An Access Control Model for NoSQL Databases

Current development platforms are web scale, unlike recent platforms which were just network scale. There has been a rapid evolution in computing paradigm that has created the need for data storage as agile and scalable as the applications they support. Relational databases with their joins and locks influence performance in web scale systems negatively. Thus, various types of non-relational databases have emerged in recent years, commonly referred to as NoSQL databases. To fulfill the gaps created by their relational counter-part, they trade consistency and security for performance and scalability. With NoSQL databases being adopted by an increasing number of organizations, the provision of security for them has become a growing concern. This research presents a context based abstract model by extending traditional role based access control for access control in NoSQL databases. The said model evaluates and executes security policies which contain versatile access conditions against the dynamic nature of data. The goal is to devise a mechanism for a forward looking, assertive yet flexible security feature to regulate access to data in the database system that is devoid of rigid structures and consistency, namely a document based database such as MongoDB

Enhancing deep transfer learning for image classification

Though deep learning models require a large amount of labelled training data for yielding high performance, they are applied to accomplish many computer vision tasks such as image classification. Current models also do not perform well across different domain settings such as illumination, camera angle and real-to-synthetic. Thus the models are more likely to misclassify unknown classes as known classes. These issues challenge the supervised learning paradigm of the models and encourage the study of transfer learning approaches. Transfer learning allows us to utilise the knowledge acquired from related domains to improve performance on a target domain. Existing transfer learning approaches lack proper high-level source domain feature analyses and are prone to negative transfers for not exploring proper discriminative information across domains. Current approaches also lack at discovering necessary visual-semantic linkage and has a bias towards the source domain. In this thesis, to address these issues and improve image classification performance, we make several contributions to three different deep transfer learning scenarios, i.e., the target domain has i) labelled data; no labelled data; and no visual data. Firstly, for improving inductive transfer learning for the first scenario, we analyse the importance of high-level deep features and propose utilising them in sequential transfer learning approaches and investigating the suitable conditions for optimal performance. Secondly, to improve image classification across different domains in an open set setting by reducing negative transfers (second scenario), we propose two novel architectures. The first model has an adaptive weighting module based on underlying domain distinctive information, and the second model has an information-theoretic weighting module to reduce negative transfers. Thirdly, to learn visual classifiers when no visual data is available (third scenario) and reduce source domain bias, we propose two novel models. One model has a new two-step dense attention mechanism to discover semantic attribute-guided local visual features and mutual learning loss. The other model utilises bidirectional mapping and adversarial supervision to learn the joint distribution of source-target domains simultaneously. We propose a new pointwise mutual information dependant loss in the first model and a distance-based loss in the second one for handling source domain bias. We perform extensive evaluations on benchmark datasets and demonstrate the proposed models outperform contemporary works.Doctor of Philosoph

Sustainable Development Report: Blockchain, the Web3 & the SDGs

This is an output paper of the applied research that was conducted between July 2018 - October 2019 funded by the Austrian Development Agency (ADA) and conducted by the Research Institute for Cryptoeconomics at the Vienna University of Economics and Business and RCE Vienna (Regional Centre of Expertise on Education for Sustainable Development).Series: Working Paper Series / Institute for Cryptoeconomics / Interdisciplinary Researc

COMPRESSIVE PARAMETER ESTIMATION VIA APPROXIMATE MESSAGE PASSING

The literature on compressive parameter estimation has been mostly focused on the use of sparsity dictionaries that encode a discretized sampling of the parameter space; these dictionaries, however, suffer from coherence issues that must be controlled for successful estimation. To bypass such issues with discretization, we propose the use of statistical parameter estimation methods within the Approximate Message Passing (AMP) algorithm for signal recovery. Our method leverages the recently proposed use of custom denoisers in place of the usual thresholding steps (which act as denoisers for sparse signals) in AMP. We introduce the design of analog denoisers that are based on statistical parameter estimation algorithms, and we focus on two commonly used examples: frequency estimation and bearing estimation, coupled with the Root MUSIC estimation algorithm. We first analyze the performance of the proposed analog denoiser for signal recovery, and then link the performance in signal estimation to that of parameter estimation. Numerical experiments show significant improvements in estimation performance versus previously proposed approaches for compressive parameter estimation

Socioecology, Acoustic Communication and Demography of Asian Elephants in Sri Lanka

