density and vulnerability. This way, climate change adaptation can be developed in accordance with socio-ecological conditions. The concluding fifth article offers an outlook on a larger framework in support of urban sustainability, based on the ecological trait concept. Together the five research papers provide pathways for urban remote sensing science and applied spatial planning that can support sustainable and resilient developments in cities
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