The Impact of Data Sovereignty on American Indian Self-Determination: A Framework Proof of Concept Using Data Science

The Data Sovereignty Initiative is a collection of ideas that was designed to create SMART solutions for tribal communities. This concept was to develop a horizontal governance framework to create a strategic act of sovereignty using data science. The core concept of this idea was to present data sovereignty as a way for tribal communities to take ownership of data in order to affect policy and strategic decisions that are data driven in nature. The case studies in this manuscript were developed around statistical theories of spatial statistics, exploratory data analysis, and machine learning. And although these case studies are first, scientific in nature, the data sovereignty framework was designed around these concepts to leverage nation building, cultural capital, and citizen science for economic development and planning. The data sovereignty framework is a flexible way to create data domains, around developed key indicators to integrate appropriate cultural capital when working with Native nations. This design is intended to put scientific theory into practice to affect everyday outcomes using data driven decision making. This framework is a proof concept and represents both applied and theoretical metrics in design strength

Tackling Uncertainties and Errors in the Satellite Monitoring of Forest Cover Change

This study aims at improving the reliability of automatic forest change detection. Forest change detection is of vital importance for understanding global land cover as well as the carbon cycle. Remote sensing and machine learning have been widely adopted for such studies with increasing degrees of success. However, contemporary global studies still suffer from lower-than-satisfactory accuracies and robustness problems whose causes were largely unknown. Global geographical observations are complex, as a result of the hidden interweaving geographical processes. Is it possible that some geographical complexities were not expected in contemporary machine learning? Could they cause uncertainties and errors when contemporary machine learning theories are applied for remote sensing? This dissertation adopts the philosophy of error elimination. We start by explaining the mathematical origins of possible geographic uncertainties and errors in chapter two. Uncertainties are unavoidable but might be mitigated. Errors are hidden but might be found and corrected. Then in chapter three, experiments are specifically designed to assess whether or not the contemporary machine learning theories can handle these geographic uncertainties and errors. In chapter four, we identify an unreported systemic error source: the proportion distribution of classes in the training set. A subsequent Bayesian Optimal solution is designed to combine Support Vector Machine and Maximum Likelihood. Finally, in chapter five, we demonstrate how this type of error is widespread not just in classification algorithms, but also embedded in the conceptual definition of geographic classes before the classification. In chapter six, the sources of errors and uncertainties and their solutions are summarized, with theoretical implications for future studies. The most important finding is that, how we design a classification largely pre-determines what we eventually get out of it. This applies for many contemporary popular classifiers including various types of neural nets, decision tree, and support vector machine. This is a cause of the so-called overfitting problem in contemporary machine learning. Therefore, we propose that the emphasis of classification work be shifted to the planning stage before the actual classification. Geography should not just be the analysis of collected observations, but also about the planning of observation collection. This is where geography, machine learning, and survey statistics meet

Semantically enhanced document clustering

This thesis advocates the view that traditional document clustering could be significantly improved by representing documents at different levels of abstraction at which the similarity between documents is considered. The improvement is with regard to the alignment of the clustering solutions to human judgement. The proposed methodology employs semantics with which the conceptual similarity be-tween documents is measured. The goal is to design algorithms which implement the meth-odology, in order to solve the following research problems: (i) how to obtain multiple deter-ministic clustering solutions; (ii) how to produce coherent large-scale clustering solutions across domains, regardless of the number of clusters; (iii) how to obtain clustering solutions which align well with human judgement; and (iv) how to produce specific clustering solu-tions from the perspective of the user’s understanding for the domain of interest. The developed clustering methodology enhances separation between and improved coher-ence within clusters generated across several domains by using levels of abstraction. The methodology employs a semantically enhanced text stemmer, which is developed for the pur-pose of producing coherent clustering, and a concept index that provides generic document representation and reduced dimensionality of document representation. These characteristics of the methodology enable addressing the limitations of traditional text document clustering by employing computationally expensive similarity measures such as Earth Mover’s Distance (EMD), which theoretically aligns the clustering solutions closer to human judgement. A threshold for similarity between documents that employs many-to-many similarity matching is proposed and experimentally proven to benefit the traditional clustering algorithms in pro-ducing clustering solutions aligned closer to human judgement. 4 The experimental validation demonstrates the scalability of the semantically enhanced document clustering methodology and supports the contributions: (i) multiple deterministic clustering solutions and different viewpoints to a document collection are obtained; (ii) the use of concept indexing as a document representation technique in the domain of document clustering is beneficial for producing coherent clusters across domains; (ii) SETS algorithm provides an improved text normalisation by using external knowledge; (iv) a method for measuring similarity between documents on a large scale by using many-to-many matching; (v) a semantically enhanced methodology that employs levels of abstraction that correspond to a user’s background, understanding and motivation. The achieved results will benefit the research community working in the area of document management, information retrieval, data mining and knowledge management

Internet of Underwater Things and Big Marine Data Analytics -- A Comprehensive Survey

