245 research outputs found

    A review of natural language processing in contact centre automation

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    Contact centres have been highly valued by organizations for a long time. However, the COVID-19 pandemic has highlighted their critical importance in ensuring business continuity, economic activity, and quality customer support. The pandemic has led to an increase in customer inquiries related to payment extensions, cancellations, and stock inquiries, each with varying degrees of urgency. To address this challenge, organizations have taken the opportunity to re-evaluate the function of contact centres and explore innovative solutions. Next-generation platforms that incorporate machine learning techniques and natural language processing, such as self-service voice portals and chatbots, are being implemented to enhance customer service. These platforms offer robust features that equip customer agents with the necessary tools to provide exceptional customer support. Through an extensive review of existing literature, this paper aims to uncover research gaps and explore the advantages of transitioning to a contact centre that utilizes natural language solutions as the norm. Additionally, we will examine the major challenges faced by contact centre organizations and offer reco

    Low carbon multi-vector energy systems: a case study of the University of Edinburgh's 2040 'Net Zero' target

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    The ultimate goal of this research was to develop a methodology to support decision-making by large (public sector) organisations regarding future energy technology choices to reduce carbon emissions. This culminated in the development of a multi-vector campus energy systems modelling tool that was applied to the University of Edinburgh as a case study. To deliver this a series of objectives were addressed. Machine learning models were applied to model building heat and electrical energy use for extrapolation to campus level. This was applied to explore the scope to reduce campus level emissions through operational changes; this demonstrated that it is difficult to further reduce the carbon emissions without technological changes given the University’s heavy reliance on natural gas-fired combined heat and power and boilers. As part of the analysis of alternative energy sources, the scope for off-campus wind farms was considered; specifically this focussed on estimation of wind farm generation at the planning stage and employed a model transfer strategy to facilitate use of metered data from wind farms. One of the key issues in making decisions about future energy sources on campus is the simultaneous changes in the wider energy system and specifically the decarbonisation of electricity; to facilitate better choices about onsite production and imports from the grid, a fundamental electricity model was developed to translate the National Grid Future Energy Scenarios into plausible patterns of electricity prices. The learning from these activities were incorporated into a model able to develop possible configurations for campus-level multi-vector energy systems given a variety of future pathways and uncertainties. The optimal planning model is formulated as a mixed-integer linear programming model with the objective to minimize the overall cost including carbon emissions. A numerical case study for the planning of three real-world campuses is presented to demonstrate the effectiveness of the proposed method. The conclusion highlights the importance of energy storage and a remote wind farm in these energy systems. Also, it is noted that there is no single solution that works in all cases where there are differences in factors such as device cost and performance, the gap between gas and electricity prices, weather conditions and the use (or otherwise) of cross-campus local energy balancing

    Synthesizing FDIR Recovery Strategies for Space Systems

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    Dynamic Fault Trees (DFTs) are powerful tools to drive the design of fault tolerant systems. However, semantic pitfalls limit their practical utility for interconnected systems that require complex recovery strategies to maximize their reliability. This thesis discusses the shortcomings of DFTs in the context of analyzing Fault Detection, Isolation and Recovery (FDIR) concepts with a particular focus on the needs of space systems. To tackle these shortcomings, we introduce an inherently non-deterministic model for DFTs. Deterministic recovery strategies are synthesized by transforming these non-deterministic DFTs into Markov automata that represent all possible choices between recovery actions. From the corresponding scheduler, optimized to maximize a given RAMS (Reliability, Availability, Maintainability and Safety) metric, an optimal recovery strategy can then be derived and represented by a model we call recovery automaton. We discuss dedicated techniques for reducing the state space of this recovery automaton and analyze their soundness and completeness. Moreover, modularized approaches to handle the complexity added by the state-based transformation approach are discussed. Furthermore, we consider the non-deterministic approach in a partially observable setting and propose an approach to lift the model for the fully observable case. We give an implementation of our approach within the Model-Based Systems Engineering (MBSE) framework Virtual Satellite. Finally, the implementation is evaluated based on the FFORT benchmark. The results show that basic non-deterministic DFTs generally scale well. However, we also found that semantically enriched non-deterministic DFTs employing repair or delayed observability mechanisms pose a challenge

    GFlowNet-EM for learning compositional latent variable models

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    Latent variable models (LVMs) with discrete compositional latents are an important but challenging setting due to a combinatorially large number of possible configurations of the latents. A key tradeoff in modeling the posteriors over latents is between expressivity and tractable optimization. For algorithms based on expectation-maximization (EM), the E-step is often intractable without restrictive approximations to the posterior. We propose the use of GFlowNets, algorithms for sampling from an unnormalized density by learning a stochastic policy for sequential construction of samples, for this intractable E-step. By training GFlowNets to sample from the posterior over latents, we take advantage of their strengths as amortized variational inference algorithms for complex distributions over discrete structures. Our approach, GFlowNet-EM, enables the training of expressive LVMs with discrete compositional latents, as shown by experiments on non-context-free grammar induction and on images using discrete variational autoencoders (VAEs) without conditional independence enforced in the encoder.Comment: ICML 2023; code: https://github.com/GFNOrg/GFlowNet-E

