888 research outputs found
A modular architecture for systematic text categorisation
This work examines and attempts to overcome issues caused by the lack of formal standardisation when defining text categorisation techniques and detailing how they might be appropriately integrated with each other. Despite text categorisation’s long history the concept of automation is relatively new, coinciding with the evolution of computing technology and subsequent increase in quantity and availability of electronic textual data. Nevertheless insufficient descriptions of the diverse algorithms discovered have lead to an acknowledged ambiguity when trying to accurately replicate methods, which has made reliable comparative evaluations impossible.
Existing interpretations of general data mining and text categorisation methodologies are analysed in the first half of the thesis and common elements are extracted to create a distinct set of significant stages. Their possible interactions are logically determined and a unique universal architecture is generated that encapsulates all complexities and highlights the critical components. A variety of text related algorithms are also comprehensively surveyed and grouped according to which stage they belong in order to demonstrate how they can be mapped.
The second part reviews several open-source data mining applications, placing an emphasis on their ability to handle the proposed architecture, potential for expansion and text processing capabilities. Finding these inflexible and too elaborate to be readily adapted, designs for a novel framework are introduced that focus on rapid prototyping through lightweight customisations and reusable atomic components.
Being a consequence of inadequacies with existing options, a rudimentary implementation is realised along with a selection of text categorisation modules. Finally a series of experiments are conducted that validate the feasibility of the outlined methodology and importance of its composition, whilst also establishing the practicality of the framework for research purposes. The simplicity of experiments and results gathered clearly indicate the potential benefits that can be gained when a formalised approach is utilised
Mining climate data for shire level wheat yield predictions in Western Australia
Climate change and the reduction of available agricultural land are two of the most important factors that affect global food production especially in terms of wheat stores. An ever increasing world population places a huge demand on these resources. Consequently, there is a dire need to optimise food production.
Estimations of crop yield for the South West agricultural region of Western Australia have usually been based on statistical analyses by the Department of Agriculture and Food in Western Australia. Their estimations involve a system of crop planting recommendations and yield prediction tools based on crop variety trials. However, many crop failures arise from adherence to these crop recommendations by farmers that were contrary to the reported estimations. Consequently, the Department has sought to investigate new avenues for analyses that improve their estimations and recommendations.
This thesis explores a new approach in the way analyses are carried out. This is done through the introduction of new methods of analyses such as data mining and online analytical processing in the strategy. Additionally, this research attempts to provide a better understanding of the effects of both gradual variation parameters such as soil type, and continuous variation parameters such as rainfall and temperature, on the wheat yields.
The ultimate aim of the research is to enhance the prediction efficiency of wheat yields. The task was formidable due to the complex and dichotomous mixture of gradual and continuous variability data that required successive information transformations. It necessitated the progressive moulding of the data into useful information, practical knowledge and effective industry practices. Ultimately, this new direction is to improve the crop predictions and to thereby reduce crop failures.
The research journey involved data exploration, grappling with the complexity of Geographic Information System (GIS), discovering and learning data compatible software tools, and forging an effective processing method through an iterative cycle of action research experimentation. A series of trials was conducted to determine the combined effects of rainfall and temperature variations on wheat crop yields. These experiments specifically related to the South Western Agricultural region of Western Australia. The study focused on wheat producing shires within the study area. The investigations involved a combination of macro and micro analyses techniques for visual data mining and data mining classification techniques, respectively.
The research activities revealed that wheat yield was most dependent upon rainfall and temperature. In addition, it showed that rainfall cyclically affected the temperature and soil type due to the moisture retention of crop growing locations. Results from the regression analyses, showed that the statistical prediction of wheat yields from historical data, may be enhanced by data mining techniques including classification.
The main contribution to knowledge as a consequence of this research was the provision of an alternate and supplementary method of wheat crop prediction within the study area. Another contribution was the division of the study area into a GIS surface grid of 100 hectare cells upon which the interpolated data was projected. Furthermore, the proposed framework within this thesis offers other researchers, with similarly structured complex data, the benefits of a general processing pathway to enable them to navigate their own investigations through variegated analytical exploration spaces. In addition, it offers insights and suggestions for future directions in other contextual research explorations
Open data and interoperability standards : opportunities for animal welfare in extensive livestock systems
Extensive livestock farming constitutes a sizeable portion of agriculture, not only in relation to land use, but in contribution to feeding a growing human population. In addition to meat, it contributes other economically valuable commodities such as wool, hides and other products. The livestock industries are adopting technologies under the banner of Precision Livestock Farming (PLF) to help meet higher production and efficiency targets as well as help to manage the multiple challenges impacting the industries, such as climate change, environmental concerns, globalisation of markets, increasing rules of governance and societal scrutiny especially in relation to animal welfare. PLF is particularly dependent on the acquisition and management of data and metadata and on the interoperability standards that allow data discovery and federation. A review of interoperability standards and PLF adoption in extensive livestock farming systems identified a lack of domain specific standards and raised questions related to the amount and quality of public data which has potential to inform livestock farming. A systematic review of public datasets, which included an assessment based on the principles that data must be findable, accessible, interoperable and reusable (FAIR) was developed. Custom software scripts were used to conduct a dataset search to determine the quantity and quality of domain specific datasets yielded 419 unique Australian datasets directly related to extensive livestock farming. A FAIR assessment of these datasets using a set of non-domain specific, general metrics showed a moderate level of compliance. The results suggest that domain specific FAIR metrics may need to be developed to provide a more accurate data quality assessment, but also that the level of interoperability and reusability is not particularly high which has implications if public data is to be included in decision support tools. To test the usefulness of available public datasets in informing decision support in relation to livestock welfare, a case study was designed and farm animal welfare elements were extracted from Australian welfare standards to guide a dataset search. It was found that with few exceptions, these elements could be supported with public data, although there were gaps in temporal and spatial coverage. The development of a geospatial animal welfare portal including these datasets further explored and confirmed the potential for using public data to enhance livestock welfare.Doctor of Philosoph
Active Learning for Reducing Labeling Effort in Text Classification Tasks
Labeling data can be an expensive task as it is usually performed manually by
domain experts. This is cumbersome for deep learning, as it is dependent on
large labeled datasets. Active learning (AL) is a paradigm that aims to reduce
labeling effort by only using the data which the used model deems most
informative. Little research has been done on AL in a text classification
setting and next to none has involved the more recent, state-of-the-art Natural
Language Processing (NLP) models. Here, we present an empirical study that
compares different uncertainty-based algorithms with BERT as the used
classifier. We evaluate the algorithms on two NLP classification datasets:
Stanford Sentiment Treebank and KvK-Frontpages. Additionally, we explore
heuristics that aim to solve presupposed problems of uncertainty-based AL;
namely, that it is unscalable and that it is prone to selecting outliers.
