MP-ABT: A Minimal Perturbation Approach for Complex Local Problems

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Structure in Machine Learning: Graphical Models and Monte Carlo Methods

This thesis is concerned with two main areas: approximate inference in discrete graphical models, and random embeddings for dimensionality reduction and approximate inference in kernel methods. Approximate inference is a fundamental problem in machine learning and statistics, with strong connections to other domains such as theoretical computer science. At the same time, there has often been a gap between the success of many algorithms in this area in practice, and what can be explained by theory; thus, an important research effort is to bridge this gap. Random embeddings for dimensionality reduction and approximate inference have led to great improvements in scalability of a wide variety of methods in machine learning. In recent years, there has been much work on how the stochasticity introduced by these approaches can be better controlled, and what further computational improvements can be made. In the first part of this thesis, we study approximate inference algorithms for discrete graphical models. Firstly, we consider linear programming methods for approximate MAP inference, and develop our understanding of conditions for exactness of these approximations. Such guarantees of exactness are typically based on either structural restrictions on the underlying graph corresponding to the model (such as low treewidth), or restrictions on the types of potential functions that may be present in the model (such as log-supermodularity). We contribute two new classes of exactness guarantees: the first of these takes the form of particular hybrid restrictions on a combination of graph structure and potential types, whilst the second is given by excluding particular substructures from the underlying graph, via graph minor theory. We also study a particular family of transformation methods of graphical models, uprooting and rerooting, and their effect on approximate MAP and marginal inference methods. We prove new theoretical results on the behaviour of particular approximate inference methods under these transformations, in particular showing that the triplet relaxation of the marginal polytope is unique in being universally rooted. We also introduce a heuristic which quickly picks a rerooting, and demonstrate benefits empirically on models over several graph topologies. In the second part of this thesis, we study Monte Carlo methods for both linear dimensionality reduction and approximate inference in kernel machines. We prove the statistical benefit of coupling Monte Carlo samples to be almost-surely orthogonal in a variety of contexts, and study fast approximate methods of inducing this coupling. A surprising result is that these approximate methods can simultaneously offer improved statistical benefits, time complexity, and space complexity over i.i.d. Monte Carlo samples. We evaluate our methods on a variety of datasets, directly studying their effects on approximate kernel evaluation, as well as on downstream tasks such as Gaussian process regression.EPSR

Highly Scalable and Secure Mobile Applications in Cloud Computing Systems

Cloud computing provides scalable processing and storage resources that are hosted on a third-party provider to permit clients to economically meet real-time service demands. The confidentiality of client data outsourced to the cloud is a paramount concern since the provider cannot necessarily be trusted with read access to voluminous sensitive client data. A particular challenge of mobile cloud computing is that a cloud application may be accessed by a very large and dynamically changing population of mobile devices requiring access control. The thesis addresses the problems of achieving efficient and highly scalable key management for resource-constrained users of an untrusted cloud, and also of preserving the privacy of users. A model for key distribution is first proposed that is based on dynamic proxy re-encryption of data. Keys are managed inside the client domain for trust reasons, computationally-intensive re-encryption is performed by the cloud provider, and key distribution is minimized to conserve communication. A mechanism manages key evolution for a continuously changing user population. Next, a novel form of attribute-based encryption is proposed that authorizes users based on the satisfaction of required attributes. The greater computational load from cryptographic operations is performed by the cloud provider and a trusted manager rather than the mobile data owner. Furthermore, data re-encryption may be optionally performed by the cloud provider to reduce the expense of user revocation. Another key management scheme based on threshold cryptography is proposed where encrypted key shares are stored in the cloud, taking advantage of the scalability of storage in the cloud. The key share material erodes over time to allow user revocation to occur efficiently without additional coordination by the data owner; multiple classes of user privileges are also supported. Lastly, an alternative exists where cloud data is considered public knowledge, but the specific information queried by a user must be kept private. A technique is presented utilizing private information retrieval, where the query is performed in a computationally efficient manner without requiring a trusted third-party component. A cloaking mechanism increases the privacy of a mobile user while maintaining constant traffic cost