    MacORAMa: Optimal Oblivious RAM with Integrity

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    Oblivious RAM (ORAM), introduced by Goldreich and Ostrovsky (J. ACM `96), is a primitive that allows a client to perform RAM computations on an external database without revealing any information through the access pattern. For a database of size NN, well-known lower bounds show that a multiplicative overhead of Ω(logN)\Omega(\log N) in the number of RAM queries is necessary assuming O(1)O(1) client storage. A long sequence of works culminated in the asymptotically optimal construction of Asharov, Komargodski, Lin, and Shi (CRYPTO `21) with O(logN)O(\log N) worst-case overhead and O(1)O(1) client storage. However, this optimal ORAM is known to be secure only in the honest-but-curious setting, where an adversary is allowed to observe the access patterns but not modify the contents of the database. In the malicious setting, where an adversary is additionally allowed to tamper with the database, this construction and many others in fact become insecure. In this work, we construct the first maliciously secure ORAM with worst-case O(logN)O(\log N) overhead and O(1)O(1) client storage assuming one-way functions, which are also necessary. By the Ω(logN)\Omega(\log N) lower bound, our construction is asymptotically optimal. To attain this overhead, we develop techniques to intricately interleave online and offline memory checking for malicious security. Furthermore, we complement our positive result by showing the impossibility of a generic overhead-preserving compiler from honest-but-curious to malicious security, barring a breakthrough in memory checking

    Utilising Emotion Monitoring for Developing Music Interventions for People with Dementia:A State-of-the-Art Review

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    The demand for smart solutions to support people with dementia (PwD) is increasing. These solutions are expected to assist PwD with their emotional, physical, and social well-being. At the moment, state-of-the-art works allow for the monitoring of physical well-being; however, not much attention is delineated for monitoring the emotional and social well-being of PwD. Research on emotion monitoring can be combined with research on the effects of music on PwD given its promising effects. More specifically, knowledge of the emotional state allows for music intervention to alleviate negative emotions by eliciting positive emotions in PwD. In this direction, the paper conducts a state-of-the-art review on two aspects: (i) the effect of music on PwD and (ii) both wearable and non-wearable sensing systems for emotional state monitoring. After outlining the application of musical interventions for PwD, including emotion monitoring sensors and algorithms, multiple challenges are identified. The main findings include a need for rigorous research approaches for the development of adaptable solutions that can tackle dynamic changes caused by the diminishing cognitive abilities of PwD with a focus on privacy and adoption aspects. By addressing these requirements, advancements can be made in harnessing music and emotion monitoring for PwD, thereby facilitating the creation of more resilient and scalable solutions to aid caregivers and PwD

    Obliquities of stars from the study of transiting exoplanets and eclipsing binaries

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    In this thesis I study stellar obliquities across a range of companion masses, and in new regimes, that aims at constraining theories of planet formation and evolution. I begin in Chapter 1 with an introduction to the state of the field of extrasolar planets, key discoveries that have motivated previous studies on the misalignments between planetary orbits and stellar spins, and highlight the gaps in our knowledge where this thesis aims to make an impact. In Chapter 2, I outline the models and tools that underpin the analysis of transit light curves and high-resolution spectra in subsequent chapters. In Chapter 3, I apply these tools to the discovery of binary systems of various mass ratios. Two such systems are rare brown dwarfs whose discoveries help calibrate models of sub-stellar evolution, and the connection to giant planet formation and evolution. In Chapters 4–6, I present new measurements of stellar obliquities across a range of companion masses. In Chapter 4 I consider two systems hosting small planets. I demonstrate a misalignment the stellar spin and the orbit of a planet twice the size of Earth. This discovery is consistent with some disc-free migration scenarios, and provides the first observational evidence of its kind that super-Earths may form far from their star. In Chapter 5, I consider a sample of 13 giant planets orbiting cool stars in weak-tide regimes. I show that their host stars display a variety of obliquities, contrary to similar planets orbiting closer to their star. Such an effect is consistent with the expectation from tidal evolution, but has not yet been tested on this scale. In Chapter 6, I study the spin angular momentum of the primary component of a binary star hosting a circumbinary planet. I demonstrate that the star is aligned with the binary and planet orbit, providing an important constraint on the formation of binary stars and circumbinary planets. Finally, in Chapter 7, I conclude and offer some thoughts on future prospects
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