Furthermore, we explore the influence of the query-pool size on the performance
of AL. Whereas it was found that the proposed heuristics for AL did not improve
performance of AL; our results show that using uncertainty-based AL with
BERT outperforms random sampling of data. This difference in
performance can decrease as the query-pool size gets larger.Comment: Accepted as a conference paper at the joint 33rd Benelux Conference
on Artificial Intelligence and the 30th Belgian Dutch Conference on Machine
Learning (BNAIC/BENELEARN 2021). This camera-ready version submitted to
BNAIC/BENELEARN, adds several improvements including a more thorough
discussion of related work plus an extended discussion section. 28 pages
including references and appendice
Big Data Computing for Geospatial Applications
The convergence of big data and geospatial computing has brought forth challenges and opportunities to Geographic Information Science with regard to geospatial data management, processing, analysis, modeling, and visualization. This book highlights recent advancements in integrating new computing approaches, spatial methods, and data management strategies to tackle geospatial big data challenges and meanwhile demonstrates opportunities for using big data for geospatial applications. Crucial to the advancements highlighted in this book is the integration of computational thinking and spatial thinking and the transformation of abstract ideas and models to concrete data structures and algorithms
Combining SOA and BPM Technologies for Cross-System Process Automation
This paper summarizes the results of an industry case study that introduced a cross-system business process automation solution based on a combination of SOA and BPM standard technologies (i.e., BPMN, BPEL, WSDL). Besides discussing major weaknesses of the existing, custom-built, solution and comparing them against experiences with the developed prototype, the paper presents a course of action for transforming the current solution into the proposed solution. This includes a general approach, consisting of four distinct steps, as well as specific action items that are to be performed for every step. The discussion also covers language and tool support and challenges arising from the transformation
Integrating Data Science and Earth Science
This open access book presents the results of three years collaboration between earth scientists and data scientists, in developing and applying data science methods for scientific discovery. The book will be highly beneficial for other researchers at senior and graduate level, interested in applying visual data exploration, computational approaches and scientifc workflows
Data Management Challenges for Internet-scale 3D Search Engines
This paper describes the most significant data-related challenges involved in
building internet-scale 3D search engines. The discussion centers on the most
pressing data management issues in this domain, including model acquisition,
support for multiple file formats, asset versioning, data integrity errors, the
data lifecycle, intellectual property, and the legality of web crawling. The
paper also discusses numerous issues that fall under the rubric of trustworthy
computing, including privacy, security, inappropriate content, and
copying/remixing of assets. The goal of the paper is to provide an overview of
these general issues, illustrated by empirical data drawn from the internet's
largest operational search engine. While numerous works have been published on
3D information retrieval, this paper is the first to discuss the real-world
challenges that arise in building practical search engines at scale.Comment: Second version, distributed by SIGIR Foru
Integrating Data Science and Earth Science
This open access book presents the results of three years collaboration between earth scientists and data scientist, in developing and applying data science methods for scientific discovery. The book will be highly beneficial for other researchers at senior and graduate level, interested in applying visual data exploration, computational approaches and scientifc workflows
Simulation Intelligence: Towards a New Generation of Scientific Methods
The original "Seven Motifs" set forth a roadmap of essential methods for the
field of scientific computing, where a motif is an algorithmic method that
captures a pattern of computation and data movement. We present the "Nine
Motifs of Simulation Intelligence", a roadmap for the development and
integration of the essential algorithms necessary for a merger of scientific
computing, scientific simulation, and artificial intelligence. We call this
merger simulation intelligence (SI), for short. We argue the motifs of
simulation intelligence are interconnected and interdependent, much like the
components within the layers of an operating system. Using this metaphor, we
explore the nature of each layer of the simulation intelligence operating
system stack (SI-stack) and the motifs therein: (1) Multi-physics and
multi-scale modeling; (2) Surrogate modeling and emulation; (3)
Simulation-based inference; (4) Causal modeling and inference; (5) Agent-based
modeling; (6) Probabilistic programming; (7) Differentiable programming; (8)
Open-ended optimization; (9) Machine programming. We believe coordinated
efforts between motifs offers immense opportunity to accelerate scientific
discovery, from solving inverse problems in synthetic biology and climate
science, to directing nuclear energy experiments and predicting emergent
behavior in socioeconomic settings. We elaborate on each layer of the SI-stack,
detailing the state-of-art methods, presenting examples to highlight challenges
and opportunities, and advocating for specific ways to advance the motifs and
the synergies from their combinations. Advancing and integrating these
technologies can enable a robust and efficient hypothesis-simulation-analysis
type of scientific method, which we introduce with several use-cases for
human-machine teaming and automated science
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