WiFi-Based Human Activity Recognition Using Attention-Based BiLSTM

Recently, significant efforts have been made to explore human activity recognition (HAR) techniques that use information gathered by existing indoor wireless infrastructures through WiFi signals without demanding the monitored subject to carry a dedicated device. The key intuition is that different activities introduce different multi-paths in WiFi signals and generate different patterns in the time series of channel state information (CSI). In this paper, we propose and evaluate a full pipeline for a CSI-based human activity recognition framework for 12 activities in three different spatial environments using two deep learning models: ABiLSTM and CNN-ABiLSTM. Evaluation experiments have demonstrated that the proposed models outperform state-of-the-art models. Also, the experiments show that the proposed models can be applied to other environments with different configurations, albeit with some caveats. The proposed ABiLSTM model achieves an overall accuracy of 94.03%, 91.96%, and 92.59% across the 3 target environments. While the proposed CNN-ABiLSTM model reaches an accuracy of 98.54%, 94.25% and 95.09% across those same environments

Governing Water in Canada: The Legislative Experiments in New Governance

Governing water in Canada is in transition. Since 2000, episodes of drought, unsafe drinking water, and polluted watersheds have affected local and First Nations communities. In reaction to these crises, provincial regulators entered a new governance phase. This regulatory turn profoundly transforms the traditional environmental regulatory approach by introducing a collaborative new governance arrangement. The legal scholarship is generally supportive of this trend, however, a dearth of empirical research exists to understand how decisions are made under this new regulatory approach. This dissertation presents an eco-resiliency framework to examine the responsiveness of this new governance mode to environmental change. The primary research question is: What lessons can be taken from resiliency theory and applied in the sphere of environmental regulation and governance? Three comparative case studies of local watershed-level committees in Ontario, Alberta, and the Yukon served as empirical evidence. The research methodology adopted a qualitative approach (i.e., participant observation and interviews with committee members) and a thorough review of the relevant legislation, administrative decisions, policy documents, and media reports. The data was analyzed in terms of the four eco-resiliency elements: flexibility, diversity, a broad perspective, and emergent change. Contrary to the themes of inclusivity and consensus found in the collaborative governance literature, the research findings exposed an insular and technocratic decision-making process that served the political interests of the province and the administrative needs of the regulatory agency. Even though, in theory, the provincial regimes under study allowed for a diverse number of stakeholders at the policy table, in practice, only a few experts influenced the decision-making. Local communities ecological health and environmental concerns including First Nations ways of knowing water were overlooked. The devolution of water governance to a local level, rather than empowering local public-interest representatives, concentrates power in the hands of a few participants. Surprisingly, the Yukon Water Board, an administrative tribunal with strict procedural requirements, offered the strongest opportunity for Aboriginal and conservation groups to raise their water concerns. The most important finding is the erosion of the environmental protection function of the state, which is obscured by this policy drift

