7,185 research outputs found

    Extending Provenance For Deep Diagnosis Of Distributed Systems

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    Diagnosing and repairing problems in complex distributed systems has always been challenging. A wide variety of problems can happen in distributed systems: routers can be misconfigured, nodes can be hacked, and the control software can have bugs. This is further complicated by the complexity and scale of today’s distributed systems. Provenance is an attractive way to diagnose faults in distributed systems, because it can track the causality from a symptom to a set of root causes. Prior work on network provenance has successfully applied provenance to distributed systems. However, they cannot explain problems beyond the presence of faulty events and offer limited help with finding repairs. In this dissertation, we extend provenance to handle diagnostics problems that require deeper investigations. We propose three different extensions: negative provenance explains not just the presence but also the absence of events (such as missing packets); meta provenance can suggest repairs by tracking causality not only for data but also for code (such as bugs in control plane programs); temporal provenance tracks causality at the temporal level and aims at diagnosing timing-related faults (such as slow requests). Compared to classical network provenance, our approach tracks richer causality at runtime and applies more sophisticated reasoning and post-processing. We apply the above techniques to software-defined networking and the border gateway protocol. Evaluations with real world traffic and topology show that our systems can diagnose and repair practical problems, and that the runtime overhead as well as the query turnarounds are reasonable

    Human Rights Treaty Commitment and Compliance: A Machine Learning-based Causal Inference Approach

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    Why do states ratify international human rights treaties? How much do human rights treaties influence state behaviors directly and indirectly? Why are some human rights treaty monitoring procedures more effective than others? What are the most predictively and causally important factors that can reduce and prevent state repression and human rights violations? This dissertation provide answers to these keys causal questions in political science research, using a novel approach that combines machine learning and the structural causal model framework. The four research questions are arranged in a chronological order that refects the causal process relating to international human rights treaties, going from (a) the causal determinants of treaty ratification to (b) the causal mechanisms of human rights treaties to (c) the causal effects of human rights treaty monitoring procedures to (d) other factors that causally influence human rights violations. Chapter 1 identifies the research traditions within which this dissertation is located, offers an overview of the methodological advances that enable this research, specifies the research questions, and previews the findings. Chapters 2, 3, 4, and 5 present in chronological order four empirical studies that answer these four research questions. Finally, Chapter 6 summarizes the substantive findings, suggests some other research questions that could be similarly investigated, and recaps the methodological approach and the contributions of the dissertation

    Mapping the intuitive investigation: Seeking, evaluating and explaining the evidence

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    The human mind has developed numerous cognitive tools to allow us to navigate the uncertainty of the world and make sense of situations and events. In this thesis I present a descriptive account of some of these tools by probing people’s ability to: evaluate, seek, and explain evidence and information. This was achieved by appraising people’s behaviour in controlled experiments – predominantly representing legal-investigative scenarios – utilising normative causal models (e.g., causal Bayesian networks), and uncovering the alternative strategies that people employed when reasoning under uncertainty. In Chapter 4, I investigate people’s ability to engage in a pattern of reasoning termed ‘explaining away’ and propose, and find empirical support towards, intuitive theories that address why the observed inference errors were made. In Chapter 5, I outline how people search for, and evaluate, evidence in a sequential investigative information-seeking paradigm – finding that people do not seek information simply to maximize a given utility function but rather are driven by additional strategies which are sensitive to factors such as demands of the task and a novel form of risk aversion. I extend these findings to forensic professionals, and utilise a naturalistic study employing mobile eye-trackers during a mock crime scene investigation to elucidate the key role that ‘asking the right questions’ plays when engaging in sense-making practices ‘in the wild’. In Chapter 6, I explore people’s preferences for certain types of information relating to opportunity and motive at various stages of the legal-investigative process. Here, I demonstrate that people prefer ‘motive’ accounts of crimes (analogous to a teleology preference) at different stages of the investigative process. In an additional two studies I demonstrate that these preferences are context-sensitive: namely, that ‘motive’ information tends to be moreincriminating and less exculpatory. In a final set of experiments, outlined in Chapter 7, I investigate how drawing causal models of competing explanations of the evidence affects how these same explanations are evaluated – arguing that graphically representing the evidence bolsters people’s understanding of the probabilistic and logical significance of the causal structures drawn. In sum, this thesis provides a rich descriptive account of how people engage in various aspects of sense-making and decision-making under uncertainty. The work presented in this thesis ultimately aims to increase the ecological and descriptive validity of normative causal frameworks utilised in the cognitive sciences – whilst informing ways to formalise decision-making practices in real-world specialised domains

    Design Science Epistemology. A pragmatist inquiry

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    This paper contributes to the clarification of a design science epistemology. It presents different epistemic types related to three stages of the design science process: 1) Evaluative and explanatory background knowledge (pre-design knowledge), 2) prospective knowledge with design hypotheses (in-design knowledge) and 3) prescriptive knowledge with design principles (post-design knowledge). The epistemological inquiry adopts a pragmatist approach and is pursued through a review of design science literature and informed by an empirical design case on digital support for social welfare allowances. The clarified design science epistemology shows a diversified epistemological landscape with several epistemic types: evaluative, critical, appreciative, normative, explanatory, prospective, prescriptive, categorial and attributive knowledge. Ways to express these epistemic types have been proposed in principal clauses. Ways of grounding have been clarified for each epistemic type. Proposals are given on how to utilize the design science epistemology in relation to design science process models and publication schemas
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