SOCIOECOLOGY, ACOUSTIC COMMUNICATION AND DEMOGRAPHY OF ASIAN ELEPHANTS IN SRI LANKA Shermin de Silva (Author) Dorothy L. Cheney (Supervisor) Comparison of behavior across species brings to light the underlying social and ecological factors that have shaped social organization and communication. Elephantids, the only living members of the Proboscidean clade are cognitively sophisticated, long-lived, putatively social mammals. I examine how vocal communication and social organization in Asian elephants (Elephas maximus) compare to African savannah elephants (Loxodonta africana), as well as basic demographic and conservation issues concerning Asian elephants. The first chapter defines fourteen distinct acoustic signals based on their acoustic features, and describes the contexts in which they occur. Most vocalizations are employed in contexts of movement, and some vocalizations are used primarily during movement or non-aggressive social interactions. This suggests that elephants actively seek out association with particular individuals. The second chapter tests the hypothesis that associations among adult female Asian elephants are governed by resourced availability, and describes the temporal structure and strength of bonds. This study population demonstrates fission-fusion social dynamics in which individuals change companions over short time scales, influenced by rainfall, but maintain stable relationships over long time scales. In the third chapter I test the hypothesis that associations are purely the consequence of the spatial distribution of resources, rather than social preference, using a modeling approach based on the spatio-temporal coordinates of individuals. In all seasons, individuals appear to move in a coordinated manner, supporting the interpretation that observed associations reflect true social preference. At the same time, resource distributions do influence the size of social units, and their movements. In the fourth chapter I review the most recent demographic studies of elephant populations in Asia as well as Africa, and highlight the lack of data for much of Asia. I outline methods based on individual identification that may be used to address this challenge to conservation and management. I apply these methods to offer demographic estimates for the study site, and examine what constitutes good practice, in the fifth chapter

Sustainable Development Report: Blockchain, the Web3 & the SDGs

This is an output paper of the applied research that was conducted between July 2018 - October 2019 funded by the Austrian Development Agency (ADA) and conducted by the Research Institute for Cryptoeconomics at the Vienna University of Economics and Business and RCE Vienna (Regional Centre of Expertise on Education for Sustainable Development).Series: Working Paper Series / Institute for Cryptoeconomics / Interdisciplinary Researc

Foundations of Cryptoeconomic Systems

Blockchain networks and similar cryptoeconomic networks aresystems, specifically complex systems. They are adaptive networkswith multi-scale spatiotemporal dynamics. Individual actions towards a collective goal are incentivized with "purpose-driven" tokens. These tokens are equipped with cryptoeconomic mechanisms allowing a decentralized network to simultaneously maintain a universal state layer, support peer-to-peer settlement, andincentivize collective action. These networks therefore provide a mission-critical and safety-critical regulatory infrastructure for autonomous agents in untrusted economic networks. They also provide a rich, real-time data set reflecting all economic activities in their systems. Advances in data science and network sciencecan thus be leveraged to design and analyze these economic systems in a manner consistent with the best practices of modern systems engineering. Research that reflects all aspects of these socioeconomic networks needs (i) a complex systems approach, (ii) interdisciplinary research, and (iii) a combination of economic and engineering methods, here referred to as "economic systems engineering", for the regulation and control of these socio-economicsystems. This manuscript provides foundations for further research activities that build on these assumptions, including specific research questions and methodologies for future research in this field.Series: Working Paper Series / Institute for Cryptoeconomics / Interdisciplinary Researc

Demographic Tipping Points as Early Indicators of Vulnerability for Slow-Breeding Megafaunal Populations

Decisions based on trends in population abundance and distribution may fail to protect populations of slow-breeding, long-lived megafauna from irrevocable decline if they ignore demographic constraints. For such taxa, we urge that effort be directed at understanding the interactions among vital rates governing population growth rates, rather than on predicting probabilities of extinction. The proximity of a population to demographic tipping points, i.e., where growth rate switches from positive to negative, can signal vulnerability to perturbation long before numbers drop below a point of no return. We define the “demographic safe space” as the combination of key vital rates that support a non-negative growth rate and illustrate this approach for Asian elephants. Through simulations, we find that even with optimal reproduction, Asian elephant populations cannot tolerate annual female mortality rates exceeding 7.5%. If adult mortality is very low (3%/year), populations can tolerate high annual mortality in calves below age 3 (up to 31.5%/year), or slow female reproduction (primiparity at 30 years or average inter-birth interval of up to 7.68 years). We then evaluate the potential impact of current threats, showing that near-optimal reproduction and high calf survival is necessary to offset even modestly increased mortality among adult female age classes. We suggest that rather than rely on simple counts or “viability” assessments, conservation planners for slow-breeding megafauna should consider demographic tipping points and strive to keep populations within their safe spaces