Governing Water in Canada: The Legislative Experiments in New Governance

Governing water in Canada is in transition. Since 2000, episodes of drought, unsafe drinking water, and polluted watersheds have affected local and First Nations communities. In reaction to these crises, provincial regulators entered a new governance phase. This regulatory turn profoundly transforms the traditional environmental regulatory approach by introducing a collaborative new governance arrangement. The legal scholarship is generally supportive of this trend, however, a dearth of empirical research exists to understand how decisions are made under this new regulatory approach. This dissertation presents an eco-resiliency framework to examine the responsiveness of this new governance mode to environmental change. The primary research question is: What lessons can be taken from resiliency theory and applied in the sphere of environmental regulation and governance? Three comparative case studies of local watershed-level committees in Ontario, Alberta, and the Yukon served as empirical evidence. The research methodology adopted a qualitative approach (i.e., participant observation and interviews with committee members) and a thorough review of the relevant legislation, administrative decisions, policy documents, and media reports. The data was analyzed in terms of the four eco-resiliency elements: flexibility, diversity, a broad perspective, and emergent change. Contrary to the themes of inclusivity and consensus found in the collaborative governance literature, the research findings exposed an insular and technocratic decision-making process that served the political interests of the province and the administrative needs of the regulatory agency. Even though, in theory, the provincial regimes under study allowed for a diverse number of stakeholders at the policy table, in practice, only a few experts influenced the decision-making. Local communities ecological health and environmental concerns including First Nations ways of knowing water were overlooked. The devolution of water governance to a local level, rather than empowering local public-interest representatives, concentrates power in the hands of a few participants. Surprisingly, the Yukon Water Board, an administrative tribunal with strict procedural requirements, offered the strongest opportunity for Aboriginal and conservation groups to raise their water concerns. The most important finding is the erosion of the environmental protection function of the state, which is obscured by this policy drift

Governing Water in Canada: The Legislative Experiments in New Governance

Governing water in Canada is in transition. Since 2000, episodes of drought, unsafe drinking water, and polluted watersheds have affected local and First Nations communities. In reaction to these crises, provincial regulators entered a new governance phase. This regulatory turn profoundly transforms the traditional environmental regulatory approach by introducing a collaborative new governance arrangement. The legal scholarship is generally supportive of this trend, however, a dearth of empirical research exists to understand how decisions are made under this new regulatory approach. This dissertation presents an eco-resiliency framework to examine the responsiveness of this new governance mode to environmental change. The primary research question is: What lessons can be taken from resiliency theory and applied in the sphere of environmental regulation and governance? Three comparative case studies of local watershed-level committees in Ontario, Alberta, and the Yukon served as empirical evidence. The research methodology adopted a qualitative approach (i.e., participant observation and interviews with committee members) and a thorough review of the relevant legislation, administrative decisions, policy documents, and media reports. The data was analyzed in terms of the four eco-resiliency elements: flexibility, diversity, a broad perspective, and emergent change. Contrary to the themes of inclusivity and consensus found in the collaborative governance literature, the research findings exposed an insular and technocratic decision-making process that served the political interests of the province and the administrative needs of the regulatory agency. Even though, in theory, the provincial regimes under study allowed for a diverse number of stakeholders at the policy table, in practice, only a few experts influenced the decision-making. Local communities ecological health and environmental concerns including First Nations ways of knowing water were overlooked. The devolution of water governance to a local level, rather than empowering local public-interest representatives, concentrates power in the hands of a few participants. Surprisingly, the Yukon Water Board, an administrative tribunal with strict procedural requirements, offered the strongest opportunity for Aboriginal and conservation groups to raise their water concerns. The most important finding is the erosion of the environmental protection function of the state, which is obscured by this policy drift

Multidisciplinary perspectives on Artificial Intelligence and the law

This open access book presents an interdisciplinary, multi-authored, edited collection of chapters on Artificial Intelligence (‘AI’) and the Law. AI technology has come to play a central role in the modern data economy. Through a combination of increased computing power, the growing availability of data and the advancement of algorithms, AI has now become an umbrella term for some of the most transformational technological breakthroughs of this age. The importance of AI stems from both the opportunities that it offers and the challenges that it entails. While AI applications hold the promise of economic growth and efficiency gains, they also create significant risks and uncertainty. The potential and perils of AI have thus come to dominate modern discussions of technology and ethics – and although AI was initially allowed to largely develop without guidelines or rules, few would deny that the law is set to play a fundamental role in shaping the future of AI. As the debate over AI is far from over, the need for rigorous analysis has never been greater. This book thus brings together contributors from different fields and backgrounds to explore how the law might provide answers to some of the most pressing questions raised by AI. An outcome of the Católica Research Centre for the Future of Law and its interdisciplinary working group on Law and Artificial Intelligence, it includes contributions by leading scholars in the fields of technology, ethics and the law.info:eu-repo/semantics/publishedVersio