The Internet of Underwater Things (IoUT) is an emerging communication ecosystem developed for connecting underwater objects in maritime and underwater environments. The IoUT technology is intricately linked with intelligent boats and ships, smart shores and oceans, automatic marine transportations, positioning and navigation, underwater exploration, disaster prediction and prevention, as well as with intelligent monitoring and security. The IoUT has an influence at various scales ranging from a small scientific observatory, to a midsized harbor, and to covering global oceanic trade. The network architecture of IoUT is intrinsically heterogeneous and should be sufficiently resilient to operate in harsh environments. This creates major challenges in terms of underwater communications, whilst relying on limited energy resources. Additionally, the volume, velocity, and variety of data produced by sensors, hydrophones, and cameras in IoUT is enormous, giving rise to the concept of Big Marine Data (BMD), which has its own processing challenges. Hence, conventional data processing techniques will falter, and bespoke Machine Learning (ML) solutions have to be employed for automatically learning the specific BMD behavior and features facilitating knowledge extraction and decision support. The motivation of this paper is to comprehensively survey the IoUT, BMD, and their synthesis. It also aims for exploring the nexus of BMD with ML. We set out from underwater data collection and then discuss the family of IoUT data communication techniques with an emphasis on the state-of-the-art research challenges. We then review the suite of ML solutions suitable for BMD handling and analytics. We treat the subject deductively from an educational perspective, critically appraising the material surveyed.Comment: 54 pages, 11 figures, 19 tables, IEEE Communications Surveys & Tutorials, peer-reviewed academic journa

Patterns, causes, and consequences of connectivity within a coral reef fish metapopulation

Population connectivity influences virtually all ecological and evolutionary processes within metapopulations including population dynamics, persistence, and divergence. A comprehensive analysis of connectivity must consider the exchange of both individuals and alleles among populations, representing demographic and genetic connectivity, respectively. For many marine species, connectivity is driven by larval dispersal. However, despite the widespread recognition that dispersal is key to predicting metapopulation dynamics and effectively managing networks of marine reserves, empirical data are scarce due to the methodological challenges of tracking larvae. This dissertation is an integrative study of the patterns, causes, and consequences of marine connectivity using the sponge-dwelling reef fish Elacatinus lori as a study system. I begin by describing the distribution and abundance patterns of E. lori and its host sponge on the Belize barrier reef. Next, I study demographic connectivity by using genetic parentage analysis to quantify dispersal. I conduct an intra-population study to identify self-recruiting dispersal trajectories and develop a method to approximate a dispersal kernel based on the distribution of habitat patches. I then complete a large-scale parentage analysis to produce the first statistically-robust marine dispersal kernel. I find that dispersal declines exponentially with respect to distance in E. lori, with no dispersal events exceeding 16.2 km. Notably, dispersal probabilities are unrelated to the number of days an individual spends in the larval phase and other biological variables. Finally, to elucidate the long-term microevolutionary consequences of genetic connectivity, I investigate spatial genetic structure in the Belizean metapopulation. In a preliminary study based on mitochondrial and microsatellite data, I find high levels of pairwise genetic differentiation between sites separated by only 20 km. In a follow-up study, I use a high-throughput multiplex approach to resolve fine-scale patterns of genetic structure throughout the species' range. Seascape genetic analyses reveal that genetic connectivity is consistent with the shape of the dispersal kernel. Collectively, this dissertation generates novel insights regarding the spatial scale at which marine fish populations are connected. Given the alarming rate of population declines on coral reefs globally, these results have important and time-sensitive conservation implications

EMPIRICAL RESEARCH ON HUMAN-AI COLLABORATIVE ARCHITECTURAL DESIGN PROCESS THROUGH A DEEP LEARNING APPROACH

北九州市立大学博士(工学)The purpose of this thesis is to explore how AI technologies intervene in the architectural design process and to discuss the importance and approaches that drive the paradigm shift towards human-AI collaboration in architectural design. The research is conducted from two perspectives: theoretical and practical. At the theoretical level, how AI technologies affect architectural design through technological evolution is analyzed, as well as the advantages, disadvantages and trends of different AI networks in sustainably analyzing and optimizing different kinds of architectural designs. Further, based on this, the methodology of how to develop a reflection on the nature of technology and data is discussed. At the practical level, AI methods that are inventive and capable of performance-based design are constructed and trained. And the basic process of human-AI collaborative architectural design is presented with an empirical study. The results of this thesis not only provide a theoretical reference and methodological basis for future research on human-AI collaborative architectural design at a broader and higher level but also attempt to explore new ideas and methods for the field of architectural design during the evolution of the old and new paradigms, ultimately realizing the purpose of sustainable development of the B&C industry.doctoral thesi

Aflatoxin contamination of groundnut: proceedings of the International Workshop, 6-9 Oct 1987, ICRISAT Center, India

An introduction by L. D. Swindale, including a general overview of the problem of aflatoxin contamination of groundnut and objectives and structure of the workshop, is followed by papers presented at the workshop in sessions on: importance of aflatoxins; aflatoxins and trade in groundnuts; aflatoxins in groundnut, monitoring and action at national level; removal of aflatoxins; methods for aflatoxin analysis; research on aflatoxin contamination of groundnut, general and genetic resistance. Group discussion reports are presented on: evaluation and monitoring of aflatoxin contamination of groundnuts and groundnut products; analytical methods for aflatoxins in groundnuts and groundnut products; research on on-farm control of aflatoxin contamination; research on control of aflatoxin contamination with reference to storage, transit, processing, etc. Recommendations on information and training, strategies and research needs are made. The individual papers are abstracted elsewher

Study on open science: The general state of the play in Open Science principles and practices at European life sciences institutes

Nowadays, open science is a hot topic on all levels and also is one of the priorities of the European Research Area. Components that are commonly associated with open science are open access, open data, open methodology, open source, open peer review, open science policies and citizen science. Open science may a great potential to connect and influence the practices of researchers, funding institutions and the public. In this paper, we evaluate the level of openness based on public surveys at four European life